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Related Experiment Video

Updated: Dec 26, 2025

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
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LLMonFHIR: A Physician-Validated, Large Language Model-Based Mobile Application for Querying Patient Electronic

Paul Schmiedmayer1, Adrit Rao1, Philipp Zagar1

  • 1Stanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA.

JACC. Advances
|May 15, 2025
PubMed
Summary

LLMonFHIR, a new mobile app, uses AI to help patients understand their health records in multiple languages and complexities. This digital health solution aims to overcome barriers to accessing electronic health records (EHRs).

Keywords:
artificial intelligencedigital healthlarge language modelliteracymobile application

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Area of Science:

  • Digital Health
  • Health Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Federal legislation mandates electronic health record (EHR) interoperability via Fast Healthcare Interoperability Resources (FHIR) APIs to enhance healthcare quality and patient empowerment.
  • Persistent barriers including limited functionality, English language, and health literacy impede equitable patient access to EHRs.
  • Individuals with chronic cardiovascular conditions face unique challenges in engaging with their personal health information.

Purpose of the Study:

  • To develop and evaluate a digital health solution addressing barriers to patient engagement with personal health information.
  • To focus on individuals managing chronic cardiovascular conditions.
  • To improve access to and understanding of electronic health records (EHRs) for diverse patient populations.

Main Methods:

  • Development of LLMonFHIR, an open-source mobile application utilizing large language models (LLMs).
  • LLMonFHIR enables users to interact with health records at varying complexity levels and in multiple languages.
  • Pilot evaluation involved physicians assessing LLMonFHIR responses for accuracy, understandability, and relevance using synthetic patient datasets.

Main Results:

  • Physicians evaluated 210 LLMonFHIR responses, yielding high median scores for accuracy (5/5), understandability (5/5), and relevance (5/5).
  • Identified challenges included summarizing health conditions and retrieving lab results, with noted response variability and omissions.
  • The need for precise data preprocessing was highlighted to ensure consistent and complete information retrieval.

Conclusions:

  • LLMonFHIR demonstrates potential in empowering individuals with limited functionality, English proficiency, or health literacy.
  • The application's multilingual and multi-complexity response generation, coupled with bidirectional text-to-speech, can enhance patient access to EHR benefits.
  • This technology can bridge gaps in health literacy and promote equitable access to personal health information.