There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians’ resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model’s generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model’s clinical impact; (6) internal validation of the model’s performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems.

在医学决策中使用贝叶斯网络 (BN) 进行了大量研究工作。然而，由于开发准确的 BN 与证明其临床实用性之间存在差距，这并没有导致 BN 在临床实践中广泛采用。本文研究了这个问题，目的是通过对描述医疗保健中 BN 的文章进行全面的文献综述，找到解决问题的解释和方法。基于从 3810 篇最相关的文章系统地缩小到 116 篇的文献集，本文分析了收益、障碍和促进因素 (BBF)使用 ITPOSMO-BBF 框架在医疗保健中实施基于 BN 的系统。一个关键发现是，文献中的作品很少考虑障碍，即使这些障碍被识别出来，它们也与促进因素无关。主要发现是，障碍可分为：（1）数据不足；(2) 临床医生对新技术的抵触情绪；(3)缺乏临床可信度；(4) 未能证明临床影响；(5) 缺乏可接受的预测性能；(6) 缺乏模型普遍性的证据。促进因素可分为： (1) 数据收集改进；(2) 软件和技术改进；(3) 具有可解释且易于使用的基于 BN 的系统；(4) 临床参与模型的开发或审查；(5)模型临床影响的调查；(6) 模型性能的内部验证；(7) 模型的外部验证。这些分组构成了通用框架的强大基础，可用于制定策略以确保在一线护理环境中采用基于 BN 的临床决策支持系统。本次审查的结果有望通过更深入地理解实践中被忽视的 BN 采纳问题并促进实施基于 BN 的系统的努力来加强研究人员之间的对话。