Optimizing Clinical Decision Support Alerts Using Explainable Artificial Intelligence (XAI)

使用可解释的人工智能 (XAI) 优化临床决策支持警报

• 批准号: 10505752
• 负责人: Siru Liu
• 金额: $ 8.96万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-10 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10505752
• 关键词: Academic Medical Centers; Accounting; Address; Affect; Area; Artificial Intelligence; Behavior; Blinded; Body Weight decreased; Cardiopulmonary Resuscitation; Cholesterol; Clinical; Clinical Decision Support Systems; Complex; Consensus; Data; Delphi Study; Development; Development Plans; Electronic Health Record; Evaluation; Fast Healthcare Interoperability Resources; Fatigue; Federal Government; Feedback; Fire - disasters; Goals; Health system; Healthcare; Healthcare Systems; Informatics; Intelligence; Lead; Learning; Logic; Machine Learning; Manuals; Maps; Mentors; Methods; Modeling; Outcome; Patients; Perception; Performance; Phase; Process; Quality of Care; Research; Research Personnel; Science; Services; Standardization; Suggestion; Taxonomy; Techniques; Time; Training; Woman; Work; base; burnout; career; career development; clinical decision support; desensitization; design; experience; health care quality; hospice environment; implementation science; improved; innovation; men; multidisciplinary; neglect; patient safety; point of care; predictive modeling; public health relevance; recruit; response; screening; skills; standardized care; support tools; tool; trustworthiness; Academic Medical Centers; Accounting; Address; Affect; Area; Artificial Intelligence; Behavior; Blinded; Body Weight decreased; Cardiopulmonary Resuscitation; Cholesterol; Clinical; Clinical Decision Support Systems; Complex; Consensus; Data; Delphi Study; Development; Development Plans; Electronic Health Record; Evaluation; Fast Healthcare Interoperability Resources; Fatigue; Federal Government; Feedback; Fire - disasters; Goals; Health system; Healthcare; Healthcare Systems; Informatics; Intelligence; Lead; Learning; Logic; Machine Learning; Manuals; Maps; Mentors; Methods; Modeling; Outcome; Patients; Perception; Performance; Phase; Process; Quality of Care; Research; Research Personnel; Science; Services; Standardization; Suggestion; Taxonomy; Techniques; Time; Training; Woman; Work; base; burnout; career; career development; clinical decision support; desensitization; design; experience; health care quality; hospice environment; implementation science; improved; innovation; men; multidisciplinary; neglect; patient safety; point of care; predictive modeling; public health relevance; recruit; response; screening; skills; standardized care; support tools; tool; trustworthiness

PROJECT SUMMARY Over the past decade, the federal government has spent more than $34 billion on the meaningful use of elec- tronic health records (EHRs). However, the acceptance rate for clinical decision support (CDS) alerts, a critical component of EHRs, is less than 10%. The large number of low relevance alerts (e.g. a weight loss alert during a cardiac resuscitation) not only increases the burden on clinicians, but can lead to the onset of alert fatigue, resulting in the neglect of important alerts and posing a serious threat to patient safety. Currently, alerts are improved primarily through manual review and by collecting user feedback. However, these methods are labor- intensive and do not allow for a timely analysis of user responses to alerts from a comprehensive aspect. The amount of alert log data is large, Vanderbilt University Medical Center generated over 3 million alert firings in 2020. There is an urgent need to utilize the data from the alert log and EHR to develop a data-driven process to generate suggestions for refining alert logic or improving clinical processes. To address this gap, I propose to use explainable artificial intelligence (XAI) combined with bias mitigation tech- niques to build predictive models that comprehensively learn user responses to alerts and in turn automatically generate responsible suggestions to improve the original logic of alerts. This project is divided into two phases with three specific aims. In the K99 phase, I will be mentored by a multidisciplinary team of experts to learn the latest XAI and bias mitigation techniques, as well as CDS evaluation and management, and to achieve the following two aims: Aim 1) Develop a standard-based taxonomy of features that affect user response to CDS alerts and Aim 2) Develop a data-driven process to generate suggestions for improving alert criteria using XAI approaches. I will then transition to the independent research phase R00 to achieve Aim 3) Evaluate generated suggestions using a mixed-methods design. Throughout this research, I expect to provide a standards-based taxonomy of features that affect user response to alerts, an innovative data-driven process capable of generating suggestions to improve alerts. I will also produce a set of expert-validated suggestions. This study could signif- icantly contribute to the CDS management and clinical processes improvements. My career development plan and the proposed research are aligned with my current skills and experiences in CDS and machine learning. Based on complementary expertise from my mentor team, I will develop competen- cies in four areas: CDS, informatics methods, implementation science, and career development and profession- alism to transfer to an independent researcher. Overall, this project can help me launch an independent research career in developing explainable, intelligent CDS tools to improve patient safety, provide standardized care, and promote an equitable and efficient healthcare system.

项目摘要 在过去的十年中，联邦政府花了超过340亿美元来有意义地使用电子 Tronic Health Records（EHRS）。但是，临床决策支持（CD）警报的接受率，关键 EHR的组成部分小于10％。大量低相关警报（例如，在 心脏复苏不仅增加了临床医生的负担，而且会导致警报疲劳发作， 导致忽视重要的警报，并对患者的安全构成严重威胁。目前，警报是 主要通过手动审查和收集用户反馈进行改进。但是，这些方法是劳动 - 密集，不允许及时分析用户对来自全面方面的警报的响应。这 警报日志数据量很大，范德比尔特大学医学中心在 2020年。迫切需要利用警报日志和EHR的数据来开发数据驱动的过程 生成提炼警报逻辑或改善临床过程的建议。 为了解决这一差距，我建议使用可解释的人工智能（XAI）以及偏见缓解技术 - 构建预测模型的有机项目，这些模型可以全面学习对警报的响应，然后自动自动学习 产生负责任的建议，以改善警报的原始逻辑。这个项目分为两个阶段 具有三个具体目标。在K99阶段，我将受到多学科专家团队的指导，以学习 最新的XAI和偏见缓解技术，以及CD评估和管理，并实现 以下两个目的：目标1）开发基于标准的分类学特征，以影响用户对CD的响应 警报和目标2）开发一个数据驱动过程，以生成使用XAI提高警报标准的建议 方法。然后，我将过渡到独立研究阶段R00以实现目标3）评估生成的 使用混合方法设计的建议。在整个研究中，我希望提供基于标准的 影响用户对警报响应的功能的分类学，这是一种创新的数据驱动过程，能够生成 提高警报的建议。我还将提出一套专家验证的建议。这项研究可能显着 明显地有助于CDS管理和临床过程改进。 我的职业发展计划和拟议的研究与我当前的技能和经验保持一致 CD和机器学习。根据我的导师团队的补充专业知识，我将发展竞争 - 在四个领域的CIE：CD，信息学方法，实施科学以及职业发展和职业 - 向独立研究人员转移的疾病。总体而言，这个项目可以帮助我启动独立研究 开发可解释的智能CD工具以提高患者安全，提供标准化护理和 促进公平，高效的医疗保健系统。