A hospital cost model Public Deposited



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  • This thesis develops a cost model for short-term, non-federal, general hospitals through a regression analysis of data from a sample of D. S. hospitals. The analysis examines the impact various factors have on hospital costs as measured by the cost per patient. The results contradict the beliefs of other researchers and contain some interesting implications for management of today's hospital system. Past researchers have been plagued with the problem of accounting for differences in hospital output. Many believed the most commonly used measures, patients and patient-days, were inadequate since they do not account for differences in case mix, complexity of cases, or quality of the care administered. The possibility of these factors affecting costs has prompted model builders to devise methods of considering them in their model. Two general methods have prevailed. One, epitomized in work done by Martin Feldstein and Judith and Lester Lave, categorizes patients according to case type and complexity of case. Then a regression model is developed to explain the differences in hospital costs as measured by cost per patient or cost per patient day. The best results obtained was an R² of .85. However, while being theoretically very appealing, this approach requires too much inaccessible data to be a pragmatically useful tool. The other approach, advanced through work done by Edwards, Miller, and Schumacher, accounts for output differences in terms of the services offered instead of those services actually rendered. Through a novel application of Guttman scaling analysis they developed a scope of service index to use in their regression model. However, errors in their regression procedure left the value of the index untested. The research in this thesis begins by closely examining the work done by Edwards, Miller, and Schumacher and testing their model in a regression analysis. The model attained an R² of only .43 so another method of regression modelling known as the dumpy variable technique was attempted to see if services could be related more closely to the dependent variable. This method attained only slightly better results. Therefore, it appeared that the ability to measure the amount of service being offered was not the problem with the service adjusted model. Instead, it indicated that some other important variables were not being considered. After conducting an exhaustive search for the missing variables, it was concluded that personnel productivity and average annual wage rate were the only variables which could adequately explain the differences in hospitals! cost per patient. A model containing these two variables produced an R² of .88 which seems to show a significant improvement over models constructed by other researchers. The overwhelming importance of personnel productivity in explaining hospital cost performance suggests that for the most part a hospital's costs are controllable through efficient management of their manpower resources. But hospitals are in a monopolistic industry and are generally not motivated by economic factors. Therefore, an external incentive for controlling costs must be provided. It is recommended that this incentive be in the form of continued governmental control of pricing practices.
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