这只是他们的另一个工具:揭示公众和患者对临床医生在围手术期护理中使用机器学习的看法。

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiomara T Gonzalez, Karen Steger-May, Joanna Abraham
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引用次数: 0

摘要

目的:在围手术期护理中成功实施机器学习增强型临床决策支持系统(ML-CDSS)需要优先考虑以患者为中心的方法,以确保符合社会期望。我们评估了公众和手术患者对在围手术期护理中使用机器学习辅助临床决策支持系统的态度和观点:我们开展了一项顺序解释性研究。第一阶段通过调查收集公众意见。第二阶段通过焦点小组和访谈了解手术患者的经历和态度:第一阶段共考虑了 281 名受访者(140 名男性 [49.8%])的数据。在没有 ML 意识的参与者中,男性报告围手术期团队更接受(OR = 2.97;95% CI,1.36-6.49)和拥护(OR = 2.74;95% CI,1.23-6.09)ML-CDSS 的可能性几乎是女性的三倍。在所有围手术期阶段,男性对 ML-CDSS 的接受度几乎是女性的两倍,OR 值从 1.71 到 2.07 不等。在第二阶段,从 10 名手术患者那里了解到的情况表明,他们一致认为 ML-CDSS 应主要发挥辅助功能。术前和术后阶段被明确认定为 ML-CDSS 可以加强护理服务的场所。患者要求外科医生通过多个平台传播有关 ML-CDSS 在其护理中的作用的教育:只要 ML-CDSS 的作用是辅助围手术期团队,公众和手术患者都能接受在围手术期护理中使用 ML-CDSS。然而,将 ML-CDSS 整合到围术期工作流程中给医疗机构带来了独特的挑战。本研究的启示可为支持大规模实施 ML-CDSS 并让患者在围手术期各阶段采用 ML-CDSS 的策略提供参考。促进 ML-CDSS 可行性和可接受性的关键策略包括:由临床医生主导讨论 ML-CDSS 在围手术期护理中的作用、建立评估 ML-CDSS 临床效用的标准以及开展患者教育。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.

Objectives: Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care.

Materials and methods: A sequential explanatory study was conducted. Stage 1 collected public opinions through a survey. Stage 2 ascertained surgical patients' experiences and attitudes via focus groups and interviews.

Results: For Stage 1, a total of 281 respondents' (140 males [49.8%]) data were considered. Among participants without ML awareness, males were almost three times more likely than females to report more acceptance (OR = 2.97; 95% CI, 1.36-6.49) and embrace (OR = 2.74; 95% CI, 1.23-6.09) of ML-CDSS use by perioperative teams. Males were almost twice as likely as females to report more acceptance across all perioperative phases with ORs ranging from 1.71 to 2.07. In Stage 2, insights from 10 surgical patients revealed unanimous agreement that ML-CDSS should primarily serve a supportive function. The pre- and post-operative phases were identified explicitly as forums where ML-CDSS can enhance care delivery. Patients requested for education on ML-CDSS's role in their care to be disseminated by surgeons across multiple platforms.

Discussion and conclusion: The general public and surgical patients are receptive to ML-CDSS use throughout their perioperative care provided its role is auxiliary to perioperative teams. However, the integration of ML-CDSS into perioperative workflows presents unique challenges for healthcare settings. Insights from this study can inform strategies to support large-scale implementation and adoption of ML-CDSS by patients in all perioperative phases. Key strategies to promote the feasibility and acceptability of ML-CDSS include clinician-led discussions about ML-CDSS's role in perioperative care, established metrics to evaluate the clinical utility of ML-CDSS, and patient education.

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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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