Implementation of a rule-based algorithm to find patients eligible for cancer clinical trials

Objective To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials. Materials and Methods To prepare approaches needed to match patients to relevant cancer clinical trials, we combined NCI’s Clinical Trials Search API...

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Veröffentlicht in:JAMIA open Jg. 7; H. 4; S. ooae131
Hauptverfasser: Bickell, Nina A, May, Benjamin, Havrylchuk, Ihor, John, Jimmy, Lin, Sylvia, Tao, Ariana, Yagnik, Radhi, Tatonetti, Nicholas P
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Oxford University Press 01.12.2024
Schlagworte:
Accuracy
Algorithms
Biomarkers
Brief Communications
Cancer
Cancer patients
Care and treatment
Clinical trials
Computational linguistics
Hospitals
Language processing
Liver
Liver cancer
Lung cancer
Medical colleges
Natural language interfaces
United States
United States
racial disparities
regular expressions
algorithms
cancer clinical trials
ISSN:2574-2531, 2574-2531
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Zusammenfassung:Objective To explore implementing regular expressions (RegEx) to streamline patient identification and classification for matching to clinical trials. Materials and Methods To prepare approaches needed to match patients to relevant cancer clinical trials, we combined NCI’s Clinical Trials Search API to extract high-level eligibility criteria, including cancer type, stage, receptor/biomarker status, with similar data of patients with appointments in the upcoming week. Using RegEx, we prospectively identified all patients with breast, liver, or lung cancers at treatment decision points at 2 Cancer Centers’ and 2 community hospitals’, classified their cancer type, stage, and receptor/biomarker status. We evaluated accuracy using RegEx against manual reviews. Results Algorithm accuracy to identify patients at treatment decision points revealed 92% True Negative and 53% True Positive rate. Staging accuracy varied from 67% to 95%, and receptor/biomarker status accuracy from 76% to 86%. Discussion and Conclusion Using RegEx significantly reduced the number of patients requiring manual review, demonstrating a reduction in manual labor and potential biases, which can improve efficiency and inclusivity of clinical trial enrollment processes, especially in resource limited or data sensitive environments. Trial Registration NCT05146297 Lay Summary Advancing cancer care requires a robust clinical trial infrastructure. Current approaches to identify patients potentially eligible for clinical trials are notoriously inefficient, requiring laborious manual review. We developed a systematic approach using a relatively simple Regular Expressions’ coding algorithm to identify cancer patients at treatment decision points when trials would be most relevant and classify them in preparation for matching clinical trials to patients. We achieved high algorithm accuracy identifying patients who were not at treatment decision points and moderate to high accuracy identifying stage and receptor/biomarker status. Regular Expressions can significantly reduce manual case review and improve the efficiency and inclusivity of clinical trial enrollment processes, especially in resource-limited or data-sensitive environments.
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ISSN:2574-2531
2574-2531
DOI:10.1093/jamiaopen/ooae131