BED OCCUPANCY AND BED MANAGEMENT

Transcription

1 PUBLIC HEALTH RESEARCH UNIT BED OCCUPANCY AND BED MANAGEMENT Report of CSO Project K/OPR/2/2/D248 Heather Baillie, William Wright, Alice McLeod, Neil Craig 1, Alastair Leyland, Neil Drummond, Andrew Boddy October Department of Public Health, University of Glasgow

2 BED OCCUPANCY AND BED MANAGEMENT Report of CSO Project K/OPR/2/2/D248 CONTENTS Page Executive Summary i I: Introduction the methods of the project 1 II: Daily Variations in Bed Occupancy 5 III: The Effects of Case-mix 22 IV: Survival Analysis influences on length of hospital stay 29 V: An Econometric Analysis of Bed Occupancy and Costs 40 VI: Procedures for Managing Beds 66 VII: Synthesis: relationships between the parts of the project 91 VIII: Conclusion 100 Appendices A: Multilevel Modelling in the Econometric Analysis B: Survival Analysis for Chronic Obstructive Airways Disease C: Relative Risk Estimates and Confidence Intervals for the Survival Analyses D: Interview Schedules E: List of Respondents

3 I: INTRODUCTION Background to the project 1.1 This project was commissioned by the Chief Scientist s Office on behalf of the Management Executive as a part of a strategy to inform purchasing and provision for acute admissions. An observed increase in emergency admissions of over 42% in the 12 year period from 1981 to 1993 in Scottish hospitals was apparent in all age groups and was not explained by demographic change. Emergency admissions were not the only group of patients to experience such an increase in activity; elective admissions increased by approximately 11% between 1981 and 1988 but have remained more or less stable since, whilst day cases expanded fourfold. Patients aged 65 and over have accounted for nearly half of the increase in emergency admissions; however, it is also the case that elective admissions have been rising in this age group. Emergency admissions have remained the focus of research because of their lack of predictability and the way in which they affect the planning of elective admissions and complicate the contracting process. 1.2 A simple review of bed occupancy rates by specialty in Scottish hospitals suggests that those with high occupancies are main-stream specialties such as medicine or general surgery, which have a high proportion of emergency admissions in their case-mix. Specialties with a higher proportion of elective admissions (such as gynaecology, ophthalmology or ENT) tend to have lower occupancy rates. This is, of course, an over-simplification because the elective/emergency ratio is a rather crude reflection of other differences in the work of particular specialties. These differences include the numbers of available beds, the influence of relatively short lengths of stay (and their possible effect on turnover intervals), and other constraints including the availability of operating theatre resources. It is in particular noticeable that the three specialties mentioned with a high proportion of elective admissions tend to have relatively short lengths of stay (compared to general medicine) which may explain part of the differences in occupancy rates, both because of the higher proportional impact of the turnover interval and because of the increased difficulties in scheduling admissions to maintain high levels of bed occupancy. These are also specialties which have seen rapid growth in the proportion of day cases over recent years. It is therefore possible that the low occupancy rates currently being observed are, in part at least, a temporary problem since it may take time to reduce bed capacity in these specialties in line with the reduction in inpatient activity. However, the situation must be more complex than this, since the decreases in length of stay and inpatient numbers have not been accompanied by any real change in occupancy rates. 1.3 It is, however, reasonable to assume that patient scheduling and patient management may differ in response to the emergency/elective admission ratio of specialties. Table 1.1 indicates pronounced differences between the occupancy ratios and proportions of emergency admissions for different specialty groups, with an association between the two measures. The activities undertaken by hospitals may also influence these measures; table 1.2 suggests that the proportion of emergency admissions in medical specialties in the major teaching hospitals is lower than in other general hospitals, but their occupancy rates remain high. 1

4 Specialty Group % occupancy ratio % emergency ratio Medical* 82.7% 60.3% Low occupancy group** 57.1% 15.8% *General Medicine, Cardiology, Metabolic Disease, Neurology, Gastroenterology **ENT Surgery, Ophthalmology, Gynaecology Table 1.1: Average occupancy ratio and proportion of emergency admissions by specialty type, Scotland Hospital Type Specialty Group % occupancy ratio % emergency ratio Large general major teaching hospital Medical* Low occupancy group** 85.3% 61.3% 51.6% 17.4% General hospital Medical* 83.4% 66.4% (some teaching) Low occupancy group** 57.5% 15.5% *General Medicine, Cardiology, Metabolic Disease, Neurology, Gastroenterology **ENT Surgery, Ophthalmology, Gynaecology Table 1.2 Average occupancy ratio and proportion of emergency admissions by specialty and hospital type, Scotland The average rate of bed occupancy for Scottish hospitals may compound several different influences: bed occupancy is likely to vary according to the specialty-mix of a hospital s beds and thus the activities it undertakes. Bed occupancy may also vary as a consequence of case-mix within specialties and may be influenced by different management practices and their application in different specialties. Variation may also derive from different social and demographic characteristics in the populations for whom individual hospitals provide care. 1.5 Specialty is important because the activities undertaken differ in ways which influence the use of beds and the scheduling of other activities (such as surgical procedures). Diagnostic case-mix within specialties and, for example, the relative proportions of elective and emergency admissions are influential because of their effects on other measures of activity (such as length of stay and throughput) and constrain at least some of the available options for bed management. The management of hospital care raises important issues at different levels and in different ways. They include larger-scale issues such as the organisation and deployment of overall bed provision within a hospital, different ways of progressing patients between areas of the hospital which provide for differing levels of, for example, nursing dependency, and the effective integration of bed use with other aspects of hospital care such as the use of investigative facilities and the scheduling of operating theatres. The competence of hospital information systems for the ongoing monitoring of performance and feedback to operational policies forms part of this management environment. In a more local context (and depending on specialty) the effectiveness of bed use is influenced by waiting list management, forward planning for the discharge of patients, and the quality of collaboration between different specialties over such matters as the transfer of patients from one to another. The architecture of a hospital and the availability of options for deploying its resources in more flexible ways may be important constraints on the efficiency of its activities. 1.6 The social and demographic characteristics of the patients (and populations) for whom a hospital provides care influence bed occupancy in both direct and indirect ways. Examples of direct influences are that older patients are likely to have longer lengths of stay and that patients from socio-economically deprived backgrounds may be more ill and require more care. Both are likely to influence throughput and thus bed occupancy. The social, demographic and geographic context of a hospital will influence its options for earlier discharge in terms of the availability of alternative care and the uses it makes of its beds. Finally, 2

5 there is some evidence that the practice of Scottish hospitals varies in a more diffuse way as a response to the needs of the populations they serve and that these effects will influence the ways in which beds are used. Aims 1.7 Estimates of future needs for acute beds and care provision assume the efficient use of beds for all acute admissions. Although some of the variation in occupancy rates between acute specialties and between hospitals may be explained by, for example, differences in demand, the mix of emergency and elective cases, length of stay and hospital or specialty size, some of the variation may reflect differences in efficiency. This report was therefore commissioned to explore the extent of and reasons for differences in the efficiency of bed use. More specifically, the research sought to examine: who takes responsibility for efficient bed use whether and how hospital/specialty targets for bed use are set what in-hospital statistics are used to monitor and review bed use perception and evidence of day-to-day and seasonal stresses arrangements made for flexible use of beds (including the concept of bed ownership ) whether discharge policies contribute to efficiency the degree and causes of variations in bed occupancy. 1.8 The intentions of the research team were to identify good management practices which support efficient bed use and to use detailed statistical modelling to explore the feasibility and implications of increasing occupancy rates and document any constraints which must inform the evaluation of departures from a chosen average target. Methods 1.9 The research has three principal components: first, statistical analyses of the efficiency of bed use as it is influenced by hospital characteristics after controlling for such variables as case-mix, diagnostic mix and the characteristics of catchment populations; second, a parallel study of organisational and management practices of the study hospitals, including arrangements for the clinical management of patients as they progress through a hospital stay; and third, an econometric analysis of the costs and benefits associated with different levels of bed occupancy. The objective of the first component was to compare the performance of hospitals in different circumstances and thus gain insight into the extent to which variation in performance may be explained by such differences and the workloads they engender. The purpose of the second component was to provide an account of differing management practices; its combination with the first provides a basis for assessing those which might improve efficiency and for identifying the benefits that might accrue from their wider adoption. The third component explored the possible costs and benefits associated with different levels of occupancy in terms of both the effects of differences in occupancy rates on unit costs and the effects on the quality of services for patients for example, the likelihood that hospitals are unable to cope with peaks in demand without recourse to sub-optimal practices such as bed borrowing and the cancellation of elective admissions. Study design 1.10 For the reasons set out in the background section and with the constraint of time in mind the study focused on one major specialty with generally high occupancy rates and a grouping of smaller specialties with generally lower rates. Both were studied in the same types of hospitals in order to include broader information about the way in which the management practices of different specialties interrelate. Although this meant the study was not concentrating on how the total bed complement of a hospital might be managed, it still permitted examination of a fairly high proportion of the bed complement of the participating hospitals. It included consideration of beds used by the study specialties elsewhere in the hospitals and beds which may be made available to other specialties. Detailed analysis was carried out on the eight Trusts who participated 3

6 in the study and in which detailed interviewing was conducted; in addition, routine activity and economic data were analysed for all Trusts. This enabled the Trusts to be located within the wider framework of the whole of Scotland The statistical component of the study made use of routinely available linked SMR1 data to determine levels of bed use for the two specialty groupings suggested above: a high occupancy, high emergency admission category and a lower occupancy, high elective admission category. Chapter 2 presents an analysis of the daily variation in bed occupancy for the Trusts participating in the study. This includes fluctuations by day of the week and by the time of year and details the times at which Trusts face extreme pressure in terms of the availability of beds. Chapter 3 considers the effects of case-mix in terms of both demographic variables such as age and sex and the differences between Trusts in the diagnostic mix of their patients with particular emphasis on how these affect length of stay (with consequent effects on occupancy rates). Chapter 5 then analyses the relationship between daily bed occupancy and the discharge of a patient from hospital, with an interest in seeing how the former affects the latter. It also provides some detail about the effect of the day of the week and case-mix for different diagnostic groups and the differences between hospitals The management component was designed to provide information relating to bed management at different levels of hospital management. Chapter 6 provides an analysis of interviews with 113 individuals from the Trusts in the study, identifying the procedures and processes of bed management in place, together with their recommendations for its improvement. It also comprises an analysis of the respondents perceptions of the likely effects of increasing bed occupancy, either through increasing admissions or through further reducing bed numbers The economic component used data on Scottish Health Service costs to consider the possibility that differences in occupancy rates reflect differences in the efficiency with which beds are managed, and that hospitals which achieve relatively high occupancy rates are in some sense using their beds more efficiently than those with lower rates. Chapter 4 considers how direct costs per case and total costs per case varied for Scottish hospitals in the two specialty groupings between 1991 and It focuses on the relationships between these costs and occupancy rates one such factors as the length of stay and type and size of hospital has been taken into account. Selection of study hospitals 1.14 Eight Trusts were selected on the basis that they had sufficient bed complements in both of the specialty groupings. The sample chosen comprised teaching hospitals or large district general hospitals to ensure that they would be comparable. To as great a degree as was possible they were chosen to reflect apparent differences in occupancy ratios in the two specialty groupings, with the added constraint that they were not to overlap with the trusts being used by the Health Economics Research Unit at the University of Aberdeen in a related project. All of the Trusts approached gave their consent to the interviews for which we are grateful. 4

7 II: DAILY VARIATION IN BED OCCUPANCY Introduction 2.1 There has been much criticism, especially from medical and nursing staff, about using the term bed occupancy when discussing the performance of hospitals (Yates, 1982). Williams said there is a vast difference between a bedstead and a bed with adequate staff and services. A high bed occupancy rate may be associated with poor medical practice and service to the community (Williams, 1968). Despite such misgivings annual bed occupancy figures are still often used to evaluate or compare how hospitals or individual specialties are using their resources. The hospital with a high average occupancy rate may not necessarily be running more effectively than the hospital with a low average. High occupancy rates can be due to longer lengths of stay rather than greater numbers of patients being treated. Furthermore since these averages are generally calculated based on an average number of available staffed beds for a year they frequently conceal bed borrowing by other specialties, five day wards, and temporary ward closures. Midnight bed counts can fail to identify patients who do not remain overnight. Even when the number of staffed beds has been counted correctly, there is no certainty that other resources such as theatre capacity or diagnostic facilities can match the available beds (Yates, 1982). In this chapter patterns of daily occupancy rates have been highlighted in order to give some insight into the wide variations in bed occupancy between hospitals and individual specialties. Data 2.2 Analysis of data from the routine system of Scottish Hospital discharge summaries (form SMR1) and data from Scottish Health Service Costs for the financial year 1994/95 were used to investigate variation in daily occupancy rates for the two chosen specialty groupings: general medicine and its associated subspecialties (such as cardiology or gastroenterology) as a high occupancy, high emergency admission category and the combined grouping of ear, nose and throat (ENT), gynaecology, and ophthalmology as a lower occupancy, high elective admission category. In this part of the analysis, however, the three specialties in the lower occupancy grouping were considered separately. Daily occupancy rates were calculated for all hospitals in Scotland, but the results reported here will focus on the eight hospitals chosen for the project. Methods 2.3 The statistical package SPSS was used to calculated the daily bed occupancy of each of the hospitals for the specialties of interest. Bed occupancy (as a percentage) was defined as: Bed occupancy = number of occupied beds x 100 number of available staffed beds 5

8 It was possible to calculate the daily number of occupied beds for specialty and hospital, because patients lengths of stay; dates of admission and discharge are recorded on SMR1. Nearly 10% of medical inpatient discharges in the financial year 1994/95, however, were recorded as having a length of stay of zero days (that is, they did not remain overnight). For these patients, a length of stay of 0.5 days was assumed. Information on the daily number of staffed beds was not available and so an average figure the number of beds reported in Scottish Health Service Costs for the financial year 1994/1995 was used. Results Distribution of average daily bed occupancy 2.4 Figure 2.1 shows the distribution of average daily occupancy rates for the medical grouping for each of the eight hospitals. The hospitals have been ranked A to H in increasing order of their annual bed occupancy for general medicine. The Figure shows the substantial variations in daily occupancy rates throughout the year which annual averages conceal. Hospital H for instance had an average rate of 90%, yet on a daily basis in the financial year 1994/1995 its occupancy ranged between 57% to 114%. This pattern was not particular to hospital H or to general medicine: each of the hospitals had similar variation for all four specialties (Figures ). bed occupancy % A B C D E F G H hospitals Figure 2.1: Distribution of daily bed occupancy for general medicine and its associated sub -specialties 6

9 bed occupancy % A B C D E F G H hospitals Figure 2.2: Distribution of daily bed occupancy for ENT surgery bed occupancy % A B C D E F G H hospitals Figure 2.3: Distribution of daily bed occupancy for gynaecology 7

10 bed occupancy % A B C D F G hospitals Figure 2.4: Distribution of daily bed occupancy for ophthalmology Variation in bed occupancy rates by day of week 2.5 Day-to-day averages highlight how the number of occupied beds changes during the week. Bed occupancy is usually lower at the weekend due to fewer admissions because there are few elective medical admissions. GP referrals are also more likely to be held off until Monday (Audit Commission, 1992). Generally, each of the eight hospitals follows a similar pattern for bed occupancy by day of the week. In the medical grouping, they have higher occupancy rates from Monday to Thursday tailing off at weekend with an increase on Sunday (Figure 2.5). In ENT and gynaecology, occupancy rates tend be higher mid-week (Figures 2.6 and 2.7). Ophthalmology was the only specialty out of the four that did not have a clear trend for all the hospitals (Figure 2.8): hospital G follows a similar pattern to ENT and gynaecology, but hospital B s occupancy peaked on Tuesday and then gradually decreased until Saturday. 8

11 100 average bed occupancy% A B C D E F G 70 Mon Tue Wed Thu Fri Sat Sun H day of week 1994/95 Figure 2.5 : Average bed occupancy by day of week for general medicine and its associated sub-specialties 90 average bed occupancy% A B C D E F 30 G 20 Mon Tue Wed Thu Fri Sat Sun H day of week 1994/95 Figure 2.6 : Average bed occupancy by day of the week for ENT surgery 9

12 90 average bed occupancy% A B C D E F G 30 Mon Tue Wed Thu Fri Sat Sun H day of week 1994/95 Figure 2.7: Average bed occupancy by day of week for gynaecology 100 average bed occupancy% A B C D F 0 Mon Tue Wed Thu Fri Sat Sun G day of week 1994/95 Figure 2.8 : Average bed occupancy by day of week for ophthalmology 10

13 Seasonality of bed occupancy 2.6 The seasonal pattern of bed occupancy is less obvious than the day-to-day variation. For general medical beds (with a high rate of emergency admissions), occupancy rates were high between January and March, with a drop in the summer months and a slight decline in December (Figure 2.9); in the high elective admission specialities, (ENT Figure 2.10 and gynaecology Figure 2.11) occupancy rates remained steady for most of the year apart from a sharp decline in December. Occupancy rates were more erratic however for ophthalmology (Figure 2.12) with no great similarity between the hospitals apart from a decline in July and December. 2.7 As well as the expected winter peak for general medical admissions and and a corresponding trough for the elective specialties (especially in ENT and gynaecology), each of the eight hospitals tended to have their own individual peaks and troughs at different times of the year. As an example, Hospital H did not appear to have an obvious winter peak for general medicine, but remained at a high level of occupancy for most of the year apart from a reduction in the summer months. 110 average bed occupancy% A B C D E F G 60 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar H month 1994/95 Figure 2.9: Average monthly bed occupancy for general medicine and its associated sub-specialties 11

14 90 average bed occupancy% A B C D E F 30 G 20 H Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar month 1994/95 Figure 2.10: Average monthly bed occupancy for ENT surgery average bed occupancy% A B C D E F G 20 Apr May Ju n Ju l Aug Sep Oct Nov Dec Ja n Feb Mar H month 1994/95 Figure 2.11 : Average monthly bed occupany for gynaecology 12

15 90 80 average bed occupancy% A B C D 20 F 10 G Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar month 1994/95 Figure 2.12: Average monthly bed occupany for ophthalmology Bed occupancy and emergency admissions 2.8 The proportions of emergency admissions for the eight hospitals in the medical specialties ranged from 58% to 84%. Hospitals with a high average occupancy tend to have a greater number of emergency admissions (Figure 2.13). For most hospitals, a high proportion of their emergency admissions are during the winter months and on certain days of the week (Audit Commission, 1992). Since occupancy rates tend to be higher at these times, this gives hospitals the potential to schedule elective admissions outwith these periods, hopefully avoiding undue pressures on their beds. 13

16 100 bed occupancy (%) A B C D E F G H emergency admissions (%) Figure 2.13: Relationship between bed occupancy and emergency admissions for general medicine and its associated sub-specialties Elective, transfer and emergency admissions. 2.9 Figures show the division of admission types in the financial year 1994/95 for each specialty in the eight hospitals. The obvious difference is the much larger proportion of emergency admissions in general medicine compared with ENT, gynaecology or ophthalmology. In general medicine, the proportion of emergency admissions for the study hospitals (apart from C) was greater than the Scottish average (60.3% of admissions in 1993). There are marked differences between hospitals in both their proportion of emergency admissions (ranging from rather less than 60% in Hospital C to about 95% in Hospital H) and the proportions of patients of transferred from another speciality (under 5% in Hospital E and 20% in Hospital B). These differences will themselves influence annual average bed occupancy rates, but they also raise other doubts about the ways in which these categories of patients are recorded in one or other hospital. Similar although less substantial differences are seen for the other specialities. hospitals A B C D E F Elective Transfer Emergency G H admissions% Figure 2.14: Proportion of elective, transfer and emergency admissions for general medicine and its associated sub-specialties 14

17 A B C D E F Elective Transfer Emergency G H Figure 2.15: Proportion of elective, transfer and emergency admissions for ENT surgery A B C D E F Elective Transfer Emergency G H Figure 2.16: Proportion of elective, transfer and emergency admissions for gynaecology 15

18 A B C D Elective Transfer Emergency F G Figure 2.17: Proportion of elective, transfer and emergency admissions for ophthalmology Periods of pressure 2.10 Levels of occupancy over 90%, 95% and 100% or over were investigated for the eight hospitals. For general medicine and its associated sub-specialties, five of the eight hospitals had occupancy rates of 100% or more for at least five days of the year (Figure 2.18). It is interesting that these days were not during the winter period but were spread over a several months. What these high occupancy rates mean in two of the hospitals is illustrated in the examples below: Hospital H high bed occupancy 14 admissions per day, on average 9 or less free beds for 60% of the year no free beds for 12% of the year Hospital E average bed occupancy 21 admissions per day, on average 16 or less free beds for 23% of the year no free beds for 1% of the year 16

19 days in the year (%) Bed occupancy >90% >95% >100% 0 A B C D E F G H hospitals Figure2.18 : Periods of pressure for general medicine and its associated sub-specialties 2.11 The three specialties with high rates of elective admissions also had to deal with high occupancy rates. Figure 2.19 shows the percentage of days in 1994/95 when the combined elective specialties had occupancy rates of 90%, 95% and 100% or more. These days were again spread over a several months. The hospitals with the highest levels of bed occupancy in general medicine were not necessarily the same as those with high rates for the elective specialties. Hospital H had the greatest percentage of days of 90% and over for general medicine, but had the lowest number for the elective specialities; hospital C had relatively few high occupancy days for general medicine, but the second highest proportion for the elective specialities. These differences may be due to different ways of managing caseload and may also reflect the effects of bed borrowing in one or other hospital. days in the year (%) Bed occupancy >90% >95% >100% A B C D E F G H hospitals Figure 2.19: Periods of pressure for combination of ENT,Gynaecology and Opthalmology 17

20 Hospital Average Occupancy % No. of days occupancy>=90% No. of days occupancy>=95% No. of days occupancy>=100% A (3.3%) 3(0.8%) 0 B (6.0%) 1(0.3%) 0 C (8.2%) 8(2.2%) 0 D (21.4%) 19(5.2%) 9(2.5%) E (23.3%) 23(6.3%) 5(1.4%) F (17.5%) 22(6.0%) 5(1.4%) G (41.1%) 88(24.1%) 45(12.3%) H (59.5%) 107(29.3%) 45(12.3%) Table 2.1: Annual bed occupancy and periods of pressure for general medicine and its associated subspecialties 2.12 Tables detail the relationship between annual average and daily occupancy rates for the four specialities in each Trust and provide a helpful insight into the implications of different rates of annual occupancy. Leaving aside uncertainties about the ways in which annual rates might be calculated, one question that might be asked about them is whether it is possible to propose a right or acceptable annual rate for comparative purposes. There are, of course, other variables to be considered in such a discussion (Chapters III and IV provide an illustration). What crude annual averages do not do is take account of the requirements of a patient s admission at different stages of a hospital stay; for this reason, they do not reflect the severity of illness (and thus the necessary skill levels of appropriate staff) or provide a measure of the flexibility needed for the random arrival of emergency admissions. Boarding patients in the beds of other specialties may provide a solution to the simple necessity of admitting a patient to hospital but it can have more complex implications for the quality of care that is provided and such other aspects of their costs as their staffing needs There can, of course, be no absolute percentage rate of bed occupancy that reflects an optimal efficiency of bed use principally, for the reasons above, that a simple statistic cannot reflect the differing circumstances of individual hospitals. Leaving these objections aside, however, Table 2.1 provides a crude indication that for general medicine occupancy rates in the low 80s may be an approximate measure of what can be expected (noting always that these data relate to a small sample of Trusts). In Table 2.1, and assuming that a 95% occupancy rate reflects an upper limit if new cases are to be accommodated, Trust D with an annual average occupancy of 82% will encounter this higher level of demand about once every three weeks. Trust F (annual average 83%) will do so every seventeen days and the speciality will need to board its patients on only five days each year. In contrast, Trusts G and H (87% and 90%) will reach the level of 95% occupancy every three or four days and will need to board patients on one day in eight Rather different considerations apply to the high elective specialities when it seems necessary to ask whether seemingly low bed occupancy rates are a sensible basis for evaluating the work they undertake. These specialities are characterised by a high proportion of planned admissions associated with relatively short lengths of stay so that at the level of bed use such other influences as the availability of operating theatre resources or patterns of staffing beds over the course of a week (neither of which are captured by bed 18

21 occupancy rates) may be more significant determinants of their efficiency. It is also worth recalling that these beds are used as a reserve for the admission of patients from the high emergency specialities and that such admissions do not necessarily contribute to bed occupancy rates Even with these qualifications, the question of a right rate of bed occupancy is not well expressed in annual percentages. For ENT surgery, Trust C (annual rate 69%) experienced 90% occupancy every sixth day and had an occupancy rate over 100% on one day in 9. For gynaecology, Trust G filled 95% of its beds every sixth day and boarded patients every ninth day for an annual rate of 76%. Lower rates of occupancy suggest a more stable balance: In Trust F (annually, 60% for ENT surgery), 90% occupancy was reached every twelfth day and every fourteenth day in Trust B for an annual average of 59%. None of these simple calculations make allowance for low rates of occupancy at weekends. Hospital Average Occupancy % No. of days occupancy>=90% No. of days occupancy>=95% No. of days occupancy>=100% A (4.6%) 5(1.4%) 1(0.3%) B (7.1%) 15(4.1%) 12(3.3%) C (17.8%) 44(12.0%) 40(11.0%) D (0.3%) 1(0.3%) 1(0.3%) E (5.8%) 16(4/4%) 11(3.0%) F (8.5%) 10(2.7%) 5(1.4%) G (10.4%) 19(5.2%) 8(2.2%) H (1.4%) 0 0 Table 2.2: Annual bed occupancy and periods of pressure for ENT surgery Hospital Average Occupancy % No. of days occupancy>=90% No. of days occupancy>=95% No. of days occupancy>=100% A (0.8%) 0 0 B (13.2%) 29(7.9%) 18(4.9%) C (11.5%) 28(7.8%) 13(3.6%) D E (2.5%) 3(0.8%) 2(0.5%) F (1.4%) 2(0.5%) 1(0.3%) G (26.0%) 65(17.8%) 43(11.8%) H (0.3%) 0 0 Table2.3: Annual bed occupancy and periods of pressure for gynaecology 19

22 Hospital Average Occupancy % No. of days occupancy>=90% No. of days occupancy>=95% No. of days occupancy>=100 % A B C (8.8%) 20(5.5%) 13(3.6%) D (10.7%) 28(7.7%) 19(5.2%) E F (9.0%) 20(5.5%) 7(1.9%) G (23.3%) 65(17.8%) 57(15.6%) H Table 2.4: Annual bed occupancy and periods of pressure for ophthalmology Conclusion 2.16 The main argument of this Chapter has been to suggest that annual average bed occupancy rates do not or do not necessarily reflect the efficiency of hospital resource use even at the simplistic level of counting beds. Hospitals reporting an average occupancy of 90% may appear to be working efficiently when in fact they are treating fewer patients with longer lengths of stay. When used for comparative purposes, crude bed occupancy rates do not take account of the legitimacy of differences resulting from factors outwith the control of the hospital or its management: these will include variation in case-mix, in diagnosticmix, and the social context of a hospital s practice when influences such as bed blocking. Although it is tempting to seek some simple comparative statistic, this first level of the project s analysis illustrates other features of the problem, especially in its demonstration that annual averages fail to reflect daily, weekly, or seasonal variations in demand. There are two sides to this issue: first, the idea that there might be some rule-of-thumb rate of expected bed occupancy is attractive in the sense of estimating whether a speciality is under- or over-performing: with the strong proviso that these rates may not reflect the other circumstances of a Trust, there are suggestions in Table 2.1, that for general medicine Trusts A and B may reflect some degree of spare capacity whereas Trust H is may be under-resourced. Two points are worth making, however: first, that this view is based more on observations about their daily bed occupancy than on their annual rates, and, second, that it is based only these rates. It takes no account of what may be more significant determinants of them principally, lengths of patient stay and (for example) the options they may have for treating patients as day-cases rather than as in-patients The second issue which may have greater relevance to the bed occupancy rates of the elective specialities concerns the extent to which simple measures of this kind reflect the patterns of work undertaken in contemporary hospital practice and thus the link between bed occupancy as a measure of such work and the actual provision of patient care. This is an important question because the introduction of contracting has shifted the focus of hospital management (and Trust accounting) away from historical measures of activity bed occupancy is one towards a more patient-focused perspective. The question is important because whether regarded as an indicator of spare capacity or as a measure of efficient resource use (as a correlate of costs), there is no a priori reason to assume that bed occupancy rates will reflect the more significant resources needed to provide patient care This view leads on to a rather different conclusion about the uses of bed occupancy rates. Clearly, there are arguments for the efficient use of beds within Trusts so that, internally, there will be a need to monitor bed use as part of the management processes of a Trust. At this level, bed occupancy rates can have value as a way of monitoring other requirements staffing needs is an obvious example. Comparisons between Trusts, on the other hand, create the need for a wholly different approach to evaluating performance 20

23 or efficiency: later sections of the Report identify both the inadequacies of present data for these purposes and provide a preliminary account of the considerations necessary if a more useful understanding of complex problems is to be reached References Audit Commission. (1992) Lying in wait: the use of medical beds in acute hospitals. Information and Statistics Division. Scottish Health Service Costs. Information and Statistics Division, National Health Service in Scotland, 1991 to Williams, B. (1968) The use and misuse of bed-occupancy and waiting list figures. Lancet 1968 i Yates, J. (1982) Hospital Beds: A problem for diagnosis and management? 21

24 III: CASE MIX Introduction 3.1 Adjusting for the case-mix of populations for the purpose of comparing units (e.g. treatments, institutions) is an essential part of health research. Despite the importance of case-mix adjustment there is no consensus on the correct method for doing so. The problem is particularly difficult when using routine hospital data because the level of information is understandably limited. The aims of this chapter are to outline some of the rationale for methods of case-mix adjustment in the analyses presented in this report and to present some results which highlight the importance of case-mix adjustment. Healthcare Resource Groups (HRGs) 3.2 In both the econometric and survival analyses, it was necessary to use information from all discharges from General Medicine and its sub-specialties. Given the range of diagnoses and operational procedures which would be involved in the data set, the main concern was how to reduce the dimensionality of the data. It was deemed sensible to use an established case-mix adjustment tool and The National Casemix Office s HRG classification, Version 3, appeared appropriate. The grouping algorithm uses the OPCS procedure codes and ICD diagnosis codes as the main sources of information to assign hospital episodes to groups which are homogenous in terms of resource utilisation. The classification can also depend on the age of the patient, the method of discharge (i.e. dead or alive) and length of stay (LOS); further details are available in the Introduction of the documentation set for HRG, Version The HRG classification of episodes results in a maximum of 572 groups which form a total of nineteen chapters. Chapter headings and the number of HRGs in each are detailed in table 3.1. The division of HRGs into chapters will be referred to again in chapter 5 when describing the results from the survival analyses. In the present chapter, these chapter headings are used to illustrate the differences between hospitals case mix. 22

25 HRG Chapter Heading n HRGs Nervous System 36 Eyes and Periorbita 12 Mouth, Head, Neck and Ears 31 Respiratory System 36 Cardiac Surgery and Primary Cardiac Conditions 38 Digestive System 65 Hepato-biliary and Pancreatic System 25 Musculoskeletal System 56 Skin, Breast and Burns 47 Endocrine and Metabolic System 19 Urinary Tract and Male Reproductive System 57 Female Reproductive System 21 Obstetrics and Neonatal Care 12 Diseases of Childhood 26 Vascular System 20 Spinal Surgery and Primary Spinal Conditions 20 Haematology, Infectious Diseases, Poisoning and Nonspecific Groupings Mental Health 17 Undefined Groups 7 Total 572 Table 3.1 : HRG chapter headings and HRG content 27 Variation in case-mix between study hospitals 3.4 For the eight study hospitals in the project, all episodes in General Medicine and its associated subspecialties in were grouped into HRGs and the corresponding HRG chapters. A comparison of the variation in case-mix between hospitals is illustrated using HRG chapters in figure 3.1. For the purpose of this illustration, only the six largest chapters are shown and all remaining episodes are grouped into Other Chapters. The workload of the hospitals is shown as the percentage of hospital episodes within each of these seven groups. 23

26 H G F hospital E D C B A % of all discharges Cardiac Surgery/Conditions Haema/Inf. Diseases etc Respiratory System Digestive System Nervous System Urinary/Male Repro. Other Chapters Figure 3.1 : HRG-mix in eight study hospitals 3.5 It is obvious that even under the broad HRG chapter headings, the case-mix of each hospital is quite different. For example, hospitals B and C have the same proportion of episodes for Cardiac Surgery and Primary Cardiac Conditions, and approximately the same proportion of episodes for Haematology, Infectious Diseases, Poisoning and Non-specific Groupings. However, hospital B has approximately twice the proportion of episodes for the Respiratory System than hospital C, and hospital C has twice the proportion for the Urinary Tract and Male Reproductive System. 3.6 Comparing case-mix at the level of HRG chapter is only an indication of how the patients treated and the treatments they receive differ between hospitals. Within each chapter there can be up to 65 distinct HRGs and within each HRG there may be important differences between patients which are not taken into account by the grouping algorithm. Age of the patient is used at a basic level to distinguish between the under and over 70 s, for example. Social factors such as marital status and deprivation are also known to affect the level of resources required by different patient groups (see Chapter 4). 3.7 Table 3.2 illustrates some of the other differences in case-mix between the eight hospitals. The average age and sex ratio of episodes do not vary markedly among the hospitals. There are, however, large variations in other factors; the proportion of patient episodes for married patients ranges from 47% to 61%, while the proportion of patient episodes for patients from the most deprived areas in Scotland (upper quartile of Carstair s scores for postcode sectors, 1991) ranges from 5% to 67%. It is recognised that important social processes can operate through such factors as family structure and deprivation which in turn influence the need for health services (e.g. longer lengths of stay). Therefore, the large variations illustrated in table 3.2 have important implications for understanding the different services being provided in each hospital. There are also wide variations in the types of episode according to whether any co-morbidities were recorded for each episode (ranging from 30% to 66%) and whether the case was an emergency admission (56% to 84%) both these factors will increase length of stay (LOS). The number of episodes which ended in a transfer to another specialty within the same hospital ranged from 4% to 27%. Indicators such as this may reflect differences in the case-mix of the patients arriving in General Medicine as well as differences in 24

27 hospital management or scope for management - larger hospitals will be more able or likely to operate between specialty transfers than hospitals with fewer specialties. Hospital age (mean) males (%) married (%) deprived (%) comorbidity (%) emergency adm. (%) transfer (%) A B C D E F G H Table 3.2 : Variation in case-mix among the eight study hospitals HRG-adjusted LOS vs. crude LOS 3.8 The following section provides an illustration of the extent to which LOS varies between hospitals and how this is influenced by case-mix. Table 3.3 details the crude and HRG-adjusted LOS for General Medicine and its associated sub-specialties in 26 hospitals for the financial years 94/95 and 95/96. The 95/96 HRG-adjusted LOS was taken from the Blue Book and the other figures were derived from the SMR1 data set. The 26 hospitals form the sample used in the econometric analysis ; a description of the sample and the methods used to calculate the scores can be found in Chapter 5. Figures below 100 indicate that LOS in these hospitals was less than the Scottish average, while figures above 100 indicate that LOS was longer than average. Hospitals are ordered in ascending crude LOS score in 94/ The purpose of this table is to illustrate two points. The first is that adjusting for case-mix using HRGs can substantially change the rating of a hospital s performance relative to the Scottish average. In 94/95, for example, the crude LOS score for hospital 24 was 79 indicating that it had one of the shortest stays of the 26 hospitals. However, adjusting for case-mix increased the LOS score to 107, implying that the stays were longer than average for the types of patients being treated there. The second point is that the LOS for individual hospitals varies from year to year. Clearly this variation over time exists for both the crude LOS scores and the adjusted ones. The point to make is that variation in the crude LOS may be attributable to changes in case-mix rather than changes that have taken place in the organisation and policy of the hospital. For example, the crude LOS score for hospital 26 increased from below average (89) to above average (103) between 94/95 and 95/96. However, by considering the HRG-adjusted LOS scores, it would appear that the hospital actually decreased LOS relative to the Scottish average Table 3.4 contains crude and adjusted LOS scores for the 26 hospitals but these are now calculated for the selected low occupancy specialties of ENT, Gynaecology and Opthamology. The table is included for reference and illustrates the same points as those made for General Medicine and its associated subspecialties. 25

28 Conclusions & Discussion 3.11 The purpose of this chapter has been to illustrate that case-mix can vary substantially among hospitals and if one wishes to compare hospitals in terms of performance or management of resources, then failing to take account of these differences can be misleading. Figure 3.1 and table 3.2 compared the eight study hospitals in terms of their HRG-mix and other case-mix indicators and both showed important differences in the composition of patient episodes. Given that the provision of services will be directly affected by these differences, clearly it is not sensible to compare the hospitals in terms of outcomes without first adjusting for differences in inputs (e.g. patients) and processes (e.g. treatments) The following chapter provides a more detailed account of the effects of case-mix on length of stay. In some cases, differences between patient groups were found to be quite small, for example, increasing length of stay with increasing deprivation. However, if these differences are considered at the hospital level, where the percentage of patients from deprived areas can vary from just 5% to over 60% (table 3.2), the effect on the hospital s average length of stay will be much greater. Other factors, such as the episode ending in a transfer to another specialty within the same hospital may reflect differences in case management as well as differences in case-mix. Episodes ending in a transfer were found to reduce LOS but this does not mean a reduction in the total time the patient spends in that hospital. The fact that between specialty transfers can account for one in four episodes in some hospitals (table 3.2) implies that beds should be considered a trust resource rather than a resource at the specialty level This chapter also provided an illustration of the effects of case-mix with tables 3.3 and 3.4 detailing LOS scores for 26 Scottish hospitals with and without adjusting for HRG-mix. These showed how inferences drawn from both cross-sectional and temporal variation in LOS could be affected by taking account of HRG-mix. Further implications of taking account of case-mix will be discussed in chapter 5 which describes the econometric analysis of bed occupancy and cost per case It was stated at the start of this chapter that adjusting for case-mix with routine data can be problematic because information is limited. For example, information on severity of disease is not available in the routine data and current methods have to rely on proxy measures such as co-morbidities and emergency admissions in an attempt to take severity into account. Therefore, while the use of adjusted measures are preferable to crude measures, adjustment should not be considered a panacea. However, making an attempt to understand the differences between hospitals that result from differences in the patients they treat and the services they provide will be provide a more informative step towards understanding how resources are managed. 26

29 Crude Score HRG Adjusted Score Hospital 1994/ /96* 1994/ /96* B D F H A C E G Table 3.3 : Crude and HRG Adjusted Length of Stay Scores for 26 Scottish Hospitals: Financial Years 1994/95 and 1995/96 General Medicine and Associated Specialities: Scotland = 100 Notes: * 1995/96 scores taken from Blue Book data 27

30 Crude Score HRG Adjusted Score Hospital 1994/ /96* 1994/ /96* D C H E F B G A Table 3.4 : Crude and HRG Adjusted Length of Stay Scores for 26 Scottish Hospitals: Financial Years 1994/95 and 1995/96 ENT, Gynaecology, Ophthalmology: Scotland = 100 Notes: * 1995/96 scores taken from Blue Book data 28

31 IV: SURVIVAL ANALYSIS Introduction to the use of survival analysis 4.1 Survival analysis is the name given to the statistical technique used to model the duration of an episode from a defined start point to an end point for a number of individuals, with particular reference to the determination of the influence of individual characteristics on this duration. A common example of the use of survival analysis would be to follow up patients until death (the end point) following the administration of a drug or treatment (the start point). The survival analysis would then be able to provide information as to whether survival was improved amongst a group of patients taking one drug over another, or to give a description of the ways in which the patient s age and comorbidities affect their survival. The analysis may be complicated by individuals leaving the study before reaching the end point for example, a patient being lost to follow-up before death. The survival time of such a patient is censored and should be included in the analysis because, although the precise time of death is not known, the time at which they left the study usually is and they were known to be alive at this time. 4.2 The modelling of hospital stays may then be conducted using survival analysis, although in this context survival may appear to be a misnomer since the duration of interest is not until death but from admission to hospital (the start point) until discharge (the end point). The analysis presented in this chapter focuses on the factors influencing the decision to discharge patients, with particular reference to the impact of daily bed occupancy rates. Patients who died during their hospital stay are therefore regarded as being censored, since no decision was taken about their discharge and they therefore could not be regarded as having reached the end point. The analysis was performed for the financial year running from 1 April 1994 to 31 March 1995 to tie in with the economic analysis over the same period; for this reason there was also left-censoring of patients who were admitted to hospital before 1 April 1994 but discharged on or after this date and rightcensoring of patients who were admitted on or before 31 March 1995 and subsequently discharged on or after this date. 4.3 The data structure is hierarchical, relating to episodes nested within patients who are treated within hospitals. This structure is likely to have consequences for the analysis; patients within the same hospital are likely to have similar decisions taken concerning their appropriate length of stay. With data available for a total of 45 hospitals a random effects model is appropriate whereby the higher order effects (the effects of hospitals upon the length of stay of their patients) are assumed to be normally distributed. 4.4 A variable such as bed occupancy is known as a time-dependent covariate its value changes from one day to the next. The survival analysis essentially uses information from every day of a patient s stay and not just the day of discharge; in addition to the occupancy at discharge it is therefore also important to consider the occupancy on each day that the patient was not discharged. The rationale behind this is that if there is a link between daily occupancy rates and discharges if, for example, patients are more likely to be discharged when occupancy rates are high then the association must run both ways and patients should be less likely to be discharged when occupancy rates are low. Multilevel modeling was used to build a random effects survival model for data including time-dependent covariates. The following section introduces the 29

32 principles of this analysis and some of the more commonly occurring issues are explored in detail. This is then followed by details of the Analyses and results for General Medicine and its associated sub-specialties.. Case study: Chronic obstructive airways disease (COAD) 4.5 The purpose of this section is to familiarise the reader with the concepts of multilevel survival analysis and explore some of the issues which will arise in each of the 14 analyses for general medicine. This will alleviate the need for repetition of key findings. The underlying algebra and many of the results are presented in Appendix B; for a fuller understanding this chapter should be read in conjunction with that Appendix. 4.6 The data relate to 6109 hospital episodes with principal diagnosis of chronic obstructive airways disease (ICD9 496). This was selected as an example of a common condition for which there may be multiple episodes per patient and for which there was a reasonably long mean length of stay and reasonable variation about this mean, implying that the possibility may exist to reduce stays and that there may be a lack of consensus as to an appropriate length of stay. 4.7 A total of 4229 patients were admitted to hospital during the course of the financial year 1994/95, generating 6109 episodes between them for an average of 1.44 episodes per patient. The mean length of each episode of care was 8.69 days (S.D. 7.90). Admissions were made to 39 hospitals, giving a mean of 157 episodes per hospital (range 1 to 798); the range in mean length of stay was from 4.19 to days. The mean number of episodes per patient varied from 1 to There were 5956 discharges meaning that 153 (2.5%) episodes were right censored. Covariate effects 4.8 The effect of each covariate variables describing the individual patient (such as age and sex) is expressed as the risk of discharge relative to a reference group. A risk greater than one implies a greater risk of discharge than the reference group on any given day and hence implies that shorter lengths of stay are associated with that covariate than with the reference group. The estimates given in table B2, for example, suggest that the relative risk of discharge for women is 0.96 relative to that of men (95% C.I ). For a continuous variable, such as age, the figure relates to a unit increase in that variable. The same table implies that each additional year of age gives a relative risk of discharge of 0.98 (95% C.I ). This risk is the same whether comparing a 61 year old to a 60 year old or a 71 year old to a 70 year old. These risks are multiplicative; the relative risk of discharge of a 60 year old is therefore 1.17 compared to a 70 year old (95% C.I ). Hospital effects 4.9 Each hospital is assumed to have a constant effect on the relative risk of discharge of its patients, and these effects are multiplicative in the same way as patient covariates. The effects of the hospitals are estimated from the data. The fact that these effects vary between hospitals is an explanation for observed differences between hospitals for the length of stay of their patients even after the standardisation for case mix and HRG mix. The degree of variation between hospitals enables the construction of a coverage interval for hospital effects (see figure B2). Patient effects 4.10 Some patients will be admitted to hospital more than once during the course of the year. In the same way that a hospital could have an effect on the length of stay of its patients possibly due to the clinical decision process regarding the correct length of stay there may be patient effects. Thus a decision to keep a patient in for an above average length of stay on one occasion may be repeated on subsequent occasions. This may reflect conditions not collected on SMR1 but which remain constant for the patient, such as social circumstances or the severity of the condition. Table B4 indicated that these patient effects were large 30

33 compared to the hospital effects for chronic obstructive airways disease about three and a half times the size. However, the remainder of this chapter has considered the analysis of episodes within hospitals with no regard to the consequences of patients generating multiple episodes with the following reasoning. Our interest is focused on the effects of the covariates and of bed occupancy in particular; the addition of the patient level of analysis did not appear to alter the covariate estimates by any reasonable magnitude (see table B4). By its nature, chronic obstructive airways disease is likely to have a larger number of multiple episodes per patient that General Medicine as a whole 72% of the patients treated in General Medical specialties throughout Scotland in only had a single episode of care. Moreover, the addition of the patient level is only the partial solution to a particularly complex problem; completeness would require the inclusion of the cross-classification of patients by hospitals, since patients may have attended more than one hospital, and of patients by HRG chapter since there may be associations within patients across different treatment categories. The effect of area of residence, although not found to be of importance for this diagnosis, should similarly be cross-classified by the hospital. The use of an episode and hospital hierarchy was therefore designed to provide a quick solution to what could be a very resource-intensive problem. Time-dependent hazards 4.11 It may be that the effect of a covariate, in terms of the relative risk of discharge, is not the same throughout a patient s stay in hospital. For example, is the risk of a female patient being discharged relative to a male always 0.96 or does this effect diminish as the length of stay increases? A cursory examination of such possibilities indicated that even when such effects are significant they tend to be extremely small and do not effect other parameter estimates (see table B3). Bed occupancy 4.12 There are a number of possible relationships which can be envisaged between bed occupancy and the relative risk of discharge. Different formulations based upon a simple linear relationship with occupancy, step functions depending upon the level of occupancy above a certain level or occupancy only factoring after a certain length of stay yielded the same relationship. The remaining analyses therefore consider just the simple linear relationship. Survival Analysis for General Medicine 4.13 The following section of the report contains results from the analysis of length of stay for all discharges from General Medicine specialties in the financial year The statistical methods have been described in the previous section detailing the pilot analysis for COAD discharges and in Appendix B. The data 4.14 There were 226,228 episodes in General Medicine and its associated sub-specialties in Scotland in , involving 52 hospitals. The main aim of the analysis was to determine the association between bed occupancy and LOS, and it was therefore necessary to omit observations from hospitals for which bed occupancy was either unavailable or appeared unreliable. Information on bed occupancy was not available for two hospitals and was considered unreliable for five hospitals. In most cases the unreliability arose from very high daily bed occupancy figures which were likely to have resulted from the average number of staffed beds, taken from the Blue Book, changing some time in the financial year. Omitting these eight hospitals resulted in losing 2,483 discharges (1.1%). 31

34 Adjusting for case-mix 4.15 The number of diagnoses and operational procedures which were carried out for all medical discharges meant that it was necessary to adopt an established case-mix adjustment tool. Each discharge was assigned an HRG code based on the National Casemix Office s grouping, Version 3. Other information recorded on the SMR1 about factors known to affect LOS was also included in the models. These factors were the age and sex of the patient, their marital status and a deprivation score of their area of residence, using 1991 Carstair s scores for postcode sectors. Also included in the models was whether or not any secondary diagnoses were recorded, the type of admission and type of discharge. Format of analyses 4.16 Due to the number of discharges, it was necessary to split the data into groups which could be handled with the available computing hardware. HRG chapters formed an obvious classification of the data and it seemed reasonable to carry out a separate analysis for each chapter. The number of discharges and the percentage of all discharges for each chapter heading are detailed in table 4.1. The largest HRG chapter, Cardiac Surgery and Primary Cardiac Conditions, had to be split further into two sets because the number of discharges could not be processed in a single model. 1 Analyses for six of the nineteen chapters were not carried out because the number of discharges were relatively small. These chapters were Eyes and Periorbita, Mouth, Head, Neck and Ears, Female Reproductive System, Obstetrics and Neonatal Care, Spinal Surgery and Primary Spinal Conditions, and Undefined Groups. In total, these chapters only formed 2.3% of all discharges. This resulted in fourteen separate analyses. 1 The Cardiac Surgery and Primary Cardiac Conditions chapter was split into two groups: group 1 contained the HRGs for Acute Myocardial Infarction, Heart Failure or Shock, Cardiac Arrest and Angina, and resulted in 32,695 episodes. All other HRGs from the chapter were combined to form group 2 which consisted of 45,094 episodes. 32

35 HRG Chapter Heading n discharges % Nervous System 21, Eyes and Periorbita Mouth, Head, Neck and Ears 1, Respiratory System 29, Cardiac Surgery and Primary Cardiac Conditions 77, Digestive System 20, Hepato-biliary and Pancreatic System 4, Musculoskeletal System 8, Skin, Breast and Burns 3, Endocrine and Metabolic System 6, Urinary Tract and Male Reproductive System 9, Female Reproductive System Obstetrics and Neonatal Care Diseases of Childhood 3, Vascular System 2, Spinal Surgery and Primary Spinal Conditions 1, Haematology, Infectious Diseases, Poisoning and Nonspecific Groupings 27, Mental Health 4, Undefined Groups 1, Total 226, Table 5.1 : Distribution of discharges by HRG chapter headings Results Case-mix effects 4.17 The main interest of the analysis was in the effect of bed occupancy on the probability of discharge. In order to address this research question and report differences between hospitals it was necessary to adjust for the case-mix of the patients as outlined in paragraph The effects of these variables on LOS are generally well understood, e.g. LOS increases with age, hence the probability of discharge decreases with age Estimates for the case-mix effects were obtained from each of the fourteen analyses except where noted. The size and direction of the effect, either an increasing or decreasing effect on the probability of discharge, changed according to HRG chapter. For the purpose of the summary presented in table 4.2, the results from the fourteen analyses for each variable were split according to how many increased and decreased the risk of discharge, and how many of each were statistically significant. Table 4.2 should give an overview of the direction and consistency of the effects - specific details of the estimates and confidence intervals can be found in Appendix C, tables C1-C5. With the exception of age, all variables were 33

36 categorical which meant that the results were estimated as the risk of discharge relative to a reference category. For all effects described in table 4.2, the reference category is given in italics. For example, the effect of sex was estimated as the risk of discharge for women relative to men Increasing age, having co-morbidities, being transferred into a specialty or being admitted as an emergency all had a decreasing effect on the probability of discharge and were relatively consistent across HRG chapters. This would be expected as all these variables are indicators for more complex cases and hence longer lengths of stay. Women tended to have longer stays than men although these effects were significant in just under half of the HRG chapters. Compared to patients who were single, married patients were discharged earlier, while being widowed or divorced had a less clear effect on LOS. Patients from more deprived areas had longer stays but these effects were generally quite small and more often than not, not statistically significant. While being discharged from a specialty was the outcome variable, different types of discharge, excluding death, were thought to be reasonable case-mix indicators. However, with the exception of being transferred to another specialty within the same hospital, which significantly reduced LOS in all HRG chapters, the effects estimated for type of discharge were not very consistent across the HRG chapters. 34

37 case-mix variable effect on probability of discharge increase n (significant) decrease n (significant) age (increasing) 0 (0) 14 (13) sex (male) female 3 (0) 11 (6) secondary diagnosis (none) one or more 0 (0) 14 (13) marital status (single) married ** 12 (12) 1 (0) widowed *,** 9 (3) 3 (1) other (incl. divorced) ** 6 (3) 7 (1) deprivation score (low deprivation quartile) medium-low deprivation quartile 4 (0) 10 (3) medium-high deprivation quartile 2 (0) 12 (4) high deprivation quartile 3 (0) 11 (2) type of admission (waiting list etc.) transfer 0 (0) 14 (12) emergency 2 (1) 12 (10) type of discharge (home) to another hospital 6 (4) 8 (6) to another specialty within same hospital 14 (14) 0 (0) other type of discharge * 10 (6) 3 (1) Table 4.2 : Summary of association between case-mix variables and risk of discharge * one effect missing due to non-convergence in the estimation procedure; this can arise when the estimate is close to zero and the number of cases are small ** marital status parameters not estimated for HRG chapter Diseases of Childhood, non-applicable. The effect of bed occupancy on LOS 4.20 The effect of bed occupancy was modelled as the change in risk of discharge associated with a 1% increase in bed occupancy; the estimates of relative risk and 95% C.I.s are shown in figure 4.1. Estimates below one can be interpreted as a decrease in risk of discharge with increasing bed occupancy and estimates above one as an increase in risk of discharge. Confidence intervals which do not include one indicate that the effect was statistically significant The pilot analysis of COAD showed that an increase in bed occupancy was associated with a decreased risk of discharge rather than an increased one. The same result was found for twelve of the fourteen HRG chapters, although the decreasing effect was statistically significant in four cases only. The HRG chapters showing a significant decrease in risk of discharge with an increase in bed occupancy were the Respiratory system, both groups of Cardiac Surgery and Primary Cardiac Conditions, and the Musculoskeletal system. 35

38 Two chapters, the Digestive system and the Hepato-biliary and Pancreatic system showed an increase in risk of discharge with an increase in bed occupancy but only the latter was statistically significant. Figure 4.1 : The effect of bed occupancy on risk of discharge. Nervous system Respiratory system Cardiac conditions 1 Cardiac conditions 2 Digestive system Hepato/Pancreatic Musculoskeletal Skin,Breast & Burns Endocrine/Metabolic Urinary/Male Repro. Childhood diseases Vascular system Haema/Inf. Dis. etc. Mental Health % C.I. R.R Even for those chapters where the effect of bed occupancy was found to be statistically significant, the size of the effect was small. Taking the Respiratory system HRG chapter as an example, if bed occupancy increased from 80% to 85%, the relative risk of discharge would be 0.98, a 2% decrease in risk of discharge. For discharges associated with the Hepato-biliary and Pancreatic system, the same change in bed occupancy would result in a relative risk of 1.02, a 2% increase in risk of discharge. The effect of day of the week on LOS 4.23 In addition to estimating the day-to-day effect of changing bed occupancy on the risk of discharge, it was also possible to investigate the pattern of discharges with the days of the week. As with the case-mix effects,, the effects of days of the week varied across the HRG chapters and a summary is presented in table 4.3. As before, the table details the number of chapters for which days of the week effects were found to be increasing or decreasing the probability of discharge and the number of cases where these effects were found to be statistically significant. Also included in the table is the median relative risk and the range of estimates obtained from all fourteen analyses. 36

39 day of the week effect on probability of discharge R.R. Monday (reference) increase n (significant) decrease n (significant) median range Tuesday 8 (3) 6 (1) 1.00 (0.80,1.26) Wednesday 10 (6) 4 (0) 1.05 (0.93,1.26) Thursday 12 (6) 2 (0) 1.05 (0.95,1.29) Friday 14 (14) 0 (0) 1.49 (1.23,2.06) Saturday 1 (1) 13 (11) 0.71 (0.62,1.43) Sunday 0 (0) 14 (14) 0.36 (0.30,0.75) Table 4.3 : Summary of association between days of the week and risk of discharge 4.24 The probability of discharge was most noticeably increased on a Friday ; the increased effect was significant for all analyses and the median R.R. was 1.49, implying the risk of discharge was increased by 50%. A significantly increased risk of discharge on Tuesday through to Thursday was found for some HRG chapters but the median size of the effect was relatively small. Not surprisingly, risk of discharge was significantly reduced on Saturday and Sunday, the exception to this week-end effect was Cardiac Surgery and Primary Cardiac Conditions 1 where the risk of discharge was significantly increased on a Saturday. The exact nature of this outlying effect would need to be investigated further. Hospital effects 4.25 It was necessary to combine the results from the fourteen analyses to obtain a single figure which would describe the overall hospital effect on the risk of discharge. A straightforward correlation between the hospital residuals from each analysis showed that the results found were generally consistent across HRG chapters. That is, if a hospital had an increasing effect on the probability of discharge for a given HRG chapter it was likely to have an increasing effect on the probability of discharge for other HRG chapters. A summary of the correlations between HRG chapters is presented in table 4.4. For each HRG chapter, the median correlation coefficient is presented along with the range of all correlations and the number out of thirteen which were statistically significant. 37

40 HRG Chapter Heading correlation median correlation range n significant Nervous System 0.54 ( ) 13 Respiratory System 0.51 ( ) 13 Cardiac Surgery and Primary Cardiac Conditions ( ) 12 Cardiac Surgery and Primary Cardiac Conditions ( ) 12 Digestive System 0.54 ( ) 13 Hepato-biliary and Pancreatic System 0.49 ( ) 12 Musculoskeletal System 0.45 ( ) 12 Skin, Breast and Burns 0.42 ( ) 12 Endocrine and Metabolic System 0.42 ( ) 12 Urinary Tract and Male Reproductive System 0.46 ( ) 12 Diseases of Childhood 0.38 ( ) 8 Vascular System 0.53 ( ) 13 Haematology, Infectious Diseases, Poisoning and Nonspecific Groupings 0.51 ( ) 13 Mental Health 0.59 ( ) 13 Table 4.4 : Correlation between hospital effects among HRG chapters 4.26 Using the number of discharges from each HRG chapter within each hospital, it was possible to calculate a weighted average of the hospital effects. This resulted in a measure of each hospital s deviation from the Scottish average in terms of risk of discharge, weighted according to their workload. Considered alone, interpretation of this weighted average is non-trivial, hence the measures and their confidence intervals are reported in table C6 in Appendix C. The composite measure and its statistical significance will be used in the final chapter where results from all analyses will be combined. It is sufficient to report at this stage that of the 45 hospitals analysed, sixteen hospitals had the effect of significantly lowering the chance of discharge, 10 hospitals significantly increased the chance of discharge and 19 hospitals had effects which were not significantly different from the Scottish average. Conclusions and Discussion 4.27 The main conclusion from this section of the study is that increased bed occupancy does not generally result in an increased risk of discharge. On the contrary, the results suggest that high bed occupancy is associated with a decreased risk of discharge. There are a number of interpretations of this result. Firstly, it may be argued that longer stays are associated with higher bed occupancy and the analyses carried out in this study have not controlled sufficiently to enable a true estimate of the relationship between the decision to discharge and decreased resources (i.e. staffed beds). It was hypothesised that occupancy could be used as a proxy measure of the pressure on beds; the results of this chapter would appear to imply that this is not the case. The results of chapter 5, the econometric analysis, indicated that for both direct cost per case and total cost per case, increasing occupancy rates changed from decreasing costs to increasing costs when crude length of stay was replaced by a standardised index. The implication of this, which is borne out by this chapter, is that occupancy rates may do little more than reflect lengths of stay. 38

41 4.28 A second argument is that the estimated linear relationship between bed occupancy and risk of discharge may be too simplistic. It is plausible that decreased resources do not feature in the decision to discharge until after a certain point in an individual s stay, e.g. until after the average LOS for a given episode. A more detailed exploration of the relationship between bed occupancy and the risk of discharge was carried out in the case study of COAD discharges and found that the negative association between the two variables remained in a number of different models. A third and final explanation of the findings is that they are not the result of confounding or model mis-specification but are a true estimate of the relationship between bed occupancy and risk of discharge. This might then imply that stays are shorter when resources are less limited; this association might arise for a number of reasons, for example, shorter waiting times for surgical procedures Interpretation of the results from this section need to be considered together with the other findings presented in this report. In particular, what exactly does bed occupancy measure in the context of available resources and hospital efficiency? It would appear that the effect of bed occupancy on LOS is far less important than many of the other variables explored in the models, such as the age, sex and marital status of the patients being treated. Adjusting for the case mix of the patients will generally explain much of the between hospital variation in LOS and adjusting for bed occupancy will have little effect in terms of explaining the variation which remains. While further analytical work could be done with the models presented in this section of the report, it is questionable whether this would lead to a better understanding of the role bed occupancy plays in the decision to discharge. Bed occupancy alone is perhaps too simplistic a measure to represent pressure on beds. Such an instrument would need to take into account factors such as the availability of resources (e.g. staffed beds), current demand (e.g. numbers in admission wards and on waiting lists) and anticipated future demand ( unforeseen events, i.e. emergency admissions). It is unlikely that a single figure could summarise all of this. 39

42 V: THE ECONOMETRIC STUDY OF BED OCCUPANCY AND COSTS Introduction 5.1 The econometric study sought to establish the effects of differences in bed occupancy rates on hospitals unit costs as part of a wider study on the possible costs and benefits associated with differences in levels of bed occupancy. The method proposed (by the Management Executive) was to use regression analysis to identify the relationship between unit inpatient costs for acute specialties and a range of variables thought to influence unit costs in addition to bed occupancy, such as length of stay and teaching status. 5.2 Data published in the Scottish Health Service Costs (the Blue Book ) for the past five financial years (1991/ /96) was used for this analysis. Of interest were the two specialty groupings: general medicine and its associated sub-specialties (such as cardiology or gastroenterology) as a high occupancy, high emergency admission category and a combination of ophthalmology, ear nose and throat, and gynaecology as a lower occupancy, high elective admission category. The reason for this distinction was to determine whether the relationship between unit costs and bed occupancy was the same for specialties with differing pressures on beds. 5.3 Tables 5.1 and 5.2 below give a summary of how some of the variables included in the cost models have been changing over the past five years. Although average length of stay appears to be decreasing, occupancy has remained fairly constant for both specialty groupings. The number of discharges for general medicine has been increasing, while the number of beds has remained more or less the same. However for the combined specialty the number of inpatient discharges and averaged staffed beds has decreased, which may be a result of more day and outpatient cases. Direct and total cost per case has been increasing for the low occupancy grouping, whilst for general medicine average cost per case has decreased over the last couple of years. Averages 1991/ / / / /96 Length of stay Occupancy Discharges Staffed beds Direct cost per case Total cost per case Table5.1: Summary statistics for 26 hospitals for medical specialties 40

43 Averages 1991/ / / / /96 Length of stay Occupancy Discharges Staffed beds Direct cost per case Total cost per case Table5.2: Summary statistics for 26 hospitals for combined specialty Selection Criteria 5.4 For this analyses 26 hospitals were selected with the following criteria: inpatients in both specialty groupings, at least 18 averaged staffed beds in the lower occupancy specialty for each of the five years respectively, one of three hospital classifications 2 (1) Large general major teaching hospital covering a full range of services(other than maternity in some cases) and with some special units, (2) General hospital with some teaching units, but not necessarily wholly teaching, (11& 12) Mixed specialist hospital. No special units. Consultant type surgery undertaken (with or without maternity). Methods 5.5 Multilevel regression was used to identify the factors that influence observed average inpatient costs. Multilevel regression techniques, as described by Rice and Leyland(1996) can identify differences both between hospitals and within hospitals over a period of time. A two level repeated measures model with years at level one nested within hospitals at level two was considered for each of the specialties separately. 5.6 Two separate models were estimated in which direct cost 3 per case and total cost per case were regressed on average bed occupancy, length of stay, dummy variables for type of hospital and years and any additional variables and interactions which appeared to influence cost per case. General hospitals with some teaching and the financial year 1995/96 were the reference categories. Interactions were calculated by multiplying the parameters together. Both direct and total costs were used in separate models to try to identify whether the relationships between unit costs and the various explanatory variables differed according to the definition of cost used. It might be expected, for example, that occupancy would have a stronger negative relationship with total cost per case, because direct costs exclude certain elements of fixed costs, which would be spread 2 General description of hospital functional classification used in Blue Book. 3 Costs of medical and nursing staff, pharmacy, PAMs, theatres, laboratory and other direct care costs. 41

44 over fewer cases if occupancy rates were increased by treating more cases. Data were not available for the whole time period to break the costs down into sub-components of direct costs. 5.7 It should be noted that bed occupancy was forced into to each of the models regardless of whether it was significant since its relationship with cost per case was of foremost interest. The estimates for bed occupancy and length of stay were centred around their means and since length of stay appeared to have a substantial effect on cost per case both a crude average of length of stay and an adjusted average; using HRG-mix (see chapter III), were added to each of the models. Definition of variables (i) average occupancy ratio % = occupied bed days x 100 staffed beds days (ii) crude length of stay = occupied bed days number of discharges (iii) direct cost per case = direct costs number of discharges (iv) total cost per case = total costs number of discharges (v) adjusted length of stay(hospital i ) = LOS Expected i ( LOS ) i LOS Expected ( LOS ) i = j n ij j LOS n ij j LOS i = mean length of stay for hospital i LOS j = mean length of stay for HRG j 42

45 Results Direct cost per case /high occupancy category Crude length of stay 5.8 Firstly crude length of stay was fitted to the model for the high occupancy specialty (see table 5.3). The constant gives the direct cost per case of a general hospital with some teaching in 1995/96 with average occupancy (84%) and average length of stay (6.8 days). The variance changes significantly from model A to B when adding length of stay and should therefore be included in the random part of the model at level 1. Further because length of stay has been decreasing (from 7.7 days in 1991/92 to 6.1 days in 1995/96) this implies there is less variation year on year within hospitals. In fact only 27% of the variation in direct cost per case can be explained by year on year variations within hospitals in 1995/96 compared with 41% in 1991/92 (see details on intra class correlation in appendix C). 5.9 Occupancy (AOR) appears not to influence the direct costs of a medical inpatient, although length of stay clearly does, as its estimate is more than twice its standard error. Hospital type has a substantial effect on direct costs with large teaching hospitals (LGMTH) on average costing an extra per case with an additional ( ) per extra day the patient stays. The dummies for the financial years 1991/ /94 are also significant and since their estimates are negative there is some evidence that costs have been increasing, although flattening out by 1994/95 (direct cost per case cheaper than 1995/96). However, for example the direct cost per case in 1992/93 of a large teaching hospital with a length of stay of 8 days (1.2 days longer than average) and an annual occupancy rate of 90%(6% greater than average) was (e.g (1.2 x89.87)+(1.2 x108)+(6 x(-1.586)) = ) 43

46 Parameter Estimate(s. e) Estimate(s. e) Model A Model B Fixed: CONSTANT 765.5(31.55) 774.7(30.56) 1991/ (27.15) (24.97) 1992/ (23.59) (21.91) 1993/ (21.1) (18.77) 1994/ (20.04) (17.05) LGMTH 287.7(55.48) 289.7(54.38) MSH -28.4(37.15) (36.28) AOR (2.104) (1.978) CRUDE LOS 84.54(15.63) 89.87(16.13) LGMTH*CRUDE LOS 111.3(24.09) 108(21.66) MSH*CRUDE LOS -5.03(17.47) 2.179(18.36) Random: Between hospitals: CONS/CONS 9057(2797) 8917(2734) Between years: CONS/CONS 4891(688.2) 4333(764.1) CRUDE LOS/CONS 854.9(341.5) CRUDE LOS/CRUDE LOS 373.8(370.1) -2(log-likelihood): Table 5.3: Models for direct cost per case for medical specialties with crude length of stay 5.10 Since model B was chosen to be the best model, its hospital effects were plotted as shown in figure 5.1. The hospitals have been ranked in increasing order of their residuals and the blue lines represent the 95% confidence intervals for their expected direct cost per case. It can be seen that the direct cost per case for the 26 hospitals ranges from 180 less than the Scottish average for the five years to 200 greater. The eight study hospitals A to H have also been highlighted. 44

47 % Confidence Interval A D G F E H C B Figure 5.1 Hospital Effects on direct cost per case for model B of medical specialti with crude length of stay Adjusted length of stay 5.11 The number of discharges (inverse) has been included in models A-D below since it also appears to influence direct cost per case when length of stay has been adjusted for HRG-mix. The number of averaged staffed beds, staffed bed days and occupied bed days could be used as alternatives to the number of discharges as they were also significant, although they are not as good predicators of direct cost per case. Adjusting length of stay has subsequently changed the sign of the estimate for average bed occupancy which is now positively associated with direct cost per case (a 1% increase in occupancy on average would increase the cost of a medical case by 4.74(model D)) The random variation at level 1 is more complex here than for the crude length of stay. Part of the variation in direct cost per case is also explained by hospital type; variation within large teaching hospitals is much greater than general hospitals, while the variation is less for mixed specialist(msh - see model B). The amount of variation in direct cost per case within hospitals also depends on the mean occupancy for that hospital i.e. 66% of the variation in direct cost per cost can be explained by year on year differences within a hospital with a mean occupancy of 90% compared with only 37% for a hospital with mean occupancy of 75%. Furthermore if the hospital with 90% occupancy was a teaching hospital, 74% of its variation in direct cost per case could be attributed to year on year differences. 45

48 Parameter Estimate(s. e) Estimate(s. e) Model A Model B Fixed: CONSTANT 634.3(58.21) 631.5(51.06) 1991/ (33.77) (26.81) 1992/ (29.45) (22.35) 1993/ (26.56) (21.07) 1994/ (24.77) (18.58) LGMTH 264.1(63.62) 267.7(63.26) MSH (41.92) (34.2) AOR 4.734(2.476) 4.505(2.294) ADJUSTED LOS 58.95(16.33) 53.55(13.47) 1/DISCHARGES 6.10e+05(2.35e+05) 6.21e+05(2.04e+05) Random: Between hospitals: CONS/CONS 9425(3051) 8281(2683) Between years: CONS/CONS 7543(1062) 5550(1383) AOR/CONS -226(99.45) AOR/AOR 57.4(28.69) LGMTH/CONS 7319(3315) MSH/CONS -1716(738.2) -2(log-likelihood): Table 5.4(a) : Models of direct cost per case for medical specialties with adjusted length of stay 46

49 Parameter Estimate(s. e) Estimate(s. e) Model C Model D Fixed: CONSTANT 577.6(61.71) 583.9(52.21) 1991/ (31.65) (27.01) 1992/ (27.44) (23.67) 1993/ (24.22) -62.9(20.2) 1994/ (22.74) 3.607(17.14) LGMTH 310.1(71.91) 299.3(65.77) MSH (41.6) (36.42) AOR 6.322(2.095) 4.739(1.968) ADJUTED LOS 58.46(16.77) 49.03(13.54) 1/DISCHARGES 8.93e+05(2.53e+05) 8.98e+05(2.15e+05) Random: Between hospitals: CONS/CONS 1.28e+04(3930) 9141(2890) Between years: CONS/CONS 3210(867.3) 3364(640.4) ADJUSTED LOS/CONS (627.8) 891.7(357.3) ADJUSTED LOS/ADJUSTED LOS 3728(1225) 506(346.9) LGMTH/CONS 9528(3414) -2(log-likelihood): Table 5.4(b) : Models of direct cost per case for medical specialties with adjusted length of stay 47

50 % Confidence Interval F A D G H E C B Figure 5.2 Hospital Effects on direct cost per case for model D of medical specialties with adjusted length of stay Direct cost per case /low occupancy category Crude length of stay 5.13 Direct cost per case has been increasing over the past five financial years. The association of direct cost per case and occupancy ratio is dependent on hospital type; costs increases with occupancy ratio for large teaching hospitals( 9.08 per case for every 1% increase) and decreases for hospitals with little or no teaching. The variation between years appears to be greater within mixed specialist hospitals compared with other types of hospitals For both the model with crude length of stay and adjusted length of stay hospital B has a considerably higher average direct cost per case than any of the other hospitals for the combined specialty grouping (see figures 5.3 and 5.4). Although this difference may be more evident for this specialty grouping, it should be recognised that hospital B consistently has a higher cost per case for all of the cost models. 48

51 Parameter Estimate(s. e) Estimate(s. e) Model A Model B Fixed: CONSTANT 710.6(30.82) 695.9(28.97) 1991/ (29.34) -201(26.19) 1992/93-180(25.54) (22.34) 1993/ (23.56) (20.57) 1994/ (22.1) (19.04) LGMTH 3.368(51.12) 1.042(48.26) MSH (36.29) (37.22) AOR -5.13(2.155) (1.687) CRUDE LOS 123.3(27.82) 101.9(27.9) LGMTH*AOR 14.23(5.6) 14.69(4.435) MSH*AOR (3.137) 1.13(3.269) Random: Between hospitals: CONS/CONS 6711(2203) 6712(2189) Between years: CONS/CONS 6038(850.7) 3309(603.8) MSH/CONS 3487(1173) -2(log-likelihood): Table 5.5 : Models of direct cost per case for combined specialty with crude length of stay 49

52 % Confidence Interval A H F G E D C B Figure 5.3 Hospital Effects on direct cost per case for model B of combined specialty with crude length of stay Adjusted length of stay 5.15 After adjusting for HRG-mix length of stay no longer appears to influence direct cost per case. The rest of the model is still similar to the model including crude length of stay, with the year on year variation of direct cost per case again being much greater within mixed specialist hospitals. This is the opposite of the medical grouping where the variation is less within such hospitals. 50

53 Parameter Estimate(s. e) Estimate(s. e) Model A Model B Fixed: CONSTANT 683.5(32.57) 675.3(30.67) 1991/ (35.91) (30.63) 1992/ (28.58) (23.53) 1993/ (26.68) (22.42) 1994/ (24.06) 2.235(29.58) LGMTH 12.54(50.28) 6.378(48.69) MSH (36.64) (38.91) AOR (2.341) (1.773) ADJUSTED LOS 33.42(37.58) 27.15(37.08) LGMTH*AOR 11.46(5.957) 13.26(4.602) MSH*AOR 1.084(3.336) 2.4(3.47) Random: Between hospitals: CONS/CONS 5590(2074) 6780(2249) Between years: CONS/CONS 7259(1023) 3466(632.6) MSH/CONS 4551(1418) -2(log-likelihood): Table 5.6 : Models of direct cost per case for combined specialty with adjusted length of stay 51

54 % Confidence Interval H A F E D C G B Figure 5.4 Hospital Effects on direct cost per case for model B of combined specialty with adjusted length of stay Total cost per case/high occupancy category Crude length of stay 5.16 The model for total cost per case is similar to that for direct cost per case, although occupied bed days (inverse) is also significant here. The variation in total cost per case within hospitals has been decreasing as for direct costs. Only 32% of the variation in total cost per case can be explained by year on year differences within hospitals in 1995/96 compared with 48% in 1991/92. 52

55 Parameter Estimate(s. e) Estimate(s. e) Model A Model B Fixed: CONSTANT 946.8(99.49) 934.9(96.17) 1991/ (44.96) (41.31) 1992/ (39.48) -123(36.34) 1993/ (35.85) (31.4) 1994/ (34.21) 0.882(28.29) LGMTH 488.7(98.64) 510.5(98.24) MSH (58.52) -52.9(58.03) AOR (3.635) -2.09(3.402) CRUDE LOS 120.1(15.2) 118.7(26.06) 1/OCCUPIED BED DAYS 7.39e+06(2.98e+06) 8.36e+06(2.88e+06) LGMTH*LOS 162.8(40.77) 166.5(35.23) MSH*LOS (29.13) (30.19) Random: Between hospitals: CONS/CONS 1.86e+04(6003) 1.95e+04(6140) Between years: CONS/CONS 1.42e+04(2017) 1.27e+04(2177) CRUDE LOS/CONS 2680(985.9) CRUDE LOS/CRUDE LOS 1004(1027) -2(log-likelihood): Table 5.7 : Models of total cost per case for medical specialties with crude length of stay 53

56 % Confidence Interval D A F G H E C B Figure 5.5 Hospital Effects on total cost per case for model B of medical specialties with crude length of stay Adjusted length of stay 5.17 Average bed occupancy although positively associated with total cost per case as for direct cost per case is not significant here. Unlike previous models where costs appear to be flattening out by the financial year 1994/95, total cost per case has decreased between 1994/95 and 1995/96. 54

57 Parameter Estimate(s. e) Estimate(s. e) Estimate(s. e) Model A Model B Model C Fixed: CONSTANT 941.1(85.19) 856.3(90.37) 914.6(78.2) 1991/ (53.73) (51.01) (47.99) 1992/ (47.48) (44.65) (42.72) 1993/ (43.36) (39.99) (37.65) 1994/ (40.92) 47.2(38.2) 34.42(33.48) LGMTH 332.1(89.55) 416.1(101) 357.8(89.77) MSH (61.4) -85.1(62.26) (55.35) AOR 3.251(3.958) 6.444(3.439) 2.768(3.59) ADJUSTED LOS 70.83(24.98) 70.37(26.63) 66.26(22.58) 1/DISCHARGES 1.04e+06(3.43e+05) 1.47e+06(3.71e+05) 1.26e+06(3.22e+05) Random: Between hospitals: CONS/CONS 1.72e+04(5963) 2.35e+04(7557) 1.51e+04(5264) Between years: CONS/CONS 2.08e+04(2924) 9190(2457) 1.35e+04(2569) ADJUSTED LOS/CONS -1676(1776) 2604(1242) ADJUSTED LOS/ADJUSTED LOS 1.05e+04(3447) 1155(1223) LGMTH/CONS 1.71e+04(7164) -2(log-likelihood): Table 5.8: Models of total cost per case for medical specialties with adjusted length of stay 55

58 % Confidence Interval D F A G H E C B Figure 5.6 Hospital Effects on total cost per case for model C of medical specialties with adjusted length of stay Direct cost per case/low occupancy category Crude length of stay 5.18 Unlike direct costs the relationship between total cost per case and occupancy does not appear to be dependent on the type of hospital; total cost per case decreases as occupancy increases regardless of hospital type. The dummy for large teaching hospitals is significant here with the total cost on average being greater per case. However as for direct costs year on year differences within hospitals explain more of the variation in total cost per case than differences between hospitals. 56

59 Parameter Estimate(s. e) Estimate(s. e) Model A Model B Fixed: CONSTANT 982(38.08) 968.4(35.29) 1991/ (41.54) (37.74) 1992/ (36.51) -176(32.86) 1993/ (33.96) (30.29) 1994/ (32.45) 8.924(28.83) LGMTH 100.3(54.81) 99.65(51.61) MSH 12.19(43.45) 8.743(43.83) AOR (2.098) (1.934) CRUDE LOS 128.6(36.76) 122.1(36.77) Random: Between hospitals: CONS/CONS 7673(2909) 7583(2809) Between years: CONS/CONS 1.32e+04(1857) 7870(1434) MSH/CONS 6729(2441) -2(log-likelihood): Table 5.9: Models of total cost per case for combined specialty with crude length of stay 57

60 % Confidence Interval A H F D E G C B Figure 5.7 Hospital Effects on total cost per case for model B of combined specialty with crude length of stay Adjusted length of stay 5.19 Length of stay no longer appears to effect total cost per case after it has been adjusted for HRG-mix, as for the direct cost per case model. 58

61 Parameter Estimate(s. e) Estimate(s. e) Model A Model B Fixed: CONSTANT 956.3(40.58) 945.3(37.71) 1991/92-204(48.59) (43.67) 1992/ (39.1) (34.61) 1993/ (36.8) 2.212(32.67) 1994/ (34.02) 23.94(29.88) LGMTH 98.99(54.93) 98.79(52.22) MSH 5.517(44.42) 1.378(45.38) AOR (2.207) (2.044) ADJUSTED LOS 41.75(48.71) 36.11(48.56) Random: Between hospitals: CONS/CONS 7360(2910) 7636(2889) Between years: CONS/CONS 1.47e+04(2069) 8522(1552) MSH/CONS 7628(2708) -2(log-likelihood): Table 5.10: Models of total cost per case for combined specialty with adjusted length of stay 59

62 % Confidence Interval H A D E F C G B Figure 5.8 Hospital Effects on total cost per case for model B of combined specialty with adjusted length of stay 60

63 Hospital Model 5.1 Model 5.2 Model 5.3 Model 5.4 Model 5.5 Model 5.6 Model 5.7 Model 5.8 A B C D E F G H Table 5.11: Hospital residuals of cost models Summary of results 5.20 Occupancy and length of stay appear to influence the cost of an inpatient case in acute specialties differently depending on whether the specialty is a high emergency, high occupancy specialty such as general medicine or a high elective, lower occupancy specialty such as ENT, gynaecology or ophthalmology. Direct cost per case on average increases with occupancy for general medicine; however, for the combined category of ENT, gynaecology and ophthalmology the relationship is dependent on type of hospital. Costs 61

64 increase with occupancy for large teaching hospitals and decreases for hospitals with little or no teaching. However, for total cost per case the relationship with occupancy for the combined specialty does not appear to be dependent on hospital type and costs decreases as occupancy increases Whether the crude average or the adjusted length of stay for the 26 hospitals is added to the model also affects which factors appear to be good predictors of the cost of an individual case. When the adjusted length of stay is fitted increasing the number of discharges, averaged staffed beds, staffed bed days and occupied bed days all reduce both direct and total cost per case for the medical specialties, yet only occupied bed days reduces total cost per case for the crude average. None of these indicators of hospital activity, however, influence the cost of an inpatient in the lower occupancy specialty In general the variation in both direct and total cost per case appears to be greater within large teaching hospitals for the high occupancy specialty, while the lower occupancy specialty has greater variation within mixed specialist hospitals. Discussion and Conclusions Methodology 5.23 Literature describing attempts to model hospital cost functions identified a number of possible problems with the proposed analysis. Firstly, the use of average cost per case or day as the dependent variable has been termed ad hoc analysis by Scott and Parkin (1995) in contrast to techniques, which are based on the theory of the firm. The latter typically use total (or total variable) costs as the dependent variable which enables coefficients on the outputs included in the independent variables to be interpreted as returns to scale. Use of a range of outputs enables the impact on costs of the multi-product nature of the hospital to be modelled. The absence of any prior evidence of the appropriate functional form can be handled through the use of flexible functional forms such as the translog cost function which includes higher order terms of the independent variable 4. In contrast, ad hoc analyses assume constant returns to scale and do not necessarily provide theoretically consistent estimates of the key relationships. For example, there is no theoretical reason why increases in bed occupancy should necessarily equate to reductions in unit costs. They may be achieved by increasing lengths of stay or they may result from increased admissions of more complex and more costly cases. The meaning of the coefficients is ambiguous in ad hoc analyses. The importance of theoretical consistency is that it enables generalisations of results outwith the data sets used in any particular model and it provides more plausible evidence of causal relationships as opposed to statistical associations thrown up by the use of a particular data set Secondly, the sample size was limited, reducing the degrees of freedom in the models used, especially given the high number of independent variables that could potentially be used in the analyses Thirdly, the use of cross-sectional data creates the identification problem. At a given point in time, each individual hospital will be at a certain point on its own cost function. Observations taken from different cost functions can not be used to estimate the cost function either of all the hospitals as a group, or the individual hospitals within the group. The composite relationship derived will not describe the marginal effects of increasing or decreasing, say, bed occupancy, on the unit costs of any of the hospitals within the group. A number of observations have to be taken from each hospital to identify hospital level effects. This problem was addressed using multilevel modelling which simultaneously increases the number of observations and hence the degrees of freedom by including in the independent variables, observations on length of stay, bed occupancy etc. over a period of years (Rice and Leyland, 1996) Fourthly, there are concerns about the appropriateness of the data used. Apart from the possibility of inaccuracies in the Blue Book data, the data themselves may have been generated by deterministic 4 The use of higher order terms reduces the degrees of freedom, a particular problem in analysis of hospital cost data in Scotland due to the small number of hospitals. This limits the number of output variables which can be used. 62

65 production and/or accounting procedures rather than random processes. Scott and Parkin (1995) found a very high R 2 and a high and highly significant intercept which are symptomatic of deterministic data processes. Analysis of Blue Book data may therefore be more appropriate using non-parametric techniques such as data envelopment analysis (Hollingsworth and Parkin, 1995). Regression analysis on the other hand is a parametric techniques which assumes random errors A second doubt regarding the appropriateness of the data in the Blue Book concerns the way in which bed occupancy is measured. Bed occupancy is an arithmetical by-product of the number of occupied beddays and the number of available beds. Length of stay is an arithmetical by-product of the number of occupied bed-days and the number of discharges. Data from the interview survey confirms the anecdotal evidence that bed borrowing occurs on a daily and on a more prolonged basis to accommodate specialty level fluctuations in demand. The question remains as to whether the available bed and bed-day data published in the Blue Book take into account these fluctuations at a specialty level, by attributing borrowed beds and occupied bed-days to the specialty borrowing them. If not, then the observed occupancy and lengths of say may not be a true reflection of actual bed occupancy Fifthly, there is likely to be multicollinearity between some of the potential independent variables In the absence of the above the problems we would have expected, other things being equal, a negative relationship between occupancy and average cost per case, and a positive relationship between average length of stay and average cost per case. However, it was anticipated that the problems described above would mean that: such relationships would be weak, non-existent or perverse; the relationships would be very sensitive to small variations in the model and data set used. Trends in key variables: In summary, over the period 1991 to 1996, length of stay has been falling in both the high and low occupancy specialties analysed in this study. In the high occupancy specialties, discharges have risen steeply and staffed beds and occupancy rates have remained constant. Overall, therefore, the increase in activity has been accommodated by shortening stays rather than improved occupancy. In the low occupancy specialties, the number of discharges and the average number of staffed beds have both fallen over the study period. However, bed occupancy has again remained largely unchanged The results from both specialty types therefore suggest that pressures on beds tend to work through into changes in length of stay and the number of staffed beds rather than changes in bed occupancy. Results from the bed management survey illustrate the practical implications these trends have had for bed management and suggests limits on the extent to which bed occupancy might be increased. The daily occupancy analyses reinforce this picture, with the relatively static average of around 85% occupancy for the period disguising wide daily fluctuations, to levels frequently in excess of 100% in the high occupancy specialties. The bed management survey suggests that this has had implications for the low occupancy specialties in terms of practices such as bed borrowing, which may mean that the crude data for average bed occupancy in the low occupancy specialties understates the true levels. Relationships between cost per case and occupancy 5.32 In the high occupancy specialty of general medicine, although the relationship between occupancy and cost per case had the right sign, the relationship was weak and insignificant. Using HRG-mix adjusted lengths of stay, the model identified a positive, and in some versions of the model, significant relationship between occupancy and cost per case In the low occupancy group of specialties, there was a significant negative relationship between occupancy and cost per case. This relationship held for both crude and HRG-mix adjusted lengths of stay, although the latter was only true for certain versions of the model. There were also hospital type-specific 63

66 relationships between occupancy and cost per case identified for the low occupancy specialties. The use of total cost per case as opposed to direct cost per case made no significant difference to the main results reported above Overall, therefore, the results are ambiguous. For the high occupancy specialties, occupancy does not seem to explain the variation in direct or total cost per case. For the low occupancy specialties, occupancy does have the expected relationship with direct and total cost per case, a 1% increase in occupancy associated with a reduction in unit costs of the order of 5 per case, based on the models using crude lengths of stay. This relationship is dependent, however, on hospital type. Relationships between length of stay and occupancy 5.35 Length of stay was positively and significantly associated with cost per case for both high and low occupancy specialties, and irrespective of whether crude or case mix-adjusted lengths of stay were used in the independent variables. The use of case mix adjusted lengths of stay did however suppress the influence of lengths of stay, that is, the coefficient on length of stay was reduced. Cost Variation 5.36 The analyses suggested that large teaching hospitals had higher costs per case after taking into account lengths of stay and bed occupancy. 95% confidence intervals of the observed unit costs minus the unit costs expected in each hospital on the basis of length of stay, occupancy etc. suggested that the outliers (positive that is, inefficient and negative that is efficient) varied according to the specification of the model used, although hospital B was a consisted positive outlier. Discussion 5.37 The conclusions that can be drawn from the results are limited by their variability, which suggests that the problems discussed above have turned out to be significant in practice. The results appear to be sensitive to the specification of each model but with no clear a priori reason why they should vary the way they do and no obvious criteria for choosing between them. For example, it is not clear why the expected relationship between occupancy and cost should hold for low occupancy specialties but not those with high occupancy, nor why for low occupancy specialties this relationship appears to be weaker for teaching hospitals than non-teaching hospitals One possible reason is suggested by the fact that a more plausible, consistent and significant relationship appears to exist between length of stay and unit costs. It may be that bed occupancy should not be thought of as an independent target variable amenable to change. May also be that the apparent room for manoeuvre at occupancy rates of approximately 85% is zero, given daily fluctuations. Evidence of this is that the relationship is stronger for low occupancy, where pressures are less. Instead, as suggested above, it may be an arithmetic and organisational by-product of other factors such as demand/discharges, bed numbers and lengths of stay. In hospitals where the latter increase due-say-to a more complex case mix as attempts are made to decant routine work into day case or outpatient settings, simultaneous increases in discharges, cost per case and occupancy would create a positive relationship between occupancy and cost per case. Obviously this is not happening in all hospitals but it may be in some, for example, in teaching hospitals where, indeed, a positive relationship was found between cost per case and occupancy in the low occupancy specialties The potential influence of case mix was explored but again gave rise to ambiguous results. This may reflect the way in which case mix was handled in the regression analyses. Case mix adjusted lengths of stay were used which subdued the effect of length of stay on cost per case. Interpretation of these results needs to 64

67 be handled with care. There are two aspects to the relationship between case mix, length of stay and unit cost. The first is the strength of the relationship between length of stay and cost. The second is whether the observed lengths of stay are legitimate in view of the case mix at the hospital concerned. Adjusting lengths of stay for case mix and using the adjusted figures in the analysis conflates these two distinct steps into one. The variation in length of stay attributable to case mix is stripped out, potentially weakening the capacity to identify the relationship between length of stay and cost. This would appear to be the case for the high occupancy specialties where the case mix adjustment suppresses the impact of lengths of stay and the relationship between occupancy and unit costs becomes positive. This suggests occupancy, positively related to length of stay which in turn is positively related to unit cost, explains a proportion of the variation in unit cost previously explained by the crude lengths of stay Overall, therefore, both the data and the methodological issues would appear to be undermining attempts to identify a robust relationship between occupancy and unit costs. Variations in the former do not appear to be explaining the latter, a conclusion reinforced by the survival analysis. The indeterminate a priori relationship between unit costs and bed occupancy is borne out by the data. The daily bed occupancy and bed management survey data underline the likely data problems which it was anticipated would obscure any relationship which does exist. To the extent that conclusions regarding efficient bed management can be drawn from the data, they suggest that the scope to reduce unit cost by targeting bed occupancy is limited with a percentage increase in occupancy reducing cost per case over the period by a figure in the order of 5. This assumes the relationship holds across all hospitals, which the survey data and the identification problem suggest will not be the case. Alternative measures of, and ways of analysing, the efficiency of bed use need to be found. References Hollingsworth, B. and Parkin, D. (1995) The efficiency of Scottish acute hospitals: An application of data envelopment analysis. Journal of Mathematics Applied in Medicine and Biology, Vol. 12, Information and Statistics Division. Scottish Health Service Costs. Information and Statistics Division, National Health Service in Scotland, 1991 to Rice, N. and Leyland, A H. (1996) Multilevel models: application to health data. Journal of Health Service Research Policy, Vol.1, No.3, Scott, A. and Parkin, D. (1995) Investigating hospital efficiency in the new NHS: The role of the translog cost function. Health Economics, Vol.4, No.6,

68 VI: PROCEDURES FOR MANAGING BEDS Introduction 6.1 Several studies and articles have been concerned with the steady rise in the number of acute emergency admissions to general medical departments in the United Kingdom over the last five years (CSAG, 1995; Kendrick, 1995; Butler, 1994; NAHAT, 1994.). One of the main problems for hospitals is the unpredictable nature of this type of demand, which can make advance planning difficult. As a response to this increase, many hospitals are adopting elements of the practice of bed management (Audit Commission, 1992; Green & Armstrong, 1993), which in its simplest form can be seen as a pro-active attempt to influence the patient flow and an emphasis on the flexible use of beds in response to the demands of the moment (Audit Commission, 1992). The Audit Commission have also identified an association between the use of a bed manager and lower turnover intervals, although they are uncertain if the relationship is directly causal (Audit Commission, 1992). 6.2 Understanding the process of bed management in response to the twin constraints of increasing, yet unpredictable, demand, thus became a key element in this study. It provides an empirical context within which the statistical accounts of bed occupancy may be judged. Method 6.3 The principal method adopted in this part of the study consisted of a semi-structured interview survey of respondents in each of the eight study trusts, conducted by BW. The interviews were audio recorded. Respondents were assured that their comments would remain anonymous. 6.4 In defining a sample of interviewees, the main determinant was that respondents at both strategic and operational levels in the organisation of the trusts should be included. To this end, a purposive sample of individuals judged to be relevant to the process of bed management in each trust was identified. These included, at strategic level, members of the trust executive (chief executives, directors of finance, directors of nursing, medical directors, directors of contracts); and at the operational level, clinical directors, specialty co-ordinators, bed, business, nurse and information managers, consultants, registrars, ward sisters, nurses and clerical assistants. 6.5 The construction of the interview schedules reflected the differing emphases of these staff groupings. That designed for staff at the strategic level emphasised the policy of bed management, with respondents being encouraged to take a trust-wide view; that intended for staff at operational levels concentrated on the practical issues of managing beds, with respondents being encouraged to reply in relation to their own particular area of work. 6.6 The schedules were designed to collect information in three main parts. These centred first on the strategic level, and included the policy and practices each trust employed, the strategies for monitoring the practice of bed management and the ownership of beds. At the tactical or operational level, we examined the management of initial referrals, the acute receiving system, procedures for transfer from receiving wards or units, the formal authority of the person responsible for bed management, procedures for dealing with fluctuations in demand and the availability and quality of data on the bed state. In addition, we investigated 66

69 the experiential and attitudinal contexts within which the process of bed management was located, with particular reference to the likely effects of a further increase in demand. 6.7 Initial contact with the trusts was by letter addressed to the Chief Executives. This described the rationale behind the project, presented an outline of its main components, identified the individuals whom we hoped to interview and contained a request for the nomination of someone within the trust with whom we could liaise. All eight trusts confirmed their willingness to participate and all nominated an individual. The researcher provided this person with the relevant list of potential interviewees and asked them to nominate any other individuals whom they judged should be interviewed. Interviews were arranged at each of the trusts between January and March The timing of these was intended to capture data during the period of highest demand on beds. 6.8 Interviews were transcribed verbatim and subjected to content analysis. Data processing was undertaken using The Ethnograph (version 4.0), a software package designed to assist in the analysis of textual data. Particular emphasis was placed on understanding respondents accounts of the procedures utilised to manage their bed complement within the context of the perceived organisational and operational constraints on that process. Findings The process of bed management 6.9 Within hospitals which took part in this study the main focus of bed management practices was on how to control and absorb the initial impact of a rise in acute emergency admissions, and then on the organisation of the flow and distribution of these patients through the wards. This can be seen as an emphasis on process, rather than the management of a static bed stock. Respondents 6.10 In total, 137 individuals were interviewed. One interview was not recorded and one individual refused to take part. Of those interviewed, 113 were selected for analysis on the basis of their containing material directly relevant to the aims of the study. A table detailing the individual respondents can be found in Appendix E. Principal organisational features of the Trusts 6.11 Three of the Trusts (A, C and F) were large general hospitals, providing a full range of services and having a major teaching role. Three more (B, E and G) were general hospitals providing some units of teaching and the remaining two (D and H) were mixed-specialty hospitals with maternity facilities, which undertook consultant type surgery. A list of the Trusts providing details of the main hospitals, other sites and the number of clinical directorates, is presented in Table

70 Trust Type Other sites No of Directorates A B Large General Hospital with major teaching role General Hospital with some teaching units Maternity Hospital Children's Hospital 7 2 small sites Geriatric Hospital One smaller site 10 C Large General Hospital on two main sites with major teaching role 5 smaller Hospitals 11 D E Mixed specialist Hospital General Hospital with some teaching units Maternity Unit Geriatric Hospital 3 Day Surgery Unit Two smaller sites Two day hospitals 12 F Two large General Hospitals on two main sites with a major teaching role Two smaller hospitals One other site 20 G General Hospital with some teaching units N/A 5 Mixed specialist Hospital One smaller hospital H 5 Table 6.1 Description of the Eight Trusts Strategic Bed Management 6.12 In all eight hospitals there was a growing recognition that rising demand necessitated the use of more formal policies and detailed planning. General Policies Trust A 6.13 In this hospital, of the seven clinical directorates five were bed holding. Each of these was responsible for the management of their own beds. There was, however, an understanding that all beds were available to be used in response to the overall demand. The Trust did not employ a bed manager. Instead, day to day bed management practices, such as transfers and boarding, were the responsibility of a Bed Bureau. The Chief Executive explained that while 68

71 the normal day to day running rests with the directorates, they are quite clear that the over-riding requirement is that we respond to the demands of patients needs. So the surgical directorate will frequently provide beds for the medical directorate on a borrowed basis [ACE] This emphasis on flexibility and local accountability is seen in the response of the Medical Director, who explained that I m fairly distant from a lot of these things day to day some of these things happen by default, some happen locally [AMD] Detailed protocols covered the operation of the Bed Bureau and the trust was in the process of upgrading its discharge planning documents. The hospital had recently restructured its acute receiving system. In addition, the trust had produced an in-house report on the increase in winter admissions; this outlined some of the local factors which contributed to the rise and described the various mechanisms which the trust employed to cope. No great difficulties were reported by management, either with bed management or boarding. However, concerns were reported, mostly by medical staff, that the reliance on a boarding strategy interfered with patient care and led to premature discharge. Trust B 6.16 The Chief Executive identified a policy of attempting to move away from the traditional system of allocating beds to specific specialities: We are trying to change them into types of wards, whether they be emergency admissions, whether they be high-dependency, recovery, whatever; special needs as opposed to being orientated towards the specialty [BCE], with the emphasis being placed on the management of patients, not beds. For this reason the trust had introduced acute receiving wards in general surgery and general medicine in order to facilitate faster patient assessment and management. Similarly, the recent appointment of a Discharge Co-ordinator (whose function was effectively that of a Bed Manager) to oversee the co-ordination of patient care and the process of boarding and discharge, reflected the emphasis on the organisation of throughput. Issues of bed management were discussed by the Operational Service Managers, whose main role was to provide a wide range of support for the Clinical Directors. During periods of high demand it was acknowledged that some medical staff experienced difficulties concerning patients boarded out into a number of different wards, but this was recognised as the exception rather than the rule. Trust C 6.17 The allocation of beds between the various specialities was based on a formula that included contract activity and average length of stay, a calculation which was reviewed at least annually and sometimes more frequently [CCE] to allow the trust to cope with any changes in the emergency admissions rate and support the programme of elective admissions. The trust also managed the bed stock in response to the influence that holiday periods had on activity, closing beds used for elective work during periods of high staff leave, such as summer and Christmas. Staff and dormant beds released by this tactic were then used to plug gaps [CMD] during the shortages caused by, for example, the mid-winter flu epidemics. The hospital attempted to co-ordinate the activity of other services outside the trust, such as the ambulance service and general practitioners, to try to ensure that the right patient is in the right bed at the right time [CMD]. The surgical specialities did not report any major problems with either bed management or boarding, although this may reflect the fact that the speciality in which the interviews took place was isolated from the general medical block. Also, during periods of pressure, some medical staff felt that there was a need for more beds, which was linked to a general dissatisfaction with the practice of boarding patients (Trust policy was to avoid boarding whenever possible). Nursing staff reported problems with uncoordinated ward rounds. Trust D 6.18 This trust had reorganised its directorate system into three, each of which included their own support services, in order to streamline communication between directorates and limit problems of bed management within them. The directorates retained authority and responsibility for managing their work internally, with 69

72 particular reference to the identification of bed management problems at an early stage. These aims were reflected in the organisation of bed management itself, in that no one individual had responsibility for managing all the hospital s beds. Each directorate employed a Business Manager who was actively involved in the monitoring of bed management issues and who assumed a supervisory role to encourage local accountability. Clinical Ward Managers (formerly ward sisters who had been provided with extra training in management and organisational issues) had the responsibility for the day to day management of beds within their own directorates Part of the rationale behind these arrangements was, according to the Chief Executive, to preclude having a Bed Manager that would carry the can for the whole hospital [DCE]. Difficulties were dealt with at the directorate level if possible, but there were formalised procedures for dealing with bed management problems if they proved to be greater than the resources available at local level. These procedures were designed to end with the involvement of executive staff, including the Chief Executive, who facilitated this by making themselves available through an on-call rota. Bed Management policy was prepared by the Business Managers, and approved after discussions with the directors of the trust. The devolution of responsibility to the local level, the emphasis on the early identification of problems, and the collection of information as frequently as required, make this system very responsive to fluctuations in demand. The well-defined and graded response to problems has two main advantages. Firstly, the problem can be addressed at the level in which it has arisen. Secondly, the solution or response carries the authority of the trust executive. Trust E 6.20 Here, bed management was seen as operating within a wider context than the procedures used within the trust itself, as indicated by the recent appointment to their Bed Management Group of a Discharge Coordinator with a background in social work. A Bed Manager was directly responsible to the Medical Director. They met on a weekly basis. The Bed Manager produced a formal report detailing bed usage, availability and projected discharges, which was sent to the Chief Executive. The Executive Directors held an informal meeting (although minutes were taken) twice a month, during which any issues or problems pertaining to bed management would be discussed and action sanctioned The Trust also had a Duty Manager, a position filled by one of the Senior Managers, whose remit was to act as co-ordinator for the whole hospital. As such they would pick up the bed management issues [EDF]. The Duty Manager met with the Bed Manager every day and received a weekly summary of the data on admissions and bed availability. The Duty Manager discussed the Bed Manager s report, plus her own executive summary, with the Chief Executive on the last day of her period of duty. Concerns were voiced by some that general medicine lacked an adequate number of beds. This led to increased boarding to surgical specialties, which were consequently having to board their own patients because of fluctuations in emergency admissions. Trust F 6.22 Trust F had no overall bed management policy. Organisation of beds was left to the individual directorates and there were no prescriptive policies telling them how to do it [FCE]. The Chief Executive added that they expected the Trust s facilities to be used as efficiently as possible [FCE] and targets for occupancy based on the Scottish Office figure of 80% and related to agreed contract levels of activity [FCE] had been set. Beds were seen as a Trust-wide resource and this was reflected in the procedure to deal with periods of high demand: a programme list [FCE] had been identified of where general medicine would spill into [FCE] when their beds were full, although the emphasis was on filling all available medical beds first. Two of the directorates (orthopaedics and general medicine), employed an individual who assumed responsibility for bed management. The Clinical Nurse Manager for general medicine had responsibility for the day to day management of beds within that directorate which she delegates to her deputy the Duty Nurse Manager. Her authority was delegated from executive level, in effect meaning that she had absolute authority to use all the beds within the trust for whatever purpose, at any time [FMD]. In the other directorates management of beds was carried out by the relevant Business or Nurse Manager The trust was in the process of a major reconfiguration of its facilities and there was general agreement amongst the executive staff that the bed management situation would improve when they received the 70

73 transfer of resources and capacity from the other main site. Good co-operation between consultants and with the nursing staff was evident in the surgical specialties, which appeared to reduce the perceived pressure on beds, even taking boarding into account. Although boarding of medical patients resulted in the cancellation of elective surgery, there appeared to be an acceptance that this was necessary to cope with increasing demand. This may have been a result of the planned and controlled boarding process. Trust G 6.24 Trust G did not employ a bed manager. Day to day management of admissions and beds was left to the directorates to organise for most of the year, in what the Medical Director described as a very devolved arrangement [GMD]. In winter, bed management became the responsibility of Clerical Officers in the Bed Bureau, part of the Medical Records Department. It was used most when the trust was in a state of crisis, (usually a medical bed crisis [GCE]). The Medical Director and the Director of Nursing both oversaw the operation of the Bureau, and when there was extra pressure on beds the Medical Director assumed overall responsibility. During such periods there was a recognised chain of contact if the Clerical Officers in the Bed Bureau experienced difficulties in allocating patients to beds. The Director of Nursing received a printout from the Patient Administration System every day. When occupancy rates reach approximately 85% she and the Medical Director received further special reports [GDN] from the Bed Bureau It was recognised by most of the interviewees, at both the strategic and tactical levels within the trust, that the Bed Bureau and the practice of bed management could function more efficiently than they did. It was also apparent that those responsible for allocating patients to beds lacked the necessary authority to facilitate this, and that as a solution, the involvement of the Medical Director or his secretary was unsatisfactory. A Bed Manager with sufficient authority to enforce decisions would help alleviate the problem according to one interviewee, who explained that the main reason this was not being implemented was because a programme was underway to implement reductions in management costs. In addition, there was a general dislike in general medicine for the policy of boarding and the effect it had on quality of care. There was also a reluctance by some medical staff to become involved in what was seen as a hospital administration problem. Trust H 6.26 In this hospital the Chief Executive explained that the bed management policy was drawn up by the Trust Management Team, which, at the time, was made up of the Director of Planning, the Clinical Director for General Medicine and the Senior Nurse Manager. Development of the policy was described as a reaction to the rise in demand for acute medical beds. The trust also employed a Bed Manager, introduced an acute receiving ward and had an observation ward attached to the A&E department The hospital operated an early warning mechanism [HDN] to deal with increases in demand. The executive management team monitored, on a regular basis, the situation relating to occupancy and admissions. The emphasis was on trying to resolve any problems caused by increases in demand within the directorates. If this was not possible the Clinical Director would go to the Medical Director, making it a trust-wide issue. If problems persisted they would contact other hospitals in the area and request assistance The trust had procedures set out for contacting neighbouring hospitals and also for communication with general practitioners when no more admissions could be accepted. The Medical Director said that they were in the process of reviewing their response to rising demand, and added that the ownership of beds by directorates and specialties was one of the aspects of this review [HMD] Medical staff complained of a chronic shortage of medical beds and reported that this, in combination with the hospital layout, caused difficulties for ward rounds and could lead to longer patient stays and blockages in the system. Some staff in the surgical specialties were concerned at the boarding of less-well patients and wanted formal criteria for such transfers. 71

74 Strategic Monitoring of Policy and Practice 6.29 In all eight hospitals bed management issues were discussed at executive level where a high level of monitoring of bed management practice was maintained, particularly during the winter period. All the trusts recognised the importance of planning for the winter demand and this is reflected in the mechanisms described below In Trust A, a Trust Management Group monitored the performance of specialties using variables such as bed occupancy, length of stay and cost. A Physical Planning Group examined issues about spare capacity, facilities, accommodation and the allocation of beds. A Bed Management Group considered such variables as length of stay, bed occupancy by specialty and the seasonal re-allocation of beds between directorates. The main aim of this group was to develop mechanisms for improvement in the areas outlined above. In Trust B, turnover rates were examined several times a year [BCE] in order to highlight any problems with length of stay or occupancy rates. The Medical Director explained that monitoring of bed management practices took place at the micro level, [BMD] with the emphasis on ward rounds, discharge planning, and improvements to the speed and co-ordination of laboratory assessments and x-rays. In Trust C, planning was initiated at the executive meetings, with short term working groups tasked to examine various aspects of the trust s performance, including high level [CMD] bed management issues. Bed usage was also one of the subjects at the monthly meetings attended by the Medical Director, Bed Manager and Chief Executive, where any problems occurring with beds on a day to day basis were discussed and solutions sought Trust D had a Performance Monitoring Group which met once a month and included the Chief Executive, the Clinical Directors and the Business Managers. Its purpose included issues relating to beds. Written protocols described the procedures for boarding patients when the medical directorate was full. In Trust E the issues relating to bed management policy and practice were monitored by the Bed Management Group, whose members included the Bed Manager, two Clinical Directors, a Senior Business Manager, a Clinical Services Manager and the Discharge Co-ordinator. During the winter period the Bed Management Group met every fortnight. Trust F had no official policy [FCE], bed management being left to the directorates. There was a Trust Executive Group which looked at issues such as bed allocation and theatre use. Occupancy rates were examined regularly and the wards reviewed bi-annually Trust G constantly reviewed the allocation of beds throughout the hospital [GCE] through a Bed Utilisation Group. Ideas approved by this body went to the Bed Implementation Group, whose function was to devise ways in which to put these ideas into practice. Membership of this group included the Medical Director and representatives from the Clinical Management Groups in each directorate. In Trust H the Trust Management Team monitored, on a regular basis, the situation relating to occupancy and admissions. They received a monthly performance report detailing the activity levels within the specialties. This was discussed with the Clinical Directors who were part of the team. Within the directorates the Clinical Director, the Service Manager and the Business Manager also monitored the bed state, and at ward level the Bed Manager attended to the day to day situation. Using this combination of high and low level monitoring, the trust compiled an overall picture of activity and levels of boarding throughout the hospital These mechanisms indicate the high level of interest bed management has attained within the practice of strategic hospital management. Even in Trusts D and F, which reported espousing a decentralised management ethos with responsibility for bed management at the operational level delegated to Directorates, executive involvement in reviewing performance was considerable. The principal theme to emerge is one of flexibility, with performance monitoring increasing pro-actively in order to head off pressures arising from variation in demand, particularly relating to predictable periods of seasonal variation. That this process is undertaken by senior management should be seen as an attempt to ensure that authoritative decisions are taken and action subsequently implemented. Direct contacts between operational bed management staff and the executive in reviewing and monitoring the performance of bed management was in evidence at all sites. This in itself implies the attempt to set up clear lines of accountability between operational and strategic staff, ensuring that senior management are engaged with events. 72

75 Ownership of Trust Beds 6.34 The rise in acute emergency admissions has been an important influence on a shift of emphasis away from the ward as the unit of organisation within hospitals to a more management orientated approach organised around the whole hospital and its directorate structure (Green & Armstrong, 1994). The introduction of bed management has been one of the main features of this approach (Green & Armstrong, 1993). Beds are seen as a resource for the hospital to manage, flexibly, in order to meet differential variation in demand between specialties or directorates (Audit Commission, 1992) In this study there were some differences between the eight Trusts on how beds were viewed, by those at the strategic level, in terms of ownership. Trust B was the most radical in its attempt to move away from the ownership of beds by specialties [BMD], seen as a necessary precondition for establishing the management of people, not beds. As a general principle, Trusts A, C, D, E and F operated on the understanding that management of beds was the responsibility of the directorates, but made it clear that the bed complement was a trust resource. There was a similar arrangement in place in Trust G, but in winter difficulties arose, partly through a tendency for the some specialities to resist relinquishing control of their beds. A similar reluctance was reported in the specialities of Trust H, and was being made one of the many aspects of bed management currently under review [HMD]. Operational bed management Initial Referral 6.36 The first step in the process at the operational level of bed management is the arrival of referrals at the trust The majority of acute emergency admissions that the hospitals dealt with came directly from general practitioners, usually following telephone contact. In two trusts (C & E) the Bed Manager received advance notification of general practitioner referrals directly from the junior doctor who took the call. This mechanism allowed the Bed Managers time to prepare a bed, locate medical notes and gave an indication of the type of admission with which they were faced. All of the trusts admitted acute medical patients, (whether GP referrals, ambulance arrivals or self referrals) through their A&E department. Seven of the hospitals undertook some form of filtering at this stage, mostly for patients displaying chest pain and coronary symptoms and used fast-track procedures to admit directly to the appropriate ward. The remaining trust (D) was about to introduce such a procedure for patients showing signs of chest pain. Two hospitals (A and C) also had several specialist units (infection, renal, Orthopaedics, gastro-intestinal bleeding etc.) which received admissions direct. Acute Receiving Systems 6.37 Each hospital attempted to absorb the impact of the bulk of emergency admissions through exercising some form of organisational control, at the earliest possible point, over the acute demand. Trusts B, C and D had established acute receiving wards for medicine and surgery, Trusts F, G and H had such wards for medicine only and Trust E had an acute receiving ward for surgery only. The main function of these was to achieve initial stabilisation and diagnosis of the condition, followed, if necessary, by planned distribution to the appropriate parent ward. Hospital F had further refined its acute receiving system by introducing a rotation system for the parent wards. These were on-take directly from the acute receiving ward every third day, an arrangement which allowed nursing staff to plan for discharges and better manage beds (FSCN). One hospital (E) was about to introduce such a ward on a pilot basis: the existing system involved the distribution of referrals, by the bed manager, between the receiving consultants in rotation. The remaining hospital (A) had just re-organised its receiving system, creating pairs of wards organised to receive and distribute admissions, again in rotation. Procedures for transfer from receiving wards 6.38 In one trust with an acute receiving ward (G) it was reported that if patients had a predicted length of stay of only a few days and the hospital was not under pressure, then they would receive their assessment and treatment in that ward without undergoing transfer to the main medical unit. However, if a longer length of stay was predicted, arrangements would be made for their transfer to the general medical wards after the initial course of assessment and diagnosis. In Trusts C, E, and H the responsibility for the organisation and 73

76 transfer of patients from the acute receiving wards rested with the Bed Manager, in Trust B with the Discharge Co-ordinator and in Trust F with the duty Nurse Manager. In Trust D the Clinical Ward Manager carried out this task and in Trusts A and G it was the duty of Clerical Officers in the Bed Bureau The mechanisms in use for facilitating transfer and the distribution of patients in each of the trusts are very similar. In Trusts with Bed Managers (B, C, E, & H) and in trust D (where the Clinical Ward Managers have responsibility for bed management), these individuals would identify available beds and liaise with ward and medical staff in order to effect the transfer and distribution of patients. In hospitals A & G the ward staff in the acute receiving ward or the pair of wards receiving that day (usually the ward sister), would contact the clerical officer in the Bed Bureau and request a bed. In Trust F the Duty Nurse manager assumed day to day responsibility for transfer and distribution under the supervision of the Clinical Nurse Manager for general medicine There was, however, an important difference between those trusts where there was an individual responsible for the transfer and boarding of patients and the collection of information on beds and those where those tasks were the function of the Bed Bureau. Bed Managers, particularly during periods of pressure, reported being able to combine the functions of finding beds and placing patients while on the wards. In the trusts with Bed Bureaux staffed by Clerical Officers (A & G), similar bed management problems sometimes necessitated the intervention of a senior executive member, particularly in Trust G where the Clerical Officers lacked authority. In both hospitals, senior management reported dissatisfaction with having to physically walk the wards looking for beds. Formal Authority 6.41 It is has been recognised that to function effectively bed managers must have authority to carry out their role (Green & Armstrong, 1993, 1994, 1995). In the trusts with Bed Managers or the equivalent (B, C, D, E, F & H) these individuals appeared to have sufficient formal authority delegated to them by senior management to facilitate the transfer and the distribution of patients within the hospital and allocate patients to available beds, especially during periods of high demand. In Trust F, for example, the Clinical Nurse Manager for general medicine and her deputy (the Duty Nurse Manager) were said to have absolute authority to use any hospital bed [FMD]. In contrast, in Trust A the role of the clerical officer was to locate beds only; authority for transfer was contained in detailed boarding protocols. It may be argued that written authority is of limited effectiveness unless it is delegated to, and exercised by, an individual. Similarly, in Trust G the clerical officers with responsibility for transfer and allocation lacked the requisite authority to enforce placement. This made it difficult for them to allocate a boarder to a ward not prepared to accept him or her, particularly during periods of crisis when beds are short and staff are under pressure. Indeed, one Clerical Officer complained that on occasions they could not even get past the ward receptionists when seeking available beds It was also evident that bed managers required a level of informal authority to function effectively. Bed Managers with a nursing background were preferred by both medical and nursing staff to those who had a clerical or administrative background. A number of bed managers reported that their nursing knowledge allowed them to select wards where the staff were equipped with appropriate nursing skills for the patients being transferred. As a result, they were able to minimise the degree of resentment and resistance which may accompany the practice of boarding between different specialties. In Trust G, where clerical staff were responsible for transfers, it was reported that nursing staff would sometimes by-pass the Bed Bureau and organise transfers between themselves. These were thus temporarily unrecorded by the Bureau, creating problems for the bed information system. This situation appeared to be compounded by the lack of any clear protocol for boarding patients outwith general medicine. As a result, staff in the Bed Bureau felt isolated and without support during bed crises The existence of a Bed Manager with formal authority, credibility and a nursing background was considered particularly important by ward staff and Nurse Managers when it came to revealing the concealment of beds. Strategies employed by nursing staff to hide beds included leaving them unmade, failing to register beds made available by temporary transfers and pass beds (where the patient is allowed home for the weekend), and leaving names of discharged patients on the bed board. The Bed Manager in Trust B, for example, regarded it as part of her job to dig out concealed beds [BBM]. The Bed Managers in hospitals C, H & E all cited concealment as one of the reasons that they physically walked the wards in 74

77 order to verify bed availability. One referred to her background in nursing as the reason that she had succeeded in halting the practice of concealment, because she knew all the tricks that nursing staff employed [CBM]. Procedures for dealing with fluctuations in demand 6.44 As indicated above, acute receiving wards and detailed receiving procedures can act as effective buffers but are still susceptible to the large fluctuations in demand that can occur, especially in winter, and to deal effectively with this extra demand trusts need well defined responses. The procedures and their combination which the trusts had in place to deal with these all differed slightly, but two broad themes were evident. The first was the conscious attempt to tackle problems with a sufficient degree of material flexibility. The second was to address increasing demand using formalised managerial flexibility The most overt expression of material flexibility took the form of boarding patients away from parent wards for short term periods until the pressure eased. Seven trusts identified boarding as a major feature of their bed management practices, and these may be further subdivided into those which specifically mentioned surgical specialties as the recipients of excess patients from medical specialties (Trusts B, E, F, G & H) and those which identified the existence of previously agreed boarding procedures or protocols (A, C & D). The ubiquitous use of boarding to deal with short-term variation in demand further highlights the necessity of giving the individual responsible for allocating patients to these beds the proper authority to carry out their task, because it is precisely at such periods that staff will be most protective of their free beds In addition to this short-term flexibility, certain trusts adopted a formal procedure of medium-term or seasonal flexibility. This also may be seen to take two forms. One concerns the flexing of the bed stock, such that beds were reallocated from certain specialties (again, mainly surgical) to general medicine for more or less predictable periods of time (that is, at least part of the winter season) (Trusts A, F & G). The second form of medium-term flexibility concerns the identification of contingency beds, normally dormant and unstaffed but available for use when required (Trusts B,E,G) The existence of formalised managerial flexibility was evidenced by the reported implementation of different management systems once a threshold of demand was reached. Three trusts had adopted such a practice. Trust G implemented its Bed Bureau, while trusts C and D used variations on the theme of crisis management to deal with the problem. Of these the latter was the most sophisticated, in terms of its defined and graded increase in senior staff involvement. The availability and quality of data on bed status 6.48 Part of the on-going process of bed management involves the collection of information on available beds. The necessity of real-time information is recognised as an essential component of the management of beds and patient throughput (Audit Commission, 1992). The credibility of the bed manager is also reflected in the accuracy of the information they provide to medical and nursing staff: no one can argue with hard data (Green & Armstrong, 1994). The necessity for the collection of accurate data is reflected in the information systems used in each of the eight trusts, all of which monitor the bed state at least twice a day. However it was still evident that the information collected was not sufficiently real-time to provide an accurate picture of the available beds during periods of high demand There was a recognition by most of those involved in the collection of information on bed management, including nurses, Bed Managers and senior staff, that accurate and up to date information could only be obtained during periods of high turnover by visiting wards personally. In Trust A, for example, during periods of high demand, executive staff would sometimes walk the wards to monitor the bed state. While there was a recognition that this was inappropriate [ACNM], there was also a view that a degree of authority and personal contact were necessary at such times. In Hospital E, it was reported that one of the reasons for the lack of real-time information was that the nursing staff responsible for the collection of this information were too busy to up-date the system during periods of pressure on beds. In Hospital F senior staff occasionally had to walk the wards to supplement the information on available beds and one Senior Nurse Manager reported that she would sometimes use the personal contact with patients to attempt to free beds by persuading some to discharge early. In Trust G a number of difficulties were reported, revealed by extra demand over the winter months. The information collected on the bed state was 75

78 neither up to date nor reliable at times of crisis. Reasons given for this included too many people up-dating the system; staff under too much pressure to up-date; and a lack of any clear definition of whose responsibility it was. One result of this was that senior staff had to collect information on the bed state at ward level. The context of bed management Introduction 6.50 In order to understand the context within which respondents made judgements about the feasibility of further increases in occupancy rates and their suggestions for improvements to bed management procedures, they were asked to consider the likely effects of such an increase, in broad terms. Such an understanding is important. Successful organisational development, especially if it requires behavioural and attitudinal change on the part of an existing workforce, is largely dependent on their overall support. Content analysis of the responses revealed high levels of agreement. The responses have therefore been combined together under discrete headings in the interests of providing a coherent and concise account of their views. Perceptions of benefit 6.51 The most frequent response was that there were no benefits at any level of an increase in admissions. A Director of Finance identified that under normal circumstances there were no financial incentives to increasing throughput in any contract: increased throughput created higher unit costs with no increase in resources from the health board: we are unlikely to see any direct relationship between increased activity and increased contract value [GDF] There was a general perception that workloads were already exceedingly heavy and bed complements at optimal levels (that is, more activity would require more beds) The principal factor determining levels of benefit was judged to be the purchaser: respondents considered that it was for the latter to decide the level of demand for beds which it wished to service, not the trust. Thus there would be no benefit to a trust from an increase in throughput unless the purchaser agreed to finance the increase in activity. Even if increases were to be funded, any benefits to a trust would be very, very marginal [CDN]. A Director of Finance identified the key issue as being whether the hospital could continue to provide high quality patient care within available resources. Indeed, extra emergency activity would create extra marginal costs, in terms of diagnostic tests, x-rays, et cetera. A Chief Executive mentioned that long-term benefit might accrue were the trust to develop a specialty and increase the mass of work undertaken within it, but any short-term increase was just a bloody penalty [BCE] Those benefits which were identified related to factors other than finance. For example, a Business Manager for general medicine said that if the increase in activity were sustained it would allow the trust to bid for more junior medical staff. The limited number of such staff was, she said, the single biggest problem in terms of being able to expand and work beds more efficiently [DBSM]. An Operational Services Manager identified the pride experienced through meeting a challenge, an intangible (and rare) beneficial effect on staff morale. A Chief Executive, in addition, describe the altruistic benefit of providing a better service for the local population through reducing waiting times. A Medical Director identified that the hospital s prestige in the community would be enhanced by their being able to cope with more emergency medical cases. Several clinicians identified an attendant improvement in the availability of patients (or conditions) for teaching and for research, thereby boosting the trust s status at national level [ASR]. A Chief Executive pointed out that the requirement to place medical patients by boarding them into surgery had increased the appreciation by surgical staff of the difficulties faced by those in medical specialities. Other intangible benefits included a long-term shift in attitudes of both staff ( there was very much a custodial kind of process of care, once the patient was in your care you decided if they could go home ) and patients ( patients are beginning to say when they feel ready to go home ) [ECD]. Perceived barriers to an increase in admissions 6.55 Although there was unanimous agreement that the major impediment to servicing any further increase in admission rates resulted from limited resources overall, specific problems were noted. Staff shortages were identified by all respondents and were perceived to exist at all levels, from consultants to domestic 76

79 staff. The difficulties were seen to become particularly acute during what one respondent called periods of extraordinary [GDN] demand, over and above the predictable annual variation. According to one Chief Executive, appointing staff to permanent jobs was easier than trying to man temporary posts, especially in winter. The reduction in junior doctors hours and the increase in their training commitment was seen to have caused consultants workloads to increase. A Specialist Registrar commented that under current arrangements, one registrar and one house officer may have responsibility for admitting up to 25 patients in a day. While this was viewed as keeping the process under control, that condition would be threatened if an increase in admissions were to occur without more staff on duty. Trusts were already seeking to cope with a shortage of junior doctors by asking consultants to work sessions carrying out some of the functions of junior doctors ( There is a limit to how many consultants will do that, for how long [CCE]), and one trust reported recruiting some G Grade nurses to do what would previously have been considered junior doctors work: doing admission, receiving, basic diagnostic work [CCE]. A nurse recalled how the apportionment of four extra but unstaffed beds to general medicine caused the department to struggle until additional nursing staff were employed. This problem was accentuated by one trust s geographical location: with a small catchment population, there was only a limited pool of qualified nursing labour. As a Director of Nursing commented, it was not easy to turn the tap on and off [DDN] Specific outcomes of increased workload consistently indicated that resources were already utilised to capacity. One effect cited by a ward sister would be for communication to suffer as nurses would have less time to talk (and listen) adequately to patients relatives, they would have no time to attend a full one to two hours ward round and would thus become dependent on information being passed to them by medical staff. She pointed out that it was unlikely they would remember to communicate 100% of the relevant information. A Bed Manager considered that waiting times would inevitably increase as nurses were pushed into hashing and bashing, doing the admission procedures and clerking, because there won t be time [CBM]. A Ward Sister in general medicine also identified time pressure as an effect, describing ward activity as becoming a big mish-mash. Furthermore she thought that the quality of care must be reduced: the standards... couldn t possibly be maintained if you haven t even got time to stop and think for a second [CWS]. This comment raises what may be an important issue, because it concerns the professionalism and authority of nursing care. Should one expect nurses to consider their activity, or should they be conceived of merely as functionaries carrying out tasks without thought? 6.57 There was substantial agreement at both strategic and operational levels of the trusts that staffing levels were adequate for periods of even high demand when the full complement were available for work. The problem respondents identified, however, was that seasonal variation in demand was linked to winter epidemics of communicable disease, to which the staff were also prone, and staffing levels were not calculated on the basis of predicted extreme rates of sickness absence. The risks of this policy were recognised by all respondents. For example, a Chief Executive said that he had been advised that staffing levels were safe but admitted that they might be getting to the limit of safety; a ward sister in general medicine described how, under pressure of work, choices with regard to the provision of nursing care had to be made, with the result that technical care tended to take priority over basic care (pressure area care, mouth care, helping people eat). The reduction in the package of care associated with hospital care was identified by a Clinical Director for general medicine as a direct result of relative reductions in resources: I observe... standards of care slipping because getting more and more patients through a fixed resource in terms of a fixed number of beds and a fixed number of staff means that each patient receives less and that may be what the public want. I suspect that the declining standards, the smaller package of care, is actually not being driven by popular demand [DCD] The scale of the pressure on beds was highlighted by one Chief Executive, who cited a 65% increase in medical emergency cases over the previous five years. During periods of highest demand on beds, the trust would cancel elective surgery to accommodate medical emergencies. For example, during the previous year there had been a period when there had been: no eye patients in the eye ward but the eye ward was full of medical emergencies [DCD]. 77

80 6.59 A Director of Nursing identified physical and mental exhaustion amongst staff in the medical directorate as a particular problem, partly because to work under constant pressure was demoralising and partly because staff sickness absence increased as a result. Providing encouragement was insufficient: I give them praise but I don t give them enough rest [BDN]. This was in contrast to staff in the surgical directorate, for whom seasonal lulls in activity, for example in July and over Christmas and New Year, had the effect of providing predictable periods of respite which, by implication, helped maintain morale. Pressure on staff was described as intolerable, horrendous [FCNM] and relentless [FDN] A second constraint concerned the dependence of Bed Bureaux on Patient Administration System (PAS) data in order to identify available beds. It was well recognised that, particularly during short-term periods of pressure on beds, data was not entered onto the PAS. This was regarded as a predictable and understandable problem: the staff responsible for entering the data were also responsible for other tasks, and would postpone data entry in favour of them. Ironically, it is during such periods of pressure that the PAS and the Bed Bureau are most likely, in principle, to be able to contribute usefully to bed management. Furthermore, not all ward-based activity was entered. For example, were a patient to attend a ward as an outpatient they would consume resources such as nursing or doctor-time. A Specialist Registrar observed that medical staff were largely ignorant of the procedures employed by the Bed Bureau. The general lack of information held by the Bureau was highlighted by one nurse, who recounted how her call to place a patient resulted in her being told where empty beds were located but not whether they were for male or female patients, information she then had to gather herself Other constraints mentioned included the limitations on boarding-out patients to quieter locations ( You d have to keep patients somewhere that was safe for them [GSR] and the time required to arrange a placement, explain it to the patient and physically carry it out), the observation that decanting patients resulted in their substitution on the parent ward by patients who were more ill and required more intensive nursing, the fact that all theatres were fully booked throughout the day and required a cultural change [GCD] before they could be routinely opened at night or at weekends. This caused difficulties because of the general expectation that while in hospital patients would have their own space and that space equals a bed [ADN]. Decanting patients was seen to cause them to feel unloved, unwanted [ADN], and perhaps more significantly, to question the competence of staff who appear to be disorganised. As a result, more complaints from patients or their families could be expected, further increasing pressure on staff Four respondents at the operational level identified theatre-time as the principal constraint on an increase in workload. A Clinical Director described how the theatres were already working to capacity ( as flat out as it possibly can [CCD]) and pointed out that there was a real limit to how much work could actually be achieved. A Specialist Registrar considered that the throughput of patients in theatre could be improved through the introduction of more nursing staff and a change in anaesthetic practice. He described his current practice as: do the surgery, do the paperwork after that operation, you will then have to go and see your next patient, prepare them and give them the local, wait for that to work and then do the surgery [CSR] In previous years, certain trusts had responded to such high demand by creating an overspend on contracts (That is by simply staffing extra beds and arguing the rationale for it post hoc). In order to prevent such an event happening again, contingency plans had been drawn up to cancel elective admissions under similar periods of pressure. This was seen to have the effect of increasing waiting times and threatening the timely completion of contracts with purchasers. Several respondents, particularly at trust Board level, stressed that only the infusion of extra funds from the Management Executive to the Health Board had prevented a repeat of the problem. The problem identified by one Chief Executive however, highlighted the difficulty of planning: one year you will get extra money, the next year there is no extra money [DCE] Two further issues could be identified as important to respondents. The first was that increasing admissions had a holistic effect on hospital organisation. Staffing costs and consumables (particularly drugs) increased across the board. Although these costs were technically marginal, they did not include damage to staff morale and risks to the quality of patient care through maintaining a workforce which perceived itself as being under stress. The second was that the physical complement of beds appeared not to be a constraining factor. Several respondents reported that beds were available to cope with fluctuation in demand, even at the 78

81 high extreme, and it was staff availability that ultimately determined whether beds themselves became available for use The common denominator underlying these responses is that increased demand must be matched by increased investment. It was recognised that, ultimately, a significant increase in demand was an issue for which purchasers had a substantial role in finding a solution: respondents at strategic level said they would wish to discuss such an effect collaboratively with purchasers in order to resource the expansion in the level of activity. Perceived effects of increased admissions on hospital costs 6.66 At all levels respondents agreed that increasing the number of admissions must increase overall costs, even if lengths of stay were reduced to compensate for the higher number of admissions. Mainly, this was seen to be because length of stay had already been trimmed to a minimal value, and any further reduction would not be offset against the requirement for more staff to manage the patients, more procedures being carried out, and more diagnostic and domestic services provided It was striking, however, that little detailed information on costs appeared to be available, even to trust directors. A Director of Finance explained that they were only required to produce costs per case on an annual, predictive basis, running from February to March of the following year: There is no incentive, there is no requirement for us to actually look at cost per day and cost per case down at the lower level [GDF] This was borne out by the general expression of ignorance about costs variation over the short term. Most Chief Executives, Directors of Finance and Directors of Nursing said that they could not give an answer to the question. One Chief Executive implied uncertainty about the relationship between activity and costs by suggesting that cost per day would probably reduce, and commenced his response to a question about cost per day by saying well let s just think about that [CCE]. A Medical Director said he had never even thought about it [GMD]. This lack of understanding was also noted at operational levels: medical staff, Ward Sisters and Nurse Managers usually responded by saying that they had no knowledge of the effect a rise in admissions might have on hospital costs. Responses were usually characterised by uncertainty: respondents used terms such as probably [AWS], presumably [ADN] and feeling [AMD] to modify the conviction of their responses to questions about the effects on costs Part of respondents uncertainty derives from the question of scale: marginal increases in demand were considered to have marginal effects on costs, a point realised, for example, in the contractual specification at one trust that marginal increases in activity of 2% or 5% (depending on speciality) could not be used as a basis for renegotiation within the current agreement. A Chief Executive, for example, estimated that the increase in expenditure to accommodate more admissions would be significant in terms of the drugs budget but not in total hospital running costs [GCE] A Medical Director emphasised that cost per day might increase in line with increasing activity, assuming no increase in resources, because reductions in length of stay would be the only feasible method of accommodating patients, but that this would result in the concentration of effort onto the higher-cost, post admission phase of patient care, when the principal diagnostic and therapeutic inputs are made. It was important, he said, to take into account speciality and patient type before estimating the effects of increasing demand on cost per day, especially as spreading overheads over a larger number of patients would tend to reduce cost per day. Other respondents thought average costs per case would decrease, but the picture would be clouded by more long-stay patients When forced to consider specific cost outcomes, respondents tended to agree that average unit costs should fall given an increased throughput for the same number of resources. This reduction, however, would be offset by an increase in marginal costs applied to dressings, drugs, diagnostic tests, portering, catering and linen services. These general effects were seen to be complicated by specific conditions which might apply, depending on the time of any increase and the type of patient involved. A Director of Finance, for example, noted that if elective admissions were reduced in order to accommodate an increase in emergencies, the effect on costs would depend on whether the lengths of stay of the emergency patients was different from those of the electives, an outcome it was impossible to predict. New techniques like keyhole surgery were 79

82 associated with reduced lengths of stay, but patients tended to be more ill while they were in hospital, thus increasing costs associated with their care Respondents foresaw a particular problem in that total costs might rise with an increase in total patient throughput, but that this increase tended to be hidden by the focus of attention on average cost per case or cost per day. In addition, reducing elective work in order to accommodate emergencies might threaten waiting list targets, which if breached, would result in financial penalties. The fact that no extra money was normally forthcoming following increased activity within a contract-year was seen to aggravate the problem: so the fact that our own cost per day is coming down is a paper exercise. It doesn t translate into any additional resource to help us run the hospital [DCD] Furthermore, a Director of Finance identified that medical and surgical costs were interrelated: while costs per medical case might reduce in the light of extra activity for no extra resources, so surgical costs per case might increase due to reduced elective activity in the face of increased boarding of medical patients The fact that activity was regulated by the purchaser s ability or willingness to fund it was identified by one Business Manager for general medicine as a key problem. The Health Board s reluctance to commit resources at the outset of the contract to accommodate the predicted winter peak in activity meant that the trust had no room for manoeuvre. The Board apparently was of the view that the increase in winter medical activity could be offset by reducing elective surgery. The trust, however, had succeeded in avoiding cancelling elective surgery the previous year, partly because of a late decision by the board to fund the extra emergency medical activity. In order to regulate the supply of services, the Business Manager considered that the trust should contract at the beginning of the year for the extra staff I m going to bring in from about October onwards on a sliding scale. I have at least two facilities I can open up here. A small facility that I would open up early in the winter, and a bigger patch when I know the peak is going to hit me... [DBSM] It would then be possible to arrange for staff to be phased in and out if we know ahead of time what financial freedom we are going to have [DBSM]. Her view was that slow decision-making by the purchaser was the root cause of subsequent difficulties, because by the time the decision had been taken, the available pool of trained nursing labour had already got jobs elsewhere: so recruiting people... for that sort of short term contract at that short notice is an unnecessary burden on the trust, and caused entirely by the purchaser [DBSM]. Perceived effects of increased admissions on length of stay 6.76 There was a general consensus among respondents that it was difficult to imagine how lengths of stay could reduce still further, following several years of consistent falls. Indeed, there were suspicions among some interviewees that lengths of stay could start to rise in the face of an increase in demand, because more and iller patients would result in bed blocking, alongside organisational problems associated with higher levels of boarding and attendant delays in providing and monitoring care. A Director of Nursing described a newly instigated policy of sending hospital nurses out into the community to conduct follow-up for oncology, for example, as a means to reducing lengths of stay There was also a perception among respondents at the strategic level that unlike previous years, when lengths of stay had been reduced in the face of increasing demand, in the current year the winter peak had been characterised by patients presenting with more complex illness requiring longer stays: This current winter peak has lasted six [to] seven weeks now. It is not being associated with massively elevated numbers of admissions but it has been associated with increased case complexity and longer durations of stay, that is obvious [GMD] Reinforcing this view, a Director of Nursing observed that the increased number of admissions in winter tended to be associated with older patients. A particular problem with this group consisted of delayed discharge of clinically well people because of shortages of community and social work support. In order to try to overcome the difficulty, both the Social Work Department and community agencies had been involved in the planning stages of the contracting process. 80

83 6.79 At ward level, respondents reported that length of stay could increase because of increased workload for diagnostic services. A Specialist Registrar suggested that if it took longer to get investigations done and a ward round was not completed until five o clock, it would be difficult to discharge patients that day. Two Ward Sisters were concerned that earlier discharges might threaten patient well-being: patients would go home earlier than they should [GWS], resulting either in an increase in readmissions or the requirement to provide a lot more support from their community services [GWS] Respondents from general medicine appeared unhappy with such a development, citing that it had nothing to do with the provision of quality care. A consultant voiced his irritation: You get the patient in the bed and before you start to offer appropriate care or investigate them properly you are trying to get them out the bloody bed and get somebody else in it [ACON] There was consensus among respondents that pressure to reduce lengths of stay would continue. The consequence would be that some patients would be discharged without sufficient observation, that monitoring was passed to their general practitioner who might decide to readmit. This caused extra delay, because a readmission might have to go through the whole clerking process in the Admission ward before going to the specialty. Clinical staff were already aware of the pressure. A Specialist Registrar had been told by his consultant, for example, that he should only take responsibility for patients actually under his care, and remain oblivious to the pressure from new patients being admitted. Reducing length of stay under pressure of demand had already led to a few patients going home just on the borderline [FSCN]. A Ward Sister in general surgery considered that the main factor determining length of stay was the consultant rather than pressure from admissions, a view echoed by a Director of Finance, who said that they had no policy on lengths of stay but left such decisions to consultants. A Specialist Registrar in Obstetrics suggested that social factors played at least some part in the resistance to shortening stays: For example in the US you might discharge patients the day after they deliver normally. For purely financial reasons. But you know obviously it s not very civilised to do that, obviously a lot of it is for social reasons and for the comfort of the woman you probably should keep her in for two or three days [GSR] Resistance to reduced lengths of stay was identified by a Chief Executive as deriving particularly from patients and by a Medical Director from general practitioners, to whom he had written asking for their understanding Not all respondents, however, were unhappy about the prospect of a reduction in lengths of stay. A consultant suggested that as older staff retired their younger replacements saw no need for longer periods in hospital, a view supported by a Ward Sister, who considered that, we sometimes encourage patients to stay too long just because we have always done it like that [AWS] Another Ward Sister considered that there was scope for the vast majority of patients to be managed as day cases, but you are going to have to change a lot of attitudes [CWS] Underlying both groups of responses, however, is the belief that it is the function of the hospital to provide care to a certain standard in order to bring about an improvement in patient well-being. Reducing lengths of stay beyond that critical point is seen as a negation of the hospital s function: if it is not in a position to improve well-being then it might as well not exist at all. A Consultant in Obstetrics and Gynaecology stated that women were discharged as soon as they were ready, so if you shorten it, they would have to be not ready. Is that reasonable? [DCON] Without formal agreement about appropriate levels of clinical outcome upon which discharge decisions are made (and such consensus appears impossible given the nature of medical practice) it seems inevitable that pressure to reduce lengths of stay as a means to improving throughput will continue to encounter resistance from staff who consider it their proper task to discharge only patients they judge to be well. 81

84 Perceived effects of 5%-15% rise in admissions 6.87 Almost all respondents at director level agreed that were a 5% increase in admissions to occur averaged across the year then their hospital would be able to absorb the extra demand under the current level of resourcing, given that they had seen admissions rising at that rate for several years, and if the rise were spread evenly throughout the year and for all specialties. They emphasised, however, that were the increase to coincide with the winter peak then serious problems would accrue, partly because it is during this time that staff sickness absence is at its height, and partly because contracts are so advanced by the early part of the year that there is no realistic possibility of reorganising work patterns to accommodate the increase However, even a 5% increase could not be managed without some cost. Two respondents suggested electives would have to be reduced. A Chief Executive, however, pointed out that as a tertiary referral centre his trust often received patients who were classified as both elective and urgent cases. Other respondents indicated that such a rise was manageable, though it depended on case-mix: such an increase among elderly patients would create accommodation problems. Two Directors of Nursing considered that even a five per cent increase would cause serious difficulties, requiring a rethink about the way certain types of patient were nursed and whether existing types of patients treated within the hospital could be treated elsewhere Respondents from one trust indicated that recent events had seen activity rise by 50% (according to the Chief Executive) or 100% (according to the Medical Director) for a period of 10 or 21 days (respectively), and the workrate had risen to cope with it without any reported effect on quality of care. The Director of Finance suggested this was evidence of the trust being grossly underoccupied previously, given that their overall bed occupancy rates were not great [ADF] Asked about the implications of a 10% rise in demand drew an almost unanimous response: unmanageable ; that would probably bring us to our knees [DCE]; impossible to accommodate without extra resources to open another ward Two trusts emphasised their responsibility for providing care irrespective of the level of demand: We are not a hospital that can close its doors. One of our neighbouring hospitals did just that this winter. One of the small hospitals said No more medical admissions and consequently we got an increasing number of patients from the [town] area. At the end of the day, we have to take, and we would manage... [GDN]; we could cope with anything [FDN] A Chief Executive said that were more flexible working arrangements to be negotiated with staff, such that weekend opening could be introduced for elective work, then the increase would be more easily dealt with. He emphasised, however, that when he had suggested such an arrangement it had been universally rejected: But this is a fundamental change now to an arrangement which has developed as a result of national pay bargaining, which is brought in, you know, all this extra pay for unsociable hours, et cetera. I think that could be eased out. Consultants are resistant, of course, because they want their weekends free. A lot of them are married with young children and they want weekends off, to be on-call or limited on-call, and they don t see, all they see, if I suggest this, is that they are being asked to do more work for the same pay sort of thing, they are already overstretched. That s not really what I m asking them. I m suggesting that they take another day off a week or whatever, or half days, and spread the load, and this I think would ease the whole workload of the hospital if we were able to do that [GCE] He pointed out, however, that such an arrangement would only be feasible in resource terms if staff relinquished the right to unsociable hours payments A Director of Nursing emphasised the scale of the problem of rising demand: I mean, a few years ago I would be saying to people that said we can t have any more... oh away and don t be silly. But I am actually... at the stage of thinking God we really can t take any more [CDN]. 82

85 The possibility of reducing bed numbers 6.95 Questions about the potential for improving occupancy rates by reducing the number of beds produced a predictable response from all interviewees: it was judged to be a bad idea, given that the total beds complement had been reduced consistently over previous years. This was itself considered partially responsible for the recent difficulties the trusts had faced in responding to seasonal variation in demand. Reducing bed numbers was thought to be something best achieved gradually, applying a trial and error technique, in order to avoid the risk of underestimating subsequent demand Respondents identified three sets of difficulties in relation to reductions in bed numbers. One concerned workload and demand, another hospital design and layout, and the third developments in clinical practice. In large measure, these were seen as being interdependent. One Chief Executive, for example, indicated that reducing the number of beds in pursuit of increased efficiency left no room for manoeuvre should demand peak at levels higher than planned for. His hospital was currently engaged in what was termed reconfiguration of beds, closing four beds in each of seven 12 bedded high dependency wards and redistributing the remaining eight to the attendant 30 bedded units. Both the Chief Executive and the Director of Finance identified that this process would reduce costs and improve staffing ratios The Director of Nursing considered that that the optimum level of bed numbers had now been reached, but thought that seasonal variation could be tackled more specifically: What I think we probably could do is to look at seasonal issues and, for example, traditionally in the summer, we close an orthopaedic ward for up to four weeks, reduce the number of surgical beds... [GDN], and suggested that this might be used to transfer some of the load on elective surgery during the winter, in order to free beds for general medicine The Medical Director identified a different solution, suggesting the concept of the acute mixed buffer beds [GMD] which are normally closed but reopened during the six week peak of winter demand: It s not being profligate, and saying we must have these beds fully staffed at all times in case. The shroud waving approach. It s saying we really don t need them most of the time, but, for six weeks in winter I really will need them [GMD].Respondents more closely involved in patient care, however, made no mention of such strategic flexibility and focused their comments on identifying the immediate pressures of their day-to-day work. A Clinical Director for General Medicine, for example, stated bluntly: We need more beds. Somebody, some day, will come to that blinding conclusion [GCD] An obstetrician argued that it was the peaks in variation in demand for beds which should form the basis of planning and resource allocations: Obstetrics is a speciality when you know you would get a sudden rise, there are times when you could get a good number of admissions overnight, and you would have to deal with all those. It s a bit different from other specialities when you can sort of put things off, with obstetrics when things go wrong they go wrong fairly quickly. So you need those beds and you need to be able to slot them in fairly promptly. [GSR] A ward sister gave what on consideration was a graphic account of peak demand on general medical beds: I believe just since last week we had 40 medical patients outwith the medical unit. That s not just a couple, it s more than one 30 bedded ward [GWS] In one trust the only feasible arrangement for closing beds was considered to be increasing day care, but several respondents identified difficulties in doing this in practice. Although the day unit was said to be not fully utilised, part of the explanation lay in the configuration of the hospital: Ophthalmology day patients, for example, were often accommodated in the ward rather than the day unit because the former was 83

86 situated adjacent to theatre, while the unit was on a different floor. Furthermore some patients were reluctant to be discharged after day surgery with the requirement to reattend early the next morning for follow-up. The Specialist Registrar who described this effect suggested that arranging for follow-up later on the second day, say after lunch, would make the effort of returning seem more worthwhile given the slightly longer period away from the site. But he was anxious that follow-up the day after surgery should not be dispensed with on medical grounds, considering it unacceptable to see the patient two days or so later: My appointment was in two days time anyway doctor so I thought I would just hang on to my painful red eye that is full of puss until you saw it at the clinic. It s not good practice [CSR] Other constraints on reducing bed numbers included the level of variation experienced in general medical admissions:...anything between 15 to a day just now [CWS] A Director of Finance thought that when the acute services were centralised onto a single site as planned there would be scope for an overall reduction in beds, but in the interim only marginal reductions in beds was feasible, despite pressure from the purchaser to reduce hospital costs. His use of the phrase squeezed out [CDF] emphasised the minimal nature of any potential reduction. This point was taken up by the Medical Director, who was concerned that beds should be closed because they were redundant, not as part of a cost cutting exercise [CMD]. While he acknowledged that excess beds were unethical and were associated with opportunity costs, he indicated that reducing bed numbers by small amounts did not lead to commensurate savings in associated costs, particularly if the same number of patients were being put through them. The Clinical Director for Ophthalmology suggested that investing in hostel accommodation for admissions and discharges might allow a more meaningful reduction in bed numbers Other structural problems were identified in reconfiguring nightingale wards. This might reduce bed numbers but could only be undertaken with significant capital input. Opportunities had been created for the re-engineering of surgical services as a result of technological and practice developments, but were more surgical beds to be lost an important source of accommodation for boarding other patients would be lost in turn. In the longer-term, changing practices in providing care for the elderly might allow the reduction of a quota of geriatric or medicine for the elderly beds, but such a move would take a lot of planning and preparation before that s going to happen [DDN] In developing a strategic argument, a Medical Director pointed out that the traditional seasonal variation in demand was a thing of the past now [GMD]. His view was that occupancy levels now ran at a high plateau year-round, with some excess peaks, thereby nullifying the suggestion that elective surgical work could be moved into the summer to free-up resources for winter medical emergency admissions. Having already instigated changes to bed management and clinical practice, he thought that there was a risk of pushing the organisation to a point beyond which I don t think you can reasonably go because the beds don t exist [GMD]. This view was echoed by a consultant from Obstetrics and Gynaecology, who remarked that one could reduce beds if you wanted to reduce the amount of work done, sure [DCON] In one trust, however, the Chief Executive thought that a cut of around 50% in the bed complement was probably achievable over the next five to ten years, partly through expanding day surgery and partly through reducing existing short lengths of stay, for example cases currently admitted for two days being in future admitted for only one. Delays were identifiable in the process of conducting investigations, and preadmission surgical assessments could be increased, allowing more patients to be admitted on the day of surgery instead of the night before. In some specialties, for example general surgery, it was reported that consultants tended not to enter the ward until five o clock in the evening, by implication reducing the scope for immediate discharge home Specific difficulties identified concerned historical working patterns in Obstetrics and Gynaecology, where changes to the theatre rota could not be implemented without wide consultation because altering the pattern for a single Consultant would have knock-on effects on those of others, particularly since some 84

87 Consultants worked across two trusts. In addition, a small number of patients could create bed blocking problems while social support arrangements were put in place: a lot of my patients are vascular and therefore have amputations and we have to wait until their house is modified and so on to allow them back. They are often in for months waiting for that [BCON] Even when the case for further reductions in bed numbers seems obvious, responses produced a sharp distinction between staff at strategic levels and those with clinical responsibility. The Chief Executive, Director of Finance and Director of Nursing of one trust, for example, were clear that further bed reductions, on top of the 200 closures which had already occurred, were not only feasible but probably beneficial in terms of quality of care and throughput. The Chief Executive reported that at any time the trust maintained around 200 empty beds. The Director of Finance pointed out that it was unreasonable to petition the Health Board for more resources to develop key areas of activity until they could demonstrate efficiency savings. This they were partly in the process of achieving by reconfiguring part of the estate into a closer physical relationship with the main hospital, allowing substantial savings at no cost in terms of patient care. The Director of Nursing argued that over and above the rationalisation of the estate, bed reductions were achievable through increasing the use of day surgery, developing more five-day wards and increasing the use of outpatient treatments. Each respondent expected opposition to these developments. The Director of Finance said that it was difficult to win arguments about closing beds because it tended to be seen as a cut in service. The Director of Nursing expected both public and professional opposition to the plans Clinical opposition in this trust was immediately apparent. Even the Medical Director voiced no enthusiasm for a significant reduction in the bed complement, citing the requirement to maintain sufficient numbers to service the new specialised units. Respondents from Obstetrics and Gynaecology reported that the outcome of earlier bed reductions had caused difficulties during the Christmas and New Year when they had tried to close a ward and all hell let loose [ABSM]. But they conceded that some further reduction was probably feasible given careful planning and organisation, though given recent service developments like the introduction of outpatient treatments in preference to inpatient, patients occupying beds represented a hard core that s difficult to alter [ACON]. Resistance in general medicine was more overt among Consultants. Two described the process as totally impossible while a Ward Sister thought a reduction was not feasible at present, and that they were being forced by bed occupancy figures and the powers that be [AWS] Other suggestions for reorganisation to allow bed reductions included the introduction of a 23-hour stay, closer co-operation with general practitioners and social workers to arrange for post-discharge care and support and the notion of the community hospital. This would be staffed by general practitioners, and designed to provide more basic care than the high-technology sophistication available at the main site Competition between specialties, wards or individuals was identified as a further problem. A Clinical Director for Obstetrics and Gynaecology, for example, gave an indication of the irritation felt by those in some specialties who had reduced bed numbers only to find that others had made no similar reduction: why should we bother any more if other people don t play the same game [ECD]. Perceptions of spare capacity Respondents discussed several issues in relation to spare capacity. These may be broadly identified as involving extensions to existing working hours, more flexible use of existing facilities, changes in clinical practice particularly through increases in day case work and outpatient procedures, and formal co-operation arrangements between neighbouring trusts The majority of respondents agreed, however, that all obvious spare capacity within existing resources had already been taken up: any spare capacity which might be identifiable was marginal under current levels of resourcing from the Health Board. Several respondents considered that more activity could be undertaken if the purchaser were prepared to buy it, but whilst closed wards might be reopened, this would only be achievable if the health board was prepared to contract for it. If the health board would like to contract for more general surgery and pay us more that would be fine, we could do that [CCE]. 85

88 6.114 Financial considerations also influenced respondents views of weekend or evening opening of theatres. This might allow an overall increase in admissions. However, such an arrangement was described by a Director of Finance as less efficient than operating five day wards because of the associated increase in costs, principally staffing-related. A Clinical Director conceded the principle of weekend working, but emphasised that financial incentives would be required because it would be an unpopular development. At ward level, two respondents described specifically operational limits on the scope for weekend opening. One said that although there was no formal closing of beds in Obstetrics at the weekend, staff levels were run down because they knew that no elective surgery would be undertaken. Another pointed out that, in fact, spare capacity at weekends could only be identified on Saturday afternoons and Sunday mornings, because patients from Friday were often not discharged until Saturday morning and admissions for the Monday list often took place on Sunday afternoon: If you were going to be admitting surgical cases, you would have to be really confident that they would be in and out the next day... [GWS] Similarly, opening five-day wards at weekends might also impinge on the ability of the ward to admit patients on Sunday night or Monday morning. Weekend staffing levels were reduced to skeletal levels, and although the potential for weekend elective work existed because the facilities were in place, it would require a completely different recruiting environment [DCD] to realise it It was recognised that, given seasonal variation in demand, general medical wards were under less pressure during the summer than they were in winter, and that it might be possible to utilise the excess for carrying out elective surgical work. Other respondents, however, described how the summer trough in admissions had proved, in practice, to be too small to allow the direct redeployment of sufficient resources into winter medical admissions, and how weekly variation in demand could be described but not predicted: we did have a two week spell early in December where... the most admissions we had was about seven,... whereas consistently since Christmas we have been having between 21 and 35 admissions, and the ward is just full all the time [DWS] Similarly the suggestion that elective activity could be phased by sending all your orthopaedic surgeons on skiing holidays in the winter so they do all their operating in the summer was practically more difficult to engineer [DBSM] Weekly variation might be utilised if a directorate such as Orthopaedics were to routinely transfer a small number of its beds to Ophthalmology for its use at weekends, but the Ward Sister who made the suggestion pointed out that such an arrangement would be difficult to organise to such a degree of precision that elective Ophthalmology patients could be securely offered beds for Monday morning The most obvious spare capacity identified by respondents was the potential to increase day care. This did not appear to be straightforward, for reasons attributed, by those in more isolated trusts, to geography. A Director of Finance reported that attempts to solve the problems associated with patients travelling long distances to attend the Day Unit by establishing a patient lodge had not been altogether successful because it was located away from the main hospital complex, making access difficult for certain groups of patients such as those for ophthalmology. A Medical Director identified that the Orthopaedic surgeons were reluctant to use the Day Case Unit for complex reasons, but believed that they would automatically increase the day surgery rates through increasing inpatient activity without a commensurate increase in Orthopaedic beds. However, a Director of Nursing considered that the geographical argument against day case work was not convincing [BDN] In addition, in another trust the scope for expansion of day case work for general medicine was thought to be limited because only 7% of the activity was elective. Specific surgical specialties, such as ENT and Ophthalmology were judged to have significant scope for more day case work, though the Chief Executive identified consultant behaviour as a particular barrier, while two clinicians identified the availability of anaesthetists as similarly problematic. A Director of Finance remarked that he was uncertain of the veracity of the view held by some colleagues, that the high level of socio-economic deprivation 86

89 experienced within his trust s catchment area was an inhibiting factor to the expansion of day care because of the large numbers of patients who were elderly, lived alone or had transport difficulties Other examples of spare capacity which related to organisational practice included the changes to the timing of ward rounds and the attempt to speed up diagnostic results. Neither was judged likely to effect more than marginal changes to bed occupancy or throughput The existence of what might be termed necessary spare capacity (specifically in Paediatrics and the Special Care Baby Unit) was identified by a Chief Executive, but he pointed out that neither facility could be used in a flexible manner to assuage demand from other specialities because of their specific purpose. The same argument was made by a Director of Nursing in relation to maternity facilities. But, in what appeared to be a contradiction, the same Medical Director stated that having some spare capacity in Obstetrics was vital because it provided a location of last resort for patients from General Medicine and Surgery when I ve got the tower block saturated [HMD] One respondent described the existence of area-wide spare capacity, in that what he called two neighbouring uneconomic hospitals which were each operating under capacity and drew patients away despite their higher unit costs because they had shorter waiting times. Other sites identified that inter-trust co-operation, rather than competition, was happening already, especially for general medical patients. For example, the Medical Directors of all the acute trusts in one Health Board area made direct contact to discuss co-operation during periods of pressure on beds. One drawback to this was that such pressure tended to hit all the trusts simultaneously. Respondents tended to be equivocal as to whether the Health Board supported such arrangements. The Director of Finance thought that he could identify an emerging agenda to rationalise activity across all the acute trusts in its area, a move he supported in principle, while the Clinical Director for Ophthalmology could see no encouragement from the Board for such a move. A Ward Sister in the same directorate said that she sensed political rivalry between the trusts preventing such a move, but also identified the fact that, at nurse-level, staff in ophthalmology in all locations tended to know one another and socialised together on occasion Inter-trust co-operation was complicated, however, by the fact that there was in some settings open, frank, hostile competition reported between neighbouring trusts to see which one is going to survive [DCD]. The presence of the competitive factor was seen to be a disincentive to more formal co-operation, described as a scandal [BCD] by one respondent, while another likened it to asking Germany and Poland to get together when there was a war on [BDN]. A Chief Executive observed that without a financial incentive, his trust was unlikely to readily help out a competing trust which they judged to be less efficient than themselves Where geographical isolation was combined with a lack of functional competition, co-operation with a neighbouring trust was considered to be good. In one location, for example, areas of mutual interest, such as sharing laundry facilities, a single Central Stores facility, IT co-operation and using sites belonging to the neighbour to conduct peripheral outpatient clinics, were being actively exploited: where we can economise and rationalise services and provide them more efficiently, it s in all our interests to do so [ADF] Contracts with fundholders were also a problem for inter-trust co-operation. In one trust transferring work elsewhere meant fundholders being asked to pay the new receiving trust on top of the existing block contract. The fundholders declined. Conclusion Three clear issues emerged from the investigation of the organisation and practice of bed management in the eight study trusts. One concerned the quality of the data upon which tactical bed allocations were made. A second concerned the operational systems in place to create, retrieve and utilise the data. The third identified organisational and procedural linkages required to relate data to actual patient-management decisions. Each of these issues may be subsumed under two broader questions: how should bed management be configured and what should be the functions of its staff? We believe that the only feasible approach to improving bed management in practice is to formally recognise it as a specific function of a hospital and to 87

90 integrate it, properly resourced, into its organisational structure. This essentially requires two developments. One is the appointment of data clerks whose primary purpose is to constantly update the status of the bed complement. The second is to formally enhance the status of the bed manager, giving him or her an expanded role in the wider process of patient throughput. In order to reinforce this enhanced role, we suggest that the term Bed Controller be applied to this function The establishment of valid, reliable and contemporary data on the status of beds is a necessary condition for both their effective management and the flow of patients through them. The existing practice of requiring ward receptionists or nursing staff to update the Patient Administration System alongside their other tasks is clearly inadequate. Data clerks should be dedicated to the task of gathering, collating and entering data as their first priority, ensuring proper deputisation during staff breaks, holidays or illness. This process should continue overnight and at weekends. These staff should register patients immediately upon their entry to a bed and similarly record all patient movements out of it. It may be appropriate for these staff to collect data at the level of the specialty, groupings of smaller specialties, or specific directorates, rather than for individual wards, though the unit for data collection will be dependent upon the circumstances of individual hospitals. Local circumstances will also influence whether the data clerks should be full or parttime. A combination of full and part-time staffing phased over each 24 hour period should allow continual reporting. The key feature of the establishment of data clerks is that they should collectively monitor the total bed complement of the trust, including both medical and surgical departments, Acute Receiving Wards and Accident and Emergency. We envisage that the specific items of data the clerks will focus upon are current bed occupancy and predicted lengths of stay, at the level of individual patients. The data clerks should be directly accountable to the Bed Controller. It is important that the clerks are specifically tasked with the collection of data on the bed state and that all ward staff recognise that they are acting with the authority of the Bed Controller. Data clerks will not only examine documentary evidence for bed occupancy (such as bed boards and ward lists) but will also physically monitor the bed state within their location in order to identify concealment. The frequency of data collection will be determined by local circumstances, but it is important that a relatively short-term review cycle is imposed in order to ensure that the database is as up to date as possible at any given point in time Removing the data collection task from the Bed Controller would leave scope for the development of this role in a central position in the hospital s management structure. Controllers should be involved in the totality of bed management, from admission to discharge, and integrated into both strategic and operational levels of hospital organisation. The principal tasks of the Bed Controller should be to manage the process of data collection, verify and maintain its accuracy, manage the database in order to provide a contemporary overview of the complete bed state within the trust or hospital and use this information to allocate patients to appropriate beds. The task of allocating patients to beds requires medical or nursing staff who seek to place a patient to contact the Bed Controller in order to be informed where the placement should occur. Having received a request for placement, the Bed Controller should identify an appropriate bed from the database, verify its availability by telephone (informing ward staff of the impending transfer) and identify its location to the requesting staff. The Bed Controller should also monitor the projected surgical admissions In order to fulfil their function as controller of beds, it is necessary that these individuals should be invested with considerable formal authority. They must have an accurate knowledge of hospital organisational procedures and a comprehensive understanding of the clinical needs of patients. In practice, such personnel are likely to be senior nurses with substantial management training. In order to establish their formal authority, Bed Controllers should be directly accountable to the trust s executive, ideally to the Medical Director. Bed Controllers should therefore have authority to use any appropriate available bed as demand necessitates In addition to the core functions of bed management, the role of the Bed Controller should also include formal input at executive level, possibly as part of a bed management group. They should have an important role in communication and liaison about bed management issues with agencies outside the trust, such as community health services, primary health care and Social Work Departments. They should be required to participate in the development and review of admission and discharge planning procedures and boarding protocols. They should be equipped to conduct analyses of the data in order, for example, to identify trends within and between specialties over the medium and long-terms. They should be enabled to conduct audit of bed occupancy data and aspects of patient throughput. Finally, they should have an 88

91 important role in identifying and finding solutions for specific problems associated with the management of beds, such as blocking and variation in demand. While the former should be conceived of as being, in principle, within the remit of the medical and nursing staff, the Bed Controller should be seen as a useful agent if the solution to the problem proves to be beyond their resources. An organisational diagram of the proposed structure can be seen below, in Figure 6.1. Medical Director wards, specialties, groups of specialties or directorates Bed Controller wards, specialties, groups of specialties or directorates wards, specialties, groups of specialties or directorates Remit Verification of data Allocation of beds Input at executive level Liaison with outside agencies Develop and review aspects of throughput Analysis and audit Data Clerks PAS Data Clerks PAS Data Clerks PAS Remit Data collection Figure 6.1: The organisation of bed management These ideas for improving bed management should be seen within the context of respondents existing experiences of bed management. It was evident that demand on beds had already increased to levels regarded as intolerable, particularly during the winter. The principal constraint on meeting this demand concerned resources. Workloads were already heavy and beds were seen to be at optimal levels. If increases in activity were to take place they had to be funded by purchasers. Developing the practice and organisation of bed management may require extra investment in manpower, particularly in relation to the appointment of dedicated data clerks, but it should minimise the requirement for senior staff to intervene (usually by walking the wards) in the data gathering process. This would allow them to concentrate on their proper function of decision-making in the light of these data, though Bed Controllers and other senior staff may still be required to intervene at ward or specialty level during periods of intense demand, where their particular authority and overall knowledge may be required The eight hospitals in the study may be seen to be reacting to the increase in acute medical admissions in broadly similar ways, by monitoring the practice of bed management at executive level and planning procedures to absorb fluctuations in demand. They have adopted receiving systems which attempt to make the assessment, treatment, processing and distribution of patients more organised. The main tactic employed to realise this, boarding, must be systematic and have the responsibilities of medical and nursing staff properly defined. There was clearly a growing recognition that beds were a trust resource and must be utilised as such. It was also clear that the lack of spare capacity identified within the trusts, which was, in part, a result of historical reductions in real levels of resource, restricted their ability to respond effectively. As a result, boarding of patients outwith general medicine often resulted in a trade-off between acute emergency and elective surgical admissions In a wider context, co-operation between trusts has the potential to alleviate some of the problems identified earlier, especially those concerning spare capacity and seasonal peaks in demand. However, competition between neighbouring trusts, introduced as part of the health market, acts as a major disincentive to more formal co-operation. There is some evidence that this is being addressed. But it remains an effective barrier to the rationalisation of bed management on a wider scale. 89

92 References Audit Commission (1992) Lying in wait: the use of medical beds in acute hospitals. London, HMSO. Butler, P. (1995) Mystery rise in emergency admissions hits hospitals. Health Service Journal, 104, 5422, 3. Clinical Standards Advisory Group. (1995) Urgent and emergency admissions to hospital. London, HMSO. Green, J. Armstrong, D. (1995) Achieving rational management: bed managers and the crisis in emergency admissions. Sociological Review, Vol. 53, No 4, Green, J. Armstrong, D. (1993) Controlling the bed state : negotiating hospital organisation. Sociology of Health and Illness, 15, Kendrick, S. (1995) Emergency admissions: what is driving the increase. Health Service Journal, 105, 5451, Morrell et al, D. C. et al (1994) Five essays on emergency pathways: a study of acute admissions to London hospitals. Kings Fund, London. NAHAT. (1994) Emergency admissions: managing the rising trend. Birmingham, NAHAT. 90

93 VII: SYNTHESIS : RELATIONSHIPS BETWEEN THE PARTS OF THE PROJECT Introduction 7.1 Earlier chapters of this Report have described the effects of different influences on the bed occupancy rates of the hospitals studied. It might be that some of these effects are associated and that relationships between them could provide a measure of the performance of the hospitals as a whole. Two assumptions underlie this expectation. First, that variation in hospital or speciality performance can to some extent be explained by the circumstances of a hospital s practice, that is, differences in the characteristics of its patients and the diagnostic mix of these patients. Clarifying variation attributable to these factors is thus a necessary preliminary to assessments of the extent to which the management practices of a hospital can influence measures of its performance or efficiency of which bed occupancy rates may be one. 7.2 This Chapter describes the relationships that may exist between the variations in performance identified in earlier sections. In the first part of this account, three variables are employed. Length of stay scores (whether crude or adjusted and expressed in terms of the Scottish average) are employed as a measure of activity in preference to bed occupancy rates for reasons that will be evident from earlier sections. Findings from the cost models ( residuals - Chapter V) are employed used to describe the extent to which costs not explained by the models are correlated with length of stay scores; a similar measure ( hospital effects ) provides an estimate of the more general influence of a hospital on such variables as its length of stay score or probabilities of discharge. The second part of the Chapter is concerned with the relationship between lengths of hospital stay and more specific management strategies identified in the course of the management survey. Crude and HRG adjusted length of stay scores 7.3 These two measures compare lengths of stay for a given hospital with the average for Scotland (= 100) either as a crude length of stay (without adjusting for differences in case-mix or diagnostic mix) or as length of stay after adjustment for the diagnostic mix (in terms of HRGs) for each hospital. If the diagnostic mix of all hospitals was the same, then the crude length of stay would equal the adjusted length of stay (the adjustment being identical for each provider). Two expectations follow: first, that for each specialty, the two measures should show a strong positive correlation; second, if hospital-wide bed management strategies are in place, hospitals which have above average lengths of stay in one specialty grouping would be likely to have above average stays for the other. 91

94 High Occupancy Low Occupancy Crude LOS Adjusted LOS Crude LOS Adjusted LOS High Crude LOS (0.00) 0.08 (0.69) 0.27 (0.19) Occupancy Adjusted LOS 0.77 (0.00) (0.52) 0.37 (0.07) Low Crude LOS 0.08 (0.69) 0.13 (0.52) (0.00) Occupancy Adjusted LOS 0.27 (0.19) 0.37 (0.07) 0.66 (0.00) 1.00 Table 7.1: Correlations (P-values) of crude and adjusted length of stay scores for high occupancy and low occupancy specialty groupings 7.4 Table 7.1 reports the correlation between crude and adjusted lengths of stay for the high and low occupancy specialities. There is close agreement between the crude and adjusted lengths of stay within each speciality group, but little evidence of an association across specialties within hospitals. The correlations were greater for the adjusted than the crude scores but they are still small. Recalling that length of stay scores reflect differences from a Scottish average, the implication is that observed lengths of stay reflect the internal practices of the specialities and the cases they treat with little effect attributable to the larger management practices of the hospitals. This conclusion has implications for the distinction between beds as a speciality or Trust resource made in Chapter VI and suggests that the former is the significant determinant of bed use. Hospital Residuals of Cost Models 7.5 The cost models developed for the econometric analysis (Chapter V; Table 4.11) generated a total of eight sets of hospital residuals. These were for combinations of direct and total costs per case, and high and low occupancy specialty groupings, and using both crude and adjusted lengths of stay as predictors. Direct costs are contained within total costs so that the two measures are related and a high correlation between them should be expected. The association between the crude and adjusted length of stay scores shown above means that the residuals from models containing the two are also likely to be correlated. Different speciality groupings within one Trust are likely to share in the same financial management process and cost relationships between the two groupings are likely to show positive correlations for this reason; these will probably be stronger for the total costs since these include an apportionment of the overheads incurred by the hospitals. 7.6 Table 7.2 describes these relationships. The correlations are highest between the residuals of models using the crude and adjusted length of stay scores within the same speciality groupings and for the same cost model (minimum 0.84). The correlations between the direct cost and total cost models, within each speciality grouping, for any combination of the crude and adjusted length of stay scores are also high (minimum 0.74). Whilst there are strong correlations between the models for total costs among the low occupancy specialities with both cost models for the high occupancy group (range ), the relationships between the residuals from the direct cost models in the low occupancy specialities and either cost model for the high occupancy specialities are not significant (range ). 7.7 The observed correlations are those that might have been anticipated, but it is necessary to add a note of caution to their interpretation. These arise from the adequacy of the cost data in Scottish Health Service Costs and uncertainty about the ways in which they are reported. First, it is not clear how fixed costs are allocated between specialities or even if these are determined in the same way from one hospital to another. Differences of this kind could mean that the proportion of direct costs within total costs varies from one speciality to another - illustrated perhaps by differences in the correlations for high and low occupancy specialities. 92

95 Direct Costs Total Costs High Occupancy Low Occupancy High Occupancy Low Occupancy Direct Costs Total Costs High Occupancy Low Occupancy High Occupancy Low Occupancy Crude LOS Adj LOS Crude LOS (0.00) Adj LOS 0.84 (0.00) Crude LOS 0.33 (0.10) Adj LOS 0.27 (0.19) Crude LOS 0.90 (0.00) Adj LOS 0.84 (0.00) Crude LOS 0.58 (0.00) Adj LOS 0.48 (0.01) Crude LOS 0.33 (0.10) (0.12) 0.32 (0.12) 0.35 (0.08) 0.84 (0.00) 0.90 (0.00) 0.49 (0.01) 0.51 (0.01) Adj LOS 0.27 (0.19) 0.35 (0.08) (0.00) 0.94 (0.00) 0.33 (0.10) 0.23 (0.27) 0.80 (0.00) 0.74 (0.00) Crude LOS 0.90 (0.00) 0.84 (0.00) 0.33 (0.10) (0.11) 0.32 (0.11) 0.27 (0.19) 0.74 (0.00) 0.81 (0.00) Adj LOS 0.84 (0.00) 0.90 (0.00) 0.23 (0.27) 0.27 (0.19) (0.00) 0.94 (0.00) 0.63 (0.00) 0.59 (0.00) Crude LOS 0.58 (0.00) 0.49 (0.01) 0.80 (0.00) 0.74 (0.00) 0.63 (0.00) (0.01) 0.53 (0.01) 0.55 (0.00) Adj LOS 0.48 (0.01) 0.51 (0.01) 0.74 (0.00) 0.81 (0.00) 0.59 (0.00) 0.55 (0.00) (0.00) 0.93 (0.00) Table 7.2: Correlations (P-values) of hospital residuals of cost models for direct and total costs, high occupancy and low occupancy specialty groupings, and models including crude and adjusted length of stay 1.00 Hospital Effects on the Probability of Discharge 7.8 Table 5.4 describes the extent to which the results of the survival analysis for length of stay were consistent from one hospital to another, for each of the 77 possible pairwise comparisons between HRG chapter headings with positive correlations for each (although with a fairly wide range from ). This is evidence of a consensus of practice for general medical diagnoses regarding the length of time a patient spends in hospital. Those hospitals which tend to keep patients in hospital for longer than the Scottish average for one diagnosis are likely to do so for all diagnoses; similarly, those with shorter stays for one HRG group tend to have shorter stays for the other HRG groups. The Relationship between Costs and Length of Stay 7.9 All the cost models described in Chapter V include length of stay: for this reason, it may be expected that there would be little correlation between the hospital cost residuals and either crude or adjusted measures of length. The reality is rather more complicated. First, the length of stay scores considered in this Chapter are those for just one year 1995/96 the most recent of the five years employed in the econometric analysis. This restriction gives a near match with the data used for the survival analysis (1994/95 the most recent linked SMR1 data) and is as near as possible to the time during which the management survey was conducted. Chapter III points out, however, that length of stay scores vary from year to year and may be sensitive to changes in the organisation and bed deployment of the hospitals analysed. The residuals obtained from the econometric models, on the other hand, relate to hospital costs over the five year period from 1991 to 1996 and have employed length of stay scores for these years. As was the case with Table 7.1, 93

96 there are also likely to be correlations between crude and adjusted length of stay scores and between the different cost models and specialty groupings. Direct Costs Total Costs High Occupancy Length of Stay Scores Low Occupancy Hospital Cost Residuals Crude LOS Adj LOS Crude LOS Adj LOS High Crude LOS (0.17) (0.86) (0.46) (0.13) Occupancy Adj LOS (0.34) 0.04 (0.86) 0.04 (0.84) (0.50) Low Occupancy High Occupancy Low Occupancy Crude LOS (0.36) (0.31) (0.36) (0.35) Adj LOS (0.62) (0.50) 0.10 (0.64) 0.02 (0.91) Crude LOS (0.07) (0.82) 0.04 (0.83) (0.38) Adj LOS (0.18) (0.80) 0.09 (0.65) (0.37) Crude LOS (0.09) (0.31) (0.52) (0.15) Adj LOS (0.21) (0.50) 0.17 (0.40) (0.74) Table 7.3: Correlations (P-values) between hospital residuals of cost models for direct and total costs, high occupancy and low occupancy specialty groupings, and models including crude and adjusted length of stay and crude and adjusted length of stay scores for high occupancy and low occupancy specialty groupings 7.10 Throughout Chapter V the models fitted indicated costs per case increasing with average length of stay. The tendency for the correlations in the above table to be negative suggests that costs per case adjusted for length of stay are likely to be lowest among those hospitals which had longer than average stays either crude or adjusted in 1995/96. This may reflect a tendency for the variation in hospital scores to have widened over time. The within-specialty correlations for the adjusted scores have particularly low absolute correlations. The Relationship between Length of Stay and Hospital Effects on the Probability of Discharge 7.11 A negative relationship is to be expected between length of stay and the probability of discharge; a low probability of discharge means an increased risk of remaining in hospital and hence leads to an increased length of stay. The probability of discharge relates to the high occupancy specialty grouping, and the correlation with that group would therefore be expected to be stronger than with the low occupancy group. Probability of discharge High Crude LOS (0.03) Occupancy Adjusted LOS (0.00) Low Crude LOS (0.12) Occupancy Adjusted LOS (0.04) Table 7.4: Correlations (P-values) between crude and adjusted length of stay scores and hospital effects on the probability of discharge for high occupancy and low occupancy specialty groupings 94

97 7.12 The hospital effects on the probability of discharge show a negative relationship with all of the length of stay scores, including those relating to the low occupancy specialty grouping, in spite of the relatively weak correlations across specialty groupings seen in table 7.1. (This relationship was stronger for the adjusted score, however). The importance of differences in the diagnostic mix of different hospitals is demonstrated by the increased correlation with the adjusted score (-0.71) than with the crude score (-0.43) for the high occupancy speciality and 0.41 (adjusted) compared to 0.31(crude) for the low occupancy group. The Relationship between Costs and Hospital Effects on the Probability of Discharge 7.13 Once again, since the cost models have been standardised for length of stay there is likely to be little correlation between costs and hospital effects on the probability of discharge. Direct Costs Total Costs Probability of discharge High Crude LOS (0.87) Occupancy Adjusted LOS (0.62) Low Crude LOS 0.22 (0.28) Occupancy Adjusted LOS 0.08 (0.70) High Crude LOS (0.38) Occupancy Adjusted LOS (0.52) Low Crude LOS 0.07 (0.73) Occupancy Adjusted LOS (0.79) Table 7.5: Correlations (P-values) between hospital residuals of cost models for direct and total costs, high occupancy and low occupancy specialty groupings and hospital effects on the probability of discharge 7.14 The negative relationship between length of stay and costs (following adjustment for length of stay) shown in table 7.2 is again evident. None of the correlations is statistically significant. Management Practices 7.15 Hospitals were categorised on five variables on the basis of the data assembled as part of the management survey (Chapter VI). Six Trusts had a policy of fast-track admissions for chest pain; two did not have an acute receiving ward for General Medicine. In two Trusts, responsibility for transfers and bed allocation was undertaken by clerical officers in a bed bureau, whilst bed managers were employed in the remainder. Four Trusts had boarding protocols, but four did not and only one had formal plans for managing short term fluctuations in emergency admissions. With a sample of only eight Trusts, it was unlikely that statistically significant differences attributable to management practice would be identified. 95

98 Fast track admissions with chest pain Length of Stay Scores Direct Costs Total Costs High Occupancy Low Occupancy High Occupancy Low Occupancy High Occupancy Low Occupancy Fast-track Not Fast- track P-value Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Probability of discharge Table 7.6: Differences associated with fast track admission policies for chest pain 7.16 The presence of a fast-track admission policy was most strongly associated with increased length of stay scores in the low occupancy specialties and with increased costs per case (both direct and total costs) in the high occupancy specialties after adjusting for length of stay. None of these differences was significant, however, nor was the reduction in the hospital s probability of discharge. 96

99 An Acute Receiving Ward for General Medicine Length of Stay Scores Direct Costs Total Costs High Occupancy Low Occupancy High Occupancy Low Occupancy High Occupancy Low Occupancy Acute Receiving Ward No Acute Receiving Ward P-value Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Probability of discharge Table 7.7: Differences associated with the presence of an acute receiving ward for General Medicine 7.17 There was no evidence of an association between the presence of an acute receiving ward and the length of stay scores for the high occupancy specialities, but a slight tendency for hospitals such wards to have shorter stays in the lower occupancy specialties. This difference, however, was markedly less pronounced following adjustment for diagnostic mix. Costs seemed to be slightly higher among the Trusts with acute receiving wards, but these differences were not significant. After standardising for case-mix as well as diagnostic mix, those Trusts with acute receiving wards did have a highly significant effect on increasing the probability of discharge for their patients. Trusts A and E (those without acute receiving wards) had the smallest residuals (-1.00 and -0.61) implying that their patients were less likely to be discharged on any given day than the patients of Trusts with receiving wards. Trust F had a two-tier admission process and also had the strongest effect of the hospitals studied Clerical Officers Responsible for Transfers and Bed Allocation 7.18 The two Trusts in which clerical officers were responsible for the transfer of patients and the allocation of beds tended to have higher length of stay scores, and a lower probability of discharge (Table 7.8). Costs appeared to be lower for these Trusts, although none of these differences was statistically significant. It was evident, however, that their length of stay was markedly longer for the low occupancy group of specialities. This difference was more pronounced after adjustment for their diagnostic mix. 97

100 Length of Stay Scores Direct Costs Total Costs High Occupancy Low Occupancy High Occupancy Low Occupancy High Occupancy Low Occupancy Clerical Officer Other Bed Manager P-value Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Probability of discharge Table 7.8: Differences associated with the use of clerical officers for bed management Boarding protocols Length of Stay Scores Direct Costs Total Costs High Occupancy Low Occupancy High Occupancy Low Occupancy High Occupancy Low Occupancy Boarding Protocol No Boarding Protocol P-value Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Probability of discharge Table 7.9: Differences associated with the existence of boarding protocols 98

101 7.19 The existence of boarding protocols appears to make no difference to the length of stay scores for either speciality grouping for crude or adjusted stays. The probability of discharge from General Medicine is slightly lower among such Trusts. Cost per case was lower among Trusts with boarding protocols: this difference was most noticeable in the high occupancy specialty grouping and for both fixed and direct cost models. Planning for Short Term Fluctuations Planning Not Planning P-value High Crude LOS Length of Occupancy Adj LOS Stay Scores Direct Costs Total Costs Low Occupancy High Occupancy Low Occupancy High Occupancy Low Occupancy Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Crude LOS Adj LOS Probability of discharge Table 7.10: Differences associated with hospitals planning for short term fluctuations 7.20 With only one Trust having formal planning for short term fluctuations, comparisons are necessarily inconclusive since they may simply reflect other differences between that Trust and the others. There were no statistically significant differences but there was weak evidence that this Trust had below average length of stay scores in the low occupancy specialties and had below average total costs for the high occupancy specialities. 99

102 VIII : CONCLUSION 8.1 Taken as a whole, the findings of the previous Chapter are not remarkable. The close agreement between crude and adjusted length of stay scores was to be expected hospitals do treat the same kinds of patients but the difference between crude and adjusted scores is worth noting as a measure of the effects of other influences on a hospital s performance. The lack of an association between the high and low occupancy specialities may be of interest to the question of improving the overall (Trust-level) deployment of beds, partly because it suggests that bed management continues to be an internal feature of speciality management and partly because it raises other questions about the boarding of patients in other specialities and the possibilities of a more proactive approach to bed management. In this context, it is worth noting that only four of the eight Trusts in the management survey had protocols for this activity. This is not a simple solution to the problem of demand for emergency admissions in high occupancy specialities, however; different specialities undertake different kinds of medical work so that the extent to which beds reflect other aspects of a speciality s activities (and its efficiency) are also likely to vary. A final comment, therefore, is to repeat earlier doubts about the extent to which simple measures (such as bed occupancy rates) are able to represent such differences and the wider pattern of activity in different areas of the hospital. The deficiencies of these measures extend to other aspects of the management of hospitals and confound a necessary distinction between the availability of resources and their deployment. They include, for example, the management of other resources (such as staff or operating theatre availability) and the presently uncertain relationship between emergency and elective admissions which in turn has implications for the management of waiting lists. 8.2 These uncertainties also take in the adequacy of the data that are available for devising summary or comparative accounts of hospital activity. Despite its statistical sophistication, the analysis we report has been handicapped by the lack of information that might provide a more appropriate description of the actual determinants of performance. While easy to make, this criticism is also one which does not have easy solutions. Tables are difficult to interpret principally because the component elements of the cost data reported in Scottish Health Service Costs are unclear and may differ in their content from one hospital to another: as examples, they provide no insight into the ways in which costs are allocated, of the ways in the costs of other hospital departments are distributed, or the ways in which staff costs of different kinds influence reports of either direct or total costs. Against this background, and as Chapter II demonstrates, bed occupancy rates especially when calculated as annual averages are meaningless. The fundamental question this project has attempted to address is essentially one of value for money: whether it is possible to compare the performance of hospitals in terms of the patients they treat and the costs they incur in doing so. Answers to this question, of course, depend on at least a partial assurance that allowance can be made for other features of a hospital s activities that may have a legitimate influence on the outcomes it achieves the obvious examples are differences in the patients it treats and the costs of doing so. The project has failed to answer this question primarily because data that might provide a basis for answering it are not available. It follows that attempts to do so by the manipulation of such data as do exist can be dangerously misleading. 8.3 There are, however, ways in which this conclusion should be modified, at least in the sense of providing pointers for future practice. Some of these have been noted above: there is an argument for assembling data in different ways when as Chapter VI suggests there would be benefits in devising a more patientfocused approach to describing the work undertaken by hospitals and for making appropriate allowance for 100

103 daily and seasonal variation. A corollary is the need to obtain a clearer picture of the work of specialities within the larger pattern of work in the hospital or Trust and the ways in which these may be related. A more complex issue is a requirement for understanding the performance of a hospital or the costs it incurs in its operational context. In simple ways, the issue concerns knowing what might be the right measure for a given hospital when it will be evident that uncontrolled comparisons between hospitals in different parts of Scotland and serving populations with differing needs can have little value. The relative risks of discharge associated with different patient characteristics reported in Appendix C although detailed and complex provide evidence of the way that variations in case-mix can influence overall lengths of stay and thus variation in the resources necessary for appropriate patient care. 8.4 One conclusion of the management survey was that the eight Trusts studied although with differing degrees of sophistication employed similar models of bed management and that these activities were not well integrated into the main management preoccupations of the Trusts. Part of the explanation may be that there was a discrepancy between a concern for the provision of care for patients and a rather separate (subsidiary) interest in the use of beds per se. This observation supports the arguments made above for alternative ways of assessing performance. At the same time, proposals for reducing the number of beds mean that the two are not actually separate and that improved bed management would be one of the prerequisites of present proposals. Whether this is feasible is a matter for debate but responses to questions about spare capacity in the management survey, taken in combination with the analysis reported in Chapter II raise doubts about the gains that might result from improved bed management. On a more positive note, the analysis described in Tables although based on small numbers suggest that improved management systems could result a more efficient use of beds. 8.5 In summary, the project has demonstrated the unsatisfactory nature of comparative measures of hospital activity and costs at a number of levels. As a consequence, it is difficult to draw useful conclusions other than to suggest the need for improved methods based on more appropriate data. Specific needs are for data that take account of the case-mix and diagnostic-mix of different hospitals (their operational context) and reflect the services they provide for patients: simple measures of activity are unable to describe these features of hospital practice and are likely to become less useful as patterns of hospital care and its management change. The evident stress experienced by the high occupancy specialities (Chapters II and VI) reflects this discrepancy between summary measures and the realities they conceal. Similar arguments apply to cost data and the extent to which their analysis can provide useful insights. The management survey has suggested ways in which the management of beds might be strengthened particularly in regard to improvements in the information systems on which beds might be managed and their integration with wider aspects of Trust management. This conclusion extends to larger questions about contracting relationships and improvements in bed use considered from an area rather than a Trust-by-Trust perspective. This means that Health Boards should be involved in these matters in ways that go beyond the contracting process. 101

104 PUBLIC HEALTH RESEARCH UNIT University of Glasgow BED MANAGEMENT AND BED OCCUPANCY: CSO PROJECT K/OPR/2/2/D248 EXECUTIVE SUMMARY Chapter 1: Introduction the Methods of the Project The study was commissioned by the Chief Scientist Office (SODoH) on behalf of the Management Executive as part of a strategy to inform purchasing and provision of acute hospital admissions. Measures of hospital activity differ by speciality and so two groups of specialities were chosen for study: (i) general medicine and its associated specialities as an example of those characterised by high rates of bed occupancy and a high rate of emergency admissions; (ii) a grouping of gynaecology, ENT and ophthalmology representing those with lower rates of bed occupancy and a high proportion of elective admissions. The study combined three methodological approaches: statistical analyses which considered ways in which annual average rates of occupancy may conceal shorter term variations in the use of beds, the effects of differences in the case-mix and diagnostic-mix of different hospitals on reported rates of occupancy; and a survival analysis which explored the influence of these different variables on the length of a patient s stay (Chapters 2-4); an econometric analysis which investigated the relationship between rates of bed occupancy and the speciality costs reported in Scottish Health Service Costs (Chapter 5). an interview survey of bed management policies and practices in eight major Trusts (identified as hospitals A H) ; these interviews were conducted at the strategic level of overall Trust management and the tactical level of more local day-to-day decision making (Chapter 6).

105 ii The data used for the statistical analyses were those reported in Scottish Health Service Costs for the financial years 1991/92 to 1995/96, in combination with SMR1 data provided by the Statistics and Information Division of the CSA. For the survival analysis, these data were translated to the National Casemix Office s Health Resource Groupings (HRGs) Version 3. Chapter 2: Daily variations in bed occupancy A high annual bed occupancy rate does not necessarily indicate efficient resource use by a hospital it may be the result of longer lengths of stay rather than greater numbers of patients being treated. Annual occupancy figures conceal more substantial variation in daily occupancy rates throughout the year: one of the Trusts taking part in the study had an annual average of 90% occupancy for its general medical beds, but on a daily basis its occupancy ranged from 57% to 114% (in 1994/95). Bed occupancy varies by the day of the week; for general medical beds, there is a high occupancy rate from Monday to Thursday which tails off at the weekend. Specialties with high rates of elective admissions tend to have higher occupancy rates in the middle of the week. The pattern of seasonality is less clear: in addition to the usual winter peak in demand for general medical beds (and a corresponding trough for the elective specialties), each of the eight Trusts had other troughs and peaks at other seasons. For general medicine and its associated specialities, hospitals with higher annual average bed occupancy rates appear to have a greater proportion of emergency admissions (ranging from 58% to 84% for the eight hospitals studied). The division between emergency, elective and transfer admissions varied considerably. The frequent use of bed borrowing among specialties in some hospitals is illustrated by the number of days that their bed occupancy exceeded 100%. Annual average occupancy rates fail to reflect these pressures: the annual occupancy of medical beds in Hospital H was 90%; this rate represents an average of 14 admissions on each day, but fewer than 10 free beds for 60% of the year and no free beds on 12% of days. In addition to case-mix and diagnostic-mix other influences within hospitals may affect their annual bed occupancy rate - such as different bed management practices. The geographical location of a hospital, and its relationship with other services, help or hinder such activities as pre-discharge planning. Bed occupancy alone is not an useful measure of hospital performance. It combines and confuses two elements: the number of patients admitted and how long those patients stay (Yates, 1982).

106 iii Chapter 3: the Influence of Case-mix Adjusting for the case-mix of populations is a necessary preliminary to comparisons of hospital activity or performance, even if restrictions on the content of routinely available NHS data limit the extent to which it is possible to do so: for example, such data do not properly reflect such influences as case complexity or case severity. Adjustment for case-mix variation in this study was undertaken on the basis of HRG groupings. The HRG-mix of the eight Trusts chosen for detailed study varied considerably: as an example, Trust B had about twice the rate of respiratory episodes of Trust C which had twice the proportion of Trust B for episodes concerning the male urinary tract and reproductive system. The eight Trusts demonstrated other case-mix differences: although age and sex did not vary markedly between them, the range of patients who were married was from 47% to 61% and the proportion of patients resident in the upper (most deprived) quartile of Carstairs score localities varied from 5% to 67%. Recorded co-morbidities varied from 30% to 66% and the proportions of emergency admissions from 56% to 84%. Episodes ending in a transfer to another speciality in the same hospital were similarly variable (4% to 27%). As the later survival analysis demonstrated, each of these differences had implications for the length of a patient s stay and thus on the bed occupancy rate reported for the hospitals. Crude and HRG-adjusted lengths of stay were calculated for 26 Scottish hospitals on which the econometric analysis was based. This adjustment demonstrated the extent to which case-mix adjustment can change a hospital s apparent performance relative to the average for Scotland: in the example of hospital 24, the crude length of stay score for general medicine was 79 (21% less than the Scottish average) but the adjusted score was 107 (7% greater than the Scottish average). A second important feature of this part of the analysis was that differences of this kind could vary from year to year and could just as well reflect changes in the patients treated in the hospital as changes in the organisation or management of the hospitals themselves. Similar observations were made for the low occupancy specialities. Understanding hospital differences that arise from differences in the patients they treat is a necessary stage in understanding how their resources are managed. Chapter 4: Occupancy rates and length of stay the results of a survival analysis Length of stay in General Medicine and its associated sub-specialties was modelled as the day-to-day risk of discharge in order to assess the effect of increased bed occupancy on the decision to discharge. The analysis involved over 200,000 hospital episodes in 45 hospitals during the financial year The analysis was principally based on health resource groups (HRGs). An increase in bed occupancy was associated with a reduced risk of discharge patients were less likely to be discharged when bed occupancy was higher. The exception was the HRG Chapter for hepatobiliary and pancreatic conditions when episodes were shorter when bed occupancy increased. The association between bed occupancy and length of stay implied either that stays are shorter when resources are less limited or that the association was further evidence that high rates of bed occupancy are simply a reflection of longer lengths of stay.

107 iv Bed occupancy as a single measure is too simple as a way of representing pressure on beds. A more appropriate measure should take account of the availability of resources (such as staffed beds), current demand (that is, waiting lists) and anticipated future demand (emergency admissions). The effect of bed occupancy on length of stay was small when compared to other factors such as the agemix of patients or the day of the week. Much of the between-hospital variation in length of stay was explained by adjusting for case-mix: adjusting for bed occupancy did little to explain the remaining variation. The overall influence of a hospital on its lengths of stay were consistent across separate analyses of the HRG groupings. Thus, if the effect of a particular hospital was to increase or decrease length of stay for one diagnostic group then the same effect would generally be found for other groups being treated in that hospital. Chapter 5: an Econometric Study of Bed Occupancy and Speciality Costs Increasing the efficiency of acute bed management has the potential to enable existing demand to be met with a reduction in acute hospital capacity or the treatment of an increased number of patients within current resources. Reduced lengths of stay and increased rates of bed occupancy are often considered to be reliable indicators of the efficiency of bed use. Other things being equal (such as case mix) either of the above changes should result in reductions in the average cost per case. If quality of care is maintained, such reductions in cost per case can be considered to represent a more efficient use of acute beds. The relationships between cost per case, length of stay and bed occupancy were examined using regression analysis of data from 26 hospitals over the financial years 1991/2 to 1995/6. The analysis was conducted for both total and direct cost per case, taking account of other factors likely to influence them, such as case mix and teaching hospital status. Other things being equal, it would be expected that cost per case would increase as length of stay increases and fall as bed occupancy rises. Between 1991 and 1996, average length of stay has fallen and the number of discharges has increased in the high occupancy specialty. The number of beds and occupancy rates have remained about the same. Real cost per case fell by 11.1%. In the group of elective specialties, the number of discharges, the number of beds and lengths of stay have all reduced, but occupancy has remained constant and the real cost per case has risen by 6.8%. In the low occupancy specialties, there is evidence that total cost per case fell as occupancy increased, although the relationship depended on hospital type for direct cost per case. For teaching hospitals the relationship was positive: direct cost per case increased with bed occupancy. In the high occupancy specialties, the relationship between occupancy and cost per case was unclear: if anything, an increase in occupancy was associated with an increase in cost per case. Costs increased with length of stay for both specialty groups. This relationship was weaker when length of stay was adjusted for diagnostic mix.

108 v Teaching hospitals tended to have higher costs per case even when length of stay and bed occupancy were taken into account. The variation in costs was greatest among teaching hospitals for the high occupancy group and among the mixed specialist hospitals in the low occupancy group. There are a number of difficulties with this kind of analysis. There is no necessary reason to expect an inverse relationship between occupancy and average cost per case: in practice, it depends on explanatory variables which are hard to control (for example, case mix, being equal between hospitals and over time). The sample size was small in relation to the number of explanatory variables. There was concern over the quality of data and many of the explanatory variables are related to each other. There are also difficulties in drawing general conclusions about hospital-level relationships between occupancy and cost from cross-sectional data from a range of hospitals. These difficulties may explain some of the unexpected and variable results obtained. Insofar as conclusions about efficient bed management can be drawn from this analysis, they suggest that the scope for reducing unit costs by targeting bed occupancy is limited: a 1% increase in occupancy reduced cost per case over the period by around 5 in those specialties where a significant relationship was found. This conclusion is supported by findings of the survival analysis (Chapter 4) and the bed management survey (Chapter 6). Alternatives to bed occupancy as a measure of activity, and better ways of analysing the efficiency of bed management are necessary. Chapter 6: a Survey of Bed Management Practices - Procedures for Managing Beds Interest in bed management as a distinct activity can be linked to the rising rate of emergency admissions, including movement away from the ward/specialty as the unit of organisation in hospitals. Issues involved in the practice of bed management emphasise emergency medical admissions, patient throughput; and the flexible use of beds. It is best viewed as part of the process of patient care rather than the management of a static bed stock. This process begins with the initial referral, and is followed by the assessment, organisation and distribution of patients within an acute receiving system. Procedures for transfer and distribution are best organised by a named individual with the necessary authority to determine the allocation of beds: Bed Managers with a nursing background are helpful in this context because they are able to facilitate smooth transfers, provide credibility, and assist in finding concealed beds. Effective bed management requires a structured approach to data collection which includes well-defined procedures and responsibilities; data about available beds are best collected by clerks who have this task as a first priority. At times of high demand, the information assembled by such a formal system should be supplemented by a named individual walking the wards. Operational systems relying on electronic data gathering require staff with responsibility for entering information as a priority task. Respondents were overwhelmingly of the opinion that there would be no significant benefits to their Trust from a further increase in admissions. Workloads were already heavy and beds were thought to be at optimal levels. If increases in activity were to take place, additional funding from purchasers would be necessary.

109 vi There was substantial agreement at both the strategic and operational levels of Trust management that staffing levels were sufficient to cope with normal levels of demand. The problem was that seasonal variations in demand were linked to winter epidemics to which staff were also prone. One result of a high absence rate in winter was that staffing levels were seen to drop to, or below, those needed to provide safe care. There was a general view that increased admissions would result in an increase in overall costs, mainly because length of stay had been reduced as much as possible. At the same time, the lack of detailed information about costs meant that it was difficult to assess the effect of an increase in admissions for unit costs over the short term. Reductions in elective admissions in order to cope with increases in emergency admissions were a matter for concern because of the possibility of financial penalties if waiting list targets were not achieved. Reducing unit costs within a contract year did not benefit Trusts because extra resources were not available from this gain in efficiency. There was a consensus that further reductions in length of stay would be very difficult. Some senior managers thought that cases presenting in the winter of 1996/97 had included a greater proportion of elderly patients and more complex cases. It was expected that pressures to reduce length of stay would continue, leading to increased re-admission rates and resistance from patients and GPs. Respondents tended to agree that a 5% increase in emergency admission rates could be managed if it occurred evenly across the year although some felt that it might lead to the cancelling of elective admissions or other problems if these additional patients were mostly elderly. There was a unanimous view that the Trusts could not cope with a 10% increase in emergency admissions within the present level of resources. Reductions in bed numbers were not seen as a feasible option. Bed reductions over few years were seen as responsible for three areas of present difficulty: these related to workload and demand; to the design and layout of hospitals; and developments in clinical practice. All three were seen as interdependent. Several issues were raised in response to questions about spare capacity. They included the option of extending existing working hours; making more flexible use of existing facilities such as operating theatres; changes in clinical practice, particularly through increases in day case work and outpatient procedures; and the need for co-operation between neighbouring Trusts. Difficulties in implementing such options were described. Three requirements for improving the management of beds emerged from the interviews. One was the quality of data upon which tactical bed allocations were made. The second concerned operational systems necessary for creating, retrieving and utilising data about available beds. The third was about the organisational and procedural links needed to relate such data to decisions about the management of patients. Bed management policy and practice in the eight Trusts did not differ to such a degree where distinct approaches to bed management could be recognised. Each was reacting to broadly similar demands; such differences as there were reflected the ways Trusts perceived the bed management function: in Trust B, this was patient-focused, and in Trust A the emphasis was on the management of the bed stock of the Trust. Bed management staff do not manage beds: they organise the placement of boarders, arrange transfers and collect information about availability. There would be benefits in expanding their role, and

110 vii increasing their authority and seniority within the hospital s management structure, as part of a more formal and systematic approach to bed management. This should include the involvement of the bed manager in the early stages of patient throughput. Bed management procedures should include an estimate of the patient s length of stay on admission and discharge planning should also begin at this time. Bed managers, with appropriate clerical support, should manage data collection in order to achieve a real time overview of the complete bed state, using this information to allocate patients to appropriate beds. This enhanced role for Bed Managers might be strengthened by using the title Bed Controller. Bed Managers need to be familiar with the functions of outside agencies such as Social Work and community services and their management procedures. They should have an input to the executive level of Trust management, having an involvement in such matters as admission and discharge procedures, boarding protocols, and ways of overcoming problems arising from blocked beds. GP referrals should not by-pass the formal acute receiving procedures. Key elements in improving the practice of bed management will be the development of more effective systems of forecasting daily, weekly and seasonal activity involving both admission and discharge procedures. This requires a clearer perspective of the process of patient care permitting continuous appraisal of the patient s pathway through the different stages of acute hospital episodes. Chapter 7: Synthesis relationships between the parts of the project There was close agreement between crude and adjusted length of stay scores for each of the high and low occupancy speciality groupings, but little evidence of an association across specialities within hospitals. Correlations between hospital residuals from the cost models (Chapter V) for both direct and total costs (from Scottish Health Service Costs) were high. These findings are difficult to interpret because of uncertainty about the ways in which cost data are reported. Costs per case adjusted for length of stay were likely to be lowest in those hospitals which had longer lengths of stay. This may reflect a tendency for variation in hospital length of stay scores to have widened over time. Hospital effects on the probability of discharge show a negative relationship with all length of stay scores. Differences in diagnostic-mix between hospitals increased the strength of this relationship. Management Practices [ Note: the findings of this section are based on statistical analyses of data from only eight Trusts] The presence of a fast-track admission policy for chest pain was associated with increased costs per case in the high occupancy specialities. There was no evidence of an association between acute receiving wards and the length of stay scores for high occupancy specialities, but a slight tendency for them to have lower length of stay scores in the low occupancy specialities. Costs may have been slightly higher in hospitals with acute receiving wards these differences were not statistically significant. Patients treated in general medicine in Trusts with acute receiving wards had a higher probability of discharge on a given day.

111 viii When responsibility for the transfer of patients and allocation of beds lay with clerical officers, length of stay scores in the low occupancy specialities tended to be greater; costs appeared to be lower in the high occupancy specialities in these Trusts. Boarding protocols did not appear to influence length of stay scores for either group of specialities. Cost per case was lower among Trusts with boarding protocols most noticeably in the high occupancy group. Only one Trust employed formal planning procedures for short-term fluctuations in demand. There were no statistically significant differences between this Trust and others, but weak evidence that this Trust had below average length of stay scores for the low occupancy specialities and below average total costs for the high occupancy group. These differences could, of course, arise from other characteristics of the Trust. Conclusion The question this project has attempted to address is whether it is possible to compare hospitals in terms of the patients they treat and the costs they incur in doing so. Simple measures of hospital performance such as bed occupancy are an unsatisfactory basis for evaluating or comparing these activities; similar arguments apply to the ways in which hospital costs are reported. There is a need to devise improved methods of data collection and for more sophisticated approaches to their statistical analysis. Present measures are unlikely to reflect the contemporary practice of hospitals and reliance on them is likely to lead to misleading conclusions. Although there is a clear relationship between high and low occupancy specialities (for example, in the common practice of boarding patients in other specialities) there is evidence that beds continue to be managed within specialities; this observation raises questions about the management of beds as a whole- Trust resource and the ways in which the demand for emergency care has implications for elective admissions and waiting list management. For the high occupancy specialities, there was evidence of stress in the provision of services, particularly at some seasons, with little reported scope for exploiting spare capacity. There was some evidence that improved approaches to bed management could lead to gains in the efficiency of bed use, but these are likely to be small. At the same time, better ways of managing beds, including inter-trust co-operation, are a necessary preliminary to reductions in bed numbers.

112 APPENDIX A SURVIVAL ANALYSIS FOR CHRONIC OBSTRUCTIVE AIRWAYS DISEASE

113 SURVIVAL ANALYSIS FOR CHRONIC OBSTRUCTIVE AIRWAYS DISEASE This appendix is written as a companion to chapter 4 with the intention of introducing the reader to the mechanics of multilevel survival analysis. A semiparametric proportional hazards model was used with Peto s method for ties such that the hazard function h of the failure of the j th individual from the k th hospital during the i th time interval t ijk, given the vector of covariates X ijk, may be written ( ; ijk ijk ) = λ( ijk ) exp( ijk β jk ) ht X t X (1) The λ( t ijk ) is the underlying or baseline hazard, typically representing the mean hazard when all of the covariates are set to zero. This is commonly fitted using a low degree polynomial which will still produce efficient estimates such that 2 n ( ; ijk ijk ) = exp ( α α1 ijk + α2 ijk + + αn ijk + ijk β jk ) ht X t t t X (2) so 2 n ( tijk ) = exp ( 0+ 1tijk + 2tijk ntijk ) λ α α α α (3) In practice quartics were found to suffice both for chronic obstructive airways disease and for the groups used in chapter 4. The time variable was centred and transformed as a mathematical aid to convergence. The first model fitted, with no covariates, was then just 4 ( α α 8 α 8 α α β ) 2 3 ( ) 0 1[ ] 2[ ] 3[ ] 4[ ] ht ; X = exp + t + t + t / + t / + X β u k jk ijk ijk ijk ijk ijk ijk ijk jk = u k 2 ( 0 σ ) ~ N, u (4) This is a simple random effects model with an effect u k associated with the k th hospital. These effects are 2 given a distribution, and the variance σ u is a measure of the variation between hospitals. Parameter estimates for the model described by equation (4) are given in table A1.

114 Parameter Estimate Standard error Fixed: α α α α α Random: 2 σ u σ e Table A1. Parameter estimates for a random effects model The additional random parameter σ 2 e that is a scale factor to allow for non-poisson variation; its implications are [ ( ; ijk ijk )] e ( ; ijk ijk ) [ ] Var h t X = σ 2 E h t X (5) The survival fraction S i the proportion of patients staying in hospital until the end of time period t i - can be estimated by S$ = exp i λ i' i ( t ) i (6) Figure A1 plots this predicted survival fraction against that observed from the data for the fifty days following admission to hospital. The estimated value of σ u 2, the between hospital variance, can be treated like any other variance. The mean hospital effect is zero and a 95% coverage interval for hospital effects is between ( , ). Assuming overall proportionality, the baseline surviving fraction for an individual in the k th hospital is ( ) $ S exp ik = λ ti e i' i uk (7) so a 95% coverage interval for hospitals would be given by exp ( ) ± λ ti e i' i. These coverage intervals are plotted together with the predicted baseline survival fraction in figure A2. This figure indicates that, for example, approximately 50% of patients remain in hospital for more than 6 days. The time taken to discharge half of the patients lay between 4 to 9 days for 95% of the hospitals; after 6 days, 2.5% of hospitals had more than 65% of their patients remaining and 2.5% had fewer than 32%.

115 The effect of adding patient covariates can be considered. Equation (8) adds two further terms relating to the patients age in years, x 1 jk, which is centred around the mean age of 70 years and a dummy variable taking the value 1 if a patient is female (x 2 jk ). ( ; ijk ijk ) = λ( ) exp ijk ( β jk + [ jk ] β1jk + 2 jk β 2 jk ) ht X t x x β u k 0 jk = u k 2 ( 0 σ ) ~ N, u (8) Table A 2 gives the parameter estimates for this model. Parameter Estimate Standard error Fixed: α α α α α β 1 jk β 2 jk Random: 2 σ u σ e Table A2. Parameter estimates for a model including patient covariates There are a number of points worth making about the comparison between tables A1 and A2. Firstly, there is little change between the estimates contained in the baseline hazard, α0,..., α4. The inclusion of explanatory variables has not changed the underlying probabilities of discharge from hospital. The components of λ( t ijk ) are regarded as nuisance parameters; they are necessary for the calculation of survival fractions but have no interpretation on their own. For this reason they are not reported in subsequent models. The interpretation of the remaining parameter estimates can be considered in terms of relative risks; the relative risk of discharge for women is e. with 95% confidence intervals given by e ± Put another way, the relative risk of discharge for women is times that of men (95% C.I ). Age is measured as a continuous variable; the relative risk associated with a one year increase in age is (95% C.I ). There has been a reduction in the between hospital variance σ 2 u ; some of the variation indicated in table A1 arose due to differences between hospitals in terms of the age and sex of their patients, controlled for in table A2. Figure A3 illustrates the different survival fractions for two groups of patients: 50 year olds and 70 year olds. Whilst 95% of 50 year olds leave hospital within 13 days of admission, 5% of 70 year olds remain in hospital for more than 23 days. The plotted fractions are for the baseline group: in this case, this is for male patients ( β 2 0 jk = ).

116 Equation (8) is based on the assumption that age is a constant hazard that is, that the effect of age is the same (in terms of the relative risk of discharge) no matter how many days the patient has been in hospital. The reality may, however, be more complicated than this. It is possible to assume that age is a timedependent hazard the relative risk of discharge changes throughout a patient s stay in hospital. This can be expressed as ( ) ( ; ) = ( ) exp + [ ] + + [ ] ht X λ t β x 70 β x β x 70 t β (9) ijk ijk ijk jk jk jk jk jk jk ijk jk This gives the estimates in table A3. Parameter Estimate Standard error Fixed: β 1 jk β 2 jk β 3 jk Random: 2 σ u σ e Table A3. Parameter estimates for a model including time-dependent hazards The effect of the interaction between age and time is small and positive, and implies that the (negative) hazard associated with age diminishes the longer a patient stay in hospital. A patient who stays until the end of the 45 th risk set a total of 44 days has a relative risk of discharge which is independent of age. After this time point, the relative risk of discharge is greater the older a patient is. This only reflects on patients who have already stayed in hospital for this length of time; consequently the predicted survival fractions shown in figure A4 differ little from those in A3. This is due to the size of the effect; although significant, the parameter estimate associated with β 3 jk represents an increase in the relative risk of 0.04% per year s age per extra day in hospital. Up to this point any mention of patients has been rather loose; the hierarchy modelled above has really been that of episodes within hospitals and has ignored the fact that multiple episodes may be generated by the same patient. The hazard relating to the i th risk set of the j th episode of the k th patient from the l th hospital with age and sex as covariates is given in equation (10). ( ) ( ; ) ( ) exp [ 70] ht X = λ t β + x β + x β β u v ijkl ijkl ijkl jkl jkl jkl jkl jkl = u + v 0 jkl kl l kl l 2 ( 0 σu ) 2 ( 0 σ ) ~ N, ~ N, v Here σ 2 u now refers to the between patient (within hospital) variance with individual patient effects u kl which are constant across multiple episodes for the same patient and σ 2 v is the between hospital variance. Estimates for this model are given in table A5. (10)

117 Parameter Estimate Standard error Fixed: β 1 jk β 2 jk Random: σ v 2 σ u σ e Table A4. Parameter estimates for a model including episode, patient and hospital levels The parameter estimates for the fixed part of the model have changed little following the inclusion of the additional (patient) level. The implied relative risks are now (95% C.I ) for women and (95% C.I ) per year for the patient s age. In the random part of the model there is a substantial increase in the between hospital variation which is compensated by a reduction in the scaling factor σ e 2. Of more interest, however, is the relationship between the between hospital and the between patient variation which suggests that 77% of the higher level variation is attributable to differences between individuals patients, with only 23% occurring due to differences between hospitals. Further patient covariates can be added into the model in an attempt to standardise for differences between patients. In particular, these include marital status (contrasting single, widowed, other and unknown with married), whether or not the patient underwent surgery and the presence of any secondary diagnoses, whether the current admission was an emergency, the eventual discharge destination in terms of being another hospital or elsewhere within the same hospital, and the deprivation quartile (in terms of the Carstairs score) of the postcode sector of residence (the fourth quartile being the most deprived, and the first quartile being taken as the baseline for comparisons). Additionally the effect of the current day of the week on the decision to discharge is modelled (comparing Tuesday to Sunday relative to Monday). The relative risks of these estimates are presented in table A5

118 Parameter Relative risk 95% C.I. Age ( ) Female ( ) Single ( ) Widowed ( ) Other marital status ( ) Marital status unknown ( ) Surgical procedure ( ) Secondary diagnosis ( ) Emergency admission ( ) Discharge other hospital ( ) Discharge same hospital ( ) Carstairs 2 nd quartile ( ) Carstairs 3 rd quartile ( ) Carstairs 4 th quartile ( ) Tuesday ( ) Wednesday ( ) Thursday ( ) Friday ( ) Saturday ( ) Sunday ( ) Table A5 Relative risks for patient covariates Table A5 indicates that the relative risks of discharge are the highest i.e. length of stays are the shortest for younger patients, women, married patients, those who have no surgery, those without secondary diagnoses, emergency admissions, and for patients being transferred to another specialty within the same hospital. There is no significant deprivation effect; it should be noted, however, that a proper exploration of area effects would require the fitting of locality as a separate level in the model. The days of the week show a heavy bias towards increased discharges on Fridays with much lower discharge rates on Saturdays and Sundays. Figure A5 convert these relative risks into their effect on the survival fraction depending on the day of admission of the patient. Two survival fractions are compared one following admission on a Monday and the other for admission on a Friday. Although the two cross over each other frequently and follow the same general pattern, at times they illustrate fairly large differences. For example, after four days in hospital (i.e. by the end of the Friday night for those admitted on the Monday, or by the end of Tuesday for those admitted on the Friday) 34% of those admitted on the Monday will have been discharged but only 2 27% of those admitted on the Friday. The between hospital variance σ u has decreased to a reduction of more than 20% over the variance following age and sex standardisation alone (given in table A2). A number of different ways of including bed occupancy in the model were considered. In many ways the other covariates can also now be regarded as nuisance parameters they have been controlled for but it is the effect of current bed occupancy on the risk of discharge which is important. The first measure is the

119 assumption of a simple linear relationship with (centred) occupancy a change in occupancy delivers a corresponding change in the hazard. The second was to consider a non-linear relationship with occupancy by assuming varying effect at different levels of occupancy. For this purpose, occupancy was broken down into five bands: below 70%, 70-80%, 80-90%, % and over 100%. It was assumed that there was no effect of occupancy on discharge policy when occupancy was below 70%; differing linear associations were assumed across the other bands. Finally, occupancy was considered to have an effect only when the length of stay had exceeded 8 days, the average for this diagnosis. Table A6 gives the relative risks associated with a 1% increase in occupancy under the three methods considered. Parameter Relative risk 95% C.I. Linear ( ) Linear 70-80% ( ) Linear 80-90% ( ) Linear % ( ) Linear >100% ( ) Linear (LOS>8 days) ( ) Table A6. Relative risks for a 1% increase in bed occupancy under three separate models Each of the models suggests that an increase in occupancy is associated with a decreased risk of discharge. This association becomes slightly stronger as the occupancy rate increases and as length of stay increases. The risks in table A6 are small but refer to a single percentage point increase in the occupancy rate; figure A6 plots the predicted survival fractions for a hospital working at a constant 90% occupancy and for one at a constant 70%. This shows the longer stays which are associated with higher occupancy rates.

120 Figure A1 Observed and predicted survival fractions

121 Figure A2 Predicted survival fraction and 95% confidence intervals

122 Figure A3 Predicted survival fractions for different age groups assuming constant hazards

123 Figure A4 Predicted survival fractions for different age groups assuming time-dependent hazards

124 Figure A5 Variation in survival fraction due to day of admission

125 Figure A6 Effect of bed occupancy on the survival fraction

126 APPENDIX B RELATIVE RISK ESTIMATES AND HOSPITAL RESIDUALS FROM SURVIVAL ANALYSES

127 Notes 1. Tables B1-B5 contain the relative risk (R.R.) estimates and confidence intervals for the case-mix effects described in section Intervals which do not contain one imply that the effect was statistically significant. 2. Table B6 contains the hospital residuals and the associated confidence intervals described in section Intervals which do not contain zero imply that the effect of the hospital was significanlty different from the average. Values below zero imply that the risk of discharge was lower than at the average hospital and values above zero imply the risk of discharge was higher than at the average hospital. Hospitals are ordered in ascending size of risk of discharge. Hospitals A-H are the eight study hospitals and the remainder have been given the anonymous codes also used in Chapter 5. Table B1: The effects of age 1, sex 2 and secondary diagnoses 3 HRG Chapter Heading age females secondary diagnosis R.R. 95% C.I. R.R. 95% C.I. R.R. 95% C.I. Nervous System 0.99 (0.99, 0.99) 0.97 (0.94,1.00) 0.87 (0.85,0.90) Respiratory System 0.99 (0.98, 0.99) 0.96 (0.94,0.99) 0.79 (0.77,0.81) Cardiac Surgery and Primary Cardiac Conditions (1) 0.99 (0.99, 0.99) 0.93 (0.91, 0.95) 0.83 (0.81, 0.85) Cardiac Surgery and Primary Cardiac Conditions (2) 0.99 (0.99, 0.99) 0.94 (0.92, 0.96) 0.82 (0.80, 0.83) Digestive System 0.99 (0.99, 0.99) 0.97 (0.94, 1.00) 0.90 (0.87, 0.92) Hepato-biliary and Pancreatic System 0.99 (0.99, 0.99) 0.99 (0.93, 1.05) 0.74 (0.70, 0.79) Musculoskeletal System 0.99 (0.99, 0.99) 0.99 (0.95, 1.04) 0.97 (0.93, 1.02) Skin, Breast and Burns 0.99 (0,99, 0.99) 1.01 (0.93, 1.09) 0.78 (0.73, 0.84) Endocrine and Metabolic System 0.99 (0.99, 0.99) 0.93 (0.88, 0.97) 0.80 (0.76, 0.85) Urinary Tract and Male Reproductive System 0.99 (0.99, 0.99) 1.00 (0.96, 1.05) 0.82 (0.78, 0.86) Diseases of Childhood 1.00 (0.99, 1.01) 0.94 (0.88, 1.01) 0.71 (0.64, 0.78) Vascular System 0.99 (0.99,0.99) 1.04 (0.96, 1.13) 0.72 (0.66, 0.78) Haematology, Infectious Diseases, Poisoning and Non-specific Groupings 0.99 (0.99, 0.99) 0.97 (0.93, 1.01) 0.88 (0.83, 0.93) Mental Health 0.82 (0.82, 0.82) 0.97 (0.91, 1.03) 0.79 (0.74, 0.83) 1 relative risk associated with each additional year of age 2 reference category = males 3 reference category = no secondary diagnoses

128 Table B2: The effects of marital status 4 HRG Chapter Heading married widowed other marital status R.R. 95% C.I. R.R. 95% C.I. R.R. 95% C.I. Nervous System 1.10 (1.07, 1.14) (0.98, 1.12) Respiratory System 1.16 (1.12, 1.21) 1.06 (1.02, 1.11) 1.09 (1.03, 1.16) Cardiac Surgery and Primary Cardiac Conditions (1) 1.21 (1.16, 1.26) 1.10 (1.05, 1.16) 1.18 (1.10, 1.27) Cardiac Surgery and Primary Cardiac Conditions (2) 1.10 (1.07, 1.14) 1.02 (0.98, 1.06) 1.09 (1.03, 1.15) Digestive System 1.07 (1.03, 1.11) 1.06 (1.01, 1.12) 0.98 (0.92, 1.05) Hepato-biliary and Pancreatic System 1.12 (1.02, 1.23) 0.95 (0.84, 1.07) 0.89 (0.76, 1.03) Musculoskeletal System 1.16 (1.09, 1.24) 1.02 (0.94, 1.11) 0.98 (0.87, 1.10) Skin, Breast and Burns 1.16 (1.06, 1.28) 1.01 (0.89, 1.15) 0.98 (0.82, 1.16) Endocrine and Metabolic System 1.18 (1.10, 1.26) 1.10 (0.99, 1.21) 0.94 (0.82, 1.07) Urinary Tract and Male Reproductive System 1.14 (1.08, 1.22) 1.05 (0.97, 1.15) 1.00 (0.87, 1.14) Diseases of Childhood Vascular System 1.15 (1.02, 1.30) 1.09 (0.93, 1.28) 1.03 (0.98, 1.18) Haematology, Infectious Diseases, Poisoning and Nonspecific Groupings 1.10 (1.04, 1.16) 0.97 (0.90, 1.06) 1.08 (0.98, 1.18) Mental Health 0.96 (0.89, 1.03) 0.87 (0.78, 0.96) 0.89 (0.80, 0.99) 4 reference category = single

129 Table B3: The effect of deprivation 5 HRG Chapter Heading medium-low deprivation medium-high high deprivation deprivation R.R. 95% C.I. R.R. 95% C.I. R.R. 95% C.I. Nervous System 0.98 (0.94, 1.03) 0.97 (0.93, 1.02) 0.97 (0.93, 1.01) Respiratory System 1.01 (0.97, 1.05) 0.98 (0.94, 1.02) 1.00 (0.96, 1.04) Cardiac Surgery and Primary Cardiac Conditions (1) 0.98 (0.94, 1.01) 0.95 (0.92, 0.99) 1.00 (0.97, 1.04) Cardiac Surgery and Primary Cardiac Conditions (2) 0.96 (0.93, 0.99) 0.95 (0.92, 0.98) 0.97 (0.94, 1.04) Digestive System 0.96 (0.92, 1.00) 0.98 (0.94, 1.02) 0.95 (0.92, 0.99) Hepato-biliary and Pancreatic System 0.93 (0.85, 1.02) 0.92 (0.85, 1.01) 0.93 (0.86, 1.02) Musculoskeletal System 1.00 (0.93, 1.07) 0.95 (0.89, 1.01) 0.94 (0.88, 1.00) Skin, Breast and Burns 0.97 (0.88, 1.07) 0.88 (0.80, 0.96) 0.94 (0.85, 1.03) Endocrine and Metabolic System 0.97 (0.90, 1.06) 0.93 (0.86, 1.00) 0.97 (0.89, 1.04) Urinary Tract and Male Reproductive System 1.04 (0.98, 1.11) 1.00 (0.94, 1.06) 0.95 (0.89, 1.01) Diseases of Childhood 0.98 (0.87, 1.10) 0.91 (0.81, 1.01) 0.91 (0.82, 1.01) Vascular System 0.92 (0.82, 1.04) 1.06 (0.94, 1.19) 1.01 (0.90, 1.14) Haematology, Infectious Diseases, Poisoning and Nonspecific Groupings 1.01 (0.94, 1.07) 1.02 (0.96, 1.08) 1.03 (0.97, 1.09) Mental Health 0.91 (0.82, 1.00) 0.96 (0.87, 1.05) 0.88 (0.80, 0.96) 5 reference category = low deprivation

130 Table B4: The effects of types of admission 6 HRG Chapter Heading admission via transfer emergency admission R.R. 95% C.I. R.R. 95% C.I. Nervous System 0.58 (0.53, 0.64) 0.95 (0.87, 1.03) Respiratory System 0.46 (0.44, 0.49) 0.68 (0.64, 0.71) Cardiac Surgery and Primary Cardiac Conditions (1) 0.64 (0.60, 0.69) 0.73 (0.68, 0.78) Cardiac Surgery and Primary Cardiac Conditions (2) 0.46 (0.45, 0.48) 0.57 (0.55, 0.59) Digestive System 0.69 (0.65, 0.72) 0.83 (0.80, 0.86) Hepato-biliary and Pancreatic System 0.52 (0.47, 0.57) 0.50 (0.46, 0.54) Musculoskeletal System 0.96 (0.88, 1.04) 1.05 (0.99, 1.11) Skin, Breast and Burns 0.61 (0.53, 0.71) 0.89 (0.80, 0.98) Endocrine and Metabolic System 0.66 (0.60, 0.73) 0.86 (0.80, 0.92) Urinary Tract and Male Reproductive System 0.51 (0.48, 0.55) 0.72 (0.68, 0.77) Diseases of Childhood 0.66 (0.55, 0.79) 0.92 (0.80, 1.06) Vascular System 0.46 (0.40, 0.52) 0.66 (0.60, 0.72) Haematology, Infectious Diseases, Poisoning and Nonspecific Groupings 0.56 (0.51, 0.61) 0.76 (0.72, 0.81) Mental Health 0.82 (0.66, 1.03) 1.23 (1.01, 1.49) 6 reference category = admission via waiting list/diary/booking

131 Table B5: The effects of types of discharge 7 HRG Chapter Heading discharged to another hospital discharged to other specialty within same hospital other type of discharge R.R. 95% C.I. R.R. 95% C.I. R.R. 95% C.I. Nervous System 1.07 (1.03, 1.12) 2.08 (2.00, 2.17) - - Respiratory System 1.08 (1.03, 1.14) 2.83 (2.74, 2.93) 1.05 (0.97, 1.14) Cardiac Surgery and Primary Cardiac Conditions (1) 0.75 (0.71, 0.79) 2.94 (2.86, 3.03) 0.91 (0.84, 1.00) Cardiac Surgery and Primary Cardiac Conditions (2) 0.80 (0.76, 0.84) 1.74 (1.68, 1.79) 1.27 (1.19, 1.36) Digestive System 0.93 (0.87, 1.00) 1.57 (1.51, 1.63) 1.31 (1.23, 1.40) Hepato-biliary and Pancreatic System 1.04 (0.91, 1.19) 1.87 (1.72, 2.03) 1.36 (1.18, 1.57) Musculoskeletal System 0.84 (0.77, 0.93) 1.67 (1.55, 1.79) 0.97 (0.83, 1.14) Skin, Breast and Burns 0.90 (0.78, 1.03) 1.83 (1.64, 2.04) 1.08 (0.87, 1.34) Endocrine and Metabolic System 0.81 (0.72, 0.91) 1.50 (1.38, 1.62) 1.26 (1.09, 1.45) Urinary Tract and Male Reproductive System 1.09 (1.00, 1.18) 1.80 (1.70, 1.91) 0.89 (0.75, 1.05) Diseases of Childhood 1.35 (1.10, 1.65) 1.67 ( ) 1.23 (0.70, 1.42) Vascular System 1.11 (0.96, 1.29) 1.32 (1.18, 1.47) 1.00 (0.70, 1.42) Haematology, Infectious Diseases, Poisoning and Nonspecific Groupings 0.88 (0.81, 0.95) 1.34 (1.24, 1.45) 1.46 (1.36, 1.56) Mental Health 0.95 (0.86, 1.05) 1.67 (1.51, 1.84) 1.13 (1.03, 1.23) 7 reference category = discharged home

132 13 Table B6 : Hospital residuals from survival analyses Hospital residual 95% C.I (-7.82,-6.22) (-5.05,-3.64) (-4.67,-3.72) (-4.63,-3.13) (-3.54,-2.14) (-3.31,-1.90) (-2.66,-1.32) (-2.55,-1.24) (-2.42,-1.12) (-2.24,-1.03) (-2.18,-0.71) (-2.01,-0.68) (-1.87,-0.52) A (-1.67,-0.33) (-1.45,-0.14) (-1.47,-0.10) (-1.36,0.00) (-1.29,0.06) E (-1.28,0.06) (-1.26,0.10) (-1.23,0.08) (-1.21,0.09) (-1.07,0.01) (-1.19,0.16) (-1.08,0.26) (-1.03,0.30) (-1.01,0.29) (-0.80,0.60) (-0.56,0.72) (-0.52,0.81) (-0.27,1.10) D 0.53 (-0.15,1.21) (-0.15,1.24) (-0.01,1.18) (-0.01,1.28) H 0.81 (0.15,1.47) G 1.02 (0.33,1.71) (0.79,2.17) C 2.02 (1.36,2.67) B 2.39 (1.72,3.07) (1.91,3.33) F 2.85 (2.18,3.52) (2.68,4.07) (6.73,8.06) (8.17,9.51)

133 14 APPENDIX C MULTILEVEL MODELLING FOR THE ECONOMETRIC ANALYSIS

134 15 MULTILEVEL MODELLING FOR THE ECONOMETRIC ANALYSIS This appendix is written as a companion to chapter 5; its purpose is to provide the reader with a broader understanding of multilevel modelling as applied to the econometric study of bed occupancy and costs. The techniques have been described in more detail elsewhere (Goldstein, 1995) and with particular reference to the health service (Rice and Leyland, 1996) and health economics (Rice and Jones, 1997). This appendix is limited to an explanation of those issues relevant to chapter 5, drawing examples from that chapter. The uses of multilevel modelling The hierarchy evident in chapter 5 is that of repeated measures made on individual hospitals. At the lowest level we have one financial year of data for each hospital, there being a total of five observations per hospital (corresponding to the five financial years from 1991/92 to 1995/96 inclusive). The response variable in each model is a measure of cost per case, be this total cost per case or direct cost per case. At the top level of the two level model are the 26 hospitals included in the analysis (there being five observations made on each of them). If the hospital is denoted by the subscript i and the year by the subscript j, then the response variable is the cost per case for the j th year in the i th hospital y ij (i = 1,,26; j = 1,,5). We are attempting to explain differences in costs both from one year to another and from one hospital to another using available information. This may relate to the hospital over the entire period; an example of such a variable is teaching status which remains unchanged from one year to the next. Alternatively this may vary from year to year within the hospital; an example of such a variable is average annual bed occupancy. There are a total of 127 data points (5 observations on 25 hospitals and only two observations on one hospital). There are a number of possible approaches to the analysis of these data, and we can consider the alternative approaches of ordinary least squares, aggregation, a fixed effects model and a multilevel model. The ordinary least squares (OLS) approach is to analyse the data as they stand. This, however, ignores the hierarchy in the data of the repeated measures being made on the same hospitals the analysis would be just as if a different 26 hospitals had been sampled each year. We would expect there to be some relationship between the cost per case for a particular hospital for two different years; a hospital with high costs for one year is more likely to have high costs every year, which may reflect something as simple as differences in its accounting procedures. The point is that y ij and y ij ' cannot be assumed to be independent of each other a key assumption of OLS regression techniques. Furthermore, consider the variables which are measured (or relate to) the hospital level. We have 26 observations relating to teaching status, one made on each hospital (with a 1 indicating that it is a teaching hospital, a 0 that it is not). An OLS regression model acts as though there are 120 such observations rather than 26, since there is no notion that the same hospitals are appearing more than once in the data. In practice this leads to an underestimation of the standard error of the regression coefficients of such hospital level variables a problem known as misestimated precision. This means that such hospital level variables are likely to be thought to have a significant relationship with the response variable when, in fact, they do not. An approach to overcome the problem of misestimated precision is to aggregate the five years into a single observation for each hospital. This means taking an average (or a weighted average) of all of the variables response and explanatory so that all variables are then measured at the

135 16 level of hospital rather than year. In essence, the y ij are condensed into a set of y i. which represent the experience of hospitals over the five year period from 1991 to However, the data now comprise just 26 observations and so there is a considerable loss of power (and information) with such a large reduction in the number of degrees of freedom. What is more, it is no longer possible to look at changes over time; if the relationship between cost per case and bed occupancy is what is of paramount interest then it is important to check that this relationship has remained constant over the period rather than just reporting the mean relationship. The third possibility mentioned above is to fit a fixed effects model. This essentially adds a dummy variable for each hospital into the regression equation as explanatory variables. The dummy relating to the first hospital, for example, would take the value 1 corresponding to responses where i = 1 and would take the value 0 otherwise. This then estimates a mean for each hospital rather than an overall mean; consequently it overcomes the problems of independence and misestimated precision. However, there is a cost; the use of 26 separate means rather than one mean for all hospitals requires the addition of 25 explanatory variables and the loss of 25 degrees of freedom. Investigating whether any of the explanatory variables was random across hospitals for example, whether the relationship between bed occupancy and cost was constant across all hospitals or if it varied between them in terms of the change in cost per case arising from a single percentage point increase in bed occupancy would again require the addition of 25 dummy variables. Such models become unwieldy very quickly. Multilevel models differ from fixed effects models in that, rather than model the effect of each hospital explicitly, they model the variation separately at each level. The separate means for each hospital can then be estimated from these variances. They have the advantage over the fixed effects models that the modelling of 26 hospital means requires the addition of one parameter the between hospital variance as opposed to the addition of 26 explanatory variables. The distributional assumptions made about hospital means that they can effectively be seen as being draws from a distribution of an infinite number of hospital means with a given variance results in hospitals borrowing strength from each other. In the fixed effects model the estimate for each hospital s mean is made from data for that hospital alone; in a multilevel model, data from all hospitals are utilised. The fixed part The fixed part of a multilevel model looks very much like any other regression model in that it may contain an intercept or constant, a number of explanatory variables which may be continuous or categorical, and interactions between these terms. Consider, for example, model A presented in table 5.3. The estimated coefficient of the constant, 765.5, represents the estimated cost per case (in pounds) when all of the other terms in the regression equation are zero. The variables 1991/92 to 1994/95 are dummy variables indicating the financial year; as such they are all zero for the financial year 1995/96 (this is called the baseline year). Similarly, the variables LGMTH and MSH are dummy variables indicating whether the hospital is a large teaching hospital or a mixed specialty hospital respectively. The baseline hospital type is the general hospital with some teaching units. AOR represents the average occupancy rate, expressed in terms of the difference in percentage points from the average of 84%. CRUDE LOS denotes the mean length of stay and is centred around an average of 6.2 days. The last two terms are interactions between the hospital type and length of stay, and are therefore both zero when either the hospital considered is a general hospital with some teaching units or when the hospital s unadjusted length of stay is 6.2 days. The figure of therefore refers to the predicted direct cost per case in

136 17 the medical specialties of a general hospital with some teaching units, in the 1995/96 financial year, which has an average occupancy rate of 84% and a mean length of stay of 6.2 days. Predicted costs for different types of hospital, different financial years, varying occupancy rates and with differing mean lengths of stay can be obtained by adding multiples of the relevant variables to this baseline figure. Accompanying the parameter estimates in table 5.3 are their standard errors. Thus, whilst the cost per case for 1991/92 was estimated to be lower than in 1995/96 for hospitals with the same occupancy and the same mean length of stay in the two years, we can be reasonably confident that costs were between and lower. The random part An OLS regression model uses a set of predictor or explanatory variables measured at the year x, x,..., x, to estimate associated regression coefficients level for each hospital, { 0ij 1ij Pij} { β β β },,..., P and thereby obtain a predicted cost $y ij 0 1 P $y = β x ij p pij p= 1 The difference between the observed and predicted values, ( y y ij $ ij ) the error term or residual. The model for the observed costs is P y = β x + e ij p pij p= 1 ij A multilevel model adds an effect or residual for each hospital, { u i }, such that P y = β x + u + e ij p pij p= 1 i ij The u i and e ij are assumed to be independent and each has its own distribution: u e i ij ~ N, 2 ( 0 σu ) 2 ( 0 σ ) ~ N, e, is denoted e ij and is called σ 2 u and σ 2 e are then referred to as the level two (between hospital) and level one (between year, within hospital) variances respectively. The estimates of these parameters are displayed in the random part of model A in table 5.3. These estimates can be used to provide 95% coverage intervals; for example, the mean for 95% of the hospitals would be expected to lie within ( ) of the overall mean under model A. At level 1 we could expect that 95% of the yearly observations of cost per case would lie within of the hospital mean. The total variation in costs can be split into its component levels

137 18 ( ) 2 2 Var y ij = σ u + σ e and for this reason this is sometimes known as a variance components model. Intra-hospital correlation The intra-hospital correlation ρ is the proportion of the total variance which lies between hospitals; when ρ = 0 there is no variation between hospitals any observed variation can be put down to year-on-year differences and the multilevel model is equivalent to an OLS regression model. As ρ increases, the proportion of the total variation which is attributable to hospitals increases; this means that the costs per case for different years within the same hospital are of increasing similarity. With the intra-hospital correlation given by 2 σu ρ = 2 2 σ + σ u e for model A of table 5.3 we get ρ = Complex level one variation So far we have made the assumption that the observed cost per case for hospitals will show the same degree of variation about their expected value regardless of any hospital or yearly characteristics. In the above notation, we have assumed that σ 2 e is constant for all hospitals and for all years. This assumption can be relaxed by considering a different error structure; model B in table 5.3, for example, fits the model y = β x + u + e + LOS e ij p pij p= 1 e e 0ij 1ij P 2 0 σe0 ~ N, 0 σe i 0ij ij 1ij σ e σe1 where LOS ij is the mean length of stay in the i th hospital for the j th year. This gives ( ) 2 2 Var y = σ + σ + 2σ LOS + σ LOS 2 2 ij u e0 e01 ij e1 ij i.e. the variance of each observation on an individual hospital about its mean is dependent on the unadjusted mean length of stay in that hospital. For model B the between year, within hospital variance at a hospital with a mean length of stay of 6.2 days (the average) was 4333; this would increase to 8622 at a hospital with a mean length of stay of 8.0 days. This has consequences for the intra-hospital correlation, which will decrease from at 6.2 days to at 8.0 days i.e. the proportion of variation attributable to differences between hospitals as opposed to variation from one year to another decreases as length of stay increases.

138 19 References Goldstein H (1995) Multilevel Statistical Models. London: Edward Arnold. Rice N, Jones AM (1997) Multilevel models and health economics. Health Economics; in press. Rice N, Leyland AH (1996) Multilevel models: applications to health data. Journal of Health Services Research and Policy 1,

139 20 APPENDIX D INTERVIEW SCHEDULES

140 21 STRATEGIC LEVEL QUESTIONS Organisational Issues Can you please describe the procedures for bed management in this trust/hospital? How do you monitor the location and number of available beds? How is this information processed and distributed, and how often? How accurate is this information? How is the complement of beds per specialty allocated, and how often is this reviewed? What about ownership of beds by the specialties? Are beds organised as a whole hospital resource, or are they seen as belonging to particular specialties/wards/consultants? Do you know the total number of beds in the Trust and how these are divided between the various specialties? If no, where could I get this inf.? What are the procedures for admitting a patient? rotas? How do the various specialties organise admissions? Can you describe the receiving Do you have placement procedures for acutely ill? Do you have written admission procedures/protocols? (Can I have copies?) Do you have any planned investigation units, acute admission wards/beds, or observation wards/beds? Do you have fast track admissions for particular conditions? Are there peak days for admissions? When a specialty s beds are full, is there particular specialty where you prefer to place patients? If yes, what are the reasons for this, is it set out in the bed/admissions policy? What are the procedures for discharging a patient? When do ward rounds take place? Are junior staff allowed to discharge, what about ward sisters? Any arrangements in place to deal with patients who require domiciliary support or a place in a nursing/residential home? Do you have liaison officers/sisters who deal with local GPs and community services? Who makes arrangements for take-home medicines, transport? Do you have pre-discharge lounges or wards? Are there any peak days for discharges? How do you plan for seasonal variations in demand? What are the main restrictions on planning? Do you ever take blocks of beds from one specialty and allocate them to another? If yes, who makes this decision?

141 22 How do you cope with short term variations in demand? Are there beds which could not be used? beds which are ring-fenced protected by waiting list initiatives etc.? private patient beds? Are beds ever concealed from the bed management team? Has the use of day surgery had any effect on your admission rates? How do the different specialties organise their operating lists? Do you have a hospital policy on theatre mix, or is this left to the surgeons to organise? How do you monitor the performance of bed management policies? What data do you use to monitor the application and performance of policy? E.g. weekly/monthly summaries etc.? LOS data? How often are your admission and discharge procedures reviewed? How useful are these reviews? Do you know who is responsible for collecting the information on the hospital s costs? (The inf. that is sent to ISD for inclusion in the annual Scottish Health Service Costs Report; the Blue Book) How often is this collected? How are boarders taken into account, are they seen as belonging to the specialty they are in at the time of recording (if yes, how long do they have to have been in specialty before they are recorded as patients), or are they counted as a patient of the specialty from which they were placed? Who makes the decision to record boarders by specialty? Economic Issues The next set of questions is about what you would consider to be the consequences of increasing bed occupancy rates... Would there be any benefits, for your trust/hospital, of increasing the number of admissions? If respondent is the Medical Director; how would you cope with a sudden rise in the number of emergency admissions? Do you have enough discretion to raise admission criteria in A&E? Could you lower the number of admissions in any way?

142 23 What would be the constraints of an increase in admissions? Are staffing levels (medical and nursing) sufficient to cope? Would you need to employ bank nurses? How would an increase in admissions affect the trust s income and total costs? How well do you think bed management practices would be able to cope? Could the organisational structure of the bed management unit cope? Are the staffing levels of the bed management unit sufficient to cope? What about the information on beds, would they/you need to collect and disseminate more often? Would you need to resort to pooling between specialties, gender pooling? What would be the financial implications? Is it likely that contracts with the HB or GPFHs would be prematurely fulfilled? What would be the effect on unit costs, for example the cost per case? What about cost per day? (If cost per case rising, ask them why) What would be the organisational effects? How would this affect elective admissions? What would be the effect on quality of care? Would it mean patients being inappropriately placed? What would be the effect on length of stay? If answer is a reduction in LOS, would a reduced LOS have any effect on cost per case or cost per day? What about re-admission rates? What about the effect on turnover? Do you have a minimum turnover interval? Could you decrease the TI, how would you do this? What would the effect of, for example, a five per cent increase in the number of admissions to this trust/hospital? What about ten per cent...fifteen per cent...? Another way to increase occupancy rates would be to reduce the number of beds in the trust/hospital...is this a feasible option? (If no, why not?) What do you think you would gain by reducing the number of beds? What would be the effects of a reduction in the number of beds? Would you make significant savings by reducing small numbers of beds?

143 24 Are yearly contracts flexible enough to allow a reduction? The re-deployability of resources? Are there political risks? Public/professional opposition? Capacity to meet waiting time targets Waiting list monies? What would be the effect on the Trust s total costs? What about the unit cost - cost per case, cost per day? (If cost per case rising, ask them why) What about spare capacity, are there any ways in which you might increase occupancy rates without reducing the number of beds? Weekend opening? Staff costs Total cost? Are NHS beds used for private work at weekends? Day case care/surgery? More high-tech surgery, more effective drugs? What about the use of long term contracts to increase re-deployability of resources? Uncertainty re costs and activity Could you co-operate with other trusts in any way? Could you sell spare capacity at marginal cost? Pricing rules, do they allow this? Ironing out fluctuations in demand? How much control do you have over demand? Could you use waiting lists to increase demand? What about five day wards How well do you think bed management policies would be able to cope? Could the organisational structure of the bed management unit cope? Are the staffing levels of the bed management unit sufficient to cope? What about the information, would you need to collect and disseminate more often? Would you need to resort to pooling between specialties, gender pooling?

144 25 What would be the financial implications? Is it likely that contracts with the HB or GPFHs would be prematurely fulfilled? What would be the effect on the total cost? What about the unit cost - cost per case, cost per day? (If cost per case rising, ask them why) What would be the organisational effects? How would this affect elective admissions? What would be the effect on quality of care? Would it mean patients being inappropriately placed? What would be the effect on length of stay? If answer is a reduction in LOS, would a reduced LOS have any effect on cost per case or cost per day? (If cost per case rising, ask them why) What about re-admission rates? What about the effect on turnover? Could you decrease the TI, how would you do this? Do you have a minimum turnover interval? How do you think bed management practices could be improved on this site? OPERATIONAL LEVEL QUESTIONS Organisational Issues Can you please describe the procedures you use for bed management in this Trust/specialty/ward? How do you monitor the location and number of available beds in your trust/specialty/ward? How is this information fed-back, and how often? How useful do you find this information? (Bed Manager - How well is this inf. received?) How is the/your complement of beds allocated, and how often is this reviewed? Is this/your allocation adequate for your needs? What about ownership of beds by the specialties? Are beds organised as a whole hospital resource, or are they seen as belonging to particular specialties/wards/consultants? How does a patient get admitted to this trust/specialty/ward?

145 26 How do you organise admissions? Can you describe your receiving rota(s)? Do you have placement procedures for the acutely ill? Do you have written admission procedures/protocols (can I have copies?) Do you have any planned investigation units, acute admission wards, or observation wards/beds? Do you have fast track admissions for particular conditions? Are there peak days for admissions? When your wards are full, is there particular specialty where you prefer to place patients? If yes, what are the reasons for this, is it set out in the bed/admissions policy? Is there a set amount of beds you can call on, or is just what is available at the time? Who makes demands on your beds? Is there any particular specialty/ward/consultant that will ask for beds, if so how many? Can you describe the discharge procedures used in this Trust/specialty/ward? Is there any delegation of authority to junior staff, or ward sisters, for discharges? Any arrangements in place to deal with patients who require domiciliary support or a place in a nursing/residential home? Do you have liaison officers/sisters who deal with local GPs and community services? Who makes arrangements for take-home medicines, transport? Do you have pre-discharge lounges or wards? Are there any peak days for discharges? How do you plan for seasonal variations in demand? What are the main restrictions on planning? Are blocks of beds ever taken from your specialty/ward and allocated to another? Do you ever receive beds from another specialty? If yes, who makes this decision? How do you cope with short term variations in demand? Are there beds which could not be used? beds which are ring-fenced protected by waiting list initiatives etc.? private patient beds? five day wards? Are beds ever concealed from the bed management team? How do you organise your operating lists in this specialty/ward? Do you have a hospital policy on theatre mix, or is this left to the surgeons to organise? Has the use of day surgery had any effect on your admission rates?

146 27 How do you monitor the performance of bed management practices in your specialty/ward? What data do you use to monitor performance? E.g. weekly/monthly summaries etc.? LOS data? How often are your admission and discharge procedures reviewed? How useful are these reviews? Do you know who is responsible for collecting the information on the hospital s costs? (The inf. that is sent to ISD for inclusion in the annual Scottish Health Service Costs Report; the Blue Book) How often is this collected? How are boarders taken into account, are they seen as belonging to the specialty they are in at the time of recording? (If yes, how long do they have to have been specialty before they are recorded as patients?) Or, are they counted as a patient of the specialty from which they were placed? Who makes the decision to record boarders by specialty? Economic Issues The next set of questions is about what you would consider to be the consequences of increasing bed occupancy rates... What would be the benefits, in this specialty/ward, of increasing the number of admissions? If respondent is in Medical Specialty; how would you cope with a sudden rise in the number of emergency admissions? Do you have enough discretion to raise admission criteria in A&E? Could you lower the number of admissions in any way? What would be the constraints of an increase in admissions? How would this affect your work? Are staffing levels (medical and nursing) sufficient to cope? Would you need to employ bank nurses? What would be the effect on quality of care? Would it mean patients being inappropriately placed? How well do you think bed management practices would be able to cope? Could the organisational structure of the bed management unit cope? Are the staffing levels of the bed management unit sufficient to cope? What about the information on beds, would you/they need to collect and disseminate more often? Would you need to resort to pooling between specialties, gender pooling?

147 28 What would be the financial implications? Is it likely that contracts with the HB or GPFHs would be prematurely fulfilled? What would be the effect on unit costs, for example the cost per case? What about cost per day? (If cost per case rising, ask them why) What would be the effect on length of stay? If a reduction in LOS, would a reduced LOS have any effect on cost per case or cost per day? What about re-admission rates? What about the effect on turnover? Do you have a minimum turnover interval? Could you decrease the TI, how would you do this? Another way to increase occupancy rates would be to reduce the number of beds in the trust/hospital...is this a feasible option? (If no, why not?) What would be the constraints of a reduction in the number of beds? Would you make significant savings by reducing small numbers of beds? Are yearly contracts flexible enough to allow a reduction? The re-deployability of resources? Are there political risks? Public/professional opposition? Capacity to meet waiting time targets Waiting list monies? What would be the effect on the Trust s total costs? What about the unit cost - cost per case, cost per day? (If cost per case rising, ask them why) What about spare capacity, are there any ways in which you might increase occupancy rates without reducing the number of beds? Weekend opening: Staff costs Total cost? Are there NHS beds which are used for private work at weekends? Day case care/surgery?

148 29 More high-tech surgery, more effective drugs? Use of long term contracts to increase the re-deployability of resources? Uncertainty re costs and activity. Could you co-operate with other trusts in any way? Could you sell spare capacity at marginal costs? Pricing rules, would they allow this? Ironing out fluctuations in demand? How much control do you have over demand? Could you use waiting lists to create demand? Five day wards How well do you think bed management practices would be able to cope? Could the organisational structure of the bed management unit cope? Are the staffing levels of the bed management unit sufficient to cope? What about the information on beds, would you need to collect and disseminate more often? Would you need to resort to pooling between specialties, gender pooling? What would be the financial implications? Is it likely that the contract with the HB or GPFHs would be prematurely fulfilled? What would be the effect on unit costs, for example the cost per case? What about cost per day? (If cost per case rising, ask them why) What would be the organisational effects? How would this affect elective admissions? What would be the effect on quality of care? Would it mean patients being inappropriately placed? What would be the effect on length of stay? If a reduction in LOS, would a reduced LOS have any effect on cost per case or cost per day? (If cost per case rising, ask them why) What about re-admission rates. How do you think bed management practices could be improved on this site?

149 30 APPENDIX E PEOPLE INTERVIEWED DURING THE MANAGEMENT SURVEY

150 31 Profession No Interviewed Chief Executive 8 Director of Finance 8 Medical Director 8 Director of Nursing 8 Clinical Director - General Medicine 7 Clinical Director Surgery 3 Clinical Director - Obstetrics & Gynaecology 2 Clinical Director Ophthalmology 1 Clinical Director ENT 1 Clinical Director Gynaecology 1 Specialty Co-ordinator General Medicine 1 Specialty Co-ordinator ENT 1 Specialty Co-ordinator Ophthalmology 1 Specialty Co-ordinator Obstetrics & Gynaecology (replaced by Consultant) Clinical Nurse Manager General Medicine 1 Clinical Nurse Manager - OT & OPH 1 Senior Nurse Manager Medical Directorate 1 Senior Nurse Manager Surgical Directorate 1 Nurse Manager - General Medicine 2 Nurse Manager - Obstetrics & Gynaecology 1 Clinical Service Manager Clinical Directorate 1 Clinical Service Manager Surgical Directorate 1 Service Manager - General Medicine 1 Service Manager - General Surgery 1 Bed Managers (various titles) 5 Business Manager - Obstetrics & Gynaecology 1 Business Manager Ophthalmology 1 Information Manager 1 General Manager - Medical Directorate 1 Table No. 1 List of respondents 1

151 32 Consultants - General Medicine 5 Consultant ENT 2 Consultant - Obstetrics & Gynaecology 2 Consultant Ophthalmology 1 Consultant - General Surgery 1 Specialist Registrar - General Medicine 5 Specialist Registrar Obstetrics & Gynaecology 2 Specialist Registrar ENT 2 Specialist Registrar Ophthalmology 1 Ward Manager - Acute Medical Receiving 2 Ward Manager - Acute Surgical Receiving 1 Ward Manager Urology 1 Ward Sister - Acute Medical Receiving 3 Ward Sister - General Medicine 3 Ward Sister ENT 3 Ward Sister Gynaecology 2 Ward Sister Dermatology 1 Ward Sister Ophthalmology 1 Community Liaison Sister 1 Staff Nurse 1 Charge Nurse 1 Clerical Assistants (Medical Records/Bed Bureau) 4 List of repondents continued.