Using Electronic Records to Detect and Learn from Ambulatory Diagnostic Errors

描述（由申请人提供）：该应用程序的总体目标是利用电子健康记录（EHR）中的数据来检测初级保健中的诊断错误，了解其原因，并为制定未来的预防策略奠定基础。诊断错误可能是初级保健中最常见的错误类型。它们也是最昂贵的，是医疗事故索赔的主要依据。尽管诊断错误很重要，但它们在患者安全方面却没有得到充分重视和研究，部分原因是它们很难被发现。帮助发现诊断错误的策略对于提高初级保健服务的质量和安全至关重要。检测方法，如错误报告系统和随机图表审查，以确定诊断错误是有限的。近年来，计算机化触发技术已被用于通过选择特定图表进行更详细的审查来识别其他环境中的不良事件。在我们的初步工作中，我们已经开发并测试了两个计算机化的触发工具，以识别可能包含初级保健诊断错误证据的图表。我们认为，完善这些工具并将其与其他变量相结合，可以提高诊断错误的检出率。在额外的初步工作中，我们测试了一种计算机化方法，该方法可用作新的触发工具，以检测与异常检测结果随访相关的诊断错误。我们现在建议验证这个潜在的触发工具。我们还建议将我们的研究扩展到VA之外，在德克萨斯州中部的一个基于实践的研究网络中建立一个大型初级保健网络。这些设置将包括内科和家庭医学，学术和非学术实践，城市和农村患者，以及重要的种族，性别，民族，年龄和社会经济多样性。我们的具体目标是：1）应用和改进基于访问模式的计算机化触发器，以检测，测量，并从各种初级保健环境中的诊断错误中学习。2)为了测试计算机化跟踪可能失访的异常检测结果的方法是否可以用作识别初级保健中诊断未遂的触发器。 在目标1中，我们将使用触发器查询临床数据库，并以电子方式收集有关初级保健访视的其他临床数据（变量）。为了测试触发器的效用，我们将比较它们的阳性预测值（PPV）与对照组，即不符合触发器标准的随机访视样本。经过培训的盲态病历审查员将验证两个子集中是否存在诊断错误。为了改进我们的触发器，我们将使用逻辑回归模型来测试将触发器与特定的独立临床变量整合的加性PPV。在目标2中，我们将以电子方式跟踪和识别患者记录，以便根据新的潜在触发因素进行进一步的图表审查。我们将通过比较触发和对照子集的PPV，测试该触发工具检测与失访检测结果相关的诊断错误的有效性。完善和验证触发诊断错误在不同的初级保健设置将为他们在全国范围内使用的质量测量和改进活动的阶段。

DESCRIPTION (Provided by the Applicant): The overall goal of the application is to utilize data from electronic health records (EHR) to detect diagnostic errors in primary care, understand their causes, and lay groundwork to formulate future prevention strategies. Diagnostic errors are likely the most common types of errors in primary care. They are also the most expensive and are the leading basis for malpractice claims. Despite their importance, diagnostic errors are an underemphasized and understudied area of patient safety, in part because they are difficult to detect. Strategies to help detect diagnostic errors are critical to improving quality and safety of primary care delivery. Detection methods such as error reporting systems and random chart reviews to identify diagnostic errors are limited. In recent years, computerized trigger techniques have been used to identify adverse events in other settings by selecting specific charts for more detailed review. In our preliminary work, we have developed and tested two computerized trigger tools to identify charts that may contain evidence of diagnostic errors in primary care. We believe that refining these tools and integrating them with additional variables could lead to a higher detection rate for diagnostic errors. In additional preliminary work, we tested a computerized method that could be used as a new trigger tool to detect diagnostic errors related to abnormal test result follow-up. We now propose to validate this potential trigger tool. We also propose to expand our research beyond the VA to a large primary care network in a practice based research network in Central Texas. These settings will include internal medicine and family medicine, academic and nonacademic practices, urban and rural patients, and significant racial, gender, ethnic, age, and socioeconomic diversity. Our specific aims are: 1) To apply and improve computerized triggers based on visit patterns to detect, measure, and learn from diagnostic errors in diverse primary care settings. 2) To test whether a method of computerized tracking for abnormal test results that are potentially lost to follow-up can be used as a trigger to identify diagnostic near-misses in primary care. In Aim 1 we will query clinical repositories with triggers and electronically collect additional clinical data (variables) about primary care visits. To test the utility of triggers, we will compare their positive predictive values (PPV) with controls, a random sample of visits that do not meet trigger criteria. Trained, blinded chart reviewers will verify the presence of diagnostic errors in the two subsets. To improve our trigger we will use a logistic regression model to test the additive PPV of integrating the trigger with specific independent clinical variables. In Aim 2, we will electronically track and identify records of patients for further chart reviews based on a new potential trigger. We will test the validity of this trigger tool for detecting diagnostic errors related to test results lost to follow-up by comparing the PPVs of the triggered and control subsets. Refining and validating triggers for diagnostic errors in diverse primary care settings would set the stage for their use nationally in quality measurement and improvement activities.