背景:尽管有大量研究评估了心房颤动(AF)的各种心律控制策略,在单个患者中确定最佳策略通常基于反复试验,没有基于国际准则/建议的一刀切的方法。决定,因此,仍然是个人的,并且很好地帮助了临床决策支持系统,特别是由人工智能(AI)指导的。QRhythm利用2阶段机器学习(ML)模型,根据一组临床因素确定给定患者的最佳节律管理策略,其中该模型首先使用监督学习来预测专家临床医生的行为,并通过强化学习确定最佳策略,以获得最佳临床结果-症状复发的复合,住院治疗,和中风。
目的:我们定性地评估了一部小说,基于AI,用于房颤节律管理的临床决策支持系统(CDSS),叫做QRhythm,它使用监督和强化学习来推荐心率控制或三种类型的节律控制策略之一-外部心脏复律,抗心律失常药物,或基于个体患者特征的消融。
方法:33名临床医生,包括心脏病学主治医师和研究员以及内科主治医师和住院医师,对节奏进行了评估,随后进行一项调查,以评估在节律管理中使用自动化CDSS的相对舒适度,并检查未来发展的领域。
结果:对33个提供者进行了调查,培训水平从住院医师到同伴到参加。在接受调查的应用程序的特征中,安全对提供者来说最重要,平均重要性评分为5分之4.7分(SD0.72)。此优先级之后是临床完整性(希望提供具有临床意义的建议;重要性等级4.5,SD0.9),向后可解释性(用于创建算法的总体透明度;重要性等级4.3,SD0.65),算法的透明度(决策背后的推理;重要性评级4.3,SD0.88),和提供者自主性(挑战模型做出的决策的能力;重要性评级3.85,SD0.83)。使用该应用程序的提供商将推荐的完整性列为他们在持续临床使用该模型时最关心的问题,其次是应用程序的功效和患者数据的安全性。对应用程序的信任各不相同;1(17%)的提供商回答说,他们有些不同意该声明,“我相信QRhythm应用程序提供的建议,\“2(33%)提供者对声明做出了中立的回应,3人(50%)有点同意这一说法。
结论:ML应用程序的安全性是被调查提供商的最高优先级,对这种模式的信任仍然各不相同。ML在医疗保健中的广泛临床接受取决于提供者对算法的信任程度。建立这种信任需要确保模型的透明度和可解释性。
BACKGROUND: Despite the numerous studies evaluating various rhythm control strategies for atrial fibrillation (AF), determination of the optimal strategy in a single patient is often based on
trial and error, with no one-size-fits-all approach based on international guidelines/recommendations. The decision, therefore, remains personal and lends itself well to help from a clinical decision support system, specifically one guided by artificial intelligence (AI). QRhythm utilizes a 2-stage machine learning (ML) model to identify the optimal rhythm management strategy in a given patient based on a set of clinical factors, in which the model first uses supervised learning to predict the actions of an expert clinician and identifies the best strategy through reinforcement learning to obtain the best clinical outcome-a composite of symptomatic recurrence, hospitalization, and stroke.
OBJECTIVE: We qualitatively evaluated a novel, AI-based, clinical decision support system (CDSS) for AF rhythm management, called QRhythm, which uses both supervised and reinforcement learning to recommend either a rate control or one of 3 types of rhythm control strategies-external cardioversion, antiarrhythmic medication, or ablation-based on individual patient characteristics.
METHODS: Thirty-three clinicians, including cardiology attendings and fellows and internal medicine attendings and residents, performed an assessment of QRhythm, followed by a survey to assess relative comfort with automated CDSS in rhythm management and to examine areas for future development.
RESULTS: The 33 providers were surveyed with training levels ranging from resident to fellow to attending. Of the characteristics of the app surveyed, safety was most important to providers, with an average importance rating of 4.7 out of 5 (SD 0.72). This priority was followed by clinical integrity (a desire for the advice provided to make clinical sense; importance rating 4.5, SD 0.9), backward interpretability (transparency in the population used to create the algorithm; importance rating 4.3, SD 0.65), transparency of the algorithm (reasoning underlying the decisions made; importance rating 4.3, SD 0.88), and provider autonomy (the ability to challenge the decisions made by the model; importance rating 3.85, SD 0.83). Providers who used the app ranked the integrity of recommendations as their highest concern with ongoing clinical use of the model, followed by efficacy of the application and patient data security. Trust in the app varied; 1 (17%) provider responded that they somewhat disagreed with the statement, \"I trust the recommendations provided by the QRhythm app,\" 2 (33%) providers responded with neutrality to the statement, and 3 (50%) somewhat agreed with the statement.
CONCLUSIONS: Safety of ML applications was the highest priority of the providers surveyed, and trust of such models remains varied. Widespread clinical acceptance of ML in health care is dependent on how much providers trust the algorithms. Building this trust involves ensuring transparency and interpretability of the model.