Severity of illness within DRGs: impact on prospective payment. 1985

S D Horn, and P D Sharkey, and A F Chambers, and R A Horn

This study compares the financial impact of a Diagnosis Related Group (DRG) prospective payment system with that of a Severity of Illness-adjusted DRG prospective payment system. The data base of about 106,000 discharges is from 15 hospitals, all of which had a Health Care Financing Administration (HCFA) DRG case mix index greater than 1. In order to pool the data over the 15 hospitals, all charges were converted to costs, normalized to Fiscal Year 1983, and adjusted for medical education and wage levels. The findings showed that, for the study population as a whole, DRGs explained 28 per cent of the variability in resource use per case while Severity of Illness-adjusted DRGs explained 61 per cent of the variability in resource use per case. When we simulated prospective payment systems based on DRGs and on Severity-adjusted DRGs, we found that the financial impact of the two systems differed by very little in some hospitals and by as much as 35 per cent of total operating costs in other hospitals. Thus, even with a data set that is relatively homogeneous (with respect to the HCFA DRG case mix index definition of hospitals), we found substantial inequities in payment when DRGs were not adjusted for Severity of Illness. These findings suggest that, with a more representative set of hospitals, the difference between unadjusted and Severity-adjusted DRG-based prospective payment could be greater than 35 per cent of a hospital's total operating costs.

UI MeSH Term Description Entries
D012044 Regression Analysis Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable. Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D003365 Costs and Cost Analysis Absolute, comparative, or differential costs pertaining to services, institutions, resources, etc., or the analysis and study of these costs. Affordability,Analysis, Cost,Cost,Cost Analysis,Cost Comparison,Cost Measures,Cost-Minimization Analysis,Costs and Cost Analyses,Costs, Cost Analysis,Pricing,Affordabilities,Analyses, Cost,Analyses, Cost-Minimization,Analysis, Cost-Minimization,Comparison, Cost,Comparisons, Cost,Cost Analyses,Cost Comparisons,Cost Measure,Cost Minimization Analysis,Cost, Cost Analysis,Cost-Minimization Analyses,Costs,Measure, Cost,Measures, Cost
D003953 Diagnosis-Related Groups A system for classifying patient care by relating common characteristics such as diagnosis, treatment, and age to an expected consumption of hospital resources and length of stay. Its purpose is to provide a framework for specifying case mix and to reduce hospital costs and reimbursements and it forms the cornerstone of the prospective payment system. Case Mix,DRG,Diagnosis Related Group,Diagnosis-Related Group,Case Mixes,DRGs,Diagnostic-Related Group,Group, Diagnostic-Related,Groups, Diagnostic-Related,Diagnosis Related Groups,Diagnostic Related Group,Diagnostic-Related Groups,Group, Diagnosis Related,Group, Diagnosis-Related,Group, Diagnostic Related,Groups, Diagnosis Related,Groups, Diagnosis-Related,Groups, Diagnostic Related,Related Group, Diagnosis,Related Groups, Diagnosis
D006760 Hospitalization The confinement of a patient in a hospital. Hospitalizations
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man

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