Timely Response to In-Hospital Deterioration Through Design of Actionable Augmented Intelligence

摘要 用于预测临床结果的增强智能（AI）模型的开发正在呈指数级增长。 自动化临床监测有助于早期发现院内恶化，如败血症和急性 肾损伤（阿基）是一种有前途的AI应用。每年有多达30万美国医院患者死于 败血症和阿基等问题，这些死亡中有5%或更多是可以预防的。更多的病人 延迟响应的后遗症造成的伤害或额外费用。与传统的基于规则的风险相比 预测，使用机器学习等方法的先进人工智能模型证明了 预测败血症和阿基损伤这些系统在实践中的有效性可能取决于AI风险如何 信息被整合到临床工作流程和技术中，但我们不知道设计或 评估有效的院内人工智能风险信息呈现和用户交互。众所周知， 可解释的人工智能是可取的，但需要解释什么以及如何有效和高效地做到这一点， 知道的有必要了解最终用户对AI在特定临床环境中的价值的看法。 我们将借鉴我们团队最近对有效临床显示设计的研究，人类-AI的理论模型， 性能，人类AI设计原则的应用，以及以人为本的设计方法的应用 设计和评估有效的方法，以支持及时应对脓毒症和阿基风险。我们的首要 目标是：确定影响临床医生对人工智能有用性的看法的因素， 有效的健康风险监测人机交互的原则，并设计人机用户界面， 有意义地改善人类AI在应对败血症和阿基时的表现。在目标1中，我们将开发一个 时间推理AI模型用于预测脓毒症和阿基的院内发展。我们将应用这个模型 回顾患者数据，作为研究活动的背景。使用图表审查，我们将量化 人类-AI系统性能的现实指标，考虑到AI模型是否具有 在临床团队怀疑或采取措施应对事件之前预测恶化。在目标2中，我们将 采访临床医生，同时审查患者在住院期间病情变化的时间进展， 包括人工智能生成的风险信息。我们将收集影响临床医生诊断的因素的定性数据。 对人工智能信息有用性的认识，以实现早期识别患者问题的目标。在目标3中， 我们将与临床医生一起开展参与式设计活动，设计有效的以人为本的人工智能显示器， 相互作用，以支持对院内脓毒症和阿基的早期反应。最后，在目标4中，使用模拟现实 病人护理任务和比较传统的病人信息技术，我们将评估的影响， 以人为本的AI设计对人类AI性能的影响。我们将生成人机交互设计 健康风险监测指南。我们的研究结果有望创新人类与人工智能互动的设计， 电子健康记录（EHR）和医疗保健监测和通信技术。

Abstract Development of augmented intelligence (AI) models for predicting clinical outcomes is growing exponentially. Automated clinical surveillance to assist in early detection of in-hospital deterioration such as sepsis and acute kidney injury (AKI) is a promising AI application. As many as 300,000 US hospital patients die each year from problems like sepsis and AKI and 5% or more of these deaths are preventable. Many more patients suffer harm or additional costs as a sequelae to delayed response. Compared to traditional rule-based risk predictions, advanced AI models using methods such as machine learning demonstrate improved reliability of predicting sepsis and AKI. The effectiveness of these systems in practice will likely depend on how AI risk information is integrated into clinical workflow and technologies, yet we are not aware of research to design or evaluate effective in-hospital AI risk information presentation and user interaction. It is widely known that explainable AI is desirable, but what needs to be explained and how to do it effectively and efficiently is not known. There is a need to understand end user perspectives on the value of AI for specific clinical contexts. We will draw on our team's recent research on effective clinical display design, theoretical models of human-AI performance, application of human-AI design principles, and application of human-centered design methods to design and evaluate effective approaches to support timely response to sepsis and AKI risk. Our primary objectives are to: identify factors that influence clinicians' perceptions of AI usefulness, generate design principles for effective health risk surveillance human-AI interaction, and design human-AI user interfaces that meaningfully improve human-AI performance when responding to sepsis and AKI. In Aim 1, we will develop a temporal reasoning AI model for predicting in-hospital development of sepsis and AKI. We will apply this model to retrospective patient data to serve as context for research activities. Using chart review, we will quantify realistic metrics of human-AI system performance that take into account whether the AI model would have predicted deterioration before the clinical team suspected or acted in response to the event. In Aim 2, we will interview clinicians while reviewing temporal progression of a patient's change in condition over their stay, including AI generated risk information. We will gather qualitative data on factors that influence clinicians' perceptions of usefulness of AI information toward the goal of early identification of patient problems. In Aim 3, we will conduct participatory design activities with clinicians to design effective human-centered AI display and interaction to support early response to in-hospital sepsis and AKI. Finally, in Aim 4, using simulated realistic patient care tasks and comparing to traditional patient information technologies, we will evaluate the impact of human-centered AI designs on human-AI performance. We will generate human-AI interaction design guidance for health risk surveillance. Our findings are expected to innovate design for human-AI interaction in electronic health records (EHRs) and health-care monitoring and communication technologies.