Evidence map›Paper›PMID 40373519›Full record

ArticleJACC. Advances2025

LLMonFHIR: A Physician-Validated, Large Language Model-Based Mobile Application for Querying Patient Electronic Health Data.

Paul Schmiedmayer, Adrit Rao, Philipp Zagar, Lauren Aalami, Vishnu Ravi, Aydin Zahedivash, Dong-Han Yao, Arash Fereydooni, Oliver Aalami

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PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Paul SchmiedmayerStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA. Electronic address: schmiedmayer@stanford.edu.
Adrit RaoStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA.
Philipp ZagarStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA.
Lauren AalamiStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA.
Vishnu RaviStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA.
Aydin ZahedivashStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA; Department of Pediatrics, Stanford University, Stanford, California, USA.
Dong-Han YaoDepartment of Emergency Medicine, Stanford University, Stanford, California, USA.
Arash FereydooniDepartment of Surgery, Stanford University, Stanford, California, USA.
Oliver AalamiStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA; Department of Surgery, Stanford University, Stanford, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo improve healthcare quality and empower patients, federal legislation requires nationwide interoperability of electronic health records (EHRs) through Fast Healthcare Interoperability Resources (FHIR) application programming interfaces. Nevertheless, key barriers to patient EHR access-limited functionality, English, and health literacy-persist, impeding equitable access to these benefits.

objectivesThis study aimed to develop and evaluate a digital health solution to address barriers preventing patient engagement with personal health information, focusing on individuals managing chronic cardiovascular conditions.

methodsWe present LLMonFHIR, an open-source mobile application that uses large language models (LLMs) to allow users to "interact" with their health records at any degree of complexity, in various languages, and with bidirectional text-to-speech functionality. In a pilot evaluation, physicians assessed LLMonFHIR responses to queries on 6 SyntheticMass FHIR patient datasets, rating accuracy, understandability, and relevance on a 5-point Likert scale.

resultsA total of 210 LLMonFHIR responses were evaluated by physicians, receiving high median scores for accuracy (5/5), understandability (5/5), and relevance (5/5). Challenges summarizing health conditions and retrieving lab results were noted, with variability in responses and occasional omissions underscoring the need for precise preprocessing of data.

conclusionsLLMonFHIR's ability to generate responses in multiple languages and at varying levels of complexity, along with its bidirectional text-to-speech functionality, give it the potential to empower individuals with limited functionality, English, and health literacy to access the benefits of patient-accessible EHRs.

Indexed as

artificial intelligencedigital healthlarge language modelliteracymobile application

Identifiers

PMID40373519
PMCPMC12144420

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.