Psychiatric Risk Assessment from the Clinician's Perspective: Lessons for the Future.

Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 0005735 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2789 (Electronic) Linking ISSN: 00103853 NLM ISO Abbreviation: Community Ment Health J Subsets: MEDLINE

Publication: 1999- : New York, NY : Kluwer Academic/Plenum Publishers
Original Publication: Lexington, Mass.

Mental Disorders*/diagnosis ; Mental Disorders*/psychology; Psychiatry/*methods ; Psychology/*methods ; Risk Assessment/*methods ; Social Work/*methods; Counselors ; Humans ; Louisiana ; Social Workers

Accurate prediction of risk-states in Serious Mental Illnesses (SMIs) is critical for reducing their massive societal burden. Risk-state assessments are notably inaccurate. Recent innovations, including widely available and inexpensive mobile technologies for ambulatory "biobehavioral" data, can reshape risk assessment. To help understand and accelerate clinician involvement, we surveyed 90 multi-disciplinary clinicians serving SMI populations in various settings to evaluate how risk assessment is conducted and can improve. Clinicians reported considerable variability in conducting risk assessment, and few clinicians explicated their procedures beyond tying it to broader mental status examinations or interviews. Very few clinicians endorsed using currently-available standardized risk measures, and most reported low confidence in their utility. Clinicians also reported spending approximately half the time conducting individual risk assessments than optimally needed. When asked about improvement, virtually no clinicians acknowledged biobehavioral, objective technologies, or ambulatory recording. Overall, clinicians seemed unaware of meaningful ways to improve risk assessment.

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Keywords: Machine learning; Objective; Risk assessment; Serious mental illness; Technology

Date Created: 20190603 Date Completed: 20201015 Latest Revision: 20201015

20240628

10.1007/s10597-019-00411-x

31154587