Preethy Nayar1, Yasar A Ozcan, Fang Yu, Anh T Nguyen. 1. Department of Health Services Research and Administration, University of Nebraska Medical Center, Omaha, USA. pnayar@unmc.edu
Abstract
BACKGROUND: Over the last couple of decades, hospitals in the United States are facing pressures to maximize performance in terms of production efficiency and quality. An increasing emphasis on value-based purchasing on the part of third-party payers as well as the prevalence of pay for performance initiatives create an imperative for more accurate assessments of health care provider performance. PURPOSES: The objectives of this study were to measure hospital performance in terms of both technical efficiency and quality using data envelopment analysis (DEA) models in urban acute care hospitals. METHODOLOGY/APPROACH: In this observational cross-sectional study of a nationally representative sample of 371 urban acute care hospitals, hospital performance was assessed using slack-based additive DEA models. The technical inputs included in the DEA models were total number of beds setup and staffed, nonphysician full-time equivalent staffing, and nonpayroll operating expenses. The technical outputs were adjusted patient days, total number of outpatient visits, and training full-time equivalent, obtained from the American Hospital Association 2008 database. The quality measures used for the quality of care dimension of performance were survival rates for acute myocardial infarction, congestive heart failure, and pneumonia obtained from the Nationwide Inpatient Sample 2008 data. FINDINGS: Less than 20% of the sample hospitals were optimally performing for both quality and efficiency. Tobit regression analysis of the DEA scores found that public, small, teaching hospitals had higher DEA efficiency and quality scores. PRACTICE IMPLICATIONS: DEA is a promising tool for benchmarking both aspects of performance: efficiency and quality of hospitals. Because quality is a multidimensional construct, the choice of an appropriate composite quality measure has to be addressed in future research. However, incorporating quality into the DEA models would be a better reflection of the hospital product.
BACKGROUND: Over the last couple of decades, hospitals in the United States are facing pressures to maximize performance in terms of production efficiency and quality. An increasing emphasis on value-based purchasing on the part of third-party payers as well as the prevalence of pay for performance initiatives create an imperative for more accurate assessments of health care provider performance. PURPOSES: The objectives of this study were to measure hospital performance in terms of both technical efficiency and quality using data envelopment analysis (DEA) models in urban acute care hospitals. METHODOLOGY/APPROACH: In this observational cross-sectional study of a nationally representative sample of 371 urban acute care hospitals, hospital performance was assessed using slack-based additive DEA models. The technical inputs included in the DEA models were total number of beds setup and staffed, nonphysician full-time equivalent staffing, and nonpayroll operating expenses. The technical outputs were adjusted patient days, total number of outpatient visits, and training full-time equivalent, obtained from the American Hospital Association 2008 database. The quality measures used for the quality of care dimension of performance were survival rates for acute myocardial infarction, congestive heart failure, and pneumonia obtained from the Nationwide Inpatient Sample 2008 data. FINDINGS: Less than 20% of the sample hospitals were optimally performing for both quality and efficiency. Tobit regression analysis of the DEA scores found that public, small, teaching hospitals had higher DEA efficiency and quality scores. PRACTICE IMPLICATIONS: DEA is a promising tool for benchmarking both aspects of performance: efficiency and quality of hospitals. Because quality is a multidimensional construct, the choice of an appropriate composite quality measure has to be addressed in future research. However, incorporating quality into the DEA models would be a better reflection of the hospital product.