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

• 批准号: 10705150
• 负责人: Gilles Clermont
• 金额: $ 63.44万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 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/10705150
• 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; adjudication; clinical care; clinical practice; data archive; data integration; electronic medical record system; human-in-the-loop; improved; knowledge base; knowledgebase; liver transplantation; machine learning model; machine learning prediction; process improvement; programs; prospective; secondary endpoint; tool

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.

摘要 医疗差错被更广泛地定义为可预防的不良临床事件。研究 表明医疗差错仍然是卫生保健和最近文献的主要挑战之一， 将医疗事故列为美国死亡的主要原因之一。紧迫性和 问题的范围促使开发旨在帮助临床医生减少 这样的错误。基于计算机的监测和警报系统， 电子医疗记录（EMR）在这方面发挥着关键作用。在以往的供资周期中， 我们小组一直在开发一种基于离群值的模型驱动警报方法， 减少医疗差错的巨大潜力。该方法使用回顾性数据来构建 机器学习模型，从患者的广泛代表性预测医生的行动 states.如果当前患者的管理措施（或其遗漏）偏离 这与对类似患者的预测管理措施有显著差异。作为一个实际的例子 系统生成的警报，考虑最近接受肝移植的患者 并接受他克莫司作为免疫抑制剂。病人有并发症， 接受矫正手术;然而，无意中，他克莫司没有在 手术由于未接受预期药物治疗代表偏离预期 在类似患者的管理实践中，它是一个临床离群值。引发警报以重新排序 因此，药物是适当的。我们当前的警报系统是静默部署在 UPMC的生产电子病历系统，并支持实时报警。 目前的提案将研究计划带到了一个大胆的新方向。警报模型将 使用各种工具进行增强，包括性能的自动评估和 除了多领域、多领域之外，还包含自适应ICU特定知识库 从EMR获得的分辨率特征。人类专家将在决定 实时生成的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，警报系统将在12个ICU中进行逐步楔形临床试验 以确定基于EHR的警报在向临床医生披露时是否会改变发生率， 他们行动的时机。次要终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。