Real World Research Using FHIR

Not yet updated for 2026
Roles: Investigator Research Leaders Informaticist Software Engineer Clinician Scientist/Trainee
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.

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

  • [1] 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.

  • [2] 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.

  • [3] 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.

  • [4] 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

  • [5] 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.

  • [6] 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:

References

[1]
A. C. Cheng et al., “Evaluating automated electronic case report form data entry from electronic health records,” Journal of Clinical and Translational Science, vol. 7, no. 1, p. e29, 2023, doi: 10.1017/cts.2022.514.
[2]
N. Zong et al., “Modeling cancer clinical trials using HL7 FHIR to support downstream applications: A case study with colorectal cancer data,” International Journal of Medical Informatics, vol. 145, p. 104308, Jan. 2021, doi: 10.1016/j.ijmedinf.2020.104308.
[3]
L. A. Lenert et al., “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, vol. 28, no. 8, pp. 1605–1611, Jul. 2021, doi: 10.1093/jamia/ocab108.
[4]
W. A. Wood et al., “ASH Research Collaborative: a real-world data infrastructure to support real-world evidence development and learning healthcare systems in hematology,” Blood Advances, vol. 5, no. 23, pp. 5429–5438, Dec. 2021, doi: 10.1182/bloodadvances.2021005902.
[5]
P. S. Brandt et al., “Design and validation of a FHIR-based EHR-driven phenotyping toolbox,” Journal of the American Medical Informatics Association: JAMIA, vol. 29, no. 9, pp. 1449–1460, Aug. 2022, doi: 10.1093/jamia/ocac063.
[6]
J. Gruendner et al., “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, vol. 10, no. 5, p. e36709, May 2022, doi: 10.2196/36709.
[7]
X. Wang, H. Lehmann, and T. Botsis, “Can FHIR support standardization in post-market safety surveillance?” in Studies in health technology and informatics, J. Mantas, L. Stoicu-Tivadar, C. Chronaki, A. Hasman, P. Weber, P. Gallos, M. Crişan-Vida, E. Zoulias, and O. S. Chirila, Eds., IOS Press, 2021. doi: 10.3233/SHTI210115.
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[10]
J. Gruendner, C. Gulden, M. Kampf, S. Mate, H.-U. Prokosch, and J. Zierk, “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, vol. 9, no. 4, p. e25645, Apr. 2021, doi: 10.2196/25645.
[11]
R. L. Bradshaw et al., “GARDE: A standards-based clinical decision support platform for identifying population health management cohorts,” Journal of the American Medical Informatics Association, vol. 29, no. 5, pp. 928–936, Apr. 2022, doi: 10.1093/jamia/ocac028.
[12]
M. Lambarki et al., “Oncology on FHIR: A data model for distributed cancer research,” in Studies in health technology and informatics, R. Röhrig, T. Beißbarth, W. Brannath, H.-U. Prokosch, I. Schmidtmann, S. Stolpe, and A. Zapf, Eds., IOS Press, 2021. doi: 10.3233/SHTI210070.
[13]
G. Jiang, R. C. Kiefer, D. K. Sharma, E. Prud’hommeaux, and H. R. Solbrig, “A consensus-based approach for harmonizing the OHDSI common data model with HL7 FHIR,” Studies in Health Technology and Informatics, vol. 245, pp. 887–891, 2017.
[14]
P. Fischer, M. R. Stöhr, H. Gall, A. Michel-Backofen, and R. W. Majeed, “Data integration into OMOP CDM for heterogeneous clinical data collections via HL7 FHIR bundles and XSLT,” Studies in Health Technology and Informatics, vol. 270, pp. 138–142, Jun. 2020, doi: 10.3233/SHTI200138.
[15]
E. R. Pfaff et al., “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, vol. 7, no. 4, p. e15199, Oct. 2019, doi: 10.2196/15199.
[16]
G. Jiang, R. Kiefer, E. Prud’hommeaux, and H. R. Solbrig, “Building interoperable FHIR-based vocabulary mapping services: A case study of OHDSI vocabularies and mappings,” Studies in Health Technology and Informatics, vol. 245, p. 1327, 2017.
[17]
H. Xu et al., “FHIR PIT: An open software application for spatiotemporal integration of clinical data and environmental exposures data,” BMC Medical Informatics and Decision Making, vol. 20, no. 1, p. 53, Dec. 2020, doi: 10.1186/s12911-020-1056-9.
[18]
A. A. Sinaci et al., “From raw data to FAIR data: The FAIRification workflow for health research,” Methods of Information in Medicine, vol. 59, no. S 1, pp. e21–e32, Jun. 2020, doi: 10.1055/s-0040-1713684.
[19]
M. Y. Garza et al., “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, vol. 2020, pp. 472–481, 2020.
[20]
J. Pawelek, K. Baca-Motes, J. A. Pandit, B. B. Berk, and E. Ramos, “The power of patient engagement with electronic health records as research participants,” JMIR Medical Informatics, vol. 10, no. 7, p. e39145, Jul. 2022, doi: 10.2196/39145.
[21]
A. Bartschke, Y. Börner, and S. Thun, “Accessing the ECG data of the apple watch and accomplishing interoperability through FHIR,” in Studies in health technology and informatics, R. Röhrig, T. Beißbarth, W. Brannath, H.-U. Prokosch, I. Schmidtmann, S. Stolpe, and A. Zapf, Eds., IOS Press, 2021. doi: 10.3233/SHTI210076.
[22]
H. Burkhardt et al., “StayHome: A FHIR-native mobile COVID-19 symptom tracker and public health reporting tool,” Online Journal of Public Health Informatics, vol. 13, no. 1, Mar. 2021, doi: 10.5210/ojphi.v13i1.11462.
[23]
N. Ploner and H.-U. Prokosch, “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, vol. 270, pp. 991–995, Jun. 2020, doi: 10.3233/SHTI200310.
[24]
A. Chatterjee, N. Pahari, and A. Prinz, “HL7 FHIR with SNOMED-CT to achieve semantic and structural interoperability in personal health data: A proof-of-concept study,” Sensors, vol. 22, no. 10, p. 3756, May 2022, doi: 10.3390/s22103756.
[25]
A. Metke-Jimenez and D. Hansen, “FHIRCap: Transforming REDCap forms into FHIR resources,” AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, vol. 2019, pp. 54–63, 2019.
[26]
S. N. Duda et al., “HL7 FHIR-based tools and initiatives to support clinical research: a scoping review,” Journal of the American Medical Informatics Association, vol. 29, no. 9, pp. 1642–1653, Jul. 2022, doi: 10.1093/jamia/ocac105.
[27]
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[28]
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[29]
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[30]
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[31]
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[32]
D. B. Wesley et al., “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, vol. 28, no. 10, pp. 2220–2225, Sep. 2021, doi: 10.1093/jamia/ocab110.
[33]
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[34]
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[35]
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Footnotes

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