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Electronic healthcare records data promises to improve the efficiency of patient eligibility screening, which is an important factor in the success of clinical trials and observational studies. To bridge the… Click to show full abstract
Electronic healthcare records data promises to improve the efficiency of patient eligibility screening, which is an important factor in the success of clinical trials and observational studies. To bridge the sociotechnical gap in cohort identification by end-users, who are clinicians or researchers unfamiliar with underlying EHR databases, we previously developed a natural language query interface named Criteria2Query (C2Q) that automatically transforms free-text eligibility criteria to executable database queries. In this study, we present a comprehensive evaluation of C2Q to generate more actionable insights to inform the design and evaluation of future natural language user interfaces for clinical databases, towards the realization of Augmented Intelligence (AI) for clinical cohort definition via e-screening.
Journal Title: Studies in health technology and informatics
Year Published: 2022
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