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Artificial Intelligence for Identifying Patient-Reported Outcome and Experience Measures in Oncology: Retrospective
Jessica Soyer1, Akram Hecini1, Sylvain Juchet1
1SKEZI, Annecy, France.
Journal of Medical Internet Research
|April 16, 2026
Summary
Artificial intelligence (AI) significantly improves the identification of patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) in oncology clinical trials. This AI-enriched approach enhances accuracy and efficiency over traditional expert methods.
Area of Science:
- Oncology research
- Health informatics
- Clinical trial methodology
Background:
- Patient perspectives are crucial for improving healthcare quality, particularly in oncology.
- Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) enhance communication and personalize care.
- AI can automate and accelerate the identification of PROMs and PREMs in research.
Purpose of the Study:
- To estimate the proportion of oncology clinical studies utilizing PROMs or PREMs.
- To compare the effectiveness of traditional expert-based identification versus an AI-enriched approach for PROMs and PREMs.
Main Methods:
- A retrospective cross-sectional study of oncology trials (2012-2022) from ClinicalTrials.gov.
- Identification of PROMs/PREMs using a traditional expert-based algorithm and an AI-enriched bidirectional encoder representations from transformers model.
- Evaluation of algorithm performance against expert decisions and analysis of factors influencing PROM/PREM inclusion.
Main Results:
- The AI-enriched method identified more studies using PROMs/PREMs (33%) than the traditional method (31%), with increasing use over time.
- The AI-enriched algorithm achieved higher accuracy (90%) compared to the expert-based algorithm (84%).
- Breast and digestive cancer trials, and later-phase/observational studies, more frequently incorporated PROMs and PREMs.
Conclusions:
- AI-enriched algorithms demonstrate superior performance in identifying PROMs and PREMs in oncology research compared to traditional methods.
- AI integration enables scalable, automated trial monitoring and supports efficient, patient-centered research.
- The AI approach can be extended to other diseases and databases for broader application.
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