Background: Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care. Objective: This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows. Methods: We conducted semistructured interviews with 15 frontline emergency department physicians and rapid response team nurses who participated in the Sepsis Watch quality improvement initiative. Interviews were audio recorded and transcribed. We used a modified grounded theory approach to identify key themes and analyze qualitative data. Results: A total of 3 dominant themes emerged: perceived utility and trust, implementation of Sepsis Watch processes, and workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by the perceived accuracy and utility of the model from personal program experience. Implementation of Sepsis Watch was facilitated by the easy-to-use tablet application and communication strategies that were developed by nurses to share model outputs with physicians. Barriers included the flow of information among clinicians and gaps in knowledge about the model itself and broader workflow processes. Conclusions: This study generated insights into how frontline clinicians perceived machine learning models and the barriers to integrating them into clinical workflows. These findings can inform future efforts to implement machine learning interventions in real-world settings and maximize the adoption of these interventions.

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中文翻译：

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将机器学习系统集成到临床工作流程中：定性研究

背景：机器学习模型具有提高诊断准确性和管理急性疾病的潜力。尽管评估和验证此类模型的工作日趋增多，但对于如何将其作为常规临床护理的一部分进行最佳翻译和实施，人们所知甚少。目的：本研究旨在探讨影响机器学习败血症预警系统（Sepsis Watch）整合到临床工作流程中的因素。方法：我们对参与脓毒症观察质量改善计划的15名一线急诊科医师和快速反应小组护士进行了半结构化访谈。采访被录音和转录。我们使用了修改后的扎根理论方法来识别关键主题并分析定性数据。结果：总共出现了3个主要主题：感知的效用和信任，脓毒症观察流程的实施以及员工注意事项。参与者描述了他们对机器学习模型的不熟悉。结果，临床医生的信任受到个人程序经验对模型的感知准确性和实用性的影响。护士开发的易于使用的平板电脑应用程序和交流策略促进了脓毒症观察的实施，该策略可与医生共享模型输出。障碍包括临床医生之间的信息流以及关于模型本身和更广泛的工作流程的知识差距。结论：这项研究对一线临床医生如何看待机器学习模型以及将其集成到临床工作流程中的障碍产生了见解。

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