ArticleFrontiers in medicine2025
Leveraging LLM to identify missed information in patient-physician communication: improving healthcare service quality.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Evidence, use cases, and implementation safeguards of large language models in primary care.Communications medicine · 2026Review
- Overview of RAG-based and LLM-based approaches to personalization in healthcare AI applications.Frontiers in artificial intelligence · 2026Review
- Large language model evaluation and feedback of transcribed simulated physician-patient verbal interactions.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and objective: 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. Methods: 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. Results: We show that showing care and empathy to patients ( Conclusion: 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.
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Registered trials
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