Electronic Medical Records and Cost Efficiency in Hospital Medical-Surgical Units

Transcription

1 Michael F. Furukawa T. S. Raghu Benjamin B. M. Shao Electronic Medical Records and Cost Efficiency in Hospital Medical-Surgical Units This study examines the impact of electronic medical records (EMRs) on cost efficiency in hospital medical-surgical units. Using panel data on California hospitals from 1998 to 2007, we employed stochastic frontier analysis (SFA) to estimate the relationships between EMR implementation and the cost inefficiency of medicalsurgical units. We categorized EMR implementation into three stages based on the level of sophistication. We also examined the effects of specific EMR systems on cost inefficiency. Our SFA models addressed potential bias from unobserved heterogeneity and heteroskedasticity. EMR Stages 1 and 2, nursing documentation, electronic medication administration records, and clinical decision support were associated with significantly higher inefficiency. Government agencies and health care providers in the United States and other developed countries consider information technology (IT) to be the future of health care (Congressional Budget Office 2008; Blumenthal et al. 2008). A critical component of IT infrastructure in hospitals consists of electronic medical record (EMR) systems, which create a digital repository of patient data that can be shared among various stakeholders involved in the delivery of health care. It is widely held that EMR, once fully implemented, can improve both cost efficiency and quality of care through the complete documentation and prompt dissemination of patient medical information for provision of care (Hillestad et al. 2005). In the context of rising expenditures, policymakers and managers have high expectations for EMR to enable improvements in efficiency and quality. The expectation of efficiency improvement from EMR is driven primarily by the well-documented productivity enhancements through IT in the general economy (Jorgenson 2001; Bresnahan, Brynjolfsson, and Hitt 2002). The business case for EMR investment includes projections of cost savings from potential reductions in length of stay; reductions in the demand for nurses; reductions in redundant or inappropriate laboratory tests, radiology procedures, and medications; and reductions in administrative expenses for medical records (Garrido et al. 2004; Hillestad et al. 2005). In theory, automation and computerization of patient records are supposed to reduce unproductive nurse time and Michael F. Furukawa, Ph.D., is an assistant professor in the School of Health Management and Policy; and T.S. Raghu, Ph.D., and Benjamin B.M. Shao, Ph.D., are associate professors in the Department of Information Systems, all in the W. P. Carey School of Business at Arizona State University. This study was funded by a grant from the Center for Health Management Research, a program of the Health Research and Educational Trust. Address correspondence to Prof. Furukawa at School of Health Management and Policy, W. P. Carey School of Business, Arizona State University, P.O. Box , Tempe, AZ Michael.Furukawa@asu.edu 110 Inquiry 47: (Summer 2010) Excellus Health Plan, Inc. ISSN /inquiryjrnl_

2 Electronic Medical Records increase time spent on direct patient care (Case, Mowry, and Welebob 2002; Turisco and Rhoads 2008). However, few studies have examined the relationship between EMR and hospital efficiency, and evidence of EMR benefits is mixed (Chaudhry et al. 2006). For example, some prior studies have found that EMR can reduce nursing documentation time by up to 30% (Poissant et al. 2005). However, other studies have shown that EMR implementation increased documentation and/or medication task times (Franklin et al. 2007; Hakes and Whittington 2008). In this study, we examined the impact of EMR implementation on hospital cost efficiency. Given that EMR systems are expected to improve outcomes at the process level, we focused on efficiency measurement in medical-surgical acute units. We used longitudinal data on medical-surgical unit costs and EMR implementation in California hospitals over the period 1998 to Frontier estimation was used to estimate the effects of EMR implementation on the cost inefficiency of medical-surgical units. Considering the importance given to hospital IT investments by government officials and policymakers, the findings from this research are intended to provide evidence of the value of health IT in the inpatient setting. Background Conceptual Framework In investigating the relationships between EMR and efficiency, our conceptual framework is based on the paradigm of structure, process, and outcomes (Donabedian 2003). In our conceptualization, management makes decisions about EMR adoption (structure), which determines the nursing workflow (process) and results in the relative cost efficiency (outcome). EMR decisions include the adoption of various EMR applications, which automate some activities in the delivery of medical care. The cost outcome in hospitals depends on the inputs and the underlying process. In medical-surgical units, cost is driven predominantly by labor productivity and prices. We hypothesized that technology will impact inefficiency of medical-surgical units. The extent to which EMR affects inefficiency will depend on how EMR affects the core tasks of the medical-surgical acute unit. Core tasks include: communications; medication administration; documentation; admission, discharge, and transfer; care coordination; patient movement; and care delivery (Bolton, Gassert, and Cipriano 2008). Importantly, in many of these tasks, EMR reduces or eliminates the reconciliation steps required in a paper-based workflow process. To the extent that EMR systems automate tasks, streamline hand-offs, and reduce time required for information reconciliation, EMR is expected to result in improvements in the efficiency of medical-surgical units. On the other hand, whether EMR can fulfill this expectation or whether it actually exerts the opposite effect remains an open empirical question. Prior Studies Many studies have applied efficiency measurement methods, such as stochastic frontier analysis (SFA) and data envelopment analysis (DEA), to examine the relationship between firm characteristics and cost/technical efficiency in hospitals (e.g., Hollingsworth, Dawson, and Maniadakis 1999; Worthington 2004; Hollingsworth 2008; Rosko and Mutter 2008; Valdmanis, Rosko, and Mutter 2008). Using SFA, Rosko et al. (2007) found that compared to hospitals in centralized health systems, membership in centralized physician/insurance systems or decentralized systems was associated with lower inefficiency. In a study of German hospitals, Herr (2008) used SFA and DEA to show that private and nonprofit hospitals were less cost efficient and less technically efficient than public hospitals. Most studies have analyzed hospital efficiency at the firm level, and few studies have examined the efficiency of medical-surgical units within the firm. Bradford et al. (2001) estimated SFA models of cost efficiency within the surgical unit of a single organization and found potential cost savings by substituting angioplasty for bypass surgery. Lee et al. (2009) used corrected ordinary least squares (COLS) and DEA to examine the efficiency of the care planning process in 111

3 Inquiry/Volume 47, Summer 2010 nursing homes and reported that for-profit facilities were more efficient than not-forprofit facilities. Some prior work has studied the relationships among IT availability, hospital costs, and technical efficiency. Blank and Van Hulst (2009) studied the relationship between IT and hospital costs in Dutch hospitals. Using panel data from 1995 to 2002, they estimated a translog cost function that included a weighted index for information and communication technologies and indexes for other specialized services. They found that IT had a significant negative relationship with hospital costs. Lee and Wan (2003) used DEA and a structural equations model to estimate the relationship between information system integration and hospital technical efficiency. Using data from 349 U.S. urban hospitals in 1997 and 1998, they found that information system integration was associated with a negative efficiency score in 1997, but a positive efficiency score in Their information system measures included an index of clinical systems, but they did not specifically examine the use of EMR. Kazley and Ozcan (2009) used DEA and windows analysis to examine the relationship between EMR use and the change in hospital technical efficiency over time. Using data on 4,606 U.S. hospitals in 2001 and 2004, they found a significant association between EMR and higher efficiency for small hospitals, but no evidence that EMR improved efficiency over time. Their measure of EMR was a binary indicator for whether the hospital had a computerized patient record with automated status. Ancarani, Di Mauro, and Giammanco (2009) examined the impact of EMR and other firm characteristics on hospital units technical efficiency. With data from a large Italian hospital in 2004, they employed DEA to derive efficiency scores and then regressed these scores on EMR use and other managerial/organizational aspects. They found that EMR had a positive and significant relationship with technical efficiency. Similarly, their measure of EMR was a binary indicator for whether the hospital ward used a computerized patient management system. This study aims to estimate the relationship between EMR implementation and the cost inefficiency of hospital medical-surgical units while controlling for unobserved heterogeneity and heteroskedasticity. Methodology Data The data on EMR implementation came from the 1998 to 2007 HIMSS Analytics Database. HIMSS Analytics, a subsidiary of the Healthcare Information and Management Systems Society (HIMSS), annually surveys a sample of U.S. nonfederal hospitals, including independent hospitals and those affiliated with integrated health care delivery systems. 1 The HIMSS Analytics Database contained details on each hospital s adoption of specific EMR applications included in this study. The database reported the contract date year for each application, as well as the current automation status (i.e., automated/live and operational). Since we could not observe the actual start date of EMR implementation, we assumed that implementation began, on average, one year after the contract date. For observations where the contract date was missing, we used the earliest reported year where the application s status was automated/live and operational. We measured the effect of EMR based on the implementation start date rather than the live and operational date for several reasons. First, we believed that process changes were likely to occur during the early phases of implementation, and we wanted to capture these workflow-related changes to business processes during this initial period. Second, EMR may become live and operational on a pilot basis rather than hospitalwide, and we were unable to determine when EMR became live and operational in each hospital s medical-surgical acute units. If there were measurement error in the contract date, this would bias against finding any effects of EMR. Thus, we believe our measure of EMR implementation is conservative and underestimates the effects of EMR on cost inefficiency. We recognize that EMR implementation is an incremental process that can continue for several years. Thus, we measured the respective effects of EMR implementation over the 112

4 Electronic Medical Records initial three-year period: the year implementation started, as well as the second and third years following implementation. Our conceptualization of EMR is an adaptation of the HIMSS EMR Adoption Model (Garets and Davis 2006). We categorized EMR into three stages reflecting increased sophistication in the automation of clinical processes. The intent of the staging approach was to objectively identify investments in EMR systems based on the level of IT support enabled in the process. The stage model also allowed us to investigate the impact of complementarities for groups of systems in the same stage. We defined a hospital to be in EMR Stage 1 if it had invested in all three ancillary systems (i.e., laboratory, radiology, and pharmacy) and a clinical data repository (CDR). The CDR system receives feeds from the ancillary systems and provides clinical workers with access to patient information. We defined a hospital to be in EMR Stage 2 if it had invested in nursing documentation (DOC) and electronic medication administration records (emar) in addition to attaining EMR Stage 1. DOC enables the creation of nursing care plans for patients, and these plans are used to standardize and document treatments provided. On the other hand, emar automates medication administration at the point of care, provides nurses with access to patient medication data, and reconciles the medication administration with physician ordering and pharmacy dispensing. Finally, we defined a hospital to be in EMR Stage 3 if, in addition to having attained EMR Stages 1 and 2, it had invested in clinical decision support (CDS) and computerized physician order entry (CPOE). EMR Stage 3 functionality is characterized by automation of clinical decision processes, including order entry management and support of clinical decision making. Data on costs and nurse staffing came from the 1998 to 2007 Annual Financial Disclosure Reports of the California Office of Statewide Health Planning and Development (OSHPD). OSHPD requires acute care hospitals to annually submit a financial report, which contains information on nurse staffing productive hours and wages for each nurse type and hospital unit. OSHPD financial statements report costs and inputs for medical/surgical acute units as a single aggregate measure for all units within each hospital. Thus, our analytical sample contains a single independent observation representing medical/surgical units at that hospital for each year. The sample for the study included medicalsurgical acute units within short-term, general acute care hospitals in California. We excluded federal, specialty, children s, and longterm acute hospitals. We excluded financial reports not based on 365 days of reported data. The analytical data set included 326 hospitals and contained 2,828 hospital-year observations. Some data points were dropped during analysis due to missing values on one or more of the measures. The descriptive statistics for the study variables are provided in Table 1. In addition, Table 2 presents the adoption rates of EMR stages and specific EMR systems by year for the hospitals in our data set. Empirical Specification We used SFA to measure cost inefficiency of medical-surgical units. SFA has been extensively applied to measure hospital performance in efficiency, technical change, and productivity. The SFA approach takes into account the random events and measurement errors associated with the data collection and estimation procedure that are either unobservable or outside the unit s control (Coelli et al. 2005). Since SFA is parametric, the coefficient estimate of labor represents the marginal product of this labor input and informs us of the elasticity output of labor. The SFA approach has been extensively used to study hospital efficiency and productivity (e.g., McKay and Deily 2008; Farsi and Filippini 2008; Mutter, Rosko, and Wong 2008). A common theme of these studies has been to examine the relationship between hospital-level efficiencies and environmental influences. The efficiency estimation approach in this research is distinct from previous studies of hospital efficiencies from two important perspectives. First, unlike previous SFA studies that have estimated cost functions at the hospital level, we estimated efficiencies for 113

5 Inquiry/Volume 47, Summer 2010 Table 1. Descriptive statistics of hospital variables used in the study Variable N Mean Standard deviation Minimum Maximum Total cost 2,828 $9.2 M $10.1 M $.046 M $89.0 M Number of discharges 2,828 5, , , Direct capital expenses/bed 2,628 $4, $4, $28.19 $54, RN cost per hour 2,776 $41.06 $11.04 $20.68 $78.77 LVN cost per hour 2,774 $25.39 $6.22 $12.15 $45.66 Aid cost per hour 2,774 $17.05 $4.53 $9.24 $33.37 Registry nurse cost per hour 2,772 $45.34 $13.10 $16.81 $78.79 Professional staff cost per hour 2,775 $43.48 $14.07 $14.42 $87.12 Support staff cost per hour 2,773 $18.99 $5.34 $9.65 $48.30 Case-mix index 2, Open heart surgery available 2, Transplant surgery available 2, Number of radiological services 2, Failure to rescue 2, Infections due to medical care 2, Decubitus ulcer 2, Postoperative respiratory failure 2, Postoperative sepsis 2, Postoperative pulmonary embolism or deep vein thrombosis 2, Risk-adjusted mortality index 2, Teaching hospital 2, Rural location 2, Percent of Medicare discharges 2, Percent of Medicaid discharges 2, RN percent 2, LVN percent 2, AID percent 2, Registry percent 2, For-profit ownership 2, Government ownership 2, California nurse staffing mandate 2, medical-surgical acute units due to data availability. Costs in medical-surgical units are driven by a comparable production process across hospitals, and this finergrained analysis enabled us to avoid bias from unobserved factors in efficiency estimation at the hospital level (Newhouse 1994), and allowed us to estimate the relationship between EMR implementation and efficiency more precisely. Second, we accounted for Table 2. EMR adoption rate (in percentages), N = 2,828 Year EMR_S1 EMR_S2 EMR_S3 CDR DOC EMAR CDS CPOE Notes: EMR_S1 5 EMR Stage 1; EMR_S2 5 EMR Stage 2; EMR_S3 5 EMR Stage 3; CDR 5 clinical data repository; DOC 5 nursing documentation; EMAR 5 electronic medication administration record; CDS 5 clinical decision support; CPOE 5 computerized physician order entry. 114

6 potential bias from unobserved heterogeneity and heteroskedasticity. Unobserved heterogeneity due to omitted variables has been shown to severely bias efficiency scores (Greene 2005; Farsi, Filippini, and Kuenzle 2005). If not accounted for, non-monotonic effects due to heteroskedasticity (Wang 2002) could also bias our estimates of the impacts of firm characteristics on efficiency. Following the standard approach in the literature, we estimated a translog cost function controlling for firm-specific characteristics. By definition, the cost function must be concave, homogeneous of degree one in input prices (PL), and non-decreasing in outputs (Y). We imposed the homogeneity condition by dividing input prices by capital expenses (PK), such that input shares add up to one. The cost function specification was as follows: ln TC it ~a 0 za i zb PK 1 lnðy it Þ it z 1 2 b 2 ðlnðy it ÞÞ 2 z X r m ln PL mit PK m it z 1 X r 2 mn ln PL mit ln PL nit z X m mn PK it k m lnðy it Þ ln PL mit PK it zt a Tzt b T 2 z X m z X s PK it t m T ln PL mit PK it h s Controls sit zu it zv it We used the number of discharges (DIS) in the hospital s medical-surgical units i at time t as the output variable (Y it ). The dependent variable, total cost (TC it ), was the direct operating cost incurred in medical-surgical units i at time t. Our measure of medicalsurgical units cost included variable expenses only and did not include overhead allocation of fixed costs. Similarly, the capital expenses (PK it ) were computed based on the total capital expense attributed to medical-surgical units. 2 The input prices (PL mit ) for labor type m included hourly prices for registered nurses (RN), licensed vocational nurses (LVN), Electronic Medical Records nurse aides (AID), registry nurses (REG), professional staff (PRO), and support staff (SUP). Following prior studies in the literature (e.g., Linna 1998; Rosko et al. 2007; McKay and Deilly 2008; Rosko and Mutter 2008), the controls (Controls sit ) in the cost function included: case-mix index (CMI), presence of open heart surgery (OHEART), presence of transplant services (TRANSP), number of radiological services (RAD_CNT), composite score of in-hospital mortality for conditions (IQC2) derived from the Agency for Healthcare Research and Quality (AHRQ) Inpatient Quality Indicators, teaching hospital status (TEACH), and rural location (RURAL). Since medical-surgical unit costs may be sensitive to nurse staffing (Savitz, Jones, and Bernard 2007), we also included nursing skill mix variables for the percentage of hours provided by RNs (RN_PCT), LVNs (LVN_ PCT), and registry nurses (REG_PCT). Additionally, we included nurse-sensitive patient outcome measures of the hospital s rate of failure to rescue (RFTR), decubitus ulcer (RULCER), infections due to medical care (RINFEC), postoperative respiratory failure (RRESP), postoperative sepsis (RSEP), and postoperative pulmonary embolism or deep vein thrombosis (RDVT) derived from the AHRQ Patient Safety Indicators. 3 We also included a quadratic time trend (T and T 2 ) and interacted time with input price variables to allow for non-hicks-neutral technical change. These time-trend variables represent the output changes that are not accounted for by input usages (e.g., capital and labor), firm-specific factors (e.g., management experience), or environmental factors (e.g., regulations). 4 California s regulation that mandated nurse staffing ratios affected all hospitals starting in April 2005 (Spetz et al. 2009). To control for potential effects of the California nurse staffing mandate, we included a dummy variable (MAN- DATE) that was initialized as zero for years before 2005 and was set to one for year 2005 and after for all hospitals. Finally, a 0 is the constant term and a i is a firm-specific dummy variable. The variables in the cost inefficiency, u it, estimation included: IT variables (EMR stage 115

7 Inquiry/Volume 47, Summer 2010 or presence of specific EMR systems), Medicare and Medicaid shares of discharges (MCAREPCT, MCAIDPCT), and indicators for for-profit (FP) or government (GOVT) ownership. In our study, we considered EMR as a firm-specific factor that influences efficiency, since EMR is not a direct input in delivering health care services in hospital medical-surgical units. 5 Medicare and Medicaid shares represent the payer mix of the hospital and capture external pressures for efficiency associated with payment policy as well as internal pressures for efficiency associated with ownership (Rosko et al. 2007). Because translog cost functions are an approximation of the true cost function, they are more likely to be robust at the point of approximation. Thus, standard practice in the productivity analysis literature is to estimate the translog cost function at the sample mean by normalization (Vita 1990). An associated benefit of normalization is that the coefficients in the estimation can be interpreted as cost elasticities at the sample mean. Since the hospitals included in our sample are diverse (as shown in Table 1), it would be difficult to generalize results from each one. By normalization at the sample mean, our findings are representative of the average hospital in our data set. Researchers have made significant progress over the last decade on the technical and functional specifications of SFA models to address unobserved heterogeneity. After reviewing the relevant literature, we employed a true fixed-effects (TFE) model to account for firm-specific time-invariant heterogeneity in the cost function represented by a i (Greene 2005; Farsi, Filippini, and Kuenzle 2005). To account for technology heterogeneity, our model allows the inefficiency term u it to vary over time. We note that there is an important distinction between heterogeneity in the production model and heterogeneity in the inefficiency model. These two issues have quite different implications for the modeling and estimation of EMR system impacts, and we have distinguished these two specific heterogeneities in our estimation. The fixedeffects specification addresses the heterogeneity in production through a i and the timevarying inefficiency term at the firm level addresses the heterogeneity in inefficiency through u it. Another related issue was heteroskedasticity associated with the variances of inefficiency measurements. We also use a model that addresses heteroskedasticity in inefficiency by parameterizing the variance of the inefficiency term (Wang 2002). Most prior SFA studies assume homoskedasticity for the estimation. However, there is no a priori reason to assume that heterogeneity would be limited to only the mean of inefficiency (Wang 2002). In our study, we relaxed the restriction on uniform variances in order to have a broader impact assessment of EMR systems. Following Wang (2002), we specified a non-negative truncated normal distribution for the inefficiency term u it as follows: u it ~N z m it, s 2 it m it ~c 0 z X c r Z rit, where Z rit is the set of r parameters r that impacts the inefficiency mean m it of hospital i at time t.! s 2 it ~ exp w r Z rit Results To focus specifically on EMR impacts, we have divided the model results into two different tables. Table 3 presents the estimations for the mean of the inefficiency term (m it ). Table 4 presents the estimations for the variance of the inefficiency term (s 2 it ). Models 1 to 3 include the EMR stage variables, and Models 4 to 6 include the specific EMR system variables from the SFA estimation. Results are presented only for estimations for the second year after the start of EMR implementation (results during the first and third years were qualitatively similar). Association of EMR Systems with Inefficiency The estimates of the effects of EMR stages on inefficiency for each specification are presented in Table 3 (Models 1 to 3). Overall, we found that EMR Stage 2 was associated with significantly higher inefficiency scores across X r 116

8 Electronic Medical Records Table 3. Parameter estimations on mean of inefficiency (1) Pooled model EMR stage models Specific systems models (3) (2) Wang (2002) (5) True fixedeffects model with true (4) True fixed- model fixed effects Pooled model effects model (6) Wang (2002) model with true fixed effects EMR Stage (2.33).023 * (2.02).018 (1.62) EMR Stage *** (3.96).031 ** (2.82).033 ** (3.28) EMR Stage * (22.03) (2.19).001 (.06) Clinical data repository.013 (1.08).009 (.66).007 (.50) Nursing documentation (21.67).048 ** (3.16).045 ** (2.83) Electronic medication administration record.071 *** (5.09).029 ** (2.95).025 ** (2.64) Computerized physician order entry * (22.47) (2.39) (2.44) Clinical decision support (2.73).021 (1.86).022 * (2.04) Percent of Medicare discharges.061 (1.28).059 (1.33).044 (1.01).049 (1.13).053 (1.21).048 (1.08) Percent of Medicaid discharges.305 *** (7.06).164 ** (2.63).099 (1.78).313 *** (7.15).131 * (2.12).185 ** (2.66) For-profit ownership *** (27.22).001 (.04).005 (.19) *** (27.26) (2.05) (2.15) Government ownership (21.02) (21.13) *** (25.62) (21.03) * (22.57) (2.31) Constant.345 (.01).099 * (2.29).164 *** (3.84).062 * (2.47).106 * (2.30).089 (1.69) Mean inefficiency score Note: t statistics are in parentheses. * p,.05. ** p,.01. *** p,

9 Inquiry/Volume 47, Summer 2010 Table 4. Parameter estimations on the variance of inefficiency (3) (6) EMR stage model Specific systems model EMR Stage (21.50) EMR Stage (2.27) EMR Stage (.28) Clinical data repository * (22.11) Nursing documentation.790 (1.28) Electronic medication administration record (2.28) Computerized physician order entry (.01) Clinical decision support (2.52) Percent of Medicare discharges (21.35) (21.19) Percent of Medicaid discharges.153 (.19).844 (.67) For-profit ownership.223 (.77).412 (1.31) Government ownership (21.45).438 (.05) Constant *** (25.84) *** (24.85) Note: t statistics are in parentheses. * p,.05. ** p,.01. *** p,.001. the true fixed-effects and Wang (2002) models. In the TFE model, EMR Stage 1 was also associated with significantly higher inefficiency. EMR Stage 3 showed no association with inefficiency in the two models, while the less powerful pooled model suggested a negative association with inefficiency. EMR Stage 2 was associated with higher inefficiency in the pooled model as well. Thus, contrary to expectation, our results suggest that EMR is associated with higher inefficiency in medical-surgical acute settings. We also assessed the association of specific EMR systems with inefficiency (Table 3, Models 4 to 6). These results were mostly consistent with those of the EMR stage models. None of the clinical data repository coefficients from EMR Stage 1 were significant in any model, so the inefficiencies associated with EMR Stage 1 we observed in Model 2 may be attributable to the other ancillary systems. Among the specific systems from EMR Stage 2, we found that both electronic medication administration records and nursing documentation were associated with significantly higher inefficiency; however, the impact of emar on inefficiency was less than that of DOC. For specific systems in EMR Stage 3, clinical decision support had a significant positive association with inefficiency from Model 6. However, computerized physician order entry was found to have a significant negative impact on inefficiency only in the pooled model. In conclusion, we found that most of the inefficiencies were associated with the implementation of nursing documentation, electronic medication administration reports, and clinical decision support. For-profit hospitals did not exhibit significant association with inefficiency across models (except in the pooled models). However, we found that government hospitals were associated with lower inefficiency (models 3 and 5). While this was different from the perception that public hospitals are inefficient, it was not entirely a surprising result. Previous studies on hospital efficiency have led to inconsistent conclusions on ownership structure and efficiency (e.g., Rosko et al. 2007; Farsi and Filippini 2008; Herr 2008). 6 In general, a higher Medicaid percentage was associated with higher inefficiency scores. The estimation results in Table 4 show that inefficiency variances are, in general, not affected by EMR system variables. Only clinical data repository was associated with a lower variance in inefficiency. Association of Hospital Characteristics with Hospital Costs While our focus was on the effects of EMR variables on inefficiencies, it is interesting to note some additional findings from the 118

10 Electronic Medical Records hospital cost function estimations. 7 California s staffing regulation was associated with a positive and significant effect on hospital costs. As expected, hospital case mix and specialized services were associated with higher costs, while rural location was associated with lower costs. The strong positive coefficient values on the time trend variable, T 2, indicated that hospital costs have been increasing over time. Interestingly, hospital costs appeared to be affected in different ways by labor prices. The hourly RN wage was associated with lower hospital costs. The interaction of the number of discharges and RN prices increased costs, and the interaction of discharges with LVN prices decreased costs. Additionally, there was a strong interaction effect for registry nurse prices and time. Among the skill mix variables, registry percentage was associated with higher costs, whereas LVN percentage was associated with lower costs. Alternative Specifications In addition to performing a number of robustness checks, 8 we considered and tested several alternative specifications to the reported models. We tested the Cobb-Douglas specification as a possible alternative to the translog functional form. The significance and signs of the inefficiency coefficients did not change in the Cobb-Douglas models. Additionally, likelihood ratio tests rejected the null hypothesis that the Cobb-Douglas model was an adequate specification. An alternative to the one-stage efficiency estimation in the reported models is to estimate a generalized least squares (GLS) cost function. Using this approach, we estimated the random effects GLS model with the EMR variables included. The results related to EMR were consistent with the reported results of the earlier models. We were concerned that the adverse impacts of EMR systems on efficiencies might be due to the lack of maturity, experience, and knowledge of their use in process improvement. Therefore, early adopters might experience an adverse impact on efficiency, whereas later adopters might have the benefit of adopting processes and systems that are more mature. To test for this possibility, we re-ran the models on a subsample that included only hospitals that implemented EMR systems after The estimation results with this subsample were again consistent with the results of the full sample study. Therefore, we ruled out the possibility that the efficiency loss was only experienced by early adopters. Discussion Projections of improvements in cost efficiency are a major thrust for promoting the implementation of EMR systems. Despite strong interest by policymakers and practitioners, the impact of EMR systems on the cost efficiency of medical-surgical units has remained an open empirical question. Our findings based on 10 years of hospital performance data suggest that EMR implementation was associated with higher cost inefficiency in hospital medical-surgical units. We found that EMR Stages 1 and 2 resulted in greater inefficiencies. While over 35% of nursing practice time is related to documentation (Hendrich et al. 2008), EMR systems have not been shown to reduce the non-valueadded activities related to documentation. Most recent studies have found that the lack of improvement in care contexts is mainly due to excessive waits for computer terminals, system down times, and duplication of documentation (Storfjell et al. 2009). The inefficiencies associated with EMR stages in prior studies are consistent with empirical evidence in our findings. Prominent among EMR Stage 1 systems is the clinical data repository system. Given that a CDR system only consolidates data from a number of multiple ancillary clinical database sources, the lack of findings for the CDR in our analysis is not a surprising result. While a CDR allows clinicians to view patient-related information in a consolidated format, it does not directly change the nurse process or the workflow. On the other hand, the electronic medication administration records system is an integral part of an approach to closed loop prescribing, dispensing, and administration. Our finding that emar had an adverse impact on cost efficiency is further supported by recent evidence of workarounds actions 119

11 Inquiry/Volume 47, Summer 2010 to circumvent the use of IT in individual hospitals (Koppel et al. 2008). Electronic medication administration requires clinical professionals to navigate between several screens to understand medication orders and review treatment history. Franklin et al. (2007) reported increased times for prescribing, medication, and pharmacy service with the introduction of a closed loop medication system that included emar. The unfavorable impact of nursing documentation on efficiency is also consistent with recent studies reported in the literature. Early studies published in the 1990s showed some significant impact of DOC on nurse process efficiency (Poissant et al. 2005). However, among the more recent studies, Wong et al. (2003) reported increased nurse times associated with documentation (consistent with our results from the TFE and Wang models), while Smith et al. (2005) and DesRoches et al. (2008) reported no significant difference in the nurse time spent on electronic documentation (consistent with our results from the pooled model). Limitations To provide a proper context for interpreting the results, a number of limitations associated with this study should be considered. First, although we used panel data for 10 years, we did not control for endogeneity and potential measurement errors. The potential endogeneity issue may be present if a hospital s inefficiency could have influenced its decision to adopt EMR or its extent of EMR adoption. While our findings empirically supported the association between EMR implementation and cost inefficiency, future research with more comprehensive data is needed to confirm the relationship as a causal one. Second, while the fixed-effects specifications control for differences across hospitals, the estimates may remain biased by the presence of time-varying unobservables. Since the actual start date of EMR implementation and integration across systems was not observed, measurement errors may also bias the estimates. A common limitation of SFA models is that the efficiency scores and rankings are sensitive to model specifications. However, as mentioned earlier, we analyzed a number of different model specifications to check for robustness of the results. Since the main objective was to investigate the impact of EMR systems, absolute efficiency scores across the models are less of a concern in interpreting our results than relative efficiency scores. In this regard, our efficiency scores were highly correlated across our model settings. 9 Conclusions The main finding from our analysis is that the purported benefits of EMR systems on cost efficiencies in hospital medical-surgical units were not apparent in our data from California. In fact, we observed that EMR implementation was associated with lower cost efficiency in medical-surgical units. It should be noted that the focus of this study was centered on cost efficiency; it is possible that hospital managers may be inclined to trade off losses in cost efficiency for gains in care quality. There is emerging evidence that EMR systems are associated with higher quality of patient care (Amarasingham et al. 2009). This suggests that policymakers and health care managers should weigh the tradeoffs of cost versus quality implicit in EMR systems implementation more carefully. Notes The authors thank HIMSS Analytics for use of its data, and thank Hung-Jen Wang for technical assistance and use of his Stata programs. 1 The 2007 HIMSS Analytics Database included 381 acute care hospital facilities in California. 2 Capital for EMR investment was reported in a separate non-revenue-producing center for data processing and was not included in the capital measures for the patient revenue-producing center for medical-surgical acute units. 3 Research has shown that some patient safety indicator (PSI) measures may be sensitive to the use of present-on-admission (POA) indicators (Houchens, Elixhauser, and Romano 2008). While the OSHPD data contained POA 120

12 Electronic Medical Records indicators, we opted not to include them in our study for several reasons. First, the validity of the POA indicators in early years was poor and may have improved over time (Coffey, Milenkovic, and Andrews 2006), making them potentially endogenous. Second, although POA is considered essential when hospital quality is the primary focus of the study, we believe that the use of POA may be inappropriate in our context since our primary interest in PSIs was to control for possible cost increases due to the overall level of adverse events. Thus, PSI measures without adjusting for POA would provide a better control for patient burden of illness (pre-existing comorbidities) as well as output quality (acquired complications). 4 Technological changes affecting costs for all hospitals were captured by the time trend variables. 5 The SFA specification does not allow for the same variable to be included in the cost function and in the inefficiency estimation. Thus, the modeler must determine whether the specific variable is likely to affect the production technology or be a determinant of inefficiency (Kumbhakar and Lovell 2003; Coelli et al. 2005; Greene 2008). Following studies in the literature (Lee and Wan 2003; Ancarani, Di Mauro, and Giammanco 2009; Kazley and Ozcan 2009), we viewed EMR as primarily an efficiency driver that enables clinical practitioners to more efficiently capture the information generated in the health care delivery process. 6 For a review of studies on hospital ownership and financial performance, see Shen et al. (2007). 7 Results are available upon request from the corresponding author. 8 As pointed out by Caudill and Ford (1993), heteroskedasticity can lead to overestimation of slope coefficients in the cost function. It has also been shown that heteroskedasticity can lead to underestimation of inefficiency for large firms and overestimation of inefficiency for small firms (Caudill, Ford, and Gropper 1995). In our setting, Model 2 is nested in Model 3 (similarly, Model 5 is nested in Model 6). The likelihood ratio test of the nested models indicated a clear support for the fully specified Wang (2002) model in both cases. 9 Higher inefficiency scores with the pooled models indicate that the true fixed-effects and Wang (2002) models are better able to control for firm-specific heterogeneity when compared to the pooled models (Farsi, Filippini and Kuenzle 2005). The Pearson correlations between the inefficiency scores are available upon request from the corresponding author. We observed that inefficiency scores were highly correlated across the related models. While the pooled model inefficiency scores were nearly uncorrelated with the Wang (2002) and TFE models, the two pooled models were highly correlated with each other. The Wang (2002) model and the TFE models were also more closely related to each other (r 5.97). Pearson correlation coefficients of inefficiency scores between the TFE models (i.e., Models 2 and 5) and between the Wang (2002) models (i.e., Models 3 and 6) were high (r..95). Therefore, even though the inefficiency scores differ (see Table 3), the inefficiency scores obtained from related models were quite consistent. References Amarasingham, R., L. Plantinga, M. Diener- West, D. J. Gaskin, and N. R. Powe Clinical Information Technologies and Inpatient Outcomes. Archives of Internal Medicine 169(2): Ancarani, A., C. Di Mauro, and M. D. Giammanco The Impact of Managerial and Organizational Aspects on Hospital Wards Efficiency: Evidence from a Case Study. European Journal of Operational Research 194(1): Blank, J. L. T., and B. L. Van Hulst Productive Innovations in Hospitals: An Empirical Research on the Relation between Technology and Productivity in the Dutch Hospital Industry. Health Economics 18(6): Blumenthal, D., C. DesRoches, K. Donelan, T. Ferris, A. Jha, R. Kaushal, S. Rao, S. Rosenbaum, and A. Shield Health Information Technology in the United States, 2008: Where We Stand. Princeton, N.J.: Robert Wood Johnson Foundation. Bolton, L. B., C. A. Gassert, and P. F. Cipriano Smart Technology, Enduring Solutions: Technology Solutions Can Make Nursing Care Safer and More Efficient. Journal of Health Information Management 22(4): Bradford, W. D., A. N. Kleit, M. A. Krousel- Wood, and R. N. Re Stochastic Frontier Estimation of Cost Models within the Hospital. Review of Economics and Statistics 83(2): Bresnahan, T. F., E. Brynjolfsson, and L. M. Hitt Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence. Quarterly Journal of Economics 117(1):

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