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The Role of Psychometric Data in Predicting Inpatient Mental Health Service Utilization

The Role of Psychometric Data in Predicting Inpatient Mental Health Service Utilization Inpatient mental health readmission rates have increased dramatically in recent years, with a subset of consumers referred to as revolving-door patients. In an effort to reduce the financial burden associated with these patients and increase treatment efficacy, researchers have begun to explore factors associated with increased service utilization. To date, predictors of increased service usage are remarkably discrepant across studies. Further exploration, therefore, is needed to better explicate the relevance of “traditional” predictors and also to identify alternate strategies that may assist in predicting rehospitalization. One method that may be helpful in identifying patients at high risk is the development of a psychometric screening procedure. As a means to this end, the present study was designed to assess the potential usefulness of psychometric data in predicting mental health service utilization. The sample consisted of 131 patients hospitalized during an index period of 8 months at an acute-care psychiatric hospital. Number of readmissions was recorded in a 9 month post-index period. Measures completed during the index admission included the Brief Psychiatric Rating Scale-Anchored (BPRS-A), Symptom Checklist-90-Revised (SCL-90-R), Kaufman Brief Intelligence Test (K-BIT), and the Beck Depression Inventory (BDI). Results indicated that psychometric data accounted for significant variance in predicting past, present and future mental health service utilization. The BPRS-A, SCL-90-R, and BDI show particular promise as time efficient psychometric screening instruments that may better enable practitioners to identify patients proactively who are at increased risk for rehospitalization. Implications are discussed with regard to patient-treatment matching and discharge planning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Psychiatric Quarterly Springer Journals

The Role of Psychometric Data in Predicting Inpatient Mental Health Service Utilization

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References (80)

Publisher
Springer Journals
Copyright
Copyright © 2001 by Human Sciences Press, Inc.
Subject
Medicine & Public Health; Psychiatry; Public Health; Sociology, general
ISSN
0033-2720
eISSN
1573-6709
DOI
10.1023/A:1010396831037
Publisher site
See Article on Publisher Site

Abstract

Inpatient mental health readmission rates have increased dramatically in recent years, with a subset of consumers referred to as revolving-door patients. In an effort to reduce the financial burden associated with these patients and increase treatment efficacy, researchers have begun to explore factors associated with increased service utilization. To date, predictors of increased service usage are remarkably discrepant across studies. Further exploration, therefore, is needed to better explicate the relevance of “traditional” predictors and also to identify alternate strategies that may assist in predicting rehospitalization. One method that may be helpful in identifying patients at high risk is the development of a psychometric screening procedure. As a means to this end, the present study was designed to assess the potential usefulness of psychometric data in predicting mental health service utilization. The sample consisted of 131 patients hospitalized during an index period of 8 months at an acute-care psychiatric hospital. Number of readmissions was recorded in a 9 month post-index period. Measures completed during the index admission included the Brief Psychiatric Rating Scale-Anchored (BPRS-A), Symptom Checklist-90-Revised (SCL-90-R), Kaufman Brief Intelligence Test (K-BIT), and the Beck Depression Inventory (BDI). Results indicated that psychometric data accounted for significant variance in predicting past, present and future mental health service utilization. The BPRS-A, SCL-90-R, and BDI show particular promise as time efficient psychometric screening instruments that may better enable practitioners to identify patients proactively who are at increased risk for rehospitalization. Implications are discussed with regard to patient-treatment matching and discharge planning.

Journal

Psychiatric Quarterly – Springer Journals

Published: Oct 3, 2004

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