Accepted for/Published in: JMIR Formative Research
Date Submitted: Jul 25, 2025
Date Accepted: Feb 18, 2026
Development and Evaluation of a Human-in-the-Loop Data Curation Training Program to Support a Digital Clinical Trial Platform: A Feasibility Study
ABSTRACT
Background:
Despite the pivotal role of clinical trials in advancing cancer care, enrollment in oncology trials remains low, contributing to recruitment challenges and health disparities. Digital health tools have emerged as promising solutions to increase awareness and accessibility, yet the clinical research workforce often lacks sustained training opportunities in data stewardship, especially at the undergraduate level. Engaging students early in structured, real-world research experiences may not only help trial accessibility but also enhance data infrastructure critical for technology-driven innovations.
Objective:
To evaluate the feasibility and educational impact of an undergraduate-led data analytics training program focused on curating oncology clinical trial data for use in a digital health application. The program aimed to enhance student understanding of clinical research infrastructure and the importance of high-quality data for downstream applications, including artificial intelligence (AI) development.
Methods:
Undergraduate students from a university-based student organization were trained to extract, standardize, and enter cancer clinical trial data from public registries into a novel AI application designed to improve trial accessibility. A post-program survey assessed participant experiences, skill acquisition, and perceived relevance to future work in clinical research or data science.
Results:
Over a 10-month period, participants processed over 2,500 clinical trial records across 5 health care institutions. Students reported high satisfaction with the program’s structure and peer collaboration, and noted substantial growth in data literacy, clinical trial familiarity, and appreciation for the role of structured data in research and AI applications.
Conclusions:
This undergraduate data analytics training model offers a scalable approach to engaging students in clinical research and data stewardship. It may serve as a foundation for developing a workforce skilled in preparing high-quality data for digital health tools and AI-driven innovations.
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Copyright
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