Case Mix Complexity Research Articles

Case mix complexity differences between teaching and nonteaching hospitals.

In the study reported here, the authors measured the differences between teaching and nonteaching hospitals in case mix complexity and provided some detail concerning the differences. They measured case complexity in a sample of 200 short-term general hospitals by the Resource Need Index (RNI) using a cross-classification of 3,490 case types with weights compiled from patient charges. Median RNI values were moderately higher for teaching than for nonteaching hospitals both for the hospital as a whole and for each clinical service except obstetrics-gynecology. The most resource-intensive case type were relatively more frequent in the teaching hospitals, but the least resource-intensive types were of about equal relative frequency in the two hospital groups. The results show that teaching hospitals could be expected to cost somewhat more per patient even if case mix were the only factor.

Hospital cost per case: analyses using a statewide data system.

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.

An Analysis of Case Mix Complexity Using Information Theory and Diagnostic Related Grouping

Case mix complexity measurements are essential to determine health care efficiency and effectiveness. Measures of patient care processes and outcomes must be adjusted for case mix before valid comparisons can be made. Hospital reimbursement, particularly prospective reimbursement, must take into account differences in case mix. In addition, a key variable for hospital classification is case mix. There are, however, no widely accepted easily computed case mix measures. Information theory measures of case mix have been developed but their acceptance has been limited by a lack of verification of their basic assumption that concentration of disease is related to clinical complexity. We discuss the rationale underlying the mathematical computaton of information theory measures and demonstrate a statistically significant relationship between clinical measures of case mix complexity and information theory measures of case mix complexity.