@article{ref1,
title="Patient perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk",
journal="General hospital psychiatry",
year="2021",
author="Yarborough, Bobbi Jo H. and Stumbo, Scott P.",
volume="70",
number="",
pages="31-37",
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. <br><br>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. <br><br>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). <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="0163-8343",
doi="10.1016/j.genhosppsych.2021.02.008",
url="http://dx.doi.org/10.1016/j.genhosppsych.2021.02.008"
}