Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study

Background Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making. Objective This study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. Methods This study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts. Results This federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors. Conclusions The use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety. International Registered Report Identifier (IRRID) DERR1-10.2196/24642

背景 诊断决策，尤其是在急诊科，是一个高度复杂的认知过程，涉及不确定性且易出错。多种因素相结合，包括患者因素（例如，病史、行为、复杂性和合并症）、医疗服务提供者 - 医疗团队因素（例如，认知负荷以及信息收集和综合）以及系统因素（例如，健康信息技术、拥挤、轮班工作和干扰）都可能导致诊断错误。利用电子触发器来识别具有某些医疗模式（例如，医疗升级）的患者记录，对于筛查诊断错误是有用的。一旦错误被识别，复杂的数据分析和机器学习技术可应用于现有的电子健康记录（EHR）数据集，以阐明影响诊断决策的潜在风险因素。 目的 本研究旨在利用大规模电子健康记录数据和机器学习技术识别与急诊科诊断错误相关的变量。 方法 本研究计划在电子健康记录数据存储库中使用触发算法，根据是否符合某些标准，生成标记为触发阳性或触发阴性的大量记录数据集。两个数据集的样本将通过病历审查进行验证，我们预计在触发阳性子集中会发现更多的诊断安全事件。机器学习将用于评估某些患者因素、医疗服务提供者 - 医疗团队因素和系统级风险因素与触发阳性和触发阴性图表的统计匹配组中的诊断安全信号之间的关系。 结果 这项由联邦政府资助的研究已获得两家学术医疗中心及其附属社区医院的机构审查委员会的批准。两个机构都在开发触发查询，并将使用触发器对样本队列进行标记。将使用关联规则挖掘、卡方自动交互检测以及分类和回归树等机器学习技术来发现重要变量，这些变量可纳入未来的临床决策支持系统，以帮助识别和降低导致诊断错误的风险。 结论 利用大规模电子健康记录数据集和机器学习来研究诊断过程中（与患者、医疗服务提供者 - 医疗团队和系统层面相关的）风险因素，可能有助于创建未来监测诊断安全的机制。国际注册报告标识符（IRRID）DERR1 - 10.2196/24642