Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development Study in Partnership With Native American Care Providers.
Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development Study in Partnership With Native American Care Providers.
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DOI：
10.2196/24377
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发表时间：
2021-09-02
影响因子：
8.5
通讯作者：
Walsh CG
中科院分区：
文献类型：
--
作者：
Haroz EE;Grubin F;Goklish N;Pioche S;Cwik M;Barlow A;Waugh E;Usher J;Lenert MC;Walsh CG
用于自杀风险预测的机器学习算法已经开发出来，在准确性方面有了显著的提高。实施这些算法来加强临床护理和减少自杀还没有得到很好的研究。这项研究旨在设计一个临床决策支持工具和适当的护理路径，以社区为基础的自杀监测和病例管理系统运作在美洲原住民保留地。参与者包括美洲原住民病例经理和主管(N=9)，他们在2个美洲原住民保留地从事自杀监测和病例管理项目。我们通过深度访谈来了解个案经理是如何看待和应对自杀风险的。访谈的结果为临床决策支持工具草案提供了依据，然后与主管一起审查，并结合适当的护理路径。病例管理人员报告说，在他们的监测系统工具中，根据预测算法接受了风险标志，特别是如果信息及时可用并与他们的临床判断结合使用。风险标志的实现需要在二分法的基础上进行编程，因此算法可以产生指示高风险和低风险的输出。为了区分连续的预测概率，我们制定了一个有利于特异性的分界点，了解到病例经理的临床判断将有助于提高敏感性。自杀风险预测算法显示了希望，但指导临床护理的实施仍然相对难以捉摸。我们的研究证明了与合作伙伴合作开发和指导风险预测算法的可操作性，以增强社区环境中的临床护理的效用。
Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. This study aims to design a clinical decision support tool and appropriate care pathways for community-based suicide surveillance and case management systems operating on Native American reservations. Participants included Native American case managers and supervisors (N=9) who worked on suicide surveillance and case management programs on 2 Native American reservations. We used in-depth interviews to understand how case managers think about and respond to suicide risk. The results from interviews informed a draft clinical decision support tool, which was then reviewed with supervisors and combined with appropriate care pathways. Case managers reported acceptance of risk flags based on a predictive algorithm in their surveillance system tools, particularly if the information was available in a timely manner and used in conjunction with their clinical judgment. Implementation of risk flags needed to be programmed on a dichotomous basis, so the algorithm could produce output indicating high versus low risk. To dichotomize the continuous predicted probabilities, we developed a cutoff point that favored specificity, with the understanding that case managers’ clinical judgment would help increase sensitivity. Suicide risk prediction algorithms show promise, but implementation to guide clinical care remains relatively elusive. Our study demonstrates the utility of working with partners to develop and guide the operationalization of risk prediction algorithms to enhance clinical care in a community setting.
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DOI：
10.1186/1748-5908-4-50
发表时间：
2009-08-07
期刊：
Implementation science : IS
影响因子：
--
作者：
Damschroder LJ;Aron DC;Keith RE;Kirsh SR;Alexander JA;Lowery JC
通讯作者：
Lowery JC
影响因子：
1.4
作者：
Linthicum, Kathryn P.;Schafer, Katherine Musacchio;Ribeiro, Jessica D.
通讯作者：
Ribeiro, Jessica D.
影响因子：
3.2
作者：
Barzilay, Shira;Yaseen, Zimri S.;Galynker, Igor
通讯作者：
Galynker, Igor
影响因子：
3.7
作者：
Betz, Marian E.;Knoepke, Christopher E.;Matlock, Daniel D.
通讯作者：
Matlock, Daniel D.
影响因子：
22.4
作者：
Franklin, Joseph C.;Ribeiro, Jessica D.;Nock, Matthew K.
通讯作者：
Nock, Matthew K.