Breaking Digital Health Barriers Through a Large Language Model–Based Tool for Automated Observational Medical Outcomes Partnership Mapping: Development and Validation Study
Our validated large language model-based tool effectively automates the transformation of clinical data into the OMOP format while maintaining high accuracy. The combination of semantic matching capabilities and a researcher-friendly interface makes data harmonization accessible to smaller research teams without requiring extensive informatics support. This has direct implications for accelerating clinical research data standardization and enabling broader participation in initiatives such as the National Institutes of Health Helping to End Addiction Long-term Initiative Data Ecosystem.
Authors
- Ümit Topaloĝlu (ORCID: https://orcid.org/0000-0002-3241-8773)
- Robert W. Hurley (ORCID: https://orcid.org/0000-0001-6591-9390)
- Martin Perkins (ORCID: https://orcid.org/0000-0002-8152-0512)
- Oğuz Akbilgiç (ORCID: https://orcid.org/0000-0003-0313-9254)
- Vithal Madhira (ORCID: https://orcid.org/0000-0001-5359-1703)
- Meredith C B Adams (ORCID: https://orcid.org/0000-0002-3969-4279)
- Cody Hudson (ORCID: https://orcid.org/0009-0001-3374-3754)
- Da Ma (ORCID: https://orcid.org/0000-0001-8628-3029)
Institutions
- Vere Software (United States) (US)
- National Cancer Institute (US)
- Wake Forest University (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-03-27
- DOI
- https://doi.org/10.2196/69004
- Citations
- 6
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 4.84