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Inefficient or Not, Hospitals Are Overcharging
Abstract
A sizable literature related to the efficiency of the U.S. hospital sector has been produced over the past 30 years. Much of this research is based on stochastic frontier analysis. This approach is problematic for a number of reasons. For one, a functional form for a hospital’s cost function must be assumed, and a limited number of forms are tractable. Second, inefficiency is measured as the expectation of a random variable with a pre-determined distribution, with no theoretical justification for the underlying assumption, that observed cost equals minimum cost plus some non-negative, random amount. Thus, the conclusion reached by most of these studies, that U.S. hospitals are inefficient, may not be foregone. Using an entirely different methodology that obviates these shortcomings, the current study suggests that whether or not hospitals are efficient, their revenues have not been increasing in proportion to the minimum cost of providing their services. This study’s estimates of the impact of input prices and technology on production costs indicate that between 2000 and 2017, hospital revenues increased at a substantially higher rate than hospital costs. This study suggests that hospitals are pricing their services well above average cost. As a result, in 2017 over $200 billion could have been transferred from patients to the hospital sector, whether due to the proclivity of hospital administrators to purchase more inputs than are necessary for production, or to subsidize activities other than patient care.
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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.
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.
Previous studies of the so-called frontier production function have not utilized an adequate characterization of the disturbance term for such a model. In this paper we provide an appropriate specification, by defining the disturbance term as the sum of symmetric normal and (negative) half-normal random variables. Various aspects of maximum-likelihood estimation for the coefficients of a production function with an additive disturbance term of this sort are then considered.
Certificate-of-need (CON) regulations can promote hospital efficiency by reducing duplication of services; however, there are practical and theoretical reasons why they might be ineffective, and the empirical evidence generated has been mixed. This study compares the cost-inefficiency of urban, acute care hospitals in states with CON regulations against those in states without CON requirements. Stochastic frontier analysis was performed on pooled time-series, cross-sectional data from 1,552 hospitals in 37 states for the period 2005 to 2009 with controls for variations in hospital product mix, quality, and patient burden of illness. Average estimated cost-inefficiency was less in CON states (8.10%) than in non-CON states (12.46%). Results suggest that CON regulation may be an effective policy instrument in an era of a new medical arms race. However, broader analysis of the effects of CON regulation on efficiency, quality, access, prices, and innovation is needed before a policy recommendation can be made.
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. Copyright
The measurement of efficiency and productivity of health service delivery has become a small industry. This is a review of 317 published papers on frontier efficiency measurement. The techniques used are mainly based on non-parametric data envelopment analysis, but there is increasing use of parametric techniques, such as stochastic frontier analysis. Applications to hospitals and other health care organizations and areas are reviewed and summarised, and some meta-type analysis undertaken. Cautious conclusions are that public provision may be potentially more efficient than private, in certain settings. The paper also considers conceptualizations of efficiency, and points to dangers and opportunities in generating such information. Finally, some criteria for assessing the use and usefulness of efficiency studies are established, with a view to helping both researchers and those assessing whether or not to act upon published results.
This paper uses a stochastic frontier multiproduct cost function to derive hospital-specific measures of inefficiency. The cost function includes direct measures of illness severity, output quality, and patient outcomes to reduce the likelihood that the inefficiency estimates are capturing unmeasured differences in hospital outputs. Models are estimated using data from the AHA Annual Survey, Medicare Hospital Cost Reports, and MEDPAR. We explicitly test the assumption of output endogeneity and reject it in this application. We conclude that inefficiency accounts for 13.6 percent of total hospital costs. This estimate is robust with respect to model specification and approaches to pooling data across distinct groups of hospitals.
A stochastic frontier cost function is fitted to data for 219 New York hospitals (1991). Economic inefficiency is estimated to be about 18 percent, comparable to published estimates for hospitals nationwide. Hospitals with over 300 beds are notably more efficient, and those with more Medicare patients are also more efficient. A reduction in excess bed capacity to control costs appears warranted. Rochester area hospitals, where a system of monopsony reimbursement exists, are more efficient. Unionized hospitals are less efficient. The stringent regulatory system in New York does not appear to effectively control costs or to affect efficiency.
Compensation 2013: Evolving models, emerging approaches
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