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基层医疗临床医生参与实施用于家族性高胆固醇血症靶向筛查的机器学习算法。

Primary care clinician engagement in implementing a machine-learning algorithm for targeted screening of familial hypercholesterolemia.

作者信息

Kim Kain, Faruque Samir C, Kulp David, Lam Shivani, Sperling Laurence S, Eapen Danny J

机构信息

Emory School of Medicine, Atlanta GA 30306, USA.

Division of General Medicine, Washington University School of Medicine, St. Louis MO 63110, USA.

出版信息

Am J Prev Cardiol. 2024 Jul 22;19:100710. doi: 10.1016/j.ajpc.2024.100710. eCollection 2024 Sep.

DOI:10.1016/j.ajpc.2024.100710
PMID:39176132
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11338952/
Abstract

OBJECTIVE

To assess the impact of a multi-pronged educational approach on the knowledge, attitudes, and behaviors regarding Familial Hypercholesterolemia (FH) management at a large academic medical center with the aim of empowering primary care clinicians (PCC) to diagnose and treat FH.

METHODS

A comprehensive educational program for PCCs on FH management was developed and piloted from July 2022 to March 2024. Components of our intervention included: 1. Implementation of a novel clinical decision support tool in the electronic medical record for FH management, 2. Development and dissemination of an interactive educational website focused on FH and its management, 3. Delivery of virtual instructional sessions to increase awareness of the tool, provide education on its use, and obtain support from institutional leadership, and 4. Direct outreach to a pilot subset of PCCs whose patients had been detected using the validated FIND FH® machine learning algorithm. Participating clinicians were surveyed at baseline before the intervention and after the educational session.

RESULTS

70 PCC consented to participate in the study with a survey completion rate of 79 % ( = 55) and 42 % ( = 23) for the baseline and follow-up surveys, respectively. Objective PCC knowledge scores improved from 40 to 65 % of responders correctly responding to at least 2/3rds of survey questions. Despite the fact that 87 % identified PCC's as most effective for early detection of FH, 100 % of PCCs who received direct outreach chose to defer care to an outpatient cardiologist over pursuing workup in the primary care setting.

CONCLUSION

Empowering PCCs in management of FH serves as a key strategy in addressing this underdiagnosed and undertreated potentially life-threatening condition. A systems-based approach to addressing these aims may include leveraging EMR-based clinical decision support models and cross-disciplinary educational partnerships with medical specialists.

摘要

目的

在一家大型学术医疗中心评估多管齐下的教育方法对家族性高胆固醇血症(FH)管理方面的知识、态度和行为的影响,旨在使初级保健临床医生(PCC)有能力诊断和治疗FH。

方法

为PCCs制定了一项关于FH管理的综合教育计划,并于2022年7月至2024年3月进行试点。我们干预措施的组成部分包括:1. 在电子病历中实施一种用于FH管理的新型临床决策支持工具;2. 开发并传播一个专注于FH及其管理的交互式教育网站;3. 提供虚拟教学课程,以提高对该工具的认识,提供其使用方面的教育,并获得机构领导层的支持;4. 直接联系一部分PCCs,这些PCCs的患者是使用经过验证的FIND FH®机器学习算法检测出来的。在干预前的基线和教育课程结束后对参与的临床医生进行了调查。

结果

70名PCC同意参与研究,基线调查和随访调查的完成率分别为79%(n = 55)和42%(n = 23)。PCC的客观知识得分从40%提高到65%,即至少三分之二的应答者能正确回答至少三分之二的调查问题。尽管87%的人认为PCCs在FH的早期检测中最有效,但接受直接联系的PCCs中有100%选择将护理推迟到门诊心脏病专家处,而不是在初级保健环境中进行检查。

结论

使PCCs有能力管理FH是解决这种诊断不足和治疗不足的潜在危及生命状况的关键策略。实现这些目标的基于系统的方法可能包括利用基于电子病历的临床决策支持模型以及与医学专家的跨学科教育伙伴关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6950/11338952/77f994193a34/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6950/11338952/e904b94c0b36/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6950/11338952/ce6e3a5c6fe2/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6950/11338952/77f994193a34/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6950/11338952/e904b94c0b36/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6950/11338952/ce6e3a5c6fe2/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6950/11338952/77f994193a34/gr2.jpg

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本文引用的文献

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患者的经历与家族性高胆固醇血症全球行动呼吁相一致。
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