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This article examines the relationship between hospital profitability and efficiency. A cross-section of 1317 U.S. metropolitan, acute care, not-for-profit hospitals for the year 2015 was employed. We use a frontier method, stochastic frontier analysis, to estimate hospital efficiency. Total margin and operating margin were used as profit variables in OLS regressions that were corrected for heteroskedacity. In addition to estimated efficiency, control variables for internal and external correlates of profitability were included in the regression models. We found that more efficient hospitals were also more profitable. The results show a positive relationship between profitability and size, concentration of output, occupancy rate and membership in a multi-hospital system. An inverse relationship was found between profits and academic medical centers, average length of stay, location in a Medicaid expansion state, Medicaid and Medicare share of admissions, and unemployment rate. The results of a Hausman test indicates that efficiency is exogenous in the profit equations. The findings suggest that not-for-profit hospitals will be responsive to incentives for increasing efficiency and use market power to increase surplus to pursue their objectives.

International audience; While home health care agencies (HHAs) play a vital role in the production of health, little research has been performed gauging their efficiency. Employing a robust approach to data envelopment analysis (DEA) we assessed overall, technical, and scale efficiency on a nationwide sample of HHAs. After deriving the three efficiency measures, we regressed these scores on a variety of environmental factors. We found that HHAs, on average, could proportionally reduce inputs by 28 % (overall efficiency), 23 % (technical efficiency) and 6 % (scale efficiency). For-profit ownership was positively associated with improvements in overall efficiency and technical efficiency and chain ownership was positively associated with global efficiency. There were also state-by-state variations on all the efficiency measures. As home health becomes an increasingly important player in the health care system, and its share of national health expenditures increases, it has become important to understand the cost structure of the industry and the potential for efficiencies. Therefore, further research is recommended as this sector continues to grow.

We compared performance, operating characteristics, and market environments of low- and high-efficiency hospitals in the 37 states that supplied inpatient data to the Healthcare Cost and Utilization Project from 2006 to 2010. Hospital cost-inefficiency estimates using stochastic frontier analysis were generated. Hospitals were then grouped into the 100 most- and 100 least-efficient hospitals for subsequent analysis. Compared with the least efficient hospitals, high-efficiency hospitals tended to have lower average costs, higher labor productivity, and higher profit margins. The most efficient hospitals tended to be nonteaching, investor-owned, and members of multihospital systems. Hospitals in the high-efficiency group were located in areas with lower health maintenance organization penetration and less competition, and they had a higher share of Medicaid and Medicare admissions. Results of the analysis suggest there are opportunities for public policies to support improved efficiency in the hospital sector.

We applied a Malmquist approach to assess levels and change of efficiency and technology in Scottish hospitals. The biased corrected Malmquist index, the efficiency change index, and the technology change index appear to vacillate improving and regressing over the four time intervals with the overall indices (2003–2007). We did not detect steady movement in these measures in one way or another, but we have advanced the literature by employing a time-series trend analysis to gauge changes without the obfuscation by white noise. By using regression, we found a definite and statistically significant trend of improvement, from the first period as it affects the final period of the performance measures. This lends some encouragement to Scottish hospital management that they are doing more with less—which is particularly relevant in times of more austerity in government spending but without a decrease in demand. A longer time period would be necessary in order to ascertain if improvements would be achieved, overall, even with increases and decreases in performance during intervening time periods.

This paper examines the impact of an endogenous cost function variable on the inefficiency estimates generated by stochastic frontier analysis (SFA). The specific variable of interest in this application is endogenous quality in nursing homes. We simulate a dataset based on the characteristics of for-profit nursing homes in California, which we use to assess the impact on SFA-generated inefficiency estimates of an endogenous regressor under a variety of scenarios, including variations in the strength and direction of the endogeneity and whether the correlation is with the random noise or the inefficiency residual component of the error term. We compare each of these cases when quality is included and excluded from the cost equation. We provide evidence of the impact of endogeneity on inefficiency estimates yielded by SFA under these various scenarios and when the endogenous regressor is included and excluded from the model.

Frontier techniques, including data envelopment analysis (DEA) and stochastic frontier analysis (SFA), have been used to measure health care provider efficiency in hundreds of published studies. Although these methods have the potential to be useful to decision makers, their utility is limited by both methodological questions concerning their application, as well as some disconnect between the information they provide and the insight sought by decision makers. The articles in this special issue focus on the application of DEA and SFA to hospitals with the hope of making these techniques more accurate and accessible to end users. This introduction to the special issue highlights the importance of measuring the efficiency of health care providers, provides a background on frontier techniques, contains an overview of the articles in the special issue, and suggests a research agenda for DEA and SFA.

BACKGROUND Since the early 1990s, specialty hospitals have been continuously increasing in number. A moratorium was passed in 2003 that prohibited physicians' referrals of Medicare patients to newly established specialty hospitals if the physician has ownership stakes in the hospital. Although this moratorium expired in effect in 2007, many are still demanding that the government pass new policies to discourage the proliferation of specialty hospitals. PURPOSE This study aimed at examining the regulatory and environmental forces that influence specialty hospitals founding rate. Specifically, we use the resource partitioning theory to investigate the relationship between general hospitals closure rates and the market entry of specialty hospitals. This study will help managers of general hospitals in their strategic thinking and planning. METHODOLOGY We rely on secondary data resources, which include the American Hospital Association, Area Resource file, census, and Center for Medicare and Medicaid Services data, to perform a longitudinal analysis of the founding rate of specialty hospital in the 48 states. Specifically, we use the negative binomial generalized estimating equation approach available through Stata 9.0 to study the effect of general hospitals closure rate and environmental variables on the proliferation of specialty hospitals. FINDINGS Specialty hospitals founding rate seems to be significantly related to general hospitals closure rates. Moreover, results indicate that economic, supply, regulatory, and financial conditions determine the founding rate of specialty hospitals in different states. PRACTICE IMPLICATIONS The results from this study indicate that the closure of general hospitals creates market conditions that encourage the market entry of specialized health care delivery forms such as specialty hospitals. Managers of surviving general hospitals have to view the closure of other general hospitals not just as an opportunity to increase market share but also as a threat of competition from new forms of health care organizations.

We seek to determine whether hospitals providing high-quality care are associated with different environmental and organizational factors than hospitals providing lower quality of care. We address this question using congestion analysis, which is an extension of data envelopment analysis (DEA). We employ a rich data set comprised of urban hospitals operating in 34 states in 2004. The data include measures of inputs, outputs, case mix, and indicators of patient safety, which we use to derive separate best-practice frontiers for high-, medium-, and low-quality hospitals. By comparing the frontier of the high-quality hospitals with the lower quality hospital frontiers, we find that higher quality hospitals perform better on the criteria of overall technical efficiency and pure technical efficiency than lower quality institutions. We found that a variety of characteristics, including expenditures, percentage of admissions that are births, Medicaid share of admissions, and government ownership are associated with differences in performance among the medium-quality hospitals. Expenditures and birth rate were associated with differences within low-quality hospital performance, as were variables related to scope and depth of activities, such as capital expenditures per bed and teaching status. We find evidence that hospitals should be able to improve both the quality and efficiency of their operations by adopting, where possible, the characteristics of the highest performing institutions.

This article focuses on the lessons learned from stochastic frontier analysis studies of U.S. hospitals, of which at least 27 have been published. A brief discussion of frontier techniques is provided, but a technical review of the literature is not included because overviews of estimation issues have been published recently. The primary focus is on the correlates of hospital inefficiency. In addition to examining the association of market pressures and hospital inefficiency, the authors also examined the relationship between inefficiency and hospital behavior (e.g., hospital exits) and inefficiency and other measures of hospital performance (e.g., outcome measures of quality). The authors found that consensus is emerging on the relationship of some factors to hospital efficiency; however, further research is needed to better understand others. The application of stochastic frontier analysis to specific policy issues is in its infancy; however, the methodology holds promise for being useful in certain contexts.

Objective. To assess the impact of employing a variety of controls for hospital quality and patient burden of illness on the mean estimated inefficiency and relative ranking of hospitals generated by stochastic frontier analysis (SFA). Study Setting. This study included urban U.S. hospitals in 20 states operating in 2001. Data Design/Data Collection. We took hospital data for 1,290 hospitals from the American Hospital Association Annual Survey and the Medicare Cost Reports. We employed a variety of controls for hospital quality and patient burden of illness. Among the variables we used were a subset of the quality indicators generated from the application of the Patient Safety Indicator and Inpatient Quality Indicator modules of the Agency for Healthcare Research and Quality, Quality Indicator software to the Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases. Measures of a component of patient burden of illness came from the application of the Comorbidity Software to HCUP data. Data Analysis. We used SFA to estimate hospital cost-inefficiency. We tested key assumptions of the SFA model with likelihood ratio tests. Principal Findings. The measures produced by the Comorbidity Software appear to account for variations in patient burden of illness that had previously been masquerading as inefficiency. Outcome measures of quality can provide useful insight into a hospital's operations but may have little impact on estimated inefficiency once controls for structural quality and patient burden of illness have been employed. Conclusions. Choices about controlling for quality and patient burden of illness can have a nontrivial impact on mean estimated hospital inefficiency and the relative ranking of hospitals generated by SFA.

Ukraine’s recent elections revealed deep divisions between eastern regions, which favored central economic planning, and western regions, which preferred more free market reforms. This study compares polyclinics in Ukraine to see if the inflexibility of Soviet-style planned economies results in lower economic efficiency in eastern regions. Using data from two geopolitical regions, Data Envelopment Analysis (DEA) scores for polyclinic efficiencies are modeled as a function of demographic and economic determinants. Surprisingly, results indicate that polyclinics in western Ukraine are less efficient. Possible explanations, including case mix intensity, responsiveness to local preferences, physician entrepreneurial behavior and a legacy of inequitable funding, are discussed.

Background Efficiency has emerged as a central goal to the operations of health care organizations. There are two competing perspectives on the relationship between efficiency and organizational performance. Some argue that organizational slack is a waste and that efficiency contributes to organizational performance, whereas others maintain that slack acts as a buffer, allowing organizations to adapt to environmental demands and contributing to organizational performance. Purposes As value-based purchasing becomes more prevalent, health care organizations are incented to become more efficient and, at the same time, improve their patients' experiences and outcomes. Unused slack resources might facilitate the timely implementation of these improvements. Building on previous research on organizational slack and inertia, we test whether efficiency and other organizational factors predict organizational effectiveness in improving Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings. Methodology We rely on data from the American Hospital Association and HCAHPS. We estimate hospital cost-efficiency by Stochastic Frontier Analysis and use regression analysis to determine whether efficiency, competition, hospital size, and other organizational factors are significant predictors of hospital effectiveness. Findings Our findings indicate that efficiency and hospital size have a significant negative association with organizational ability to improve HCAHPS ratings. Practice implications Although achieving organizational efficiency is necessary for health care organizations, given the changes that are currently occurring in the U.S. health care system, it is important for health care managers to maintain a certain level of slack to respond to environmental demands and have the resources needed to improve their performance.

Ukraine's history has given it a split personality (e.g. divergent cultural influences on economic and managerial behavior), as was observed in the recent political developments both prior to and following the December 2004 elections. Eastern regions were heavily influenced by Russo-Soviet rule, while western regions have more of a European outlook. This study, which is largely exploratory, compares recent trends in hospital efficiency in Ukraine to see if this split personality manifests itself in differential rates of improvement. Given the inflexibility of Soviet-style planned economies, it is hypothesized that western regions will show greater improvement in economic efficiency that can be attributed to higher levels of managerial and medical entrepreneurship. Data for this study comes from three oblasts (i.e. geopolitical regions), one in the west and two in the east, spanning from 1997 to 2001. Data envelopment analysis (DEA) was used to estimate technical efficiency for the hospitals. After correcting for bias, a second-stage Tobit regression was estimated. Results indicate that hospitals in the west improved efficiencies, while those in the east stayed constant. These western areas of the nation, being more amenable to western management and medical 'business' practice, may be quicker to pick up on new techniques to increase healthcare delivery efficiencies. This may stem from the more limited effects of a shorter history of incorporation into a Soviet-style planned and controlled economy in which individual decision-making and entrepreneurship was suppressed in favor of central decision-making by the state.

This research summarizes an analysis of the impact of environment pressures on hospital inefficiency during the period 1990-1999. The panel design included 616 hospitals. Of these, 211 were academic medical centers and 415 were hospitals with smaller teaching programs. The primary sources of data were the American Hospital Association's Annual Survey of Hospitals and Medicare Cost Reports. Hospital inefficiency was estimated by a regression technique called stochastic frontier analysis. This technique estimates a "best practice cost frontier" for each hospital that is based on the hospital's outputs and input prices. The cost efficiency of each hospital was defined as the ratio of the stochastic frontier total costs to observed total costs. Average inefficiency declined from 14.35% in 1990 to 11.42% in 1998. It increased to 11.78% in 1999. Decreases in inefficiency were associated with the HMO penetration rate and time. Increases in inefficiency were associated with for-profit ownership status and Medicare share of admissions. The implementation of the provisions of the Balanced Budget Act of 1997 was followed by a small decrease in average hospital inefficiency. Analysis found that the SFA results were moderately sensitive to the specification of the teaching output variable. Thus, although the SFA technique can be useful for detecting differences in inefficiency between groups of hospitals (i.e., those with high versus those with low Medicare shares or for-profit versus not-for-profit hospitals), its relatively low precision indicates it should not be used for exact estimates of the magnitude of differences associated with inefficiency-effects variables.

This study investigates the factors associated with hospital provision of prevention and health promotion services. The authors conceptualize the provision of these services as a hospital response to the community health concerns of environmental stakeholders. The response depends on hospital recognition and interpretation of institutional and resource dependence pressures and is related to interorganizational linkages, resource dependencies, and information processing structure. Data for the study came from 3,453 U.S. hospitals. The authors found that hospital provision of prevention and health promotion services is positively related to alliance and network membership, the diffusion of such services among other area hospitals, the use of community health status information, and hospital size. Also, for-profit hospitals provide fewer prevention and health promotion services than not-for-profit hospitals. These findings have policy and management implications.

The benefits attributed to heath networks have made them a popular form of interorganizational collaboration in the health care industry. However, studies of the hospital industry have suggested th...

This study examined the impact of managed care and other environmental factors on hospital inefficiency in 1631 US hospitals during the period 1990-1996. A panel, stochastic frontier regression model was used to estimate inefficiency parameters and inefficiency scores. The results suggest that mean estimated inefficiency decreased by about 28% during the study period. Inefficiency was negatively associated with health maintenance organization (HMO) penetration and industry concentration. It was positively related with Medicare share and for-profit ownership status.

This study used stochastic frontier analysis to study variations in inefficiency in US hospitals. Cost‐inefficiency (i.e., differences between best practice and actual expenses) is assumed to be affected by ownership status, competition, regulatory pressure, and market demand conditions. The level of analysis is the hospital (n = 3,262) and data for 1994 were used. The market was defined as the county in which the hospital was located. A two‐stage approach was used in the analysis. In the first stage, translog cost‐functions were estimated. Outputs used in the cost function analysis include inpatient discharges, post‐admission days, outpatient visits, medical education, and case‐mix index. Following Jondrow's technique, inefficiency scores (i.e., the difference between predicted least costs and actual costs) were estimated. Inefficiency estimates were not sensitive to changes in assumptions about the distribution of the error term. In the second stage, the estimated inefficiency scores were used as dependent variables to test hypotheses about the impact of internal and external environmental pressures on cost‐inefficiency. Since the distribution of the estimated inefficiency scores was censored, Tobit equations were estimated. The second stage analysis found that measured inefficiency was negatively related with industry concentration (Herfindahl index), public payment policy, and unemployment rate and positively related with for‐profit status.

A two-stage approach is used in a stochastic frontier analysis of the factors affecting hospital efficiency. In the first stage, a translog cost-function is used to estimate inefficiency scores. In the second stage, inefficiency scores are regressed against independent variables to test hypotheses that come from X-inefficiency Theory. The study was based on 1989 data for 195 Pennsylvania acute care hospitals. This data base was chosen because of the availability of patient-level severity of illness data, a measure of output that is not available from most data sources. The stochastic frontier analysis models estimated mean inefficiency scores that ranged from 0.075 to 0.180. The addition of the DRG case mix index (CMI) reduced estimated inefficiency by more than 50%. The incremental effect of a severity of illness variable to an equation with CMI was very small. The second-stage results suggest inefficiency and are inversely associated with regulatory pressures and industry concentration.

Background: Recently, questions have been raised regarding the effectiveness of helicopters in trauma care. We conducted a retrospective study to evaluate the effect of on-scene helicopter transport on survival after trauma in a statewide trauma system. Methods: Data were obtained from a statewide trauma registry of 162,730 patients treated at 28 accredited trauma centers. Patients transported from the scene by helicopter (15,938) were compared with those transported by ground with advanced life support (ALS) (6,473). Interhospital transfers and transports without ALS were excluded. Statistical analysis was performed using one-way analysis of variance and logistic regression. Results: Patients transported by helicopter were significantly (p < 0.01) younger, were more seriously injured. and had lower blood pressure. They were also more likely to be male and to have systolic blood pressure < 90 mm Hg. Logistic regression analysis revealed that when adjusting for other risk factors, transportation by helicopter did not affect the estimated odds of survival. Conclusion: A reappraisal of the cost-effectiveness of helicopter triage and transport criteria, when access to ground ALS squads is available, may be warranted.

Two policy changes in 1988, one administrative and one legislative, allowed greater Medicare coverage of subacute care in skilled nursing facilities (SNFs). The Medicare Catastrophic Coverage Act (MCCA) of 1988, in conjunction with an administrative directive, or transmittal, from the Health Care Financing Administration (HCFA), changed the Medicare SNF benefit structure substantially. In this study, we specified a simultaneous equation system to explain the effects of the benefit changes on Medicare use. The results suggest that the two policychanges increased Medicare use in Pennsylvania SNFs substantially; however, the increase was associated with facility and case mix characteristics, which suggest that the increase was largely attributable to reclassification of current patients from other payer categories, Medicaid and self-pay, rather than new admissions. The effects of the MCCA and the HCFA transmittal on increased Medicare use were unanticipated and have important implications for the way in which subacute care is defined and financed infuture benefit discussions.

The purpose of this study was to examine behavioral differences between for-profit (FP) and not-for-profit (NFP) nursing homes. Previous studies have failed to establish consistent behavioral differences. This study uses a simultaneous equation model to control for potential endogeneity among system variables, with model parameters estimated using 3SLS. The study provides evidence that NFPs provide significantly higher quality of care to Medicaid beneficiaries and to self-pay residents than do FPs, as evidenced by better staffing and better outcomes among nursing homes with residents at higher risk for adverse outcomes.

The Institute of Medicine recommends the use of key quality indicators (resident outcomes that suggest the presence of good or bad care) as tools for interfacility comparison of the quality of care provided in nursing homes. In the spirit of this recommendation, the Health Care Financing Administration released selected results from its Medicare and Medicaid Annual Certification Survey to guide consumers in purchasing nursing home services. However, because this information is published without adjustment for resident characteristics that can influence the outcomes of nursing home care, its utility for policy-makers or consumers interested in variation in nursing home quality is limited. This research study utilizes federal and state survey data to evaluate variations in mortality, pressure ulcers, urethral catheterization, and physical restraint use in 438 Medicare-certified skilled nursing care facilities in Pennsylvania. A standardization function adjusting for resident characteristics known to influence outcomes is developed and estimated by ordinary least squares regression. Results suggest considerable variation in rates for these indicators across Pennsylvania nursing home facilities. Alternative uses for risk-adjusted key quality indicators as tools for improving nursing home quality and assisting potential consumers of nursing home care in making better informed choices are considered.

Cost-function analysis of hospitals has been criticized for not including severity adjustments. We tested a scalar hospital-specific severity index, derived from Admission MedisGroup Scores. Alternative versions (i.e., linear/nonlinear) of the index were evaluated by estimating cost functions on a sample of 201 Pennsylvania hospitals. The scalar index was a strong predictor of costs. The results also suggest that the omission of a severity variable in a hospital cost function may cause a specification error.

U.S. Hospitals rely heavily on debt financing to fund major capital investments. Hospital efficiency is at least partly determined by the amount and quality of plant and equipment it uses. As such, a hospital's access to debt and credit rating may be related to its efficiency. This study explores this relationship using a broad sample of hospitals and associated bond issuance histories. Employing stochastic frontier analysis (SFA), we measure cost inefficiency to gauge the impact of debt issuance and debt rating. We find that hospitals with recent bond issues were less inefficient. Although we do not find a perfectly linear relationship between debt rating and inefficiency, we have evidence that hints at such a relation. Finally, we find an increase in inefficiency in the years following bond issues, consistent with the possibility of a debt death spiral.

The Medicare prospective payment system (PPS) contains incentives for hospitals to improve efficiency by placing them at financial risk to earn a positive margin on services rendered to Medicare patients. Concerns about the financial viability of small rural hospitals led to the implementation of the Medicare Rural Hospital Flexibility Program (Flex Program) of 1997, which allows facilities designated as critical access hospitals (CAHs) to be paid on a reasonable cost basis for inpatient and outpatient services. This article compares the cost inefficiency of CAHs with that of nonconverting rural hospitals to contrast the performance of hospitals operating under the different payment systems. Stochastic frontier analysis (SFA) was used to estimate cost inefficiency. Analysis was performed on pooled time-series, cross-sectional data from thirty-four states for the period 1997-2004. Average estimated cost inefficiency was greater in CAHs (15.9 percent) than in nonconverting rural hospitals (10.3 percent). Further, there was a positive association between length of time in the CAH program and estimated cost inefficiency. CAHs exhibited poorer values for a number of proxy measures for efficiency, including expenses per admission and labor productivity (full-time-equivalent employees per outpatient-adjusted admission). Non-CAH rural hospitals had a stronger correlation between cost inefficiency and operating margin than CAH facilities did.

To use an advance in data envelopment analysis (DEA) called congestion analysis to assess the trade-offs between quality and efficiency in U.S. hospitals.Urban U.S. hospitals in 34 states operating in 2004.Input and output data from 1,377 urban hospitals were taken from the American Hospital Association Annual Survey and the Medicare Cost Reports. Nurse-sensitive measures of quality came from the application of the Patient Safety Indicator (PSI) module of the Agency for Healthcare Research and Quality (AHRQ) Quality Indicator software to State Inpatient Databases (SID) provided by the Healthcare Cost and Utilization Project (HCUP).In the first step of the study, hospitals' relative output-based efficiency was determined in order to obtain a measure of congestion (i.e., the productivity loss due to the occurrence of patient safety events). The outputs were adjusted to account for this productivity loss, and a second DEA was performed to obtain input slack values. Differences in slack values between unadjusted and adjusted outputs were used to measure either relative inefficiency or a need for quality improvement.Overall, the hospitals in our sample could increase the total amount of outputs produced by an average of 26 percent by eliminating inefficiency. About 3 percent of this inefficiency can be attributed to congestion. Analysis of subsamples showed that teaching hospitals experienced no congestion loss. We found that quality of care could be improved by increasing the number of labor inputs in low-quality hospitals, whereas high-quality hospitals tended to have slack on personnel.Results suggest that reallocation of resources could increase the relative quality among hospitals in our sample. Further, higher quality in some dimensions of care need not be achieved as a result of higher costs or through reduced access to health care.

The rising cost of health care has created great interest in developing methods to increase the efficiency of health care organizations. Despite this interest most analyses of prospective payment and other programs designed to control expenditures have examined costs and not efficiency. This article examines a new technique--data envelopment analysis (DEA)--that facilitates the conduct of efficiency studies. The utility of DEA is analyzed by comparing this technique with other methods used to measure efficiency, by discussing the application of DEA in the health care industry and by assessing the validity of results from DEA studies. The article concludes with an assessment of the strengths and weaknesses of DEA and suggestions for refining this technique.

The New Jersey all-payer prospective payment system compensates hospitals for charity care and bad debts. This study examines its impact on the provision of care to self-pay patients. Self-pay patients include two types of uninsured individuals: (1) patients who cannot afford to pay their bill and (2) more affluent patients who can afford to pay but who evade collection. Using data for the period 1979–85, the study employed a sample of seventy-nine New Jersey hospitals that entered the all-payer system during the years 1980–82. A regression equation, which included independent variables to control for the community's pool of uninsured residents and the hospital's share of this pool, was estimated for the number of self-pay discharges. The results indicate that the volume of care provided to self-pay patients increased when the New Jersey all-payer system was introduced. The results also show that teaching hospitals and facilities in urban areas discharge a disproportionately large number of self-pay patients. Analysis of the operating margin ratio suggests that the all-payer system helped to restore the financial viability of hospitals that tend to provide larger amounts of services to the uninsured.

Twenty stochastic frontier analysis (SFA) studies of hospital inefficiency in the United States were analyzed. Results from best-practice methods were compared against previously used methods in hospital studies to ascertain the robustness of SFA in estimating cost inefficiency. To compare past studies and analyze new data, SFA methods were varied by (a) the assumptions of the structure of costs and distribution of the error term, (b) inclusion of quality and product descriptor measures, and (c) use of simultaneous and two-stage estimation techniques. SFA results were relatively insensitive to several model variations.

The primary objective of this study is to assess whether systematic differences in inefficiency are associated with hospital membership in different types of systems. We employed the Battese/Coelli simultaneous stochastic frontier analysis (SFA) technique to estimate hospital cost inefficiency. Mean estimated inefficiency was 8.42%. Membership in different types of systems was related to estimated cost inefficiency (p < .05). Compared to hospitals that were members of centralized health systems, membership in centralized physician/insurance or decentralized systems was associated with decreased inefficiency; membership in independent systems was associated with increased inefficiency.

Rationale: US Hospitals rely heavily on debt financing to fund major capital investment. Hospital efficiency is at least partly determined by the amount and quality of plant and equipment it uses. As such, a hospital's access to capital and credit rating may be related to its efficiency. Objectives: This paper measures the relationship between hospital efficiency, debt issuance and debt ratings. It further explores the direction of these relationships. Methodology: Prior research indicates the possibility of a debt death spiral, wherein inefficient organizations suffer lower credit ratings which reduce their access to capital, making it harder to purchase new equipment that could make them more efficient, effectively perpetuating and deepening the cycle. Using information on US hospitals and their bond issuance decisions, this study attempts to determine if there is evidence of such death spirals. This study collects information on approximately 1,600 US hospitals, linking it to bond issuance and Medicare Cost Report data. A Heckman correction for selection is implemented because some hospitals may choose not to issue bonds. Stochastic frontier analysis is used to estimate hospital cost inefficiency. The independent variables include outputs (admissions, outpatient visits, inpatient days in non-acute care units and teaching activities), input prices (price of capital and area wage rate), and product mix adjusters (Medicare case mix index, outpatient surgery mix, risk adjusted mortality rate, the use of high-tech services and the portion of emergency outpatient visits). Inefficiency effects variables include ownership status, Herfindahl-Hirshman Index, payer mix, network affiliation, HMO penetration, bond insurance and bond rating. While technically a cross-sectional analysis, this study uses data that includes hospital bond issues lagged as much as three years behind the study period. As such, it explores the relationship between past period bond rating and current period productivity. Since higher bond ratings are associated with lower interest expense, this may enable hospitals to invest more in capital, which will help improve efficiencies. Alternatively, hospitals might have to earn those higher ratings with prior period efficiency. Results Higher bond ratings are associated with lower cost inefficiency. There is also a positive association between issuance and efficiency, indicating that hospitals that have issued debt recently have seen lower cost inefficiency. This study also finds that inefficiency increases with the passage of time since the last bond issuance, introducing at least two possible explanations. It could indicate that the efficiency benefits of capital expansion are short-lived, lasting just a few years. Alternatively, such findings would also occur if hospitals sought to improve efficiency prior to issuance in order to earn better ratings.

There were 4,919 registered, short-term, community hospitals in the 2004 American Hospital Association (AHA) Annual Survey of Hospitals; 60 percent of those hospitals were non-profit (NP), 23 percent of them were public (non-federal government owned and operated), and 17 percent were for-profit (FP). In general, while the absolute number of hospitals in the United States has decreased in recent years, the share of hospitals that are FP has increased. For example, in 1997, the AHA reported 5,057 registered, short-term, community hospitals, of which 59 percent were NP, 25 percent were public, and 16 percent were FP.

Uncompensated care can create financial difficulties for hospitals. The problem is likely to worsen as the number of individuals lacking health insurance continues to grow. The objective of this study is to measure how uncompensated care affects hospitals' ability to provide the services for which they do receive compensation. Applying output-based data envelopment analysis (DEA) under various assumptions on the disposability of outputs to a sample of Pennsylvania hospitals, we find that, on average, hospitals could have produced 7% more output if they had all operated on the best-practice frontier and that uncompensated care reduced the production of other hospital outputs by 2%. Thus, even if hospitals were to operate efficiently, they might still face financial distress as a result of providing uncompensated care. The findings in our study suggest that policy makers should continue looking at ways to increase funding to hospitals providing uncompensated care while not distorting economic incentives to reduce excessive costs.

Background: Growing reliance on service provision through systems and networks creates the need to better understand the nature of the relationship between service collaboration and hospital performance and the conditionsthat affect this relationship. Objective: We examine 1) the effects of service provision through health systems and health networks on hospital cost performance and 2) the moderating effects of market conditions and service differentiation on the collaboration-cost relationship. Research Design: We used moderated regression analysis to test the direct and moderating effects. Data on 1368 private hospitals came from the 1998 AHA Annual Survey, Medicare Cost Reports, and Solucient. Measures: Service collaboration was measured as the proportion of hospital services provided at the system level and at the network level. Market conditions were measured by the levels of managed care penetration and competition in the hospital's market. Results: The proportion of hospital services provided at the system level had a negative relationship with hospital cost. The relationship was curvilinear for network use. Degree of managed care penetration moderated the relationship between network-based collaboration and hospital cost. Conclusion: The benefits of service collaboration through systems and networks, as measured by reduced cost, depend on degree of collaboration rather than mere membership. In loosely structured collaborations such as networks, costs reduce initially but increase later as the extent of collaboration increases. The effect of network-based collaboration is also tempered by managed care penetration. These effects are not seen in more tightly integrated forms such as systems.

A significant part of the restructuring in the health care industry has involved hospitals joining health networks and health systems. While the proclaimed purpose of this strategy has been to improve hospital performance, studies have found that not all member hospitals reap the desired outcomes. Variations in performance have been linked to, among other things, service provision at the network/system level versus individual hospital level. This study examined the impact of network and system use to provide services on hospital X-inefficiency (i.e., the difference between actual and optimal costs) in a national sample (n = 1,368) of U.S. urban, general, hospitals. Stochastic frontier analysis (SFA) revealed a mean hospital X-inefficiency of 14.85%. Results suggest that hospitals providing a moderate to high proportion of services at the network or system level were more efficient than hospitals that did not use networks or systems for service provision. Low users of networks or systems and nonusers had comparable levels of efficiency.

The need for uncompensated care has increased during a period in which hospitals are confronted with public and private-sector fiscal pressures. Using a panel design (1995--1998) on Pennsylvania private, not-for-profit general hospitals, we found the provision of uncompensated care is positively associated with financial surpluses, the provision of uncompensated care by neighboring hospitals, bed capacity, proportion of outpatient visits that are emergency, and the unemployment rate (a proxy for need for uncompensated care). Other analysis found that the provision of uncompensated care was not associated with operating surplus, except in hospitals that provide very large amounts of uncompensated care. Provision of services to Medicaid patients and HMO penetration had a negative impact on profitability.

A panel design was used to analyze changes in performance variables related to profitability, volume, and efficiency in a national sample of major teaching hospitals from 1990 to 1999. After steady increases beginning in 1990, the average total margin ratio peaked in 1996 and by 1999 it declined by almost 50 percent. Average operating margins were negative every year. Major teaching hospitals responded to financial pressures by expanding outpatient activity, reducing length of stay, downsizing inpatient capacity, and increasing labor productivity. Membership in multi-hospital systems increased by over 70 percent through the 1990s. Increases in average real cost per adjusted admission peaked in 1994 and fell by 6 percent from 1994 to 1999.

This study examined the impact of health maintenance organization (HMO) market penetration and other internal and external environmental factors on hospital X-inefficiency in a national sample (N = 1,966) of urban U.S. hospitals in 1997. Stochastic frontier analysis, a frontier regression technique, was used to measure X-inefficiency and estimate parameters of the correlates of X-inefficiency. Log-likelihood restriction tests were used to test a variety of assumptions about the empirical model that guided its selection. Average estimated X-inefficiency in study hospitals was 12.96 percent. Increases in managed care penetration, dependence on Medicare and Medicaid, membership in a multihospital system, and location in areas where competitive pressures and the pool of uncompensated care are greater were associated with less X-inefficiency. Not-for-profit ownership was associated with increased X-inefficiency.

Crafting a payment mechanism for hospitals that provides for the legitimate operating needs of efficient institutions is an enduring health policy dilemma. The Prospective Payment System used by Medicare and some other payers in the US has been criticized for not adjusting for differences in severity of illness within diagnosis-related groups (DRGs). Previous studies have examined the relationship between profitability and severity of illness at the hospital level. This study examines the relationships between severity of illness and cost, revenue, and profit at the patient level. Two measures of severity (disease stage and number of unrelated diseases) were significant predictors of cost per case, and often had better predictive power than DRGs. In most instances, payers did not compensate adequately for severity so that higher values for the severity variables resulted in financial losses for the hospital.

This article reports on a study of the labor efficiency of 461 nursing homes located in Pennsylvania. Data envelopment analysis was used to estimate efficiency scores. Tobit equations were estimated for the entire sample and for subsamples consisting of for-profit (FP) and not-for-profit (NFP) nursing homes. The authors found that the major factors explaining efficiency were managerial and environmental characteristics such as ownership, occupancy rate, size, payment source, wage rate, and per capita income, rather than quality characteristics of nursing homes. Analysis of the FP and NFP subsamples suggests that many NFP homes may respond to environmental pressures by increasing their efficiency, whereas FP homes tend to operate at a high level of efficiency irrespective of environmental and regulatory pressures.

This study applies Porter's model of competitive advantage to the nursing home industry. Discriminant analysis is used to identify organizational and environmental characteristics associated with nursing homes which have demonstrated valued strategic outcomes, and to distinguish the more successful nursing homes from their rivals. The results of the discriminant analysis suggest that nursing homes with superior payer mix outcomes are distinguishable from their less successful rivals in areas associated with a focused generic strategy. The study suggests that nursing homes which are better staffed, of smaller size and lower price are more likely to achieve high levels of self-pay utilization. Independent living units, continuing care retirement communities in particular, are likely to act synergistically with nursing home organizational characteristics to enhance competitive advantage by linking the value chain of the nursing home to that of retirement housing. Nursing homes with higher proportions of Medicare were found to provide a unique product when compared to their rivals. Profit status does not discriminate better self-pay strategic utilization, but for-profit facilities are more likely to pursue a Medicare strategy. Concern was raised that, as nursing homes become more strategically oriented, Medicaid access may become more problematic.

Congress intended the Medicare Catastrophic Coverage Act (MCCA) of 1988 to reduce the risk for illness-related catastrophic financial losses in the elderly. The act was short-lived, facing repeal just one year after passage. Many elderly persons were convinced that the costs of the program outweighed the benefits. However nursing home payment provisions of the MCCA may have affected out-of-pocket expenses paid by the elderly for long-term care more than consumers realized at the time of repeal. A transmittal memorandum, issued by the Health Care Financing Administration independent of Congressional action, enhanced consumers' ability to qualify for Medicare nursing home benefits. We investigated the effects of the Medicare policy change on nursing home payer mix and out-of-pocket expenses in 489 Pennsylvania nursing homes. We found that substantial shifts in payer mix from self-pay to Medicare payment sources occurred, reducing out-of-pocket expenses. Unfortunately the debate over the MCCA's repeal did not include discussion of the improved nursing home benefit structure. These findings, and the fate of the MCCA legislation, reinforce the importance of comprehensive information and clear communication in promoting health care reform.

We examined the relationship between variations in intra-DRG severity of illness classifications and hospital profitability. Unlike in previous studies, we created a direct hospital-level measure of severity, formed from MedisGroup severity scores. We estimated separate regression equations for total margin, operating margin, net revenue per admission, and expense per admission. We examined data for 201 Pennsylvania hospitals and found that hospital profits were inversely related to the severity of illness index. Expense per admission was positively related to severity; however the relationship between severity and net revenue per admission was not significant. The results suggest that hospitals with a more severe case mix may not recover the full costs of providing services. Thus payment reform should include adjustments for severity of illness.

This research study utilizes indicators from federal and state surveys to evaluate variation in outcomes in 438 Medicare certified skilled nursing care facilities in Pennsylvania. First, a standardization function adjusting for patient characteristics known to influence outcomes was developed and estimated. The relationships between organizational and environmental characteristics and the chosen outcome indicators (i.e., differences between the actual and expected rate of mortality, pressure ulcers, urethral catheterization and physical restraints) were then analyzed by weighted least squares regression. Results suggest considerable interfacility variation in rates for these outcome indicators. A portion of this variation is significantly attributable to resident characteristics (P < or = 0.05). However, variation in outcomes in Pennsylvania facilities is also associated with facility characteristics (e.g., size and for-profit status), and environmental characteristics (e.g., per capita income and bed supply). Implications for nursing home management and policy are considered.