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 ...