Learning alerting models for clinical care from EMR data and human knowledge

从 EMR 数据和人类知识中学习临床护理警报模型

基本信息

批准号：
10521549
负责人：
Gilles Clermont
金额：
$ 64.49万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-30 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10521549
关键词：
Address Area Automobile Driving Behavior Caregivers Cause of Death Clinical Clinical Management Clinical Trials Complication Computerized Medical Record Computers Conduct Clinical Trials Data Decision Support Model Development Evaluation Evaluation Studies Event Funding Generations Goals Growth Healthcare Human Hybrids Immunosuppressive Agents Impact evaluation Individual Information Resources Inpatients Intensive Care Units Intervention Knowledge Learning Literature Machine Learning Medical Errors Methodology Methods Modeling Monitor Operative Surgical Procedures Outcome Outpatients Output Patient-Focused Outcomes Patients Pattern Performance Pharmaceutical Preparations Physicians Pilot Projects Play Practice Management Process Production Research Resolution Resources Role Running System Tacrolimus Time Training Work adjudicate base clinical care clinical practice data archive human-in-the-loop improved knowledge base liver transplantation machine learning model machine learning prediction programs prospective secondary endpoint tool

项目摘要

Abstract Medical errors are more broadly defined as adverse clinical events that are preventable. Studies show that medical errors remain one of the key challenges of health care and recent literature ranks medical errors as one of the leading causes of death in the US. The urgency and the scope of the problem prompt the development of solutions aimed to aid clinicians in reducing such errors. Computer-based monitoring and alerting systems that rely on information in electronic medical records (EMRs) play a key role in this effort. In the previous funding cycles, our group has been developing an outlier-based model-driven alerting methodology with significant potential to reduce medical errors. The method uses retrospective data to build machine learning models that predict physician actions from a broad representation of patient states. An alert is raised if a management action (or its omission) for the current patient deviates significantly from predicted management actions for similar patients. As an example of an actual alert generated by the system, consider a patient who has recently undergone a liver transplant and receives tacrolimus as immunosuppressive agent. The patient suffers a complication and undergoes corrective surgery; however, inadvertently, tacrolimus is not reordered following the surgery. Since not receiving the expected medication represents a deviation from predicted management practice in similar patients, it is a clinical outlier. Raising an alert to reorder the medication is therefore appropriate. Our current alerting system is silently deployed on the production electronic medical record system at UPMC and supports alerting in real-time. The current proposal takes the research program in a bold new direction. Alerting models will be enhanced using a variety of tools, including automatic evaluation of performance and the inclusion of an adaptive ICU-specific knowledge-base in addition to multi-domain, multi- resolution features derived from the EMR. Human experts will play a major role in determining appropriateness and usefulness of alerts when generated in real-time, contribute to the dynamic growth of the knowledge base, and evaluate the quality of the explanations provided for the alerts. Finally, the alerting system will be deployed across 12 ICUs in a step-wedge clinical trial to determine whether EHR-based alerting, when revealed to clinicians, modifies the rate and timing of their actions. Secondary end-points will include alert performance metrics, process- related outcomes, and patient-centered outcomes.

抽象的医疗错误更广泛地定义为可预防的不良临床事件。研究表明医疗错误仍然是医疗保健和最新文献的主要挑战之一将医疗错误列为美国的主要原因之一。紧迫性和问题的范围促使制定解决方案，旨在帮助临床医生减少这样的错误。基于计算机的监控和警报系统依赖于信息电子病历 (EMR) 在这项工作中发挥着关键作用。在之前的融资周期中，我们的团队一直在开发一种基于异常值的模型驱动的警报方法减少医疗失误的巨大潜力。该方法使用回顾性数据来构建机器学习模型可以根据患者的广泛代表性来预测医生的行为州。如果当前患者的管理行为（或其遗漏）出现偏差，则会发出警报与对类似患者的预测管理行动有显着差异。作为一个实际的例子系统生成的警报，考虑最近接受过肝脏移植的患者并接受他克莫司作为免疫抑制剂。患者出现并发症并且接受矫正手术；然而，不经意间，他克莫司并未在以下情况下重新订购：外科手术。由于没有接受预期的药物治疗代表与预期的偏差在类似患者的管理实践中，它是一个临床异常值。发出警报以重新排序因此药物治疗是适当的。我们当前的警报系统默默地部署在 UPMC 生产电子病历系统并支持实时警报。当前的提案将研究计划带向了一个大胆的新方向。警报模型将是使用各种工具进行增强，包括自动评估性能和除了多领域、多领域知识之外，还包含自适应 ICU 特定知识库来自 EMR 的分辨率特征。人类专家将在确定方面发挥重要作用实时生成警报的适当性和有用性有助于动态知识库的增长，并评估为知识库提供的解释的质量警报。最后，该警报系统将在阶梯式临床试验中部署在 12 个 ICU 中确定基于 EHR 的警报在向临床医生透露时是否会修改速率和他们行动的时机。次要端点将包括警报性能指标、流程相关结果和以患者为中心的结果。