Controlling for Quality in Hospital Inefficiency Estimation
- Presenter:
Mon June 5, 2006 9:30-10:45 Room Alumni Lounge
Rationale: Recent increases in the costs of healthcare in the U.S. apparently are not accompanied by commensurate increases in quality, leading many to speculate that inefficiencies exist. In addition, the growing recognition of the important role that resource allocation decisions play in quality improvement has augmented interest in healthcare inefficiency measurement. Conceptually, healthcare quality improvement can reduce resource use by eliminating medical errors and unnecessary procedures. Alternatively, quality improvement can result in greater resource use because it requires more or better services. While stochastic frontier analysis (SFA) has become a popular method to estimate the inefficiency of hospitals, and it may be a powerful analytic tool, existing applications of SFA may not adequately control for the quality in analyses of hospital inefficiency. Without proper control, inefficiencies may be over- or under-estimated. This study assesses the role of quality in hospital inefficiency estimation.
Methodology: We identify existing strategies to control for hospital quality that have been employed in SFA estimation of hospital inefficiency. Employing SFA methods, we compare hospital inefficiency estimates with no hospital quality controls to the estimates with the following controls for hospital quality: 1) structural measures only (e.g., hospital teaching status), 2) structural and aggregated outcome measures (e.g., risk-adjusted, all-causes mortality), and 3) structural measures and multiple outcome measures designed to capture the multi-dimensional nature of hospital quality (e.g., risk-adjusted mortality rates for a variety of conditions and procedures and patient safety event rates).
Our sample is comprised of over 1,200 U.S., urban, general hospitals for the period 1999-2002. The quality variables are obtained by applying the Inpatient Quality Indicator (IQI) and Patient Safety Indicator (PSI) modules of the Agency for Healthcare Research and Quality (AHRQ) Quality Indicator (QI) software to 20 Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID). Other control variables in our hospital cost function include the input, output, price, and product descriptor variables commonly used in hospital applications of SFA from data files compiled by the American Hospital Association (AHA), the Center for Medicare & Medicaid Services (CMS), and Solucient.
Results: Preliminary analysis with 2001 data suggests that it is important to employ controls for quality in hospital applications of SFA. Although the average estimated inefficiency measure across hospitals is unaffected by the inclusion of quality controls, a number of the hospital quality variables entered the model significantly, and the rankings of hospitals by inefficiency scores was impacted by the choice of quality control strategy. Moreover, an inaccurate assessment of the impact of quality on cost can result from the use of a single outcome measure only.
Conclusions: We provide a rationale and empirical support for the inclusion of controls for quality in hospital applications of SFA. Researchers able to include variables that control for the multi-dimensional nature of hospital quality should do so. Researchers faced with data constraints that limit the quality variables they can include should be aware of the limitations of their analysis when interpreting and applying their results.
