JMIR MEDICAL INFORMATICS

Vorisek et al

Review

Fast Healthcare Interoperability Resources (FHIR) forInteroperability in Health Research: Systematic Review
Carina Nina Vorisek1, MD, MSc; Moritz Lehne1, PhD; Sophie Anne Ines Klopfenstein1,2, MD; Paula Josephine
Mayer1; Alexander Bartschke1, MA; Thomas Haese1, BSc; Sylvia Thun1, MD, PhD
1Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany

2

Institute for Medical Informatics, Charité – Universitätsmedizin Berlin, Berlin, Germany

Corresponding Author:
Carina Nina Vorisek, MD, MSc
Core Facility Digital Medicine and Interoperability
Berlin Institute of Health at Charité – Universitätsmedizin Berlin
Anna-Louisa-Karsch-Str. 2
Berlin, 10178
Germany
Phone: 49 30 450 543049
Email: carina-nina.vorisek@bih-charite.de

Abstract
Background: The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology.
However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for healthresearch, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as“Public Health & Research” and “Evidence-Based Medicine” while using already established web technologies. Therefore, FHIR
could help standardize data across different data sources and improve interoperability in health research.
Objective: The aim of our study was to provide a systematic review of existing literature and determine the current state of
FHIR implementations in health research and possible future directions.
Methods: We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases forstudies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published
before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting
Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393).
Data synthesis was done in tables and figures.
Results: We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%,n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%,n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization ofdata (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of thestudies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease,genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable toother use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized
Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18),
International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data
model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain “Public Health &Research.” Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and theneed for a FHIR server.
Conclusions: Our review found that FHIR can be implemented in health research, and the areas of application are broad andgeneralizable in most use cases. The implementation of international terminologies was common, and other standards such as
the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations suchas the change of FHIR content, lack of FHIR implementation, safety, and legal matters need to be addressed in future releases toexpand the use of FHIR and, therefore, interoperability in health research.

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Vorisek et al(JMIR Med Inform 2022;10(7):e35724) doi: 10.2196/35724
KEYWORDS
Fast Healthcare Interoperability Resources; FHIR; interoperability; health research; health care; health information technology;
research; clinical research; public health; epidemiology

Introduction
Within the current COVID-19 pandemic, there was a broad
realization of the currently limited data collection processes andhow powerful the exchange of scientific data could be ifinteroperability between health care and research was provided[1]. Although there was a large amount of data in the healthcare ecosystem, there was lack of data that adheres to Findable,
Accessible, Interoperable, and Reusable [2] principles for users
to find, use, analyze, and share data on COVID-19. This applies
specifically to academic health research where the lack ofinteroperability between health care and research often inhibits
the use of existing data sources for research. Commonly, thedata collections of health research are stored in decentralized,
autonomous data infrastructures which requires integration into
common frameworks to enable centralized search and access.
However, processing national and cross-national scientific data
across different institutions and software systems requires
international standards and terminologies: the Observational
Health Data Sciences and Informatics (OHDSI) Observational
Medical Outcomes Partnership (OMOP) common data model(CDM) is used in observational research, whereas the Clinical
Data Interchange Standards Consortium (CDISC) Operational
Data Standard (ODM) is used specifically for the exchange ofdata within clinical trials [3]. CDISC is providing standards
such as standardized raw data sets (Study Data Tabulation
Model; SDTM), also considered a CDM, as well as standardized
analysis data sets models. Further established standards are theterminologies Systematized Nomenclature of Medicine ClinicalTerms (SNOMED CT) and Logical Observation Identifiers,
Names, and Codes (LOINC). SNOMED CT is the mostcomprehensive clinical health care terminology worldwide
providing more than 350,000 concepts, whereas LOINC is astandard for laboratory tests and clinical observations. One of
the latest emerging standards for the exchange of health data is
the standard Fast Healthcare Interoperability Resources (FHIR).
FHIR is a standard used in health information technology
introduced in 2011 by the Standard Developing Organization
Health Level Seven International (HL7). FHIR is based on
previous HL7 standards (HL7 versions 2 and 3 and ClinicalDocument Architecture) and combines their advantages withestablished modern web technologies such as a Representational
State Transfer (REST) architecture; application programming
interface (API), XML, and JSON formats; and authorization
tools (Open Authorization). In FHIR, all exchangeable content
is defined by distinct basic building blocks—referred to as
resources—which define the content and structure of informationand can refer to each other using reference mechanisms [4].

different solutions can be implemented [5]. To cover information
not included in the basic resources, FHIR provides a built-in
extension mechanism and can be adapted for specific use cases
while ensuring interoperability. Additional rules and constraints
within resources can be defined in profiles. Therefore, FHIR
covers various domains of health care with its resources andcan be used for different purposes and in various contexts andworkflows.
With regard to health research, there is still a lack of use ofinternational standards when exchanging data between healthcare and research institutions. However, there have been recent
regulative and legislative changes promoting standards andinteroperability in health care [6-8]. In addition, there are
initiatives of HL7 promoting FHIR’s use in health research,such as the Vulcan HL7 FHIR Accelerator aiming to connect
clinical research and health care, the MedMorph project aiming
to advance public health by using standards such as FHIR, andthe collaboration of HL7 and OHDSI on a single common data
model [9-11]. As many research platforms and modern data
management systems, such as the Extensible Neuroimaging
Archive Toolkit open-source imaging informatics platform, use
extensible REST APIs [12,13], FHIR may be the new standard
to fill the interoperability gap in health research with its REST
architecture. Existing reviews on FHIR investigate the generaluse of FHIR in digital health [14] or its use in electronic healthrecords [15]. However, to the best of our knowledge, the use of
FHIR in health research has not been systematically investigated.
Therefore, the aim of our study was to provide a systematic
review of existing literature to determine the current state ofuse cases, implementation, goals, and limitations of FHIR in
health research.

Methods
Protocol, Registration, and Ethical Considerations
This systematic review was conducted in accordance with the(PRISMA) Preferred Reporting Items for Systematic Reviews
and Meta-Analyses guidelines [16]. The review was registeredwith the International Prospective Register of Systematic
Reviews (PROSPERO; CRD42021235393) [17]. As data
originated from published studies, ethical approval for this study
was not requested.

Inclusion and Exclusion Criteria
We included studies investigating the use of FHIR in healthresearch. We did not focus on particular patient populations,interventions, control groups, or outcomes, except the use of
FHIR in health research. Details on inclusion and exclusion
criteria are presented in Textbox 1.

The base FHIR specification serves as a foundation providing
basic resources, frameworks, APIs, and a platform in whichhttps://medinform.jmir.org/2022/7/e35724

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Textbox 1. Inclusion and exclusion criteria for paper review.
Inclusion criteria•

Studies focusing on the use of FHIR in health care research•

Original papers published in peer-reviewed journals in English•

Studies with publication dates no earlier than 2011

Exclusion criteria•

Studies focusing on the use of FHIR in electronic health records, mobile and web apps, decision support, and data protection or security•

General overviews on FHIR•

Comments, books, editorials, or reviews•

Language other than English•

Studies conducted before 2011

Information Sources and Search Strategy
A comprehensive literature search was performed through the
PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore,
and Cochrane Library databases. In addition, citation tracking
and reference list checking were performed. The goal of thesearch strategy was to retrieve all relevant studies related to our
research question published between 2011 and 2022. Searchterms were therefore relatively broad to make sure that all
potentially relevant studies were identified. Search terms used
for the database searches were “FHIR” and “Fast Healthcare
Interoperability Resources.” Information on the detailed searchstrategy for each database is provided as an appendix to thisreview (Multimedia Appendix 1). The search was conductedon February 26, 2022.

Study Selection and Data Collection Process
Study selection included 2 screening levels: (1) screening of
titles and abstracts of all studies identified in the literature searchand (2) full-text review of studies that had not been excludedin the first step. Review at the first stage of screening was
performed independently by 2 authors (ML and SAIK) using
the Rayyan web app [18]. Remaining disagreements wereresolved by a third author (CNV). Further full-text screening
at the second stage and data extraction were performed by 6
authors (CNV, ML, SAIK, PJM, AB, and TH), anddisagreements of at least two authors at this stage were resolvedby the last author (ST).

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Data Extraction and Analysis
Data synthesis was conducted in tables and figures. For
categorical variables, simple and relative frequencies andproportions were used. To identify the networks of coauthors,
we also performed a network analysis that investigated, for all
authors of the included studies, whether they were coauthors ina study. Results were visualized in a network graph. We did notassess bias in studies due to the lack of quantitative tools
applicable to technical papers on standards. All analyses weredone with R statistical software (version 4.0.5; R Foundationfor Statistical Computing) [19] and the tidyverse packages [20].
All data and analyses scripts are provided in a GitHub repository[21].

Results
Study Selection and Extraction
A total of 998 articles were identified through the databasesearches (344 from MEDLINE, 359 from Embase, 201 fromWeb of Science, 84 from IEEE Xplore, and 10 from CochraneLibrary). No additional records were identified through citation
tracking and reference list checking. We excluded 477 duplicatesand 422 articles that did not meet the inclusion criteria or met
the exclusion criteria. Among the 99 full-text articles assessed
for eligibility, an additional 50 studies were excluded. Finally,
49 [1,12,22-68] articles met the inclusion criteria and were
included in the systematic review (Figure 1). Details on the
exclusion reasons for the full-text evaluation can be found in
Multimedia Appendix 2, and the exclusion reasons for theabstract evaluation can be found in the GitHub repository [21].

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Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram for identifying articles eligible for inclusion.
FHIR: Fast Healthcare Interoperability Resources.

Characteristics of Included Studies
Publication dates ranged from 2016 to 2022 with the median in
2020. Of the 49 included studies, 73% (n=36) were publishedbetween 2020 and 2022. The increase of publications from 2020
onward is visualized in Figure 2, showing the temporal trendof all FHIR publications identified in the databases with thesearch terms “FHIR” OR “Fast Healthcare Interoperability
Resources” and the number of publications included into theanalysis per year.
The results of the network analysis of coauthorships are shownin Figure 3. Of a total of 256 authors, most (85%, n=217)
appeared only once in the included studies, and no author

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occurred more than 6 times within the included studies. Most
coauthorship networks were restricted to individual studies,with occasional connections between networks (ie, authors
having published studies with different groups of coauthors).
Of the 49 studies, the majority were conducted in Germany(47%, n=23) [12,26,28-31,34,35,40-42,45-47,52,53,56-58,60,62,
63,69], the United States (27%, n=13) [22,25,36,44,48-50,61,
64-66,68,70], and Australia (6%, n=3) [1,43,67]. The remaining
studies were performed in Austria (2%, n=1) [32], Canada (2%,n=1) [24], France (2%, n=1) [51], Greece (2%, n=1) [59], Japan(2%, n=1) [27], Pakistan (2%, n=1) [38], Spain (2%, n=1) [55],
Switzerland (2%, n=1) [39], Taiwan (2%, n=1) [23], and theUnited Kingdom (2%, n=1) [37].

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Figure 2. Number of publications per year (all: all FHIR publications identified in the databases with the search terms “FHIR” OR “Fast Healthcare
Interoperability Resources”; included: studies included in this review). FHIR: Fast Healthcare Interoperability Resources.

Figure 3. Network of coauthorships. Each point represents an author. Point size and color indicate the number of publications of this author (between
1 and 6). Lines indicate that authors have coauthored at least one paper together. Line thickness represents the number of coauthorships.

Research Domain and Area of FHIR Application
Of the 49 studies, most (73%, n=36) studies covered the researchdomain of clinical research, of which 10 (20%) studies wereclinical trials [22,29-31,36,39,43,56,65,66]; 3 (6%) studies
focused on solutions in public health and epidemiology[38,40,64], and the remaining studies did not specify theirresearch domain (20%, n=10; Figure 4) [24,32,41,42,45-47,

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50,63,69]. The included studies used FHIR for the
standardization of data (41%, n=20) [23,26,30,34,41,45-48,
51-53,57-60,63,66,67,70], data capture (29%, n=14)[1,12,22,24,27,35-37,43,44,55,61,64,65], recruitment (14%,n=7)[28,29,31,32,49,56,62],
analysis(12%,
n=6)[25,38,42,50,68,69], and consent management (4%, n=2; Table
1) [39,40]. Details on the included studies are presented in Table
2.

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Figure 4. Number of studies according to research domain.

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Table 1. Numbers of studies according to area of FHIR application, medical specialty, and international standard.
Area

Studies (N=49), n (%)

FHIRa application
Standardization of data

20 (41)

Data capture

14 (29)Recruitment

7 (14)

Analysis

6 (12)

Consent management

2 (4)Medical specialty
Generic approach

27 (55)

Infectious disease

8 (16)

Oncology

6 (12)

Genomics

4 (8)Pulmonary hypertension

1 (2)Neuroimaging research

1 (2)Genomic cancer medicine

1 (2)Environmental health

1 (2)International standard

a

Other

21 (43)None

18 (37)LOINCb14 (29)SNOMED CTc

18 (37)

ICD-10d

9 (18)OMOPe

6 (12)

FHIR: Fast Healthcare Interoperability Resources.

b

LOINC: Logical Observation Identifiers Names and Codes.

c

SNOMED CT: Systematized Nomenclature of Medicine Clinical Terms.

dICD-10: International Classification of Diseases 10th Revision.

e

OMOP: Observational Medical Outcomes Partnership.

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Table 2. Characteristics of studies.
Source, year

Country

Item mapped to FHIRa

Objective for FHIR use

FHIR resources

Banach et al [56], Germany
2021

Medical and demographic data Estimation of the number of potentially eligible pa- —bfrom free-text eligibility criteria tients for planning multicenter trials based on freetext criteria and using a consented data set based on
FHIR

Bauer et al [1],2020

Australia

Questionnaire

Ontology-based standard questionnaire for linking
genomic data with clinical outcomes

Questionnaire

Bialke et al [40],2018Germany

Modular consent templates

Support improvement for consent definition andconsent documentation

Consent

Bild et al [28],2020

Germany

Informed consent template

Cross-site interoperability layer for representing the Consent and Patient
validity of data use policies derived from signed informed consent templates and regulatory framework

Brandt et al [71], United States
2021

Phenotype definitions from the Repository of structured phenotype definitions for
Phenotype Knowledgebase
automation of cohort identification.
repository

Patients, Encounter, Procedure, Medication orders, Condition, and Observation

Cheng et al [44],2021

EHRc Data

Patient, Observation, AllergyIntolerance,MedicationOrder, and Condition

United States

Seamless data exchange between the REDCapd research electronic data capture and any EHR systemwith a FHIR APIe

Deppenwieset al[57], 2021

Germany

Oncology data

Provide a transformation tool from oncology data
XML files to FHIR for oncological data to enableclinical research

Medication, MedicationStatement, and Procedure

Eapen et al [24],2019CanadaElectronic form components

Management, editing, and rendering of electronic
forms in the form of an open-source framework

Questionnaire and QuestionnaireResponse

Fischer et al [35], Germany
2020

Common data set from a German pulmonary hypertension
registry

Feasibility of HL7f FHIR Bundle and XSLTg as a

Garza et al [61],2020

Concomitant medications, de- Developing and implementing a systematic mapping
mographics, eligibility, labs,
approach for evaluating HL7 FHIR standard coverage
medical history, therapeuticin multicenter clinical trials.
area–specific, procedure, encounters, vital signs, other, administrative, questionnaires,and study drug administration

United States

h

i

j

generic ETL process to populate an OMOP CDM

Patient, Encounter, andObservation
Observation, Patient,
Specimen, Encounter,
Diagnostic Report, andCondition

González- Castro Spainet al [55], 2021

Clinical patient data (fromEHR) and patient-generated
dataCollection and aggregation of survivorship data (use Patient, Condition, Obsercases colon cancer and breast cancer)
vation, MedicationStatement, Encounter, andProcedure

Gruendner et al[69], 2020

Germany

Clinical patient data

Analysis within and across institutions—

Gruendner et al[42], 2021

Germany

Metadata

Developing a Metadata Schema based on FHIR togather metadata on clinical, epidemiological, andResearchStudy, Questionnaire, and DocumentReference

public health studies; elevate data FAIRnessk; andwiden analysis possibilities across health researchdomains
Guérin et al [51], France
2021

Clinical and omics data in oncology

Improve and accelerate retrospective and prospective MolecularSequence andclinical and genomic data sharing in oncology
Observation

Gulden et al [31], Germany
2018

Eligibility criteria of clinical
trials

Recruitment of patients for clinical trials using eligi- Condition and Patientbility criteria

Gulden et al [30], Germany
2021

Clinical trial data

Multisite clinical trial registry

Hong et al [25],2017

Ovarian cancer data

Support of clinical statistics and analysis leveraging Patient, Observation,
standardized data exchange and access services based Condition, and Procedure
on FHIR

United States

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Source, year

Country

Item mapped to FHIRa

Objective for FHIR use

Hund et al [53],2021Germany

Process data

Developing a framework to enable standardized,
ActivityDefinition, Binashared processes using Business Process Model and ry, Bundle, CodeSystem,
Notation and FHIR for arbitrary biomedical research Endpoint,Group, NamingSystem, Organization,
Practitioner, PractitionerRole, ResearchStudy,
StructureDefinition, Subscription, and Task

Jiang et al [70],2017

United States

Clinical research data

Development and assessment of a consensus-based

Kilintzis et al[59], 2022

GreeceFHIR resources

Observation

approach for harmonizing the OHDSIl CDM with
HL7 FHIR
Clinical information from inm

ICU COVID-19 patients

Fusion of clinical information with chest sounds and Media
imaging of COVID-19 ICU patients

Klopfenstein et al Germany[41], 2021

Metadata of clinical, epidemio- Developing a Metadata Schema based on FHIR tological and public health stud- gather metadata on clinical, epidemiological, andiespublic health studies; elevate data FAIRness; andwiden analysis possibilities across health researchdomains

ResearchStudy, Questionnaire, and DocumentReference

Khalique andKhan [38], 2017

Pakistan

EHR

Khvastova et al[12], 2020

Germany

Open-source research platform Feasibility study for the full integration of FHIR into PatientXNAT(XNATn)Analysis or mining of EHR data and contextual infor- —mation to assess the population’s health

Lackerbauer et al Austria[32], 2019

Informed consent or questionnaires

Automated verification of answers

Questionnaire and QuestionnaireResponse

Lambarki et al[58], 2021

Germany

Oncology data

Use and apply a harmonized FHIR-based modular
data set in a federated data platform for translational
cancer research to store data in a structured mannerand enable data transfer

Condition, Observation,
Procedure, MedicationStatement, Patient, Organization, Specimen,
ClinicalImpression, Encounter, and ServiceRequest

Lee et al [23],2020

TaiwanIPSo

FHIR-based global infectious disease surveillanceand case-tracking model

MedicationStatement,
Medication, AllergyIntolerance, Condition, Immunization, Procedure, Organization, Observation,
CarePlan, and Location

Lenert et al [50],2021

United States

Clinical data

Availability of data for research

Patient, Encounter, Condition, Procedure, Observation, MedicationRequest, and MedicationAdministration

Leroux et al [67], Australia
2017

Data model

Mapping CDISCp ODMq to FHIR

Patient, Observation,
EpisodeOfCare, Encounter, QuestionnaireResponse, Questionnaire,
and CarePlan

Majeed et al [60], Germany
2021

General patient information,encounter, or visit related information; individual data points;
observations; measurements;and surveys

Developing a generic ETL framework to process
Patient, Observation, andpatient data into FHIR and enable data integration in Encountera single central data warehouse as a prerequisite fortranslational research

Metke-Jimenez et Australia
al [43], 2019

REDCap forms

Data export from REDCap into FHIR resources

Encounter, Observation,
Condition, and Patient

Peng et al [52],2021

Genomic Variant Cell Format
data

Coverage of Variant Cell Format data in OMOP
CDM with and without using FHIR as intermediate
layer

MolecularSeqeunce, Patient, and Condition

Germany

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Source, year

Country

Item mapped to FHIRa

Objective for FHIR use

FHIR resources

Pfiffner et al[22], 2016

United States

ResearchKit data

Patient-reported outcomesContract, Questionnaire,
QuestionnaireResponse,
Patient, and Observation

Reinecke et al[29], 2020

Germany

Patient ID lists

Data-driven recruitment of patients for clinical trials, List
storage of patient lists, and generation of notifications

Rinaldi et al [45], Germany
2021

Microbiology data

Standardization of clinical data from patient care and DiagnosticReport, Obsermedical research in the field of infection control
vation, Specimen, andServiceRequest

Rinaldi et al [47], Germany
2021

OpenEHR Template

Mapping infection control related data across 3 different standards—OpenEHR, FHIR, and OMOP
CDM—to maximize analysis capabilities

DiagnosticReport, Observation, Specimen, ServiceRequest, and Encounter

Sass et al [26],2020

Germany

COVID-19 data

Standardized data model

Patient, Consent, Observation, Condition, Procedure, Encounter, Medication, and MedicationStatement

Sass et al [46],2021Germany

Medication chapter of the Ger- Representation of structured medication dataman Procedure
Classification and Identificationof Medicinal Products–compliant

Patient, Procedure, MedicationStatement, andMedication

medication terminology
Tanaka et al [27], Japan
2020

SS-MIX2r

Mapping electronic medical record items between
SS-MIX2 and HL7 FHIR

Patient, Encounter, Condition, AllergyIntolerance, Observation, Specimen, ServiceRequest,
MedicationRequest, andMedicationDispense

Ulrich et al [34],2016

Germany

Metadata or CRFs

Metadata repository

Questionnaire

Wagholikar et al[36], 2017

United States

Common data model demoClinical apps sharing via a platform
graphics, laboratory results, anddiagnoses

Wang et al [48],2021

United States

FDAt’s Adverse Event Report- Potential use of FHIR for postmarket safety surveil- AdverseEventlance for drug products
ing System data

Weber et al [39],2020

Switzerland

Electronic consent form

Designing of a FHIR-based eConsent app for Android Contractand evaluation of acceptance

Wettstein et al[62], 2021

Germany

Clinical data

Using FHIR for automated and distributed feasibility Group, ResearchStudy,
queries to find available cohort sizes across institu- and Task
tions

Wettstein et al[63], 2021

Germany

Medical routine data

HL7 FHIR version R4 is used to define the necessary Group, ResearchStudy,communication messages as well as process input
and Task
and output variables.

Wu et al [37],2018

United Kingdom

EHR data and unstructured
documents

Semantic search system for obtaining clinical insights Patient and DocumentRefrom unstructured clinical notes
ference

Xu et al [64],2020

United States

Data set of patients with “asth- Impact of airborne pollutant exposures on asthma
ma-like” conditions(research question)

Zong et al [65],2020

United States

Colorectal cancer report

Automatic population of eCRFs in colorectal clinical Questionnaire and Quescancer trials
tionnaireResponse

Zong et al [66],2021

United States

Colorectal cancer data model

Framework for capturing common data elementsfrom CRFs and FHIR resources to——DiagnosticReport andObservation

identify clinical information needs

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Source, year

Country

Item mapped to FHIRa

Objective for FHIR use

FHIR resources

Zong et al [68],2020

United States

EHR

Discovery of genotype-phenotype associations

Condition, and Observation

FHIR: Fast Healthcare Interoperability Resources.

b

Not available.

c

EHR: electronic health record.

dREDCap: Research Electronic Data Capture.

eAPI: application programming interface.

f

HL7: Health Level Seven International.

g

XSLT: Extensible Stylesheet Language Transformations.

hETL: Extract-Transform-Load.

i

OMOP: Observational Medical Outcomes Partnership.

j

CDM: common data model.

k

FAIR: Findable, Accessible, Interoperable, and Reusable.

l

OHDSI: Observational Health Data Sciences and Informaticsm

ICU: intensive care unit.

n

XNAT: Extensible Neuroimaging Archive Toolkit.

o

IPS: International Patient Summary.

p

CDISC: Clinical Data Interchange Standards Consortium.

q

ODM: Operational Data Model.

r

SS-MIX2: Standardized Structured Medical Information Exchange2.

s

CRF: Case Report Form.

t

FDA: U.S. Food and Drug Administration.

Study Objectives
In terms of medical specialty, most (55%, 27/49) of the studies[24,27-32,34,36-42,44,46,48,49,53,56,60-63,67,70] were using
a generic approach—implementable in any kind of specialty(Table 2). Of the remaining studies, 16% (8/49) use cases
focused on infectious disease [1,22,23,26,45,47,50,59], whereas
12% (6/49) focused on oncology [25,55,57,58,65,66] and 8%(4/49) on genomics [43,52,68,69]. Further medical specialties
were environmental health (2%, 1/49) [64], genomic cancer
medicine (2%, 1/49) [51], neuroimaging research (2%, 1/49)[12], and pulmonary hypertension (2%, 1/49) [35]. Despite
studies implementing FHIR in specific use cases, 55% (12/22)of the studies [1,12,22,23,25,35,43,50,52,58,64,69] reported
generic solutions conferrable to other use cases. Details on study
objectives with regards to FHIR use can be found in Table 2
and Multimedia Appendix 3.

International Standards
Among the 49 studies, 37% (n=18) did not report on or use
additional standards or terminologies [12,22-24,27,28,30-32,
38,39,48,50,55,57,64,66,69]. SNOMED CT [1,25,26,35,37,43,
45-47,51,55,56,65,70] and LOINC [25,26,35,37,42-45,
47,49,51,55,56,58,61,65,68,70] were reported to be used by
29% (n=14) and 37% (n=18) of the studies, respectively; 18%(n=9) of the studies used International Classification of Diseases
10th Revision [25,26,35,37,49,51,58,65,68] and 12% (n=6)
used OMOP CDM [26,29,35,47,52,60]; and 43% (n=21) of thestudies used additional standards which were categorized under“Other” (Table 1) [26,34-37,40,42,43,45-47,49,51,56,58-60,
62,63,67,70]. The implemented FHIR resources by each study
are listed in Table 2; 5 (10%) studies did not precisely list their
FHIR resources used [36,38,56,64,69]. Information on the FHIR
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version used was provided by 45% (n=22) of the studies[22,23,25,26,28,30,32,35,40,42,48,49,57,59,60,66,68,70], which
can be found in Multimedia Appendix 4.

Limitations of FHIR Use
With regard to the limitations of FHIR use, Bild et al [28],Lackerbauer et al [32], and Metke-Jimenez et al [43] reported
the possible content changes of new versions of FHIR resources.
Generalizability was a concern in the studies of Khalique et al[38] and Zong et al [65]. The need for a FHIR server [69] andthe requirement for a protocol for deidentification [1] wereadditional limitations. Reinecke et al [29] had not tested theexchange of data between locations and therefore could notprovide information in terms of use and results of their
prototype. Wagholikar et al [36] implemented a limited subset
of FHIR resources in their platform and therefore the filteringof FHIR resources using complex query formats was notsupported. In terms of electronic consents, safety and legal
matters were major concerns [39]. Zong et al [68], investigating
the discovery of genotype-phenotype associations, reported thelack of information on differences in genetic data as well as
extra mapping efforts since the data were from multiple sources.
In addition, there was a lack of resources preventing thedemonstration of use in the study. Generalizability was also a
concern in this study in terms of exploring the FHIR framework
within other variants and noncancer phenotypes in future work.

Discussion
Principal Findings
This systematic review summarizes the current state of use cases
implementing FHIR in health research. As FHIR was developed
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JMIR MEDICAL INFORMATICS
in 2011, we included studies from 2011 to 2022 and found that
half of studies were published between 2020 and 2022,
displaying an increased use of FHIR in the past years.
Interestingly, the first publication of our included studies
emerged in 2016, indicating a 5-year latency between the
publication of the FHIR standard and the publication of studies
addressing its use in health research. Germany and the UnitedStates were the countries with the highest number ofpublications, which might be due to recent regulatory
measurements and initiatives: in the United States, the 21stCentury Cures Act requires the use of FHIR for health data;and in Germany, the medical informatics initiative aiming toclose the gap between research and health care used FHIR in
their already established use cases. Our network analysis showedthat authorships were dispersed relatively equally across studies,
not dominated by individual research groups or authors.
Most studies aimed to primarily standardize their data for healthresearch and reported using additional international standards
and terminologies. Within studies using FHIR for data capture,
the FHIR resource “Questionnaire” was often used. Further
areas of FHIR use were analysis, recruitment, and consent
management. The literature shows that fast and efficient patient
screening for clinical trial recruitment support systems isimportant, and there is a current lack of standards andinteroperability of in these systems, as well as with regard toeligibility criteria [72].
The majority of studies followed a more generic approach rather
than implementing FHIR for a specific use case. The studies
establishing use cases focused on infectious diseases,
specifically COVID-19, as well as genomics, oncology, andimaging—which are all specialties more advanced in terms ofdigitalization. Among these use cases, only a small number ofstudies reported limited generalizability of their results.
Though provided by FHIR specifically for research, resources
out of the domains “Public Health & Research” and“Evidence-Based Medicine” were used in only 4 studies. A
recently published study investigated the feasibility of the FHIR
resource “ResearchStudy” in a metadata registry for COVID-19
research and found that there was a need for the use ofextensions on more than 20% of the data items [41]. However,
the resources “ResearchStudy” and “ResearchSubject” arecurrently under revision and will likely be tailored more toresearchers’ needs when released with FHIR version R5 in 2022
[73].
Our analysis found that FHIR was used as a complement toother standards. Studies reporting on terminologies mostly used
SNOMED CT and LOINC, both terminologies supported by
FHIR within its value sets. There were 6 studies that used FHIR
in addition to OMOP CDM, a standard widely used inobservational research. Using OMOP CDM, a recommended
way of transforming and transferring data from existing
databases—Extract-Transform-Load tools are used for eachsource separately. To connect multiple heterogeneous databases,
FHIR can be used as an intermediate format for local data
extraction [35]. Reinecke et al [29] also extended the OMOP
CDM with FHIR to exchange electronic health record data toconnect the CDM to several health care systems. However, there
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Vorisek et al
were also limitations as Leroux et al [67] mapped CDISC SDTM
and FHIR and found that CDISC SDTM’s use of controlled
terminology is inhibiting semantic interoperability solutions;
FHIR uses semantic standards accepted in health care that areusually precoordinated (eg, SNOMED CT and LOINC), whereas
CDISC SDTM uses only controlled terminology inpostcoordination. Therefore, there would be the need forsponsors to translate terminologies used within systems. Leroux
et al [67] proposed the new FHIR resources “ClinicalStudyPlan”
and “ClinicalStudyData”—equivalent to ODM “Study” and“ClinicalData” elements—which could overcome the semantic
incompatibility. However, mappings with data transformation
may lead to information loss and errors; therefore, developing
ODM toward FHIR would be preferable, and the draft of ODM
version 2.0 already includes better support for FHIR [74,75].
In addition, HL7 and CDISC have jointly released a mapping
implementation guide to help transform FHIR content into
CDISC Clinical Data Acquisition Standards Harmonization
Implementation Guide or SDTM Implementation Guide data
sets. [76,77].
With regard to limitations using FHIR, there were certain
drawbacks reported by the included studies such as the possible
change in the content of different versions of FHIR resources,
safety, legal matters, and the need for a FHIR server. Not all
studies tested the use of FHIR in practice and, therefore, could
not provide results on the actual FHIR implementation.

Limitations
One limitation of our study is the lack of quality evaluation due
to missing established tools for evaluating technical papers on
standardization in health care. For technical evaluations,structured information on additional standards, software, and
FHIR version was missing in several studies. Therefore, ouranalysis on additional used standards might be biased as halfof the studies did not report on using other international
standards or terminologies. In addition, there were studies that
did not list their FHIR resources clearly or at all. We aimed toguarantee an optimal systematic review process targeting
academic peer-reviewed literature that is available in English;
however, limitations remained as we may have missed relevant
studies that were not published in the target language.
Furthermore, we assumed that the published literature provides
a surplus on successful FHIR initiatives because, in general,
unsuccessful initiatives tend to stay unpublished [78]. Thus, ourreview may suffer from publication bias. In addition, this study
investigated studies with a clear focus on FHIR in healthresearch. However, there might be research projects using FHIR
without FHIR being the central message or included in title andabstract.

Conclusions
To the best of our knowledge, this is the first systematic review
investigating the use of FHIR in health research. It was shownthat FHIR has been successfully implemented in clinical, publichealth, and epidemiological research at the stages of recruitment
and consent management, data capture, and standardization as
well as analysis of patient data. The implementation ofinternational terminologies such as SNOMED CT and LOINC
is common and, together with the REST API, stands out in
JMIR Med Inform 2022 | vol. 10 | iss. 7 | e35724 | p. 12(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
comparison with other health research standards. Other standards
such as OMOP CDM were used as a complement to FHIR insome studies, and a future aim could be the development of an
infrastructure for the seamless integration and communicationof health information across different standards. This approachis reinforced by the current development of collaborations ofdifferent Standards Developing Organizations such as OHDSIand FHIR and the improved support of FHIR in combination

Vorisek et alwith CDISC. Resources of the domain “Public Health &Research” and “Evidence-Based Medicine” were rarely usedand could further elevate interoperability in health research,specifically after their modifications in FHIR version R5.
However, this approach will need to address current limitations
but could, if successfully implemented, elevate digitalized healthresearch.

Acknowledgments
This work was done as part of the National Research Data Infrastructure for Personal Health Data Consortium [79]. We gratefully
acknowledge the financial support of the Deutsche Forschungsgemeinschaft (German Research Foundation; NFDI 13/1).

Conflicts of Interest
ST is the vice chair of Health Level Seven Deutschland. The remaining authors declare no other conflicts of interests.

Multimedia Appendix 1
Search strategy for each database.
[DOCX File , 32 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Reasons for the exclusion of full-text evaluation.
[DOCX File , 61 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Word cloud showing the keywords of the main FHIR use objectives of the studies. FHIR: Fast Healthcare Interoperability
Resources.
[PNG File , 31 KB-Multimedia Appendix 3]

Multimedia Appendix 4
Additional information on the included studies.
[DOCX File , 43 KB-Multimedia Appendix 4]

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Abbreviations
API: application programming interface
CDISC: Clinical Data Interchange Standards Consortium
CDM: common data model
FHIR: Fast Healthcare Interoperability Resources
HL7: Health Level Seven International
LOINC: Logical Observation Identifiers Names and Codes
ODM: Operational Data Standard
OHDSI: Observational Health Data Sciences and Informatics
OMOP: Observational Medical Outcomes Partnership
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PROSPERO: International Prospective Register of Systematic Reviews
REST: Representational State Transfer
SDTM: Study Data Tabulation Model
SNOMED CT: Systematized Nomenclature of Medicine Clinical Terms

Edited by C Lovis; submitted 29.12.21; peer-reviewed by S Hume, J Hüsers; comments to author 25.02.22; revised version received
22.04.22; accepted 18.05.22; published 19.07.22
Please cite as:
Vorisek CN, Lehne M, Klopfenstein SAI, Mayer PJ, Bartschke A, Haese T, Thun S
Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review
JMIR Med Inform 2022;10(7):e35724
URL: https://medinform.jmir.org/2022/7/e35724
doi: 10.2196/35724
PMID:©Carina Nina Vorisek, Moritz Lehne, Sophie Anne Ines Klopfenstein, Paula Josephine Mayer, Alexander Bartschke, Thomas
Haese, Sylvia Thun. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 19.07.2022. This is an
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