Towards a Hospital Coordination Performance Model for Patient-Centric Care Network

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1 Towards a Hospital Coordination Performance Model for Patient-Centric Care Network Shahadat Uddin 1 and Liaquat Hossain 2 1 Project Management Graduate Programme, University of Sydney, Australia 2 Project Management Graduate Programme, University of Sydney, Australia {shahadat.uddin, liaquat.hossain}@sydney.edu.au Abstract. In this study, we introduce a network enabled coordination model to examine the coordination performance of a patient-centric care network that evolves during patient hospitalization period. Though a number of hospital coordination models have been developed (i.e. Guided Care Model, Chronic Care Model) for the current healthcare system focusing on the quality of service and patient expectation with less hospitalization cost, it is apparent that the use of patient-centric care network as a way to explore coordination performance in the healthcare literature is lacking to date. We emphasize this type of coordination model for health care organizations in this research and develop hypotheses related to the attributes of patient-centric care network and hospital level of coordination performance. The health insurance claim datasets from Hospital Contribution Fund (HCF), a large Australian health insurance company, are analyzed to examine our proposed coordination performance model for healthcare organizations. We consider degree, tie strength, and connectedness as network attributes whether patients length of stay (LOS) during their hospitalization period is used to measure coordination performance. Results show that the attributes of patient-centric care network such as degree, tie strength, and connectedness have impacts on the performance of coordination. Keywords: coordination model; hospital performance; hospitalization period; patient-centric care network. 1. Introduction Coordination, the management of task interdependencies [1], has been shown to provide enhancement in performance not only in non-medical settings such as organizational learning [2] and product development cycle [3], but also in healthcare settings, particularly chronic disease management, and emergency and intensive care [4]. Coordination is very critical to organizations for their smooth running to achieve their desired organizational goals. From the perspective of patient perceptions of quality of care, coordination was identified as one of the most important factors in a study of patient-centered care [5]. In addition, effective coordination among staff reduces adverse events as like unusual death and wrong treatment in the care of

2 hospitalized patients [6]. Coordination is one way that healthcare organizations have attempted to meet the demands they face, for example, demand for improved quality of care and clinical outcomes, and demands for high level of patient satisfaction. During hospitalization period, patients receive services from different hospital service units. Also, depending on the unavailability of hospital doctors and patient medical condition patients need to be seen by outside specialist doctors. For instance, a patient might be served by medical test department before go through for a hip replacement surgery by surgeons or specialist doctors. After surgery that patient will be served by primary care unit for post surgery care. At this stage, the patient might need to be seen by external doctors depending on patient medical condition. This forms a patient-centric network as in figure 1, illustrating one directional relation of patients with different hospital units; receiving services from them. There are also relations and interdependencies among all hospital departments, as shown by dotted line in figure 1, which have impacts on the patient network attributes such as frequency of doctor visits and hospital length of stay for patient. Surgery Department Do surgery Visit Specialist Doctors Patient in Hospital Medical test Department Medical examination Take care Primary Care Team Fig. 1. Patient-centric care network that evolve during inpatient stay in the hospital Our goal is to model the performance of coordination by analyzing the patientcentric care network that evolves during patient hospitalization period with different hospital service units. We believe that by better understanding the attributes of patient-centric care network during inpatient stay of patient in the hospital, we will be in a better position to design the hospital settings that support those attributes. The following two questions motivate this research: 1. How do the attributes of patient-centric care network, as illustrated in figure 1 that develops during patient hospitalization period affect the hospital performance? 2. What are the characteristics of those attributes of patient-centric care network (figure 1) that produce efficient hospital performance?

3 2. Hospital Coordination Performance & Social Network Analysis Coordination has been considered a central issue in the growing literature on integrated delivery system, such as hospital healthcare system. It is defined as the additional activity need to be performed to assemble and synchronize differentiated work efforts so that they function properly and harmoniously during the course of attaining or achieving organizational goals [7]. From the perspective of an organization such as healthcare organization, coordination has three components: (a) set of actors; doctors, nurses, and other staff working in healthcare organizations or hospitals (b) who perform task; which includes the service given to the hospital inpatient and outpatient (c) to achieve goals; better patient care with high level of satisfaction [8-10]. There are many typologies that have been developed to distinguish global patterns of coordination in organization. In one such typology which was successfully applied to healthcare organization, coordination is categorized into two major types: programming and feedback [11]. Programming approach of coordination requires the standardization and predetermination of work processes. This approach includes the use of rules, regulations, schedules, plans, procedures, policies, and protocols to identify activities to be performed and specify work responsibilities to be followed. Coordination by programming, which requires little time for personal interaction, is found most effective when minimum interdependencies exist among staff and when work requirements are well understood and predictable [12]. In contrast, coordination by feedback involves the exchange of information, orally or in written form, among actors or staff. In organizational level, exchange of information and feedback among staff is needed for adapting to unforeseen circumstances. In general, healthcare organizations or hospitals are considered to face situations where either stable or uncertain or a combination of both work requirements is needed for their smooth functioning to promote patient healthcare outcomes. The uncertainty or the unpredictability, which entails high levels of staff interdependencies [13], are coming in large part from unpredictable arrival of patients in hospital emergency departments and from the variability of patient responses to medical intervention. On the other hand, only stable work requirements are needed for some hospital admissions. For example, patients who need surgery for knee or hip replacement might be best cared going through well-planned procedures in an environment where only stable work requirements are required. No matter which approach of coordination is exercised during the course of inpatient hospital stay, it has significant impact on patient-centric care network as illustrated in the figure 1. Therefore, both programming and feedback modes of coordination are critically important and should be used to their highest extent for healthcare organizations or hospitals to fulfill patient requirements. We apply the theory and measures of SN to quantify the network position of actors in modeling the network-based coordination performance. In patient-centric care network, we have five actors which have direct network relation, either oneway or two-way, among themselves. Excluding the patient actor, the rest four actors have two-way direct network relations among themselves. The patient actor has one-way incoming network relation with the rest four actors. Considering the goal of these rest four actors to provide better care to the patient actor, we can think

4 the patient-centric care network as a star network where the central actor ( patient ) has one-way incoming network relation with the rest four actors which have twoway direct network relations among themselves. As the network structure determines the application of SN measures [14], we cannot apply all SN measures in quantifying the actor network positions in our patient-centric care network. For instance, betweenness measure of SN theory cannot be applied as the network is fully connected and the patient actor is a black-hole, only receive care or support from all other actors. However, we can apply other SN measures such as degree, tie strength, or connectedness which all consider a single actor and its direct network relation with other actors at one instance. 2.1 Proposed Coordination Performance Model Considering the network properties and goal of the patient-centric care network (figure 1) we propose a coordination performance model (figure 2) where network properties are considered as independent variables and goal is used as dependent variable. Age Moderating Variables Sex Network Structure Degree Tie Strength Connectedness Coordination Performance Length Of Stay (LOS) Fig. 2. Coordination performance model for patient-centric care network The framework of our proposed model, as illustrated in figure 2, is intended to assess the coordination performance of health service providers or hospitals. The model is constructed in a way to assess the patients length of stay (LOS) in the hospitals as a product of attributes of network position of patients in the patientcentric care network. We use sex and age as moderating variables to cluster our complete dataset, and also to perform micro-level analysis for our proposed hypotheses. These two moderating variables are found as important predictors for patient length of stay for their hospital admissions. Garcia-Vidal et al. [15] found the evidence of the association between patient age and prolonged stay in hospital. The value of LOS also differs for male and female for the same disease. For instance, Skobeloff et al. [16] found a higher incidence of asthma admissions for adult female

5 than adult male asthmatic patients and female asthmatic patients experience longer hospital stays per admission than male patients. We use degree, tie strength, and connectedness as independent variables to measure network position of patient in his or her care network and LOS as dependent variable for measuring hospital coordination performance as illustrated in figure 1. Degree: According to graph theory, degree of a vertex or node in a graph is the total number of edges incident to that vertex. In the context of social network analysis, degree of an actor is the count of the number of ties to other actors in the network. Tie Strength: According to Granovetter [17], the strength of relation between two actors can be expressed as a combination of the amount of time and the reciprocal services which characterize the tie between them. Connectedness: Network connectedness or simply connectedness, the third network variable, defines the frequency of communication between actors in the network. In the patient-centric care network, it dictates the frequency of doctor visits to a particular patient. Length Of Stay (LOS): The only dependent variable is LOS which is used to measure hospital coordination performance. If defines total number of days that a patient stay at hospital in the course of his or her treatment. 2.2 Research Hypothesis Based on the principle of coordination to health care organizations and in alignment with our coordination performance model, we propose the following hypotheses: Hypothesis 1: There is a significant relationship between network position of a patient in the patient-centric care network and the perceived level of coordination performance shown by hospital or health care organizations for that patient. To assess this hypothesis, we present another three sub-hypotheses to evaluate the principle theory. They are: (sh1) Degree correlates to length of stay (LOS). (sh2) Tie strength (with doctor) correlates to length of stay (LOS). (sh3) Connectedness correlates to length of stay (LOS). Hypothesis 2: The relations sh1, sh2 and sh3 are mediated by moderating variables of age and sex of patient. This means these socio-demographic characteristics of patients can be used to predict the relation between network position of patient and hospital perceived level of coordination performance. 3. Dataset In this study, we test the hypotheses for our proposed coordination performance model by using health insurance claim data. The dataset is provided by a non-profit health insurance organization, hospital contribution fund (HCF), a large health

6 insurance organization in Australia. It includes member claim data from January 2005 to February There are three different categories of claims coming from members, hospitals or health service providers, and doctors to HCF: ancillary claim, medical claim, and hospital claim. Ancillary claims are auxiliary claims such as medical services of the type dental, optical, physiotherapy, dietician, and pharmaceutical. All claims coming from specialist doctors except of the ancillary type are medical claims. The claims for the services provided as a hospital inpatient in a private or public hospital approved by the Department of Health, Australia are considered as hospital claim. There are about million ancillary claims, 8.98 millions medical claims, and 3.1 millions hospital claims HCF received for the health service providers from its 0.44 million members over the data collection time period from January 2005 to February Because of this huge dataset, we have to choose subset of data for experimentation in order to test our proposed hypothesis. Further, as our proposed model aim to model coordination performance of the patient hospitalization, we cannot consider claims of patients who do not have any admission to hospital for their illness. So, for the testing of our model, we considered only the subset of data for total hip replacement (THR) and knee surgery (KS) patients. For these two types of service, patients must need admission to hospitals; thus meeting our data inclusion criteria for data analysis to test our coordination performance model. Both of these two types of hospital admissions have medical and hospital claims along with few ancillary claims as summarized in table 1. Table 1. Summary of dataset considered to test the proposed model Total THR KS #. Claims Hospital Medical Ancillary #. Hospital Admission As stated in our proposed model, we have three independent variables of degree, tie strength and connectedness, and one dependent variable of length of stay (LOS). We measure degree by counting the total number of doctor visits that a patient has during his or her hospitalization period. As each doctor visit to patient is responsible for a medical claim to HCF, the total medical claims that a patient has for his or her hospital admission is the value for degree variable. The ratio of the expenses for medical claims in comparing to the total hospitalization expenses is the patient tie strength with doctor. To calculate patient connectedness with doctors, we divide the total number of doctor visits by the number of different doctors who visit the patient during his or her hospitalization period. An examination of the patient degree, tie strength and connectedness for both THR and KS datasets reveals common non-normal distributions for all of them which allow us to apply Spearman test to validate the relation of our proposed hypotheses.

7 4. Result and Discussion We design both macro-level and micro-level test to validate our proposed hypotheses. At macro-level, we carried out Spearman test on THR and KS datasets to determine if there is a relationship between the attributes of patient-centric care network and hospital perceived level of performance. At micro-level, we cluster our datasets by using the demographic characteristics of age and sex of patient. Then we do the correlation test to see how the relations found at macro-level work on these clusters. 4.1 Macro-level Analysis The Spearman correlation coefficient values as stated in table 2 show positive correlation for two relations and negative correlation for one relation. (sh1) Degree has a positive correlation with length of stay (LOS). We find that an increased in degree correlates with an increased LOS (rho=0.753, p=0.001 for THR and rho=0.700, p=0.001 for KS). The increased number of visits by hospital doctors and external doctors during patient hospitalization period makes his or her inpatient stay in the hospital longer. (sh2) Tie strength (with doctor) has negative correlation with length of stay (LOS). The Spearman correlation test shows a negative result for this sub-hypothesis (rho= , p=0.001 for THR and rho=-0.657, p=0.001 for KS) such that stronger tie strength with doctors makes the LOS of patient shorter for his or her hospital admission. Strong tie strength with doctors indicates either doctors visit patients frequently or doctors spend more time with patients during their visits or both. Both frequent visit and longer visit duration make doctors more aware about patient medical complaints which make their hospital stay shorter. (sh3) Connectedness has positive correlation with length of stay (LOS). Results indicate a positive correlation (rho=0.663, p=0.001 for THR and rho=0.534, p=0.001 for KS) between patient connectedness with doctors and patient length of stay (LOS) in the hospital for the same hospital admission. Table 2. Result macro-level between patient-centric care network variables and LOS Length Of Stay (LOS) Total Hip Replacement (THR) Knee Surgery (KS) Degree ** ** Tie Strength (Doctor) ** ** Connectedness ** ** Note: X denotes unnecessary or out of scope testing **. Correlation is significant at the 0.01 level (2-tailed).

8 KS THR 4.2 Micro-level Analysis The results we found at micro-level (see table 3) also support our findings at macrolevel. In addition, our micro-level results show the differences of the strength and pattern of relations for different clusters between attributes of patient-centric care network and patient LOS during hospitalization period. Gender Effect: We cluster both the THR and KS datasets on the basis of the sex of patient: male and female. Degree and connectedness show strong positive relations with LOS while tie strength has strong negative correlation with LOS. A further investigation to the correlation values for male and female group reveals that female shows stronger relation (in positive direction for degree and connectedness and in negative direction for tie strength) between all combination of the independent variables and LOS than male. Patient Age: On the basis of patient age, we cluster the THR and KS datasets in two groups: age group 1 (AG1) and age group 2 (AG2). All THR patients who are younger than the average age (58.70 years) belong to AG1; others belong to AG2. Similarly, all KS patients who are older than the average age (49.41 years) belong to AG2; the rest belongs to AG1. All clusters show strong relation, both in positive and negative direction, between any combination of independent variables and LOS. However, AG1 and AG2 show different pattern of relations for THR and KS datasets. AG1 shows stronger correlation than AG2 to THR dataset for any combination of independent variables and LOS. On the other hand, stronger relation is found for AG2 to KS dataset than AG1. Table 3. Result at micro-level for the correlation test between network attributes of patientcentric care network and LOS considering moderating variables. Length Of Stay (LOS) Sex Age Male Female AG1 AG2 Degree ** ** ** ** Tie Strength (Doctor) ** ** ** ** Connectedness ** ** ** ** Degree ** ** ** ** Tie Strength (Doctor) ** ** ** ** Connectedness ** ** ** ** Note: X denotes unnecessary or out of scope testing **. Correlation is significant at the 0.01 level (2-tailed). The first hypothesis in this research shows the relationship between the attributes of patient-centric care network and hospital performance of coordination which is measured by patient LOS at hospital. Degree and connectedness have positive correlation whether tie strength has negative correlation with LOS. These relations between network attributes and LOS can be used to design hospital settings which will support relational characteristics of those network attributes. According to our second hypothesis, the relations as described in the first hypothesis are mediated by

9 patient age and sex. By combining these two hypotheses, a current hospital setting can be reviewed in order to find its current state of coordination performance. We use only LOS to quantify coordination performance in our proposed model. There are evidences in the current literature using other direct measure such as total hospitalization cost [18] and indirect measure as like patient satisfaction level [19] for evaluating performance in hospital settings. There exists a strong positive correlation between LOS and total hospitalization cost in our dataset for both THR (rho=0.893, p=0.001) and KS (rho=0.837, p=0.001) patients. Thus, both of them will show similar pattern of correlation with any other network variable. For example, if any of them, say LOS, shows strong positive correlation with another variable then total hospitalization cost will also reveal a strong positive relation with that variable. Another possible measure for coordination performance, patient satisfaction level, is beyond the scope of this paper because of the source of data which come from health insurance claim information. Result findings in this study show a positive correlation between degree and LOS, and a negative correlation between tie strength with doctor and LOS for both THR and KS patient. If a patient is seen by doctors more frequently, the LOS for that patient will be higher. On the other hand, if doctors spend more time during their visits to patient, they understand the patient better and can treat the patient efficiently which could result an early hospital discharge of patient with higher satisfaction level. Spending more time with patient during their visits leads a higher amount of fees need to be paid to doctors which eventually make a stronger tie strength value with doctors for the correspond patient according to the way it is calculated in this research. Therefore, the duration of doctor visits is more important than the number of doctor visits to patient. Healthcare managers or health insurance providers can use our relational hypotheses for developing healthcare practice culture, or motivating healthcare practitioner and other staff in order to find out a way about how to control or reduce LOS for THR and KS patient. For instance, the negative relation of the subhypothesis sh2 denotes that the stronger relation between doctor and patient will make LOS shorter. By considering this relation, the healthcare managers may devote to develop an organizational culture where doctors or specialist surgeons spend more time with patients during their visits as a way of making stronger tie strength with patients. This research is not without its limitations. Though people have hospital admissions for many other diseases like heart stroke and cancer, this research uses only THR and KS datasets. Further, we do not consider whether a patient has anyone at home to look after him or her after being discharged from hospital or not. If a patient has relative at home, hospital may discharge him or her early after giving proper training to the person who will take care of the patient at home. On the other hand, if a patient does not have anyone at home, he or she needs to stay at hospital until the complete recovery from disease, which will result longer LOS in hospital. Despite these drawbacks, this is possibly the first study to model hospital coordination performance by analyzing the attributes of patient-centric care network that develop during patient hospitalization period.

10 5. Conclusion In this research, we measure coordination performance using SNA techniques in the context of patient admissions to healthcare organizations. We present a model for coordination performance based on the analysis of patient-centric care network that evolve during patient hospitalization period. We hypothesized that the network attributes of this patient-centric care network have effect on the patient LOS. Using these relations from our hypotheses, the organizational setting of any healthcare providers can reviewed and judged. The results from both at macro-level and microlevel seem to be complementing each other. In addition, at micro-level we find interesting pattern of network relation between independent variables and LOS of our proposed coordination performance model as like female shows stronger correlations between network attributes and LOS than male for both THR and KS datasets. Reference 1. Malone, T. and K. Crowston, The interdisciplinary study of coordination. ACM Computing Surveys (CSUR), (1): p Morgan, K., The learning region: institutions, innovation and regional renewal. Regional studies, : p Tang, D. and X. Qian, Product lifecycle management for automotive development focusing on supplier integration. Computers in Industry, (2-3): p Fargason Jr, C. and C. Haddock, Cross-functional, integrative team decision making: essential for effective QI in health care. QRB. Quality review bulletin, (5): p Gerteis, M., et al., Through the patient's eyes: understanding and promoting patient-centered care. 1993: Jossey-Bass San Francisco. 6. Andrews, L., et al., An alternative strategy for studying adverse events in medical care. Lancet, (9048): p Haimann, T. and W. Scott, Management in the modern organization. 1974: Houghton Mifflin Harcourt (HMH). 8. Baligh, H., Decision rules and transactions, organizations and markets. Management Science, 1986: p Malone, T. What is coordination theory. 1988: Citeseer. 10. Malone, T. and K. Crowston. What is coordination theory and how can it help design cooperative work systems? 1990: ACM New York, NY, USA. 11. Argote, L., Input uncertainty and organizational coordination in hospital emergency units. Administrative Science Quarterly, 1982: p Young, G., et al., Patterns of coordination and clinical outcomes: a study of surgical services. Health Services Research, (5 Pt 1): p Flood, A., The impact of organizational and managerial factors on the quality of care in health care organizations. Medical Care Research and Review, (4): p Uddin, M. and L. Hossain. Towards Coordination Preparedness of Soft-Target Organisation. 2009: Springer-Verlag New York Inc. 15. Garcia-Vidal, C., et al., Factors associated with prolonged hospital stay in community-acquired pneumonia. Enfermedades infecciosas y microbiología clínica, (3): p Skobeloff, E., et al., The influence of age and sex on asthma admissions. Jama, (24): p Granovetter, M., The strength of weak ties. American journal of sociology, (6): p Sylvia, M., et al., Guided care: cost and utilization outcomes in a pilot study. Disease Management, (1): p Longest, B., Relationships between coordination, efficiency, and quality of care in general hospitals. Hospital Administration, (4): p. 65.