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Related Experiment Videos

Classifying psychiatric inpatients: seeking better measures.

J Durbin1, P Goering, G Pink

  • 1Health Systems Research Unit, Clarke Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. jdurbin@hsru.clarke-inst.on.ca

Medical Care
|April 23, 1999
PubMed
Summary

A new severity index improved prediction of psychiatric inpatient length of stay (LOS) beyond standard data. This severity measure offers potential for more accurate case-mix reimbursement systems.

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Area of Science:

  • Psychiatry
  • Health Services Research
  • Medical Informatics

Background:

  • Current case-mix reimbursement for psychiatric inpatients is limited by a lack of effective patient grouping systems.
  • Patient factors beyond diagnosis influence care delivery in psychiatry, necessitating new measures of patient need.

Purpose of the Study:

  • To compare the predictive accuracy of the Computerized Severity Index (CSI) for psychiatric inpatient length of stay (LOS) against routinely collected discharge abstract data.
  • To evaluate the CSI's ability to account for variations in LOS.

Main Methods:

  • Retrospective chart review of 355 psychiatric discharges with primary diagnoses of psychotic or major depressive disorders.
  • Severity ratings using the CSI were combined with demographic and diagnostic data from discharge abstracts.

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  • Multivariate regression analyses were employed to model LOS.

    • Main Results:

    • The CSI significantly improved LOS prediction models, explaining an additional 9% to 11% of the variation.
    • For psychotic disorders, maximum severity during hospitalization was the key LOS predictor.
    • For depressive disorders, an increase in severity post-admission best predicted LOS.

    Conclusions:

    • Severity ratings derived from chart review enhance LOS prediction for psychiatric inpatients compared to discharge abstract variables.
    • Further research is required to integrate severity ratings into a comprehensive psychiatric case-mix grouping system.
    • Accurate prediction of LOS is crucial for risk assessment in case-mix-based reimbursement systems.