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Bioengineering
Volume 12
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10.3390/bioengineering12010017
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Article

Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules

by
Yunguo Yu
1,
Cesar A. Gomez-Cabello
2,
Svetlana Makarova
1,
Yogesh Parte
1,
Sahar Borna
2,
Syed Ali Haider
2,
Ariana Genovese
2,
Srinivasagam Prabha
2 and
Antonio J. Forte
1,2,*
1
Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
2
Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
*
Author to whom correspondence should be addressed.
Bioengineering 2025, 12(1), 17; https://doi.org/10.3390/bioengineering12010017
Submission received: 19 November 2024 / Revised: 20 December 2024 / Accepted: 27 December 2024 / Published: 28 December 2024
(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)
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Abstract

Current clinical care relies heavily on complex, rule-based systems for tasks like diagnosis and treatment. However, these systems can be cumbersome and require constant updates. This study explores the potential of the large language model (LLM), LLaMA 2, to address these limitations. We tested LLaMA 2′s performance in interpreting complex clinical process models, such as Mayo Clinic Care Pathway Models (CPMs), and providing accurate clinical recommendations. LLM was trained on encoded pathways versions using DOT language, embedding them with SentenceTransformer, and then presented with hypothetical patient cases. We compared the token-level accuracy between LLM output and the ground truth by measuring both node and edge accuracy. LLaMA 2 accurately retrieved the diagnosis, suggested further evaluation, and delivered appropriate management steps, all based on the pathways. The average node accuracy across the different pathways was 0.91 (SD ± 0.045), while the average edge accuracy was 0.92 (SD ± 0.122). This study highlights the potential of LLMs for healthcare information retrieval, especially when relevant data are provided. Future research should focus on improving these models’ interpretability and their integration into existing clinical workflows.
Keywords: diagnostics; clinical decision support; Artificial Intelligence; large language models; data retrieval diagnostics; clinical decision support; Artificial Intelligence; large language models; data retrieval
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MDPI and ACS Style

Yu, Y.; Gomez-Cabello, C.A.; Makarova, S.; Parte, Y.; Borna, S.; Haider, S.A.; Genovese, A.; Prabha, S.; Forte, A.J. Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules. Bioengineering 2025, 12, 17. https://doi.org/10.3390/bioengineering12010017

AMA Style

Yu Y, Gomez-Cabello CA, Makarova S, Parte Y, Borna S, Haider SA, Genovese A, Prabha S, Forte AJ. Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules. Bioengineering. 2025; 12(1):17. https://doi.org/10.3390/bioengineering12010017

Chicago/Turabian Style

Yu, Yunguo, Cesar A. Gomez-Cabello, Svetlana Makarova, Yogesh Parte, Sahar Borna, Syed Ali Haider, Ariana Genovese, Srinivasagam Prabha, and Antonio J. Forte. 2025. "Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules" Bioengineering 12, no. 1: 17. https://doi.org/10.3390/bioengineering12010017

APA Style

Yu, Y., Gomez-Cabello, C. A., Makarova, S., Parte, Y., Borna, S., Haider, S. A., Genovese, A., Prabha, S., & Forte, A. J. (2025). Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules. Bioengineering, 12(1), 17. https://doi.org/10.3390/bioengineering12010017

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