Combining human and machine intelligence for clinical trial eligibility querying

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 29; no. 7; pp. 1161 - 1171
• Main Authors: Fang, Yilu, Idnay, Betina, Sun, Yingcheng, Liu, Hao, Chen, Zhehuan, Marder, Karen, Xu, Hua, Schnall, Rebecca, Weng, Chunhua
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 14.06.2022
• Subjects: Artificial Intelligence; COVID-19; Eligibility Determination - methods; Humans; Natural Language Processing; Patient Selection; Research and Applications; human–computer collaboration; cohort identification; informatics; eligibility prescreening
• ISSN: 1527-974X; 1067-5027; 1527-974X
• DOI: 10.1093/jamia/ocac051

Abstract Objective To combine machine efficiency and human intelligence for converting complex clinical trial eligibility criteria text into cohort queries. Materials and Methods Criteria2Query (C2Q) 2.0 was developed to enable real-time user intervention for criteria selection and simplification, parsing error correction, and concept mapping. The accuracy, precision, recall, and F1 score of enhanced modules for negation scope detection, temporal and value normalization were evaluated using a previously curated gold standard, the annotated eligibility criteria of 1010 COVID-19 clinical trials. The usability and usefulness were evaluated by 10 research coordinators in a task-oriented usability evaluation using 5 Alzheimer’s disease trials. Data were collected by user interaction logging, a demographic questionnaire, the Health Information Technology Usability Evaluation Scale (Health-ITUES), and a feature-specific questionnaire. Results The accuracies of negation scope detection, temporal and value normalization were 0.924, 0.916, and 0.966, respectively. C2Q 2.0 achieved a moderate usability score (3.84 out of 5) and a high learnability score (4.54 out of 5). On average, 9.9 modifications were made for a clinical study. Experienced researchers made more modifications than novice researchers. The most frequent modification was deletion (5.35 per study). Furthermore, the evaluators favored cohort queries resulting from modifications (score 4.1 out of 5) and the user engagement features (score 4.3 out of 5). Discussion and Conclusion Features to engage domain experts and to overcome the limitations in automated machine output are shown to be useful and user-friendly. We concluded that human–computer collaboration is key to improving the adoption and user-friendliness of natural language processing.