Classifying psychiatric inpatients - Seeking better measures

BACKGROUND. Use of case-mix reimbursement in psychiatric inpatients has bee n limited as a result of a lack of systems which effectively group patients according to required resource needs. In recognition of the fact that many patient factors, in addition to diagnosis influence delivery of care in ps ychiatry, new measures of patient need are emerging. OBJECTIVE. This study compared improvement realized by using a multidimensi onal measure of patient severity, the Computerized Severity Index (CSI), to predict length of stay (LOS) in psychiatric inpatients over that achieved by using patient variables routinely collected in the discharge abstract. METHOD. Through retrospective chart review, severity ratings were made on 3 55 psychiatric discharges with primary diagnoses of psychotic or major depr essive disorders. Those ratings were combined with demographic and diagnost ic data available in discharge abstracts and were then entered into multiva riate regression analyses to model LOS. RESULT. CSI ratings significantly contributed to prediction models, which a ccounted for an additional 9% to 11% of variation in LOS over discharge abs tract data. Among patients with psychotic disorders, maximum severity durin g hospitalization was the best predictor of LOS, whereas among patients wit h depressive disorders, it was an increase in severity following admission. CONCLUSION. Severity ratings, based on chart review, improved prediction of LOS over discharge abstract variables for psychiatric inpatients in two di agnostic groups. Further research is needed to estimate the impact of incor porating severity ratings into a grouping system for all psychiatric inpati ents. Estimation of predictive accuracy is important to determine the amoun t of risk passed on to providers in a payment system based on psychiatric c ase mix.