Patient Feedback on the Use of Predictive Analytics for Suicide Prevention - PubMed (original) (raw)
. 2021 Feb 1;72(2):129-135.
doi: 10.1176/appi.ps.202000092. Epub 2020 Nov 3.
Affiliations
- PMID: 33138714
- DOI: 10.1176/appi.ps.202000092
Patient Feedback on the Use of Predictive Analytics for Suicide Prevention
Mark A Reger et al. Psychiatr Serv. 2021.
Abstract
Objective: There is significant debate about the feasibility of using predictive models for suicide prevention. Although statistical considerations have received careful attention, patient perspectives have not been examined. This study collected feedback from high-risk veterans about the U.S. Department of Veterans Affairs (VA) prevention program called Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment (REACH VET).
Methods: Anonymous questionnaires were obtained from veterans during their stay at a psychiatric inpatient unit (N=102). The questionnaire included three vignettes (the standard VA script, a more statistical vignette, and a more collaborative vignette) that described a conversation a clinician might initiate to introduce REACH VET. Patients rated each vignette on several factors, selected their favorite vignette, and provided qualitative feedback, including recommendations for clinicians.
Results: All three vignettes were rated as neutral to very caring by more than 80% of respondents (at least 69% of respondents rated all vignettes as somewhat caring to very caring). Similar positive feedback was obtained for several ratings (e.g., helpful vs. unhelpful, informative vs. uninformative, encouraging vs. discouraging). There were few differences in the ratings of the three vignettes, and each of the three scripts was preferred as the "favorite" by at least 28% of the sample. Few patients endorsed concerns that the discussion would increase their hopelessness, and privacy concerns were rare. Most of the advice for clinicians emphasized the importance of a patient-centered approach.
Conclusions: The results provide preliminary support for the acceptability of predictive models to identify patients at risk for suicide, but more stakeholder research is needed.
Keywords: Machine learning; REACH VET; Suicide prediction; Suicide prevention; Veterans.
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