DRGs and severity of illness measures: an analysis of patient classification systems
Journal of Medical Systems
Measuring technical efficiency in health care organizations
Journal of Medical Systems
Development of a scalar hospital-specific severity of illness measure
Journal of Medical Systems
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: Theory, Methodology and Application
Towards More Optimal Medical Diagnosing with Evolutionary Algorithms
Journal of Medical Systems
Medical diagnostic process optimization through the semantic integration of data resources
Computer Methods and Programs in Biomedicine
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A two-stage approach is used in a stochastic frontier analysis of the factors affecting hospital efficiency. In the first stage, a translog cost-function is used to estimate inefficiency scores. In the second stage, inefficiency scores are regressed against independent variables to test hypotheses that come from X-inefficiency Theory. The study was based on 1989 data for 195 Pennsylvania acute care hospitals. This data base was chosen because of the availability of patient-level severity of illness data, a measure of output that is not available from most data sources. The stochastic frontier analysis models estimated mean inefficiency scores that ranged from 0.075 to 0.180. The addition of the DRG case mix index (CMI) reduced estimated inefficiency by more than 50%. The incremental effect of a severity of illness variable to an equation with CMI was very small. The second-stage results suggest inefficiency and are inversely associated with regulatory pressures and industry concentration.