Personalizing Prostate Cancer Education for Patients Using an EHR-Integrated LLM Agent
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909906697715712 |
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| author | Hao, Yuexing Holmes, Jason Waddle, Mark R. Davis, Brian J. Yu, Nathan Y. Vickers, Kristin Preston, Heather Margolin, Drew Lockenhoff, Corinna E. Vashistha, Aditya Kalantari, Saleh Ghassemi, Marzyeh Liu, Wei |
| author_facet | Hao, Yuexing Holmes, Jason Waddle, Mark R. Davis, Brian J. Yu, Nathan Y. Vickers, Kristin Preston, Heather Margolin, Drew Lockenhoff, Corinna E. Vashistha, Aditya Kalantari, Saleh Ghassemi, Marzyeh Liu, Wei |
| contents | 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 out of 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 out of 3), complete (2.7 out of 3), and safe (2.7 out of 3), with moderate personalization (2.3 out of 3). This study highlights the potential of LLM agents to improve patient engagement and health education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19100 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Personalizing Prostate Cancer Education for Patients Using an EHR-Integrated LLM Agent Hao, Yuexing Holmes, Jason Waddle, Mark R. Davis, Brian J. Yu, Nathan Y. Vickers, Kristin Preston, Heather Margolin, Drew Lockenhoff, Corinna E. Vashistha, Aditya Kalantari, Saleh Ghassemi, Marzyeh Liu, Wei Human-Computer Interaction 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 out of 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 out of 3), complete (2.7 out of 3), and safe (2.7 out of 3), with moderate personalization (2.3 out of 3). This study highlights the potential of LLM agents to improve patient engagement and health education. |
| title | Personalizing Prostate Cancer Education for Patients Using an EHR-Integrated LLM Agent |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.19100 |