Using Artificial Intelligence and Large Language Models to Reduce the Burden of Registry Participation

James P. McGlothlin, Timothy Martens

2025

Abstract

Health care disease registries and procedural registries serve a vital purpose in support of research and patient quality. However, it requires a significant level of clinician effort to collect and submit the data required by each registry. With the current shortage of qualified clinicians in the labor force, this burden is becoming even more costly for health systems. Furthermore, the quality of the abstracted data deteriorates as over-worked clinical staff review and abstract the data. The modern advancement in electronic medical records has actually increased this challenge by the exponential growth in data volume per patient record. In this study, we propose to use large language models to collect and formulate the registry data abstraction. For our initial work, we examine popular and complicated patient registries for cardiology and cardiothoracic surgery. Initial results demonstrate the promise of artificial intelligence and reenforce our position that this technology can be leveraged.

Paper Citation

in Harvard Style

McGlothlin J. and Martens T. (2025). Using Artificial Intelligence and Large Language Models to Reduce the Burden of Registry Participation. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 789-796. DOI: 10.5220/0013306200003911

in Bibtex Style

@conference{healthinf25,
author={James McGlothlin and Timothy Martens},
title={Using Artificial Intelligence and Large Language Models to Reduce the Burden of Registry Participation},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={789-796},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013306200003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Using Artificial Intelligence and Large Language Models to Reduce the Burden of Registry Participation
SN - 978-989-758-731-3
AU - McGlothlin J.
AU - Martens T.
PY - 2025
SP - 789
EP - 796
DO - 10.5220/0013306200003911
PB - SciTePress