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

Abstract

Objective: Assess patient understanding of, potential concerns with, and implementation preferences related to automated suicide risk identification using electronic health record data and machine learning.

Method: Focus groups (n = 23 participants) informed a web-based survey sent to 11,486 Kaiser Permanente Northwest members in April 2020. Survey items assessed patient preferences using Likert and visual analog scales (means scored from -50 to 50). Descriptive statistics summarized findings.

Results: 1357 (12%) participants responded. Most (84%) found machine learning-derived suicide risk identification an acceptable use of electronic health record data; however, 67% objected to use of externally sourced data. Participants felt consent (or opt-out) should be required (mean = -14). The majority (69%) supported outreach to at-risk individuals by a trusted clinician through care messages (57%) or telephone calls (47-54%). Highest endorsements were for psychiatrists/therapists (99%) or a primary care clinician (75-96%); less than half (42%) supported outreach by any clinician and participants generally felt only trusted clinicians should have access to risk information (mean = -16).

Conclusion: Patients generally support use of EHR data (not externally sourced risk information) to inform automated suicide risk identification models but prefer to consent or opt-out; trusted clinicians should outreach by telephone or care message to at risk individuals.

Keywords: Attempt; Death; Electronic health records; Machine learning; Patient perspective; Risk; Suicide.