  Objective: To enhance health literacy and accessibility of health information
for a diverse patient population by developing a patient-centered artificial
intelligence (AI) solution using large language models (LLMs) and Fast
Healthcare Interoperability Resources (FHIR) application programming interfaces
(APIs). Materials and Methods: The research involved developing LLM on FHIR, an
open-source mobile application allowing users to interact with their health
records using LLMs. The app is built on Stanford's Spezi ecosystem and uses
OpenAI's GPT-4. A pilot study was conducted with the SyntheticMass patient
dataset and evaluated by medical experts to assess the app's effectiveness in
increasing health literacy. The evaluation focused on the accuracy, relevance,
and understandability of the LLM's responses to common patient questions.
Results: LLM on FHIR demonstrated varying but generally high degrees of
accuracy and relevance in providing understandable health information to
patients. The app effectively translated medical data into patient-friendly
language and was able to adapt its responses to different patient profiles.
However, challenges included variability in LLM responses and the need for
precise filtering of health data. Discussion and Conclusion: LLMs offer
significant potential in improving health literacy and making health records
more accessible. LLM on FHIR, as a pioneering application in this field,
demonstrates the feasibility and challenges of integrating LLMs into patient
care. While promising, the implementation and pilot also highlight risks such
as inconsistent responses and the importance of replicable output. Future
directions include better resource identification mechanisms and executing LLMs
on-device to enhance privacy and reduce costs.
