Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Milan, Italy.
Department of Social and Political Sciences, Bocconi University, Milan, Italy.
J Med Internet Res. 2024 Nov 25;26:e55897. doi: 10.2196/55897.
There is a growing enthusiasm for machine learning (ML) among academics and health care practitioners. Despite the transformative potential of ML-based applications for patient care, their uptake and implementation in health care organizations are sporadic. Numerous challenges currently impede or delay the widespread implementation of ML in clinical practice, and limited knowledge is available regarding how these challenges have been addressed.
This work aimed to (1) examine the characteristics of ML-based applications and the implementation process in clinical practice, using the Consolidated Framework for Implementation Research (CFIR) for theoretical guidance and (2) synthesize the strategies adopted by health care organizations to foster successful implementation of ML.
A systematic literature review was conducted based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search was conducted in PubMed, Scopus, and Web of Science over a 10-year period (2013-2023). The search strategy was built around 4 blocks of keywords (artificial intelligence, implementation, health care, and study type). Only empirical studies documenting the implementation of ML applications in clinical settings were considered. The implementation process was investigated using a thematic analysis and coding procedure.
Thirty-four studies were selected for data synthesis. Selected papers were relatively recent, with only 9% (3/34) of records published before 2019. ML-based applications were implemented mostly within hospitals (29/34, 85%). In terms of clinical workflow, ML-based applications supported mostly prognosis (20/34, 59%) and diagnosis (10/34, 29%). The implementation efforts were analyzed using CFIR domains. As for the inner setting domain, access to knowledge and information (12/34, 35%), information technology infrastructure (11/34, 32%), and organizational culture (9/34, 26%) were among the most observed dimensions influencing the success of implementation. As for the ML innovation itself, factors deemed relevant were its design (15/34, 44%), the relative advantage with respect to existing clinical practice (14/34, 41%), and perceived complexity (14/34, 41%). As for the other domains (ie, processes, roles, and outer setting), stakeholder engagement (12/34, 35%), reflecting and evaluating practices (11/34, 32%), and the presence of implementation leaders (9/34, 26%) were the main factors identified as important.
This review sheds some light on the factors that are relevant and that should be accounted for in the implementation process of ML-based applications in health care. While the relevance of ML-specific dimensions, like trust, emerges clearly across several implementation domains, the evidence from this review highlighted that relevant implementation factors are not necessarily specific for ML but rather transversal for digital health technologies. More research is needed to further clarify the factors that are relevant to implementing ML-based applications at the organizational level and to support their uptake within health care organizations.
PROSPERO 403873; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=403873.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/47971.
机器学习（ML）在学术界和医疗保健从业者中越来越受欢迎。尽管基于 ML 的应用程序在患者护理方面具有变革性的潜力，但它们在医疗保健组织中的采用和实施仍零星存在。目前有许多挑战阻碍或延迟了 ML 在临床实践中的广泛实施，并且对于这些挑战是如何被解决的，我们的了解是有限的。
本研究旨在（1）使用实施研究整合框架（CFIR）进行理论指导，研究基于 ML 的应用程序在临床实践中的特征和实施过程，以及（2）综合医疗保健组织采用的策略，以促进 ML 的成功实施。
根据 PRISMA（系统评价和荟萃分析的首选报告项目）指南进行了系统文献综述。在 10 年的时间内（2013-2023 年），在 PubMed、Scopus 和 Web of Science 中进行了搜索。该搜索策略围绕着 4 个关键字块（人工智能、实施、医疗保健和研究类型）构建。仅考虑记录 ML 应用程序在临床环境中实施情况的实证研究。使用主题分析和编码程序来调查实施过程。
选择了 34 项研究进行数据综合。所选论文相对较新，只有 9%（3/34）的记录发表于 2019 年之前。基于 ML 的应用程序主要在医院内实施（29/34，85%）。就临床工作流程而言，基于 ML 的应用程序主要支持预后（20/34，59%）和诊断（10/34，29%）。使用 CFIR 领域分析实施工作。就内部环境领域而言，获得知识和信息（12/34，35%）、信息技术基础设施（11/34，32%）和组织文化（9/34，26%）是影响实施成功的最常见维度。就 ML 创新本身而言，被认为相关的因素包括其设计（15/34，44%）、相对于现有临床实践的相对优势（14/34，41%）和感知复杂性（14/34，41%）。就其他领域（即流程、角色和外部环境）而言，利益相关者的参与（12/34，35%）、反思和评估实践（11/34，32%）以及实施领导者的存在（9/34，26%）是确定为重要的主要因素。
本综述揭示了与医疗保健中基于 ML 的应用程序实施过程相关且应考虑的因素。虽然 ML 特定维度的相关性，如信任，在几个实施领域中清晰可见，但本综述的证据强调，相关的实施因素不一定是 ML 特有的，而是数字健康技术的普遍因素。需要进一步研究，以进一步澄清与组织层面实施 ML 相关的因素，并支持其在医疗保健组织中的采用。
