Address correspondence to Ryan L. Mutter, Ph.D., Agency for Healthcare Research and Quality, Center for Delivery, Organization and Markets, Rockville, MD; e-mail: email@example.com. Michael D. Rosko, Ph.D., is with the Graduate Program in Health and Medical Services Administration, School of Business Administration, One University Place, Widener University, Chester, Pennsylvania. Herbert S. Wong, Ph.D., is with the Agency for Healthcare Research and Quality, Center for Delivery, Organization and Markets, Rockville, MD.
Measuring Hospital Inefficiency: The Effects of Controlling for Quality and Patient Burden of Illness
Article first published online: 8 SEP 2008
No claim to original U.S. government works. © Health Research and Educational Trust
Health Services Research
Volume 43, Issue 6, pages 1992–2013, December 2008
How to Cite
Mutter, R. L., Rosko, M. D. and Wong, H. S. (2008), Measuring Hospital Inefficiency: The Effects of Controlling for Quality and Patient Burden of Illness. Health Services Research, 43: 1992–2013. doi: 10.1111/j.1475-6773.2008.00892.x
- Issue published online: 12 NOV 2008
- Article first published online: 8 SEP 2008
- Hospital efficiency;
- stochastic frontier analysis;
- hospital quality;
- patient safety
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.