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.

通过开发基于大型语言模型和快速医疗互操作性资源的人工智能解决方案，提高多样化患者群体的健康素养和医疗信息可及性，通过开放接口让用户与其健康记录进行互动，评估其对于患者健康素养的有效性，并指出了不同患者个体的适应性差异与人工智能应用中存在的挑战，表明了对于患者护理中语言模型的集成的可行性和风险，未来的方向包括资源识别机制和在设备上执行语言模型以提高隐私和降低成本。