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患者对预测分析在预防自杀中的应用的反馈。

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

机构信息

U.S. Department of Veterans Affairs (VA) Puget Sound Health Care System, Seattle (all authors); Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle (Reger, Gebhardt, Buchholz).

出版信息

Psychiatr Serv. 2021 Feb 1;72(2):129-135. doi: 10.1176/appi.ps.202000092. Epub 2020 Nov 3.


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

摘要

目的:关于使用预测模型进行自杀预防的可行性存在重大争议。尽管统计方面的考虑已经受到了仔细的关注,但尚未研究患者的观点。本研究从高风险退伍军人那里收集了有关美国退伍军人事务部(VA)预防计划的反馈,该计划称为康复参与和协调健康-退伍军人增强治疗(REACH VET)。

方法:从精神科住院病房的退伍军人(N=102)中获得匿名问卷。问卷包括三个情景(标准 VA 脚本、更具统计性的情景和更具协作性的情景),描述了临床医生可能发起的对话,以介绍 REACH VET。患者对每个情景进行了多项因素的评分,选择了他们最喜欢的情景,并提供了定性反馈,包括对临床医生的建议。

结果:超过 80%的受访者(至少有 69%的受访者认为所有情景都是有些关心到非常关心)对所有三个情景的评分都为中性到非常关心。对几个评分(例如,有帮助与无帮助、信息丰富与信息不足、鼓励与不鼓励)也获得了类似的积极反馈。三个情景的评分差异很小,每个脚本都有至少 28%的样本被选为“最喜欢”的脚本。很少有患者认为讨论会增加他们的绝望感,而且隐私问题也很少。大多数给临床医生的建议都强调了以患者为中心的方法的重要性。

结论:这些结果初步支持了使用预测模型来识别有自杀风险的患者的可接受性,但需要更多利益相关者的研究。

相似文献

[1]
Patient Feedback on the Use of Predictive Analytics for Suicide Prevention.

Psychiatr Serv. 2021-2-1

[2]
Evaluating Clinician Attitudes After Local Implementation of the Veterans Affairs Predictive Analytic Model for Suicide Prevention.

J Psychiatr Pract. 2022-1-6

[3]
Evaluation of the Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment Suicide Risk Modeling Clinical Program in the Veterans Health Administration.

JAMA Netw Open. 2021-10-1

[4]
The Veterans Health Administration REACH VET Program: Suicide Predictive Modeling in Practice.

Psychiatr Serv. 2023-2-1

[5]
Impact of Implementation Facilitation on the REACH VET Clinical Program for Veterans at Risk for Suicide.

Psychiatr Serv. 2024-8-1

[6]
Implementation strategy to increase clinicians' use of the caring letters suicide prevention intervention.

Psychol Serv. 2023

[7]
Integrating Predictive Modeling Into Mental Health Care: An Example in Suicide Prevention.

Psychiatr Serv. 2018-10-10

[8]
Qualitative Evaluation of a Caring Letters Suicide Prevention Intervention for the Veterans Crisis Line.

Psychiatr Serv. 2023-12-1

[9]
Development of the Veterans Crisis Line Caring Letters Suicide Prevention Intervention.

Health Serv Res. 2022-6

[10]
Differential Preferences for the Caring Contacts Suicide Prevention Intervention Based on Patient Characteristics.

Arch Suicide Res. 2020

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Clin Psychol Sci. 2025-5

[3]
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[4]
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[5]
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Child Adolesc Psychiatr Clin N Am. 2024-7

[6]
Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors.

J Clin Psychiatry. 2022-8-31

[7]
Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice.

BMC Psychiatry. 2022-7-23

[8]
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Prev Med. 2021-11

[9]
Patient perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk.

Gen Hosp Psychiatry. 2021

[10]
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Biol Psychiatry Cogn Neurosci Neuroimaging. 2021-9

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