Literature DB >> 33138714

Patient Feedback on the Use of Predictive Analytics for Suicide Prevention.

Mark A Reger1, Brooke A Ammerman1, Sarah P Carter1, Heather M Gebhardt1, Sasha M Rojas1, Jacob M Lee1, Jonathan Buchholz1.   

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.

Entities:  

Keywords:  Machine learning; REACH VET; Suicide prediction; Suicide prevention; Veterans

Year:  2020        PMID: 33138714     DOI: 10.1176/appi.ps.202000092

Source DB:  PubMed          Journal:  Psychiatr Serv        ISSN: 1075-2730            Impact factor:   3.084


  4 in total

1.  Patient perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk.

Authors:  Bobbi Jo H Yarborough; Scott P Stumbo
Journal:  Gen Hosp Psychiatry       Date:  2021-03-04       Impact factor: 3.238

Review 2.  Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.

Authors:  Ellen E Lee; John Torous; Munmun De Choudhury; Colin A Depp; Sarah A Graham; Ho-Cheol Kim; Martin P Paulus; John H Krystal; Dilip V Jeste
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2021-02-08

3.  Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice.

Authors:  Bobbi Jo H Yarborough; Scott P Stumbo; Jennifer L Schneider; Julie E Richards; Stephanie A Hooker; Rebecca C Rossom
Journal:  BMC Psychiatry       Date:  2022-07-23       Impact factor: 4.144

4.  Implementing the national suicide prevention strategy: Time for action to flatten the curve.

Authors:  John K Iskander; Alex E Crosby
Journal:  Prev Med       Date:  2021-07-19       Impact factor: 4.018

  4 in total

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