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In this paper, we establish relationships between hospital cost per case and the independent variables; case mix complexity, case mix severity, factor input prices, and hospital characteristics. Two hundred and sixteen thousand discharges from Maryland's acute general hospitals are grouped into 383 Diagnostic Related Groups which are used to compute an information theoretic measure of case mix complexity. Multiple linear regression equations are developed which predict up to 88% of the variance of between-hospital cost per case. The most highly significant predictors of cost per case are complexity, patient age, proportion of high risk patients, average length of stay, and nonphysician salary levels. Two distinct groups of hospitals, metropolitan and rural, are defined and models are developed for each. We discuss the implications of these findings for the identification and regulation of unexpectedly high cost hospitals and for prospective cost per case reimbursement.
Schumacher et al. (Mon,) studied this question.