Utility of Predictive Systems to identify Inpatient Diagnostic Errors: The UPSIDE Study

项目摘要/摘要 自从医学研究所发表了《TO》以来，许多关于患者安全的研究已经进行 错误是人“2000年，对医院环境中的诊断错误的研究相对匮乏。 拟议研究的广泛、长期目标是更好地了解发病率、原因和 住院环境中诊断错误的危险因素。这项工作将为下一步的研究提供基础 制定干预措施以减少这些错误，包括预测性工具、干预目标和 未来干预试验结果评估的方法学。为实现这一总目标，我们将 制定了以下具体目标：1)确定死亡患者中诊断错误的发生率 或在入院后两天或以上通过 结构化、标准化的病历裁决流程，2)将裁决数据与数据相结合 以确定哪些特定因素会导致诊断错误的风险，并使用风险 估计，以计算导致这些错误的因素的发生率和影响，以及3)创建机器- 学习模型，可用于回溯性地识别诊断错误可能导致 已经发生了。这项研究将对2000名普通住院患者进行回顾性评估。 20家美国医院的医疗单位参与了一项全国性的研究合作，并为 将数据提供给基准测试和采购组织(Vizient)。使用SAFER诊断(SAFER-Dx)和 诊断误差评估和研究(DEER)分类工具，两者都适用于住院设置， 裁判员将审查电子病历数据，并确定是否存在诊断 使用严格的培训和持续的审查流程来确保站点、裁判员、 还有时间。标准的建模技术将被用来理解每一项人口归因风险 鹿处理失败的原因是诊断错误以及几个患者、提供者和 系统层面的风险因素。最后，将使用先进的机器学习方法来创建能够 识别发生诊断错误的患者，性能优于标准方法，例如 Logistic回归。综合起来，这些方法将提供一幅广泛和具有代表性的事件图景。 在遭受伤害的住院患者中的诊断错误，开发患者和 基于系统的因素，使诊断错误或多或少发生，并建立高级、高效和 支持各种机构的未来监控和改进计划所需的可扩展工具。 这项研究将建立一个基础，医疗保健系统可以从中评估和实现卓越的 在住院环境中进行诊断。

PROJECT SUMMARY/ABSTRACT While much research has been conducted on patient safety since the Institute of Medicine published “To Err is Human” in 2000, there is a comparative dearth of research on diagnostic errors in the hospital setting. The broad, long-term objectives of the proposed research is to better understand the incidence, causes, and risk factors for diagnostic errors in the inpatient setting. This work will provide foundational research for the development of interventions to reduce these errors, including predictive tools, targets for intervention, and a methodology for outcome assessment in future trials of interventions. To achieve this overall goal, we will carry out the following specific aims: 1) To determine the incidence of diagnostic errors among patients who die in hospital or are transferred to the ICU two days or more after admission to a general medicine service through a structured, standardized adjudication process of patient records, 2) To combine adjudication data with data from Vizient to determine which specific factors contribute to risks for diagnostic errors, and to use risk estimates to calculate incidence and impact of factors contributing to those errors, and 3)To create machine- learning models that can be used to retrospectively identify patients in whom a diagnostic error was likely to have taken place. The research will involve a retrospective evaluation of 2000 patients admitted to general medicine units at 20 US hospitals participating in a national research collaborative and which also contribute data to a benchmarking and purchasing organization (Vizient). Using the Safer-Diagnosis (Safer-Dx) and Diagnostic Error Evaluation and Research (DEER) taxonomy tools, both adapted for the inpatient setting, adjudicators will review electronic medical record data and determine the presence or absence of diagnostic errors using a rigorous training and continuous review process to ensure reliability across sites, adjudicators, and time. Standard modelling techniques will be used to understand the population-attributable risk of each of the DEER process failure points to diagnostic error as well as the contributions of several patient, provider, and system-level risk factors. Lastly, advanced machine-learning methods will be used to create models that can identify patients in whom diagnostic error occurred, with superior performance to standard approaches such as logistic regression. Together, these approaches will provide a broad and representative picture of the incidence of diagnostic errors among hospitalized patients who have suffered harm, develop models of patient and system-based factors that make a diagnostic error more or less likely, and build advanced, efficient, and scalable tools needed to support future surveillance and improvement programs for a variety of institutions. This research will establish a foundation from which healthcare systems can assess and achieve excellence in diagnosis in the inpatient setting.