Personalizing prostate cancer education for patients using an EHR-Integrated LLM agent

Cancer patients often lack timely education and personalized support due to clinician workload. This quality improvement study develops and evaluates a Large Language Model (LLM) agent, MedEduChat, which is integrated with the clinic's electronic health records (EHR) and designed to enhance prostate cancer patient education. Fifteen non-metastatic prostate cancer patients and three clinicians recruited from the Mayo Clinic interacted with the agent between May 2024 and April 2025. Findings showed that MedEduChat has a high usability score (UMUX = 83.7/100) and improves patients' health confidence (Health Confidence Score rose from 9.9 to 13.9). Clinicians evaluated the patient-chat interaction history and rated MedEduChat as highly correct (2.9/3), complete (2.7/3), and safe (2.7/3), with moderate personalization (2.3/3). This study highlights the potential of LLM agents to improve patient engagement and health education.

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Publication Details

Journal
npj Digital Medicine
Published
2025-12-18
DOI
https://doi.org/10.1038/s41746-025-02166-0
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

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article

Personalizing prostate cancer education for patients using an EHR-Integrated LLM agent

Mark Raymond Waddle, Yuexing Hao, Shakiba Kalantari, Nathan Y. Yu et al.
npj Digital Medicine
Machine Learning in Healthcare
article

Personalizing prostate cancer education for patients using an EHR-Integrated LLM agent

Mark Raymond Waddle, Yuexing Hao, Shakiba Kalantari, Nathan Y. Yu, Brian J. Davis, Marzyeh Ghassemi, Aditya Vashistha, Jason Holmes, Kristin S. Vickers, Wei Liu, Heather R. Preston, Drew Margolin, Corinna E. Löckenhoff
article en

Abstract

Cancer patients often lack timely education and personalized support due to clinician workload. This quality improvement study develops and evaluates a Large Language Model (LLM) agent, MedEduChat, which is integrated with the clinic's electronic health records (EHR) and designed to enhance prostate cancer patient education. Fifteen non-metastatic prostate cancer patients and three clinicians recruited from the Mayo Clinic interacted with the agent between May 2024 and April 2025. Findings showed that MedEduChat has a high usability score (UMUX = 83.7/100) and improves patients' health confidence (Health Confidence Score rose from 9.9 to 13.9). Clinicians evaluated the patient-chat interaction history and rated MedEduChat as highly correct (2.9/3), complete (2.7/3), and safe (2.7/3), with moderate personalization (2.3/3). This study highlights the potential of LLM agents to improve patient engagement and health education.

npj Digital MedicineVol. 8(1)
Cornell University (US), WinnMed (US), Mayo Clinic in Arizona (US), Mayo Clinic Hospital (US), Mayo Clinic in Florida (US), Massachusetts Institute of Technology (US)
President's Council of Cornell Women, National Cancer Institute
Openalex Percentile: Top 78%
Machine Learning in Healthcare
DOIOpen Access PDFOpenAlex
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