Vivian Valdmanis

Sensitivity analysis for DEA models: An empirical example using public vs. NFP hospitals

Abstract The research presented here applies the Data Envelopment Analysis (DEA) to a sample of public (government-owned) and not-for-profit hospitals operating in Michigan in 1982. The focus of this research is twofold. First, assessing the relative efficiency between these two ownership types using the DEA rather than cost or profit functions can add insights into the production practices of these two ownership forms of hospitals. Second, testing the sensitivity of the DEA will add credence to this approach and resulting measures. Ten different specifications of the DEA are employed to test the sensitivity of the model.

Rural hospital performance and its correlates

The cost, technical, allocative and scale efficiencies of a sample of rural U.S. hospitals are calculated via linear programming models. Tobit analysis is used to assess possible correlates of each of the efficiency measures. A large amount of dispersion in operating efficiency is found within our data set; the majority of the dispersion is due to technical inefficiency. In general, for-profit hospitals are found to outperform not-for-profit and public hospitals. Demand characteristics, quality of care, and the mix of services offered are also found to influence performance.

Ownership and technical efficiency of hospitals.

The findings in this paper revealed the sample public hospitals to be more efficient relative to the sample NFP hospitals. Tight governmental control over the resources allocated to public hospitals may be one reason why these hospitals appear more efficient. Conversely, NFP hospitals appear less efficient but, in general, offer more diversified and sophisticated medical services. Therefore, even in the absence of the profit motive, the nonprofit and public sector are different enough to result in different productivity levels. This paper illustrates the applicability of the nonparametric technique in the measurement of relative technical (overall and pure) productive performance between NFP and public hospitals. The measures of inputs and outputs were relatively rough, and measures of scope of services were also crude. However, as data collection techniques improve and more precise information becomes available, this methodological approach can prove useful in the analysis of hospital efficiency and, in turn, the shaping of hospital policy.

EVALUATING HOSPITAL PERFORMANCE WITH CASE-MIX-ADJUSTED OUTPUTS

In this article, we compared hospital efficiency using a multiple input-output approach in two ways: one way used a straightforward count of inpatient days and outpatient services as outputs; and the second used a case mix-adjusted count of inpatient services and outpatient care as outputs. Our results show that there was no difference when we incorporated the case-mix index, either as a weighting device or as a separate output. However, this result may be due to our having a relatively homogeneous sample, (i.e., large metropolitan hospitals). Variations using this approach may occur when using a more heterogeneous sample, such as comparing hospitals of all sizes or rural versus urban hospitals.

Competitive effects on teaching hospitals

Abstract In this study we assess the performance of US teaching hospitals operating in 1995. Since teaching hospitals must increasingly compete with non-teaching hospitals for managed care contracts based on price, decreasing costs can only come from either reducing inefficiencies or decreasing the ‘public good’ production of teaching and research. We use a data envelopment analysis (DEA) approach to measure the relative technical and scale efficiencies on a sample of 254 US teaching hospitals. The next step of our research is to assess in a bivariate context the effect market competition has on the teaching hospitals in our sample. We find that competition (as measured by the number of managed care contracts per hospital and the number of patients covered by these contracts per hospital) has positive effects on the teaching hospitals. In other words, as competition increases so does the teaching hospital’s relative efficiency. We also regress each hospital’s relative efficiency scores on ownership form, organization structure, teaching effort, and competitive market variables. We find that increased competition leads to higher efficiency without compromising teaching intensity.

Principal-agent problems in health care systems: an international perspective

A central feature of all health care systems is the flow of finances from the population, via a variety of agencies, to the providers of health care. Each transfer of funds within the system involves a principal-agent problem, in the sense that a principal is entrusting funds to an agent with the intention that some desired aspect of health care delivery can be secured. This paper examines within the context of a principal-agent model three key elements of the health care system: the raising of finance, the transfer of funds to hospitals, and spending by hospitals. At each of these stages there is a danger that the objectives of society for the health care system are lost. In order to illustrate the issues involved, five mature systems of health care are examined: Austria, Germany, the United Kingdom, the Netherlands and the United States of America. The paper concludes that three aspects of the flow of funds are crucial to securing adequate control: the means of controlling patient entry to hospitals; the mechanism for remunerating hospitals for additional patients; and the control of physicians by hospital management.

The effects of teaching on hospital productivity

Abstract In addition to providing direct patient care, some hospitals are also used as training sources for residents. Because of these additional responsibilities, total costs are typically higher in teaching hospitals than in their non-teaching counterparts. In this paper, we use a data envelopment analysis (DEA) methodology to assess the relative technical efficiency of the 213 teaching hospitals in our sample. Because of DEAs flexibility, we were able to specify multiple inputs and outputs in determining the ‘best practice frontier’. Using this frontier as a benchmark, we determined the excess resources employed by technically inefficient hospitals. Expanding the use of a DEA, we were also able to determine how much of the inefficiency was due to excess use of residents, i.e., ‘congestion’. Systematic differences in terms of hospital ownership, teaching dedication, and teaching intensity were included in the analysis. We found an average inefficiency score of 0.80, indicating that these hospitals could have reduced inputs by 20% while maintaining output levels. Inefficiency attributed to the congestion of residents amounted to 20% of the total inefficiency score.

Charity care: do not-for-profits influence for-profits?

This study further examines whether not-for-profit hospitals exert pressure on for-profit hospitals to provide charity care and whether for-profit hospitals react differently than not-for-profit hospitals to managed care pressures and hospital competition in providing charity care. A two equation model is estimated using 1996 data from California hospitals. The results indicate that in mixed ownership markets, for-profit hospitals provide significantly less charity care as not-for-profit hospitals in the market provide more. Unexpectedly, study for-profit hospitals were not more influenced by price competition than other hospitals with respect to charity care. Having a unique role in providing charity care may justify continuing tax exemption for not-for-profit hospitals and enhance interest in payment and other policies with regard to conversions to ensure that not-for-profit hospitals continue to be represented in market areas.

Comparing French and US hospital technologies: a directional input distance function approach

French and US hospital technologies are compared using directional input distance functions. The aggregation properties of the directional distance function allow comparison of hospital industry-level performance as well as standard firm-level performance with regard to productive efficiency. In addition, the underlying constituents of efficiency – in the short run, congestion and technical inefficiency, and in the long run, scale inefficiency – are analysed by decomposing the overall measure. By virtue of using the directional distance function, it is also possible to obtain an estimate of a lower bound on allocative inefficiency. It is found that French and US hospitals use quite different technologies. Long run scale inefficiencies cause most of the French hospitals’ inefficiency, while short run technical inefficiency is the main source of overall productive inefficiency in the US hospitals.

Efficiency and productivity changes in large urban hospitals 1994-2002: ownership, markets, and the uninsured.

Based on the Current Population Survey, 46.6 million Americans did not have health insurance in 2005 (Center on Budget and Policy Priorities, 2006). Lack of insurance is often associated with lower utilization rates, which may in turn adversely affect health status (Ayanian, Weissman, Schneider, Ginsburg, & Zaslavsky, 2000). Since universal health insurance is not provided for in the US, uninsured individuals must either self-pay or rely on charity care provided by hospitals and health clinics. The majority of charity care is produced in the public sector, either at the state, county, or local level (federal hospitals primarily serve a particular segment of the population – e.g., veterans in the case of Veterans Administration hospitals). Public hospital provision of “safety net” hospital services is particularly prevalent in large urban areas (Lipson & Naierman, 1996). These safety net hospitals are defined by the Institute of Medicine as having an “open door policy to serve all patients regardless of their ability to pay and provide substantial levels of care to Medicaid, the uninsured, and other vulnerable patients” (IOM, 2000). Private not-for-profit (NFP) hospitals also provide charity care but to a lesser extent than public providers, especially since the imposition of cost cutting measures both by Medicare and Medicaid (federal programs that fund health care for the elderly and indigent, respectively) and by managed care. Given that approximately 15% of US GDP is allocated to health care, cost cutting measures are laudable; however, care still needs to be provided for individuals who cannot afford it, and the burden of providing this care has to be borne somewhere in the health care system.