Developing e-Triggers to Detect Telemedicine Related Diagnostic Safety Events

尽管在美国快速、大规模地部署远程医疗带来了好处，但使准确的 通过远程医疗进行诊断涉及几个值得进一步研究的潜在挑战。目前，它不是 众所周知，远程医疗的实施如何影响远程诊断，远程诊断的定义是 通过远程交互和传输准确、及时地解释患者的健康问题 数据，包括向患者清楚地传达该解释。但远程医疗的早期证据- 相关的误诊已经出现，尽管诊断错误的准确频率与 远程医疗访问以及导致这些诊断错误的因素尚不清楚。甚至在那之前 在全球大流行期间，诊断错误在卫生保健领域很常见，而且报告不足。这个项目的目标是 确定远程诊断错误的促成因素，开发有效检测这些错误的方法，并启用 风险评估战略，以防止它们。我们将首先用定性的方法来理解因素。 增加远程诊断错误的风险，并确定哪些线索可用于检测远程诊断错误。 然后，我们将利用这些发现来开发电子触发器(e-触发器，即挖掘大量 用于识别可能不良事件的信号的临床和管理数据)以识别与远程医疗相关的 诊断错误。因此，我们将在之前工作的基础上，使用电子触发算法来检测护理模式 使人想起漏诊或延误的诊断。审查和分析电子触发器识别的案例可以发现 安全问题，并提供与诊断过程和相关因素相关的故障信息 各种因素。这将为个人、团队和医疗保健人员的改进工作生成学习和反馈 组织。最后，我们将举办联合设计研讨会，并利用这些发现来开发一个自我 系统、提供商和患者的评估工具，用于识别和降低远程诊断错误的风险。 该项目将利用我们在贝勒医学院现有的强大合作伙伴关系，并 美国退伍军人事务部(VA)和杠杆数据库包含电子健康记录 (EHR)从1000多万人中挑选，以实现以下具体目标。 目的1：通过与临床医生的访谈，评估增加远程诊断错误风险的因素 工作人员、患者安全人员、远程医疗专家和患者。 目标2：开发电子触发器，以确定与远程医疗有关的诊断错误并确定潜在的 促成因素。 目的3：开发一种自我评估工具来评估远程医疗相关诊断错误的风险。 用于识别错误的电子触发器组合可用于质量改进活动和实施 减少差错的解决方案。风险评估工具将使卫生系统、提供者和患者能够自我 评估他们出现远程诊断错误的风险，并采取前瞻性行动以降低此类风险。

Despite the benefits of a rapid, large-scale deployment of telemedicine in the US, making an accurate diagnosis via telemedicine involves several potential challenges that warrant further study. Currently, it is not well known how telemedicine implementation impacts ‘telediagnosis,’ defined as the co-production of an accurate and timely explanation of a patient’s health problems through remote interactions and transmitted data, including the clear communication of that explanation to the patient. But early evidence of telemedicine- related misdiagnosis is already emerging even though the precise frequency of diagnostic errors related to telemedicine visits and the factors that contribute to these diagnostic errors are unknown. Even before the global pandemic, diagnostic errors were common and underreported in health care. The goal of this project is to identify contributory factors for telediagnosis errors, develop methods to efficiently detect them, and enable risk-assessment strategies to prevent them. We will first use qualitative methods to understand factors increasing the risk of telediagnosis errors and identify what clues can be used to detect a telediagnosis error. We will then use these findings to develop electronic triggers (e-triggers, i.e., tools to mine vast amounts of clinical and administrative data to identify signals for likely adverse events) to identify telemedicine-related diagnostic errors. We will thus build on our prior work on using e-trigger algorithms to detect patterns of care suggestive of missed or delayed diagnoses. Review and analysis of e-trigger identified cases can uncover safety concerns and provide information on breakdowns related to the diagnostic process and contributory factors. This will generate learning and feedback for improvement efforts by individuals, teams, and healthcare organizations. Finally, we will perform co-design workshops and use these findings to develop a self- assessment tool for systems, providers, and patients to identify and mitigate the risk of telediagnosis errors. The project will capitalize on our existing strong collaborative partnerships at Baylor College of Medicine and the U.S. Department of Veterans Affairs (VA) and leverage databases containing electronic health records (EHRs) from over 10 million individuals to accomplish the following specific aims. Aim 1: Evaluate factors that increase the risk of telediagnosis errors using interviews with clinicians, staff, patient safety personnel, telemedicine experts and patients. Aim 2: Develop e-triggers to identify telemedicine-related diagnostic errors and identify potential contributory factors. Aim 3: Develop a self-assessment tool to evaluate the risk of telemedicine-related diagnostic errors. A portfolio of e-triggers to identify errors can be used for quality improvement activities and implementation of solutions to reduce error. The risk assessment tool will enable health systems, providers, and patients to self- assess their risk for telediagnosis errors, and act prospectively to reduce such risks.