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

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Abstract

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 computation 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.

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