Researchers Propose Framework for Tools To Identify Diagnosis Errors
A group of AHRQ-funded researchers has proposed a framework for evaluating electronic trigger tools to find diagnostic errors. E-trigger tools, which mine patient data to identify a likely error or adverse event, are considered a promising method to identify diagnostic errors efficiently. The researchers, writing in BMJ Quality & Safety, identified a framework consisting of seven elements: identification and prioritization of diagnostic error of interest; definition of criteria to detect an error; determination of potential data sources; construction of an algorithm to obtain data cohort; testing and data review; assessment of e-trigger performance; and iterative refinements. Access the abstract.
Narrative Review
Application of electronic trigger tools to identify targets for improving diagnostic safety
Abstract
Progress in reducing diagnostic errors remains slow partly due to poorly defined methods to identify errors, high-risk situations, and adverse events. Electronic trigger (e-trigger) tools, which mine vast amounts of patient data to identify signals indicative of a likely error or adverse event, offer a promising method to efficiently identify errors. The increasing amounts of longitudinal electronic data and maturing data warehousing techniques and infrastructure offer an unprecedented opportunity to implement new types of e-trigger tools that use algorithms to identify risks and events related to the diagnostic process. We present a knowledge discovery framework, the Safer Dx Trigger Tools Framework, that enables health systems to develop and implement e-trigger tools to identify and measure diagnostic errors using comprehensive electronic health record (EHR) data. Safer Dx e-trigger tools detect potential diagnostic events, allowing health systems to monitor event rates, study contributory factors and identify targets for improving diagnostic safety. In addition to promoting organisational learning, some e-triggers can monitor data prospectively and help identify patients at high-risk for a future adverse event, enabling clinicians, patients or safety personnel to take preventive actions proactively. Successful application of electronic algorithms requires health systems to invest in clinical informaticists, information technology professionals, patient safety professionals and clinicians, all of who work closely together to overcome development and implementation challenges. We outline key future research, including advances in natural language processing and machine learning, needed to improve effectiveness of e-triggers. Integrating diagnostic safety e-triggers in institutional patient safety strategies can accelerate progress in reducing preventable harm from diagnostic errors.
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