Real World Research Using FHIR

Learning objectives
  1. Identify examples of how FHIR has already been used by researchers.
    Researchers have used FHIR to extract data from EHRs, define phenotypes that are portable across systems and institutions, and enable research that requires integration with clinical workflows.
Relevant roles:
  • Investigator
  • Research Leaders
  • Informaticist
  • Software Engineer
  • Clinician Scientist/Trainee

Researchers have published work using FHIR in a number of areas, usually related to:

Below are summaries of recent published research in each of these areas. At the bottom of this page is a list of other published research involving FHIR.

1 Extracting data

  • Cheng et al. (2023) used FHIR to automatically populate clinical trial case report forms. In a COVID-19-related trial at Vanderbilt University Medical Center, they populated 10,081 of 11,952 values (84%) using FHIR, with 89% agreement with human entry. Manual review indicated that disagreement with human entry was often due to errors in manual data entry, suggesting that the FHIR-based approach could improve accuracy over manual chart abstraction.

  • Zong et al. (2021) evaluated using FHIR to automatically populate case report forms for a colorectal cancer clinical trial. The were able to successfully extract a portion of the data needed to fill case report forms with 98.5% positive predictive value and 96.2% sensitivity using clinical records from 331 Mayo Clinic patients.

  • Lenert et al. (2021) describes a way of using FHIR to replace custom data pipelines for extracting COVID-19 data from EHRs. They used FHIR to populate PCORnet and OMOP databases with significantly less delay than previous approaches. (For more information on PCORnet and OMOP, please see Common Data Models.) This approach should be portable across EHRs and institutions.

  • Wood et al. (2021) discusses building a “Data Hub” that includes clinical data from multiple institutions for hematology research. They support ingesting data from both FHIR endpoints and OMOP databases with the goal of reducing the informatics burden on participating institutions.

2 Phenotype portability

  • Brandt et al. (2022) created cross-institution phenotype definitions (aka cohort definitions) using a combination of FHIR and Clinical Quality Language (CQL)1. Three institutions successfully used their system to identify cases and non-cases by generating queries to run against existing OMOP databases. Institutions that do not have OMOP research databases could possibly use FHIR and CQL directly to identify cases and non-cases, though the authors did not test this.

  • Gruendner et al. (2022) also developed a cross-institution phenotyping approach using FHIR and CQL. Their system had four components:

    1. User-interface for writing queries.
    2. Backend for translating the user input to a standardized format.
    3. Middleware to transport the query.
    4. Execution service to convert the standardized query to FHIR.

    Their approach could be deployed at multiple institutions and scaled to millions of records using synthetic data.

3 Interacting with clinical workflows

FHIR can be used to integrate third-party applications with EHRs using SMART on FHIR, a standard that enables “write once, use everywhere” integration. Please see the SMART on FHIR Introduction page for more information on real-world research use cases, including clinical decision support and data integration.

SMART on FHIR can also be used to provide patients access to their clinical data directly from their provider’s EHR. As of April 2023, we did not find examples of this capability in research.

REDCap, a survey tool that can be used with EHRs in the clinical workflow, can also be used with FHIR. For more information on using REDCap with FHIR, please see the introductory REDCap page.

4 Other references

Below is a list of studies that used FHIR in some way. These may help you come up with ideas of how FHIR can benefit your research:

  • Adverse event reporting
    • Wang, Lehmann, and Botsis (2021)
  • Clinical decision support
    • Jung et al. (2022)
  • Clinical statistics and analysis
    • Hong et al. (2017)
  • Cohort building
    • Gruendner et al. (2021)
    • Bradshaw et al. (2022)
  • Data models
    • Lambarki et al. (2021)
    • Jiang, Kiefer, Sharma, et al. (2017)
    • Fischer et al. (2020)
    • Pfaff et al. (2019)
    • Jiang, Kiefer, Prud’hommeaux, et al. (2017)
  • Environmental health and spatiotemporal data
  • Findable, Accessible, Interoperable, and Resusabe (FAIR) data principles
    • Sinaci et al. (2020)
  • Multi-site research
    • Garza et al. (2020)
  • Patient-facing apps
    • Pawelek et al. (2022)
    • Bartschke, Börner, and Thun (2021)
    • Burkhardt et al. (2021)
    • Ploner and Prokosch (2020)
    • Chatterjee, Pahari, and Prinz (2022)
  • REDCap
  • Reviews of studies that use FHIR
    • Duda et al. (2022)
    • Vorisek et al. (2022)
    • Griffin et al. (2022)
  • Social determinants of health
    • Watkins et al. (2020)


Bartschke, Alexander, Yannick Börner, and Sylvia Thun. 2021. “Accessing the ECG Data of the Apple Watch and Accomplishing Interoperability Through FHIR.” In Studies in Health Technology and Informatics, edited by Rainer Röhrig, Tim Beißbarth, Werner Brannath, Hans-Ulrich Prokosch, Irene Schmidtmann, Susanne Stolpe, and Antonia Zapf. IOS Press.
Bloomfield, Richard A., Felipe Polo-Wood, Joshua C. Mandel, and Kenneth D. Mandl. 2017. “Opening the Duke Electronic Health Record to Apps: Implementing SMART on FHIR.” International Journal of Medical Informatics 99 (March): 1–10.
Bradshaw, Richard L, Kensaku Kawamoto, Kimberly A Kaphingst, Wendy K Kohlmann, Rachel Hess, Michael C Flynn, Claude J Nanjo, et al. 2022. “GARDE: A Standards-Based Clinical Decision Support Platform for Identifying Population Health Management Cohorts.” Journal of the American Medical Informatics Association 29 (5): 928–36.
Brandt, Pascal S., Jennifer A. Pacheco, Prakash Adekkanattu, Evan T. Sholle, Sajjad Abedian, Daniel J. Stone, David M. Knaack, et al. 2022. “Design and validation of a FHIR-based EHR-driven phenotyping toolbox.” Journal of the American Medical Informatics Association: JAMIA 29 (9): 1449–60.
Burkhardt, Hannah, Pascal Brandt, Jenney Lee, Sierramatice Karras, Paul Bugni, Ivan Cvitkovic, Amy Chen, and William Lober. 2021. “StayHome: A FHIR-Native Mobile COVID-19 Symptom Tracker and Public Health Reporting Tool.” Online Journal of Public Health Informatics 13 (1).
Chatterjee, Ayan, Nibedita Pahari, and Andreas Prinz. 2022. “HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study.” Sensors 22 (10): 3756.
Cheng, Alex C., Mary K. Banasiewicz, Jakea D. Johnson, Lina Sulieman, Nan Kennedy, Francesco Delacqua, Adam A. Lewis, et al. 2023. “Evaluating Automated Electronic Case Report Form Data Entry from Electronic Health Records.” Journal of Clinical and Translational Science 7 (1): e29.
Duda, Stephany N, Nan Kennedy, Douglas Conway, Alex C Cheng, Viet Nguyen, Teresa Zayas-Cabán, and Paul A Harris. 2022. “HL7 FHIR-Based Tools and Initiatives to Support Clinical Research: A Scoping Review.” Journal of the American Medical Informatics Association 29 (9): 1642–53.
Fischer, Patrick, Mark R. Stöhr, Henning Gall, Achim Michel-Backofen, and Raphael W. Majeed. 2020. “Data Integration into OMOP CDM for Heterogeneous Clinical Data Collections via HL7 FHIR Bundles and XSLT.” Studies in Health Technology and Informatics 270 (June): 138–42.
Garza, Maryam Y., Michael Rutherford, Sahiti Myneni, Susan Fenton, Anita Walden, Umit Topaloglu, Eric Eisenstein, et al. 2020. “Evaluating the Coverage of the HL7 ® FHIR ® Standard to Support eSource Data Exchange Implementations for Use in Multi-Site Clinical Research Studies.” AMIA ... Annual Symposium Proceedings. AMIA Symposium 2020: 472–81.
Griffin, Ashley C, Lu He, Anthony P Sunjaya, Andrew J King, Zubin Khan, Martin Nwadiugwu, Brian Douthit, et al. 2022. “Clinical, Technical, and Implementation Characteristics of Real-World Health Applications Using FHIR.” JAMIA Open 5 (4): ooac077.
Gruendner, Julian, Noemi Deppenwiese, Michael Folz, Thomas Köhler, Björn Kroll, Hans-Ulrich Prokosch, Lorenz Rosenau, et al. 2022. “The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study.” JMIR Medical Informatics 10 (5): e36709.
Gruendner, Julian, Christian Gulden, Marvin Kampf, Sebastian Mate, Hans-Ulrich Prokosch, and Jakob Zierk. 2021. “A Framework for Criteria-Based Selection and Processing of Fast Healthcare Interoperability Resources (FHIR) Data for Statistical Analysis: Design and Implementation Study.” JMIR Medical Informatics 9 (4): e25645.
Hong, Na, Naresh Prodduturi, Chen Wang, and Guoqian Jiang. 2017. “Shiny FHIR: An Integrated Framework Leveraging Shiny r and HL7 FHIR to Empower Standards-Based Clinical Data Applications.” Studies in Health Technology and Informatics 245: 868–72.
Jiang, Guoqian, Richard C. Kiefer, Deepak K. Sharma, Eric Prud’hommeaux, and Harold R. Solbrig. 2017. “A Consensus-Based Approach for Harmonizing the OHDSI Common Data Model with HL7 FHIR.” Studies in Health Technology and Informatics 245: 887–91.
Jiang, Guoqian, Richard Kiefer, Eric Prud’hommeaux, and Harold R. Solbrig. 2017. “Building Interoperable FHIR-Based Vocabulary Mapping Services: A Case Study of OHDSI Vocabularies and Mappings.” Studies in Health Technology and Informatics 245: 1327.
Jones, James, Daniel Gottlieb, Joshua C Mandel, Vladimir Ignatov, Alyssa Ellis, Wayne Kubick, and Kenneth D Mandl. 2021. “A Landscape Survey of Planned SMART/HL7 Bulk FHIR Data Access API Implementations and Tools.” Journal of the American Medical Informatics Association 28 (6): 1284–87.
Jung, Sungwon, Sungchul Bae, Donghyeong Seong, Ock Hee Oh, Yoomi Kim, and Byoung-Kee Yi. 2022. “Shared Interoperable Clinical Decision Support Service for Drug-Allergy Interaction Checks: Implementation Study.” JMIR Medical Informatics 10 (11): e40338.
Lambarki, Mohamed, Jori Kern, David Croft, Cäcilia Engels, Noemi Deppenwiese, Alexander Kerscher, Alexander Kiel, Stefan Palm, and Martin Lablans. 2021. “Oncology on FHIR: A Data Model for Distributed Cancer Research.” In Studies in Health Technology and Informatics, edited by Rainer Röhrig, Tim Beißbarth, Werner Brannath, Hans-Ulrich Prokosch, Irene Schmidtmann, Susanne Stolpe, and Antonia Zapf. IOS Press.
Lenert, Leslie A, Andrey V Ilatovskiy, James Agnew, Patricia Rudisill, Jeff Jacobs, Duncan Weatherston, and Kenneth R Deans Jr. 2021. “Automated Production of Research Data Marts from a Canonical Fast Healthcare Interoperability Resource Data Repository: Applications to COVID-19 Research.” Journal of the American Medical Informatics Association 28 (8): 1605–11.
Mandel, Joshua C, David A Kreda, Kenneth D Mandl, Isaac S Kohane, and Rachel B Ramoni. 2016. “SMART on FHIR: A Standards-Based, Interoperable Apps Platform for Electronic Health Records.” Journal of the American Medical Informatics Association 23 (5): 899–908.
Metke-Jimenez, Alejandro, and David Hansen. 2019. “FHIRCap: Transforming REDCap Forms into FHIR Resources.” AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science 2019: 54–63.
Pawelek, Jeff, Katie Baca-Motes, Jay A Pandit, Benjamin B Berk, and Edward Ramos. 2022. “The Power of Patient Engagement with Electronic Health Records as Research Participants.” JMIR Medical Informatics 10 (7): e39145.
Pfaff, Emily Rose, James Champion, Robert Louis Bradford, Marshall Clark, Hao Xu, Karamarie Fecho, Ashok Krishnamurthy, et al. 2019. “Fast Healthcare Interoperability Resources (FHIR) as a Meta Model to Integrate Common Data Models: Development of a Tool and Quantitative Validation Study.” JMIR Medical Informatics 7 (4): e15199.
Ploner, Nico, and Hans-Ulrich Prokosch. 2020. “Integrating a Secure and Generic Mobile App for Patient Reported Outcome Acquisition into an EHR Infrastructure Based on FHIR Resources.” Studies in Health Technology and Informatics 270 (June): 991–95.
Sayeed, Raheel, Daniel Gottlieb, and Kenneth D. Mandl. 2020. “SMART Markers: Collecting Patient-Generated Health Data as a Standardized Property of Health Information Technology.” Npj Digital Medicine 3 (1): 9.
Sinaci, A. Anil, Francisco J. Núñez-Benjumea, Mert Gencturk, Malte-Levin Jauer, Thomas Deserno, Catherine Chronaki, Giorgio Cangioli, et al. 2020. “From Raw Data to FAIR Data: The FAIRification Workflow for Health Research.” Methods of Information in Medicine 59 (S 01): e21–32.
Vorisek, Carina Nina, Moritz Lehne, Sophie Anne Ines Klopfenstein, Paula Josephine Mayer, Alexander Bartschke, Thomas Haese, and Sylvia Thun. 2022. “Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review.” JMIR Medical Informatics 10 (7): e35724.
Wagholikar, Kavishwar B., Rahul Jain, Eliel Oliveira, Joshua Mandel, Jeffery Klann, Ricardo Colas, Prasad Patil, et al. 2017. “Evolving Research Data Sharing Networks to Clinical App Sharing Networks.” AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science 2017: 302–7.
Wang, Xingtong, Harold Lehmann, and Taxiarchis Botsis. 2021. “Can FHIR Support Standardization in Post-Market Safety Surveillance?” In Studies in Health Technology and Informatics, edited by John Mantas, Lăcrămioara Stoicu-Tivadar, Catherine Chronaki, Arie Hasman, Patrick Weber, Parisis Gallos, Mihaela Crişan-Vida, Emmanouil Zoulias, and Oana Sorina Chirila. IOS Press.
Watkins, Michael, Benjamin Viernes, Viet Nguyen, Leonardo Rojas Mezarina, Javier Silva Valencia, and Damian Borbolla. 2020. “Translating Social Determinants of Health into Standardized Clinical Entities.” Studies in Health Technology and Informatics 270 (June): 474–78.
Wesley, Deliya B, Joseph Blumenthal, Shrenikkumar Shah, Robin A Littlejohn, Zoe Pruitt, Ram Dixit, Chun-Ju Hsiao, Christine Dymek, and Raj M Ratwani. 2021. “A Novel Application of SMART on FHIR Architecture for Interoperable and Scalable Integration of Patient-Reported Outcome Data with Electronic Health Records.” Journal of the American Medical Informatics Association 28 (10): 2220–25.
Wood, William A., Peter Marks, Robert M. Plovnick, Kathleen Hewitt, Donna S. Neuberg, Sam Walters, Brendan K. Dolan, et al. 2021. “ASH Research Collaborative: A Real-World Data Infrastructure to Support Real-World Evidence Development and Learning Healthcare Systems in Hematology.” Blood Advances 5 (23): 5429–38.
Xu, Hao, Steven Cox, Lisa Stillwell, Emily Pfaff, James Champion, Stanley C. Ahalt, and Karamarie Fecho. 2020. “FHIR PIT: An Open Software Application for Spatiotemporal Integration of Clinical Data and Environmental Exposures Data.” BMC Medical Informatics and Decision Making 20 (1): 53.
Zong, Nansu, Daniel J. Stone, Deepak K. Sharma, Andrew Wen, Chen Wang, Yue Yu, Ming Huang, et al. 2021. “Modeling Cancer Clinical Trials Using HL7 FHIR to Support Downstream Applications: A Case Study with Colorectal Cancer Data.” International Journal of Medical Informatics 145 (January): 104308.


  1. CQL is an HL7® standard that expresses inclusion and exclusion logic.↩︎