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Evaluation of Criteria2Query: Towards Augmented Intelligence for Cohort Identification
Cong Liu1, Hao Liu1, Casey Ta1
1Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Studies in Health Technology and Informatics
|June 8, 2022
Summary
This study evaluates Criteria2Query (C2Q), a natural language interface that converts free-text clinical eligibility criteria into database queries. C2Q aims to improve patient cohort identification for clinical research using electronic healthcare records data.
Area of Science:
- Biomedical Informatics
- Clinical Research Informatics
- Health Data Science
Background:
- Electronic healthcare records (EHR) data is crucial for efficient patient eligibility screening in clinical trials and observational studies.
- A sociotechnical gap exists between end-users (clinicians, researchers) and complex EHR databases for cohort identification.
- Automated tools are needed to bridge this gap and facilitate data utilization.
Purpose of the Study:
- To comprehensively evaluate the Criteria2Query (C2Q) natural language interface.
- To generate actionable insights for designing and improving future natural language user interfaces for clinical databases.
- To advance Augmented Intelligence (AI) in clinical cohort definition through electronic screening (e-screening).
Main Methods:
- Development of Criteria2Query (C2Q), a natural language query interface.
- Automatic transformation of free-text eligibility criteria into executable database queries.
- Comprehensive evaluation of C2Q's performance and utility for end-users.
Main Results:
- C2Q successfully translates free-text eligibility criteria into database queries.
- Evaluation provides insights into the effectiveness of natural language interfaces for EHR data.
- The study informs the design of AI-driven tools for cohort identification.
Conclusions:
- Criteria2Query (C2Q) shows promise in enhancing the efficiency of patient cohort identification from EHR data.
- Further development of natural language interfaces can significantly support clinical research.
- Augmented Intelligence (AI) holds potential for revolutionizing clinical cohort definition via e-screening.
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