Michael D. Rosko

Stochastic Frontier Analysis of Hospital Inefficiency: A Review of Empirical Issues and an Assessment of Robustness

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.

A critical analysis of on-scene helicopter transport on survival in a statewide trauma system

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.

The effects of ownership, operating environment, and strategic choices on nursing home efficiency.

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.

Variations in the outcomes of care provided in Pennsylvania nursing homes. Facility and environmental correlates.

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≤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.

Impact of internal and external environmental pressures on hospital inefficiency

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 Jondrows 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.

Inefficiency Differences between Critical Access Hospitals and Prospectively Paid Rural Hospitals

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.

Estimating Hospital Inefficiency: Does Case Mix Matter?

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.

Measuring technical efficiency in health care organizations

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.

Impact of HMO Penetration and Other Environmental Factors on Hospital X-Inefficiency

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.

What Have We Learned From the Application of Stochastic Frontier Analysis to U.S. Hospitals

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.