Evaluation of Criteria2Query: Towards Augmented Intelligence for Cohort Identification.

Journal: Studies in health technology and informatics
Published Date: Jun 6, 2022

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.

Authors

  • Cong Liu
    Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St, Chicago, IL, 60607, USA.
  • Hao Liu
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Casey Ta
    Department of Biomedical Informatics, Columbia University, New York, New York, USA.
  • James Roger
    Department of Biomedical Informatics, Columbia University, New York, NY, USA.
  • Alex Butler
    Department of Biomedical Informatics, Columbia University, New York, NY, USA.
  • Junghwan Lee
    Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
  • Jaehyun Kim
    Department of Biomedical Informatics, Columbia University, New York, USA.
  • Ning Shang
    Columbia University, New York, New York.
  • Chunhua Weng
    Department of Biomedical Informatics, Columbia University.

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