J Durbin1, P Goering, G Pink, M Murray. 1. Health Systems Research Unit, Clarke Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. jdurbin@hsru.clarke-inst.on.ca
Abstract
BACKGROUND: Use of case-mix reimbursement in psychiatric inpatients has been 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 psychiatry, new measures of patient need are emerging. OBJECTIVE: This study compared improvement realized by using a multidimensional 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 355 psychiatric discharges with primary diagnoses of psychotic or major depressive disorders. Those ratings were combined with demographic and diagnostic data available in discharge abstracts and were then entered into multivariate regression analyses to model LOS. RESULT: CSI ratings significantly contributed to prediction models, which accounted for an additional 9% to 11% of variation in LOS over discharge abstract data. Among patients with psychotic disorders, maximum severity during hospitalization was the best predictor of LOS, whereas among patients with 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 diagnostic groups. Further research is needed to estimate the impact of incorporating severity ratings into a grouping system for all psychiatric inpatients. Estimation of predictive accuracy is important to determine the amount of risk passed on to providers in a payment system based on psychiatric case mix.
BACKGROUND: Use of case-mix reimbursement in psychiatric inpatients has been 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 psychiatry, new measures of patient need are emerging. OBJECTIVE: This study compared improvement realized by using a multidimensional 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 355 psychiatric discharges with primary diagnoses of psychotic or major depressive disorders. Those ratings were combined with demographic and diagnostic data available in discharge abstracts and were then entered into multivariate regression analyses to model LOS. RESULT: CSI ratings significantly contributed to prediction models, which accounted for an additional 9% to 11% of variation in LOS over discharge abstract data. Among patients with psychotic disorders, maximum severity during hospitalization was the best predictor of LOS, whereas among patients with 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 diagnostic groups. Further research is needed to estimate the impact of incorporating severity ratings into a grouping system for all psychiatric inpatients. Estimation of predictive accuracy is important to determine the amount of risk passed on to providers in a payment system based on psychiatric case mix.
Authors: Bentson H McFarland; Anne E Kovas; Shelby L Haugan; David A Pollack; Jo M Mahler Journal: Int J Methods Psychiatr Res Date: 2005 Impact factor: 4.035