识别在电子病历系统中实施人工智能辅助临床决策支持的促进因素和障碍

IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Systems Pub Date : 2024-09-18 DOI:10.1007/s10916-024-02104-9
Joseph Finkelstein, Aileen Gabriel, Susanna Schmer, Tuyet-Trinh Truong, Andrew Dunn
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引用次数: 0

摘要

近年来,计算技术的进步推动了人工智能(AI)医疗保健技术的发展。集成到电子健康记录(EHR)中的人工智能辅助临床决策支持(CDS)被证明在改善临床护理方面具有巨大潜力。随着人工智能辅助临床决策支持的迅速普及,人们意识到,如果不仔细考虑与这些工具的实施和维护有关的社会技术问题,就会导致意想不到的后果,错失良机,并使这些潜在的有用技术得不到最佳利用。48 小时出院预测工具(48DPT)是一种新的人工智能辅助电子病历 CDS,用于促进出院规划。本研究旨在从方法学角度评估 48DPT 的实施情况,并使用经过验证的实施科学框架确定采用和维护的障碍和促进因素。研究采用了 RE-AIM(Reach、Effectiveness、Adoption、Implementation、Maintenance)的主要维度和实施研究综合框架(CFIR)的构架,对使用 48DPT 的 24 位主要利益相关者进行了访谈分析。通过对 48DPT 实施情况的系统评估,我们描述了实施过程中的促进因素和障碍,如缺乏认识、缺乏准确性和信任、可及性有限以及透明度等。根据我们的评估,确定了人工智能辅助电子病历 CDS 成功实施的关键因素。未来人工智能辅助电子病历数据采集系统的实施工作应从项目一开始就让主要的临床利益相关者参与人工智能工具的开发,支持人工智能模型的透明度和可解释性,为临床用户提供持续的教育和入职培训,并从临床人员那里获得有关数据采集系统性能的持续意见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.

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来源期刊
Journal of Medical Systems
Journal of Medical Systems 医学-卫生保健
CiteScore
11.60
自引率
1.90%
发文量
83
审稿时长
4.8 months
期刊介绍: Journal of Medical Systems provides a forum for the presentation and discussion of the increasingly extensive applications of new systems techniques and methods in hospital clinic and physician''s office administration; pathology radiology and pharmaceutical delivery systems; medical records storage and retrieval; and ancillary patient-support systems. The journal publishes informative articles essays and studies across the entire scale of medical systems from large hospital programs to novel small-scale medical services. Education is an integral part of this amalgamation of sciences and selected articles are published in this area. Since existing medical systems are constantly being modified to fit particular circumstances and to solve specific problems the journal includes a special section devoted to status reports on current installations.
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