# LLM on FHIR -- Demystifying Health Records

arXiv: https://arxiv.org/abs/2402.01711 · HTML: https://mlsanity.com/paper/2402.01711/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)

## Built on by (most cited first, 9 of 9)

- [Almanac Copilot: Towards Autonomous Electronic Health Record Navigation](https://mlsanity.com/paper/2405.07896/) (2024)
- [MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs](https://mlsanity.com/paper/2405.14748/) (2024)
- [Enhancing Clinical Decision Support and EHR Insights through LLMs and the Model Context Protocol: An Open-Source MCP-FHIR Framework](https://mlsanity.com/paper/2506.13800/) (2025)
- [EHR-MCP: Real-world Evaluation of Clinical Information Retrieval by Large Language Models via Model Context Protocol](https://mlsanity.com/paper/2509.15957/) (2025)
- [Dynamic Fog Computing for Enhanced LLM Execution in Medical Applications](https://mlsanity.com/paper/2408.04680/) (2024)
- [LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs](https://mlsanity.com/paper/2505.08704/) (2025)
- [Large Language Models for Automating Clinical Data Standardization: HL7 FHIR Use Case](https://mlsanity.com/paper/2507.03067/) (2025)
- [FHIR-RAG-MEDS: Integrating HL7 FHIR with Retrieval-Augmented Large Language Models for Enhanced Medical Decision Support](https://mlsanity.com/paper/2509.07706/) (2025)
- [LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and Differential Diagnosis](https://mlsanity.com/paper/2506.19702/) (2025)

## Abstract (arXiv)

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.