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Open Access Research article

Development of mental health quality indicators (MHQIs) for inpatient psychiatry based on the interRAI mental health assessment

Christopher M Perlman1*, John P Hirdes1, Howard Barbaree2, Brant E Fries34, Ian McKillop1, John N Morris5 and Terry Rabinowitz6

Author Affiliations

1 School of Public Health and Health Systems, University of Waterloo, 200 University Ave West, Waterloo N2L 3G1, Canada

2 Waypoint Centre for Mental Healthcare, 500 Church St, Penetanguishene, L9M 1G3, Canada

3 Institute of Gerontology and School of Medicine, University of Michigan, 300 North Ingalls St, Ann Arbor, 48109, USA

4 Ann Arbor VA Healthcare Center, 2215 Fuller Rd, Ann Arbor, 48105, USA

5 Hebrew Senior Life, 1200 Centre Street, Boston, 02131, USA

6 Fletcher Allen Health Care, 111 Colchester Avenue, Burlington, 05401, USA

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BMC Health Services Research 2013, 13:15  doi:10.1186/1472-6963-13-15

Published: 10 January 2013

Abstract

Background

Outcome quality indicators are rarely used to evaluate mental health services because most jurisdictions lack clinical data systems to construct indicators in a meaningful way across mental health providers. As a result, important information about the effectiveness of health services remains unknown. This study examined the feasibility of developing mental health quality indicators (MHQIs) using the Resident Assessment Instrument - Mental Health (RAI-MH), a clinical assessment system mandated for use in Ontario, Canada as well as many other jurisdictions internationally.

Methods

Retrospective analyses were performed on two datasets containing RAI-MH assessments for 1,056 patients from 7 facilities and 34,788 patients from 70 facilities in Ontario, Canada. The RAI-MH was completed by clinical staff of each facility at admission and follow-up, typically at discharge. The RAI-MH includes a breadth of information on symptoms, functioning, socio-demographics, and service utilization. Potential MHQIs were derived by examining the empirical patterns of improvement and incidence in depressive symptoms and cognitive performance across facilities in both sets of data. A prevalence indicator was also constructed to compare restraint use. Logistic regression was used to evaluate risk adjustment of MHQIs using patient case-mix index scores derived from the RAI-MH System for Classification of Inpatient Psychiatry.

Results

Subscales from the RAI-MH, the Depression Severity Index (DSI) and Cognitive Performance Scale (CPS), were found to have good reliability and strong convergent validity. Unadjusted rates of five MHQIs based on the DSI, CPS, and restraints showed substantial variation among facilities in both sets of data. For instance, there was a 29.3% difference between the first and third quartile facility rates of improvement in cognitive performance. The case-mix index score was significantly related to MHQIs for cognitive performance and restraints but had a relatively small impact on adjusted rates/prevalence.

Conclusions

The RAI-MH is a feasible assessment system for deriving MHQIs. Given the breadth of clinical content on the RAI-MH there is an opportunity to expand the number of MHQIs beyond indicators of depression, cognitive performance, and restraints. Further research is needed to improve risk adjustment of the MHQIs for their use in mental health services report card and benchmarking activities.

Keywords:
Quality; Performance; Indicators; Outcomes; interRAI; Mental health; Psychiatry; Assessment system