ORIGINAL RESEARCH article
Front. Med.
Sec. Family Medicine and Primary Care
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1631565
This article is part of the Research TopicPatient-Centered Care: Strengthening Trust and Communication in Healthcare RelationshipsView all 13 articles
Leveraging LLM to Identify Missed Information in Patient-Physician Communication: Improving Healthcare Service Quality
Provisionally accepted
- 1University of Toronto, Toronto, Canada
- 2University College London, London, England, United Kingdom
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Electronic medical records (EMRs) have significantly changed the dynamics of physician-patient interactions, leading to a shift in communication patterns. Although various studies have developed guidelines for these new dynamics, different EMRs result in different modes of interaction, which can contribute to missed information during clinical encounters. Therefore, this study aims to develop a method that can automate the identification process of missed information to increase patient safety and satisfaction.A total of 98 transcripts of clinical consultations from two primary care clinics in the United States were used for identifying missed information and patient unsatisfactory factors. We first examine those factors through ordinal logistic regression. Then we leveraged large language model (Phi-3.5) to develop the automation model for identifying missed information of physicians.We show that showing care and empathy to patients (𝛽=1.283, OR = 3.609 [95% CI: 1.836, 7.091], 𝑝<0.001) and explaining things clearly to patients (𝛽=1.620, OR = 5.051 [95% CI: 2.138, 11.938], 𝑝<0.001) can significantly increase the level of patient satisfaction. And our model has an average accuracy of 90.09% with F1-score of 93.75% on identifying missed information during clinical practices in primary care.This study demonstrates the potential of automated analysis using Phi-3.5 to evaluate the identification of communication gaps in physician-patient interactions, ultimately enhancing patient safety and satisfaction. Further research is needed to refine this approach and explore its application across diverse healthcare settings.
Keywords: large language model (LLM), Automation, Patient Satisfaction, Patient Safety, Missed Information
Received: 19 May 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 Zhou, Cohen, Zhou and Montague. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Enid Montague, University of Toronto, Toronto, Canada
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