LLMonFHIR
Background: To 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—...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | JACC: Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772963X2500198X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849321383763378176 |
|---|---|
| author | Paul Schmiedmayer, PhD Adrit Rao Philipp Zagar, MS Lauren Aalami, MS Vishnu Ravi, MD Aydin Zahedivash, MD, MBA Dong-han Yao, MD Arash Fereydooni, MD Oliver Aalami, MD |
| author_facet | Paul Schmiedmayer, PhD Adrit Rao Philipp Zagar, MS Lauren Aalami, MS Vishnu Ravi, MD Aydin Zahedivash, MD, MBA Dong-han Yao, MD Arash Fereydooni, MD Oliver Aalami, MD |
| author_sort | Paul Schmiedmayer, PhD |
| collection | DOAJ |
| description | Background: To 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. Objectives: This 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. Methods: We 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. Results: A 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. Conclusions: LLMonFHIR'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. |
| format | Article |
| id | doaj-art-1491f57470614d43a7b1773cee03e4e4 |
| institution | Kabale University |
| issn | 2772-963X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JACC: Advances |
| spelling | doaj-art-1491f57470614d43a7b1773cee03e4e42025-08-20T03:49:46ZengElsevierJACC: Advances2772-963X2025-06-014610178010.1016/j.jacadv.2025.101780LLMonFHIRPaul Schmiedmayer, PhD0Adrit Rao1Philipp Zagar, MS2Lauren Aalami, MS3Vishnu Ravi, MD4Aydin Zahedivash, MD, MBA5Dong-han Yao, MD6Arash Fereydooni, MD7Oliver Aalami, MD8Stanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA; Address for correspondence: Dr Paul Schmiedmayer, James H. Clark Center, Stanford Mussallem Center for Biodesign, 318 Campus Drive, Stanford, California 94305-5428, USA.Stanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USAStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USAStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USAStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USAStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA; Department of Pediatrics, Stanford University, Stanford, California, USADepartment of Emergency Medicine, Stanford University, Stanford, California, USADepartment of Surgery, Stanford University, Stanford, California, USAStanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA; Department of Surgery, Stanford University, Stanford, California, USABackground: To 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. Objectives: This 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. Methods: We 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. Results: A 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. Conclusions: LLMonFHIR'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.http://www.sciencedirect.com/science/article/pii/S2772963X2500198Xartificial intelligencedigital healthlarge language modelliteracymobile application |
| spellingShingle | Paul Schmiedmayer, PhD Adrit Rao Philipp Zagar, MS Lauren Aalami, MS Vishnu Ravi, MD Aydin Zahedivash, MD, MBA Dong-han Yao, MD Arash Fereydooni, MD Oliver Aalami, MD LLMonFHIR JACC: Advances artificial intelligence digital health large language model literacy mobile application |
| title | LLMonFHIR |
| title_full | LLMonFHIR |
| title_fullStr | LLMonFHIR |
| title_full_unstemmed | LLMonFHIR |
| title_short | LLMonFHIR |
| title_sort | llmonfhir |
| topic | artificial intelligence digital health large language model literacy mobile application |
| url | http://www.sciencedirect.com/science/article/pii/S2772963X2500198X |
| work_keys_str_mv | AT paulschmiedmayerphd llmonfhir AT adritrao llmonfhir AT philippzagarms llmonfhir AT laurenaalamims llmonfhir AT vishnuravimd llmonfhir AT aydinzahedivashmdmba llmonfhir AT donghanyaomd llmonfhir AT arashfereydoonimd llmonfhir AT oliveraalamimd llmonfhir |