Application of a Machine Learning to Enhance e-Triggers to Detect and Learn from Diagnostic Safety Events

急诊科（艾德）诊断错误的频率在很大程度上是未知的，但很可能是 显著迫切需要创建提供诊断安全数据的测量方法， 临床医生和领导者可以根据这些数据采取行动，以防止诊断损害。电子触发器（e- 触发器）工具挖掘大量的临床和管理数据，以识别可能的不良事件信号 并证明有能力识别诊断错误。这些工具比 其他方法，并且可以将需要人工审查的记录数量减少到最高伤害风险的记录。 在之前的工作中，我们使用基于规则的电子触发算法来识别提示遗漏的护理模式。 或在初级保健和住院治疗中延迟诊断。例如，几天后， 后来由一个计划外的住院治疗可能表明潜在的问题，诊断过程中， 诊所的访问。我们还提出了一个知识发现框架，安全Dx触发器工具框架， 使卫生保健组织（HCO）能够开发和实施电子触发工具，以衡量诊断 使用综合电子健康记录（EHR）数据。对这些案例的审查和分析可以 发现安全问题，并提供诊断过程故障和相关贡献的信息 这些因素反过来又可以产生学习和反馈，以便改进。 来自机器学习（ML）和数据科学的复杂技术可以帮助告知“第二个” 比基于规则的电子触发器更好地识别诊断错误和/或危害的电子触发器算法 这需要大量的手工操作和图表审查。与基于规则的系统相比，ML技术 可以帮助从例子中学习，并准确地检索诊断错误的图表，而不需要“手 电子触发器的制作我们将把电子触发器应用于包含纵向病人护理的综合电子病历 数据（病程记录、测试、转诊），提供患者诊断过程的详细描述。使用 国家VA数据，包括来自900万退伍军人的数据，以及来自Geisinger卫生系统的数据， HCO为约300万患者提供服务，我们提出以下目标： 目标1 -开发、完善、测试和应用Safer Dx电子触发器，以实现检测、测量和学习 不同急诊科（艾德）设置中的诊断错误。我们将计算出 艾德诊断错误的基础上，这些电子触发器，并描述了可预防的诊断损害的负担。 目的2 -探索机器学习技术，产生强大，准确的模型来预测诊断错误 使用从包含诊断错误的专家标记的患者记录中获得的EHR富集数据（来自目标1）。 据我们所知，这是ML在诊断错误测量中的第一个应用， 扩大由专家驱动的电子触发器开发和完善工作。新开发的电子触发器可以进行试点测试 并在其他HCO实施，使他们能够从数字数据中创建可操作的安全相关见解。

The frequency of diagnostic errors in emergency departments (ED) is largely unknown but likely to be significant. There is a compelling need to create measurement methods that provide diagnostic safety data to clinicians and leaders who in turn can act upon these data to prevent diagnostic harm. Electronic trigger (e- trigger) tools mine vast amounts of clinical and administrative data to identify signals for likely adverse events and have demonstrated capability to identify diagnostic errors. Such tools are more efficient and effective than other methods and can reduce the number of records requiring human review to those at highest risk of harm. In prior work, we used rules-based e-trigger algorithms to identify patterns of care suggestive of missed or delayed diagnoses in primary care and inpatient settings. For instance, a clinic visit followed several days later by an unplanned hospitalization could be indicative of potential problems with the diagnostic process at the clinic visit. We also proposed a knowledge discovery framework, the Safer Dx Trigger Tools Framework, to enable health care organizations (HCOs) to develop and implement e-trigger tools to measure diagnostic errors using comprehensive electronic health record (EHR) data. Review and analysis of these cases can uncover safety concerns and provide information on diagnostic process breakdowns and related contributory factors, which in turn could generate learning and feedback for improvement purposes. Sophisticated techniques from machine learning (ML) and data science could help inform ‘second generation’ e-trigger algorithms that better identify diagnostic errors and/or harm than rules-based e-triggers that require substantial manual effort and chart reviews. In contrast to rules-based systems, ML techniques could help learn from examples and accurately retrieve charts with diagnostic error without the need for “hand crafting” of an e-trigger. We will apply e-triggers to comprehensive EHRs that contain longitudinal patient care data (progress notes, tests, referrals) that provide an extensive picture of patients’ diagnostic journeys. Using national VA data, including data from 9 million veterans, and data from Geisinger health system, a pioneer HCO that serves approximately 3 million patients, we propose the following aims: Aim 1 – To develop, refine, test, and apply Safer Dx e-triggers to enable detection, measurement, and learning from diagnostic errors in diverse emergency department (ED) settings. We will calculate the frequency of diagnostic errors in the ED based on these e-triggers and describe the burden of preventable diagnostic harm. Aim 2 - To explore machine learning techniques that yield robust, accurate models to predict diagnostic errors using EHR-enriched data derived from expert-labeled patient records containing diagnostic errors (from Aim 1). To our knowledge this is the first ML application in diagnostic error measurement, which could help scale up expert-driven e-trigger development and refinement. Newly developed e-triggers can be pilot tested and implemented at other HCOs, enabling them to create actionable safety-related insights from digital data.