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

急诊科(ED)的诊断错误频率在很大程度上是未知的，但很可能是 意义重大。迫切需要创建测量方法，以提供诊断安全数据 临床医生和领导可以根据这些数据采取行动，以防止诊断伤害。电子触发器(e- 触发)工具挖掘海量的临床和管理数据，以识别可能的不良事件的信号 并且已经证明了识别诊断错误的能力。这些工具比其他工具更有效率和效力 其他方法，并可将需要人工审查的记录数量减少到危害风险最高的记录。 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暗示错过的护理模式 或在初级保健和住院环境中延迟诊断。例如，几天后就去看了一次诊所 之后意外住院可能表明诊断过程中存在潜在问题 去诊所就诊。我们还提出了一个知识发现框架，即更安全的Dx触发器工具框架，以 使医疗保健组织(HCO)能够开发和实施电子触发工具来测量诊断 使用全面的电子健康记录(EHR)数据时出现错误。对这些案例的审查和分析可以 发现安全问题，并提供有关诊断流程故障和相关因素的信息 这些因素反过来又可以产生学习和反馈，以达到改进的目的。 来自机器学习(ML)和数据科学的复杂技术可以帮助了解第二个 生成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 这需要大量的人工工作和图表审查。与基于规则的系统相比，ML技术 可以帮助从示例中学习并准确地检索带有诊断错误的图表，而不需要手动操作 制作“电子触发器”。我们将电子触发器应用于包含纵向患者护理的全面电子病历 数据(进展记录、测试、转诊)，提供患者诊断过程的全面描述。vbl.使用 国家退伍军人数据，包括900万退伍军人的数据，以及先驱盖辛格健康系统的数据 为服务约300万名病人的医管局，我们建议的目标如下： 目标1-开发、改进、测试和应用更安全的Dx电子触发器，以实现检测、测量和学习 不同急诊科(ED)设置中的诊断错误。我们将计算出 基于这些e-触发器的ED中的诊断错误，并描述可预防的诊断伤害的负担。 目标2-探索机器学习技术，以产生稳健、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丰富的数据(来自目标1)。 据我们所知，这是诊断误差测量中的第一个ML应用，这可能有助于 扩大专家驱动的电子触发器开发和改进。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与安全相关的可操作的见解。

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.