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

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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.