Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45209, first published .
Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review

Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review

Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review

Authors of this article:

Fouzia Amar 1 Author Orcid Image ;   Alain April 1 Author Orcid Image ;   Alain Abran 1 Author Orcid Image

Review

École de technologie supérieure - ETS, Montreal, QC, Canada

Corresponding Author:

Fouzia Amar, MEng

École de technologie supérieure - ETS

1100 Notre Dame Ouest

Montreal, QC, H3C 1K3

Canada

Phone: 1 514 396 8800

Email: famar2022@gmail.com


Background: The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard.

Objective: The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies.

Methods: A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard.

Results: A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language–based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability.

Conclusions: This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

J Med Internet Res 2024;26:e45209

doi:10.2196/45209

Keywords



Background

The development and deployment of electronic health records (EHRs) worldwide has led to several interoperability challenges, including extracting valuable information from free-text clinical notes, integrating unstructured EHR patient data, and reusing them without ambiguity for clinical research purposes [1,2].

Interoperability is defined by the Institute of Electrical and Electronics Engineers (IEEE) as the “ability of two or more components to exchange information and to use the information that has been exchanged” [3]. A total of 4 interoperability levels were proposed by the Healthcare Information and Management Systems Society organization: (1) foundational, (2) structural, (3) semantic, and (4) organizational (Figure 1) [4].

Figure 1. Interoperability levels (Healthcare Information and Management Systems Society).

Important research efforts are underway to develop health care standards, including Fast Healthcare Interoperability Resources (FHIR) standards for the electronic exchange of health care information. This standard was developed by the Health Level Seven International (HL7) organization aiming at the evolution of messaging standards to achieve semantic interoperability [5]. FHIR combines the best features of HL7 version 2, version 3, and clinical document architecture, while leveraging the latest web standards.

FHIR interoperability solutions are designed from a set of modular components called “resources” that have been designed as small reusable components defining a set of properties for the capture of the structure of the domain data acquisition for a particular use. The version 4.3.0 release of FHIR R4B proposes 143 different “resources” classified into five categories to handle health data: (1) foundation resources, (2) base resources, (3) clinical resources, (4) financial resources, and (5) specialized resources (Multimedia Appendix 1).

Many elements in FHIR use coded values and allow the use of external terminologies, such as (1) Systematized Nomenclature of Medicine–clinical terms (SNOMED CT)—comprehensive multilingual clinical terminology used in EHR. Its components include concepts (codes), descriptions (terms), and relationships—and (2) Logical Observation Identifiers Names and Codes (LOINC), a set of universal names and ID codes used to identify laboratory and clinical test results.

Common terminology aims to address the problem of ambiguity in exchanged data [6]. However, most legacy systems do not take advantage of a terminology-based approach to data exchange and still use incompatible terminologies or unstructured data in their exchanges with EHR. Thus, to ensure semantic interoperability with these legacy applications, it is mandatory to perform manual data transformations, mapping, or other alternate solutions to ensure that the exchanged data are interpreted correctly by the information systems involved.

The FHIR standard is of great interest to researchers and EHR ecosystem developers [7], and several technology giants, such as Amazon, Google, IBM, Microsoft, Oracle, and Salesforce, offer ready-to-use cloud services to promote the adoption of FHIR [8].

In this study, we report on a systematic mapping Review (SMR) and propose a classification of the FHIR literature based on FHIR resources, terminologies, research approaches, and innovative contributions. The goal of this SMR was to provide state of the art, trends, and analysis of research related to semantic interoperability using FHIR. This activity specifically aimed to identify the FHIR and terminology used, as well as the different proposed approaches, contribution types, and trends.

Related Work

Of the number of literature reviews on the challenges of health information systems interoperability, only 4 specifically address FHIR standard issues [2,9-11]. This section summarizes the findings of this study.

The study by Ayaz et al [10] reviewed 80 studies published between 2012 and 2019 that identified the opportunities and challenges associated with using FHIR. Another FHIR systematic review by Lehne et al [9] identified the main issues to be considered when exchanging data based on only 2 data sources and presented an overview of 131 selected studies up to February 4, 2019. A third systematic literature review by Setyawan et al [2] on data interoperability issues and possible solutions selected 39 studies published between 2018 and 2021 from 3 data sources (ie, IEEE Xplore, ScienceDirect, and Scopus). Finally, a systematic review of semantic interoperability in the study by de Mello et al [11] selected 28 studies from 2012 to 2020 from 8 different sources.

An overview of these 4 studies is presented in Table 1 in terms of their study goal or objective, period covered, number of studies selected and analyzed, source databases, and their respective mapping and research questions (RQs).

Table 1. Overview of the 4 literature reviews on semantic issues when using Fast Healthcare Interoperability Resources (FHIR).
IDStudy, yearGoal or objectivePeriod coveredStudies, nSource databasesMapping and research questions
R1Ayaz et al [10], 2021A systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applicationsJanuary 2012-December 201980
  • ACM
  • IEEE
  • Springer
  • Google Scholar
  • PubMed
  • ScienceDirect
  • Q1: Types or models of FHIR implementation?
  • Q2: Common resources used in FHIR implementation?
  • Q3: Applications that benefit from the use of FHIR?
  • Q4: Approaches applied to map or migrate data from previous standards to FHIR?
  • Q5: Goals of FHIR?
  • Q6: Challenges and open questions related to the FHIR domain?
R2Lehne et al [9], 2019Screen all FHIR publications and identify the main topicsFebruary 4, 2019131
  • Web of science
  • PubMed
  • N/Aa
R3Setyawan et al [2], 2021Identify integration problems of HL7b FHIR implementation and provide technical solutions and nontechnical solutions to these problems2018-202139
  • ScienceDirect
  • IEEE
  • SCOPUS
  • Q1: State of the art of data integration and interoperability in HL7 FHIR implementation?
  • Q2: Problems the researcher has regarding Data Integration and Interoperability in HL7 FHIR Implementation?
  • Q3: Potential solutions and future research regarding Data Integration and Interoperability in HL7 FHIR Implementation?
R4de Mello et al [11], 2022Present a comprehensive systematic literature review of semantic interoperability in electronic health record2010-202028
  • ACM
  • IEEE Xplore
  • Google scholar
  • MEDLINE
  • PubMed
  • ScienceDirect
  • SpringerLink
  • Web of Science
  • Q1: State of the art in health standards applied in health records?
  • Q2: Challenge and open questions to semantic interoperability in health records.
  • Q3: Health standards adopted in the studies?
  • Q4: Terminologies or health repositories used?
  • Q5: Approaches used?
  • Q6: Main security concerns used?
  • Q7: Evaluation approaches used?

aN/A: not applicable.

bHL7: Health Level Seven International.

To the best of our knowledge, no systematic mapping review has been published that specifically addresses the semantic issues raised when using FHIR.

Some of the lessons learned in these studies and their limitations are presented in Table 2. The key findings are summarized as follows:

  • Most used FHIR resources from general FHIR implementation up to 2019: Observation, Patient, Practionner, Condition, Medication, AllergyIntolerance, Medication, Device [10].
  • The study by Ayaz at al [10] provides a distribution of the different techniques used for the mapping approach (eg, mapping HL7 v2 to FHIR and mapping FHIR to others).
  • The formula string using FHIR as the keyword is general and allows maximum results [9]. This study showed that ontology or terminology approaches were subject to a higher number of studies up to 2018.
  • One study [2] used a limited number of databases (only 3), and the formula string may limit the obtained results.
  • We learned from the study by de Mello et al [11] that the main approaches used are based on ontology and terminology, particularly SNOMED CT, International Classification of Diseases (ICD)-9 or ICD-10.
Table 2. Lessons learned and limitations of related work.
IDLessons learnedLimitations
R1
  • Interesting mapping framework OR data model into the category of the most selected studies
  • Common resources used
  • More databases are needed
  • Formula for search may limit the result to semantic FHIRa studies
  • Are the identified resources still of interest for semantic interoperability?
  • Mapping to terminologies is not covered
R2
  • Formula used help to select the maximum number of studies
  • Topics of interest: ontology, terminology, data models ontology, terminology, data, and information model, mapping
  • The ontology, terminology, and data models is the subject of a high number of the selected studies
  • Limited list of databases
  • The number of studies continues to increase as of 2018
R3
  • N/Ab
  • Limited database sources
  • The search formula may limit the result as it must have the following: (challenge* OR problem* OR issue* OR trend*)
R4
  • Period: 2010-2020
  • Different approaches based on ontology and the use of terminology, especially SNOMED CTc, ICDd-9 or ICD-10
  • The use of OWLe and RDFf to solve semantic issues

  • HL7g FHIR is not in the list of identified standards based on the selected studies
  • The search based on semantic interoperability may limit the result as it used: “semantic interoperability” AND (“health record” OR “medical record” OR “patient record” OR “hospital record”) AND standard
  • A small number of articles selected

aFHIR: Fast Healthcare Interoperability Resources.

bN/A: not applicable.

cSNOMED CT: Systematized Nomenclature of Medicine–clinical terms.

dICD: International Classification of Diseases.

eOWL: web ontology language.

fRDF: resource description framework.

gHL7: Health Level Seven International.

These previous studies do not provide an overview of post-2019 developments related to semantic interoperability approaches and trends using FHIR. An extensive and more recent study is needed using many databases and a search string that includes the most possible semantic keywords.

In contrast to related works, this SMR focuses on the semantic level of interoperability using FHIR and reports on research directions over the last decade.


Overview

SMRs and systematic literature reviews typically use the same methodology, but their goals differ. The systematic literature review is driven by specific questions and the review aims to respond to these specific questions. In contrast, an SMR targets more high-level questions aimed at a specific topic, with the goal of providing a classification of the research results. An SMR can be helpful to researchers because it provides an overview of the literature at a given time for a specific topic of interest [12].

According to the study by Petersen et al [13], the mapping review process follows three steps: (1) planning the mapping, (2) conducting the mapping, and (3) reporting the results of the mapping. Furthermore, specific guidelines for a systematic mapping review in the software engineering domain as mentioned in the study by Brereton et al [14] suggest more detailed activities at each step (Figure 2):

  • Planning: identification of the different items used for the research process, including RQs, resources and databases, search terms and formulas, inclusion and exclusion criteria, and questions for quality assessment (QA).
  • Conducting: initial extraction of the studies was performed in this step. An initial preselection was performed, and both the inclusion and exclusion criteria were applied. The selected studies were then evaluated and filtered using QA questions.
  • Reporting: during this step, mapping was conducted using the final selection of the studies, and a discussion of the results was presented.
Figure 2. Mapping and review process for this Fast Healthcare Interoperability Resources study.

Research Questions

A set of 5 questions was defined (Table 3).

Table 3. Research questions.
RQa IDRQ
RQ1What are the main resources types studied or used in FHIRb semantic interoperability studies?
RQ2Which terminologies are used in FHIR semantic interoperability studies?
RQ3What are the semantic research approaches most frequently applied to achieve semantic interoperability?
RQ4What are the main types of contributions within the selected studies?
RQ5What are the publication sources and types?

aRQ: research question.

bFHIR: Fast Healthcare Interoperability Resources.

Mapping Search Strategy

Search String

The search string was established iteratively: first, by using “FHIR” as a search term in 3 databases and screening the results to look for more keywords dealing with the subject of interest, and second, by identifying additional terms and their synonyms based on the RQs.

The following is the search string composed using Boolean operators based on the identified terms: FHIR AND (Semantic OR terminology OR vocabulary OR Ontology OR mapping OR classification).

The Zotero reference management tool [15] was used to collect, organize, annotate, and review the abstract and full text to include or exclude studies that met the eligibility criteria. Zotero helps manage the large number of studies extracted for research.

Search Databases

The following 10 databases were used to identify and extract studies that satisfied the predefined search string: IEEE Xplore, ScienceDirect, Springer, Scopus, ACM, Web of Science, MEDLINE or PubMed, Compendex, Inspec, and Semantic Scholar.

Eligibility Criteria

Textbox 1 shows the inclusion and exclusion criteria for identified and extracted studies. Articles were excluded based on titles and abstracts as well as full-text reading in some cases.

Textbox 1. Inclusion and exclusion criteria for identified or extracted studies.

Inclusion criteria

  • Published between 2012 and 2022 (start date of 2012 was chosen because it was the date of publication of the first draft of the Fast Healthcare Interoperability Resources [FHIR]).
  • Published in English or French

Exclusion criteria

  • Studies not accessible in full text
  • Books
  • Studies reported on 2-3 pages
  • Duplicate studies: We retained the most recent data
  • Technical reports or summaries of conferences and editorials
  • Studies that do not address semantics using FHIR
  • Studies addressing enhancements to FHIR terminology and comparisons

Quality Assessment

QA of a research publication is a crucial selection step to ensure that the selected studies have the appropriate impact for mapping reviews and further analysis [12]. For this purpose, we defined quality criteria scores based on the following questions.

  • QA1: Are the objectives of the research clearly defined?
  • QA2: Does the author present results that meet the stated research objectives?
  • QA3: Is the method or technique used to achieve semantic interoperability clearly described?
  • QA4: Is a literature review provided?
  • QA5: Is the author’s contribution clearly stated?
  • QA6: Is a result validation strategy presented?

Quality evaluation of the identified and extracted studies was performed by answering each QA question. The selected studies satisfied at least 4 of the 6 QA criteria. Others were rejected.

Data Extraction and Data Synthesis

This SMR was conducted in 2022, with publications published from 2012 to 2022. A summary of the data extraction showing the selection filter effect is presented in Figure 3.

  • 2601 results were obtained in the first step
  • 2372 remained after applying the exclusion criteria for studies published out of the range of 2012‑2022
  • 1701 remained after the applying the exclusion criteria on venue type is possible, when available
  • 1692 for studies remained after applying the English or French language restriction
  • 70 studies remained after removing duplicates, applying the additional inclusion, exclusion criteria and QA.

The 70 studies retained are listed in Multimedia Appendix 2 [16-85]. The extracted data are presented in Multimedia Appendix 3.

Figure 3. Search steps and results from databases. QA: quality assessment.

This section presents findings related to the 5 RQs listed in Table 3.

RQ1: Main FHIR Resources Types Studied or Used

We reviewed 70 selected studies and identified the types of FHIR resources that were used. Table 4 lists the 62 types of resources identified in the corresponding studies that have used them. In Figure 4, the resource types are presented, from most frequently used to least used.

The 10 most referred or used resource types in these studies were Observation (31/297, 10.4%), Patient (29/297, 9.8%), Medication (19/297, 6.4%), Condition (16/297, 5.4%), CodeSystem (15/297, 5.1%), Encounter (11/297, 3.7%), Procedure (11/297, 3.7%), Medication Request (10/297, 3.4%), Practitionner (10/297, 3.4%), and ValueSet (9/297, 3%).

The HL7 organization classifies the list of resources into five categories: (1) foundation, (2) base, (3) clinical, (4) financial, and (5) specialized resources. Each of these categories was further divided into 5 subcategories, except for the financial resources category, which was subdivided into 4 subcategories.

The resources inventoried in the 70 selected studies were mostly in 3 clinical subcategories: summary, medications, and diagnosis (Figure 5 and Multimedia Appendix 1). There is one exception to Patient and Encounter resources, which are in the base category and mainly used for identification purposes. We also noted the terminology subcategory (foundation category) among the top 10 most used resources. Terminology resources were used to map concepts to FHIR terminology (eg, SNOMED CT and LOINC).

Table 4. Fast Health Care Interoperability Resources types used and related studies.
Resource typeStudy IDStudies, n
ObservationS2, S42, S43, S68, S47, S60, S48, S6, S7, S8, S9, S11, S12, S40, S28, S13, S14, S15, S54, S55, S56, S38, S22, S61, S23, S62, S5, S34, S69, S7030
PatientS1, S42, S43, S68, S65, S46, S47, S60, S48, S50, S39, S9, S11, S13, S14, S15, S16, S54, S55, S30, S56, S38, S22, S23, S5, S34, S52, S69, S7029
MedicationS1, S68, S36, S47, S63, S4, S60, S48, S6, S39, S9, S14, S54, S55, S31, S22, S61, S5, S6919
ConditionS25, S68, S60, S6, S39, S8, S9, S11, S12, S40, S14, S15, S54, S22, S61, S6216
CodeSystemS37, S9, S29, S10, S40, S41, S17, S18, S20, S61, S33, S14, S15, S21, S2815
EncounterS1, S68, S60, S39, S9, S11, S16, S38, S23, S62, S511
ProcedureS68, S60, S39, S8, S9, S40, S61, S5, S62, S12, S6911
PractitionnerS68, S47, S60, S50, S9, S15, S16, S34, S23, S7010
MedicationRequestS1, S68, S63, S4, S39, S53, S62, S22, S61, S6910
ValueSetS37, S9, S29, S40, S41, S17, S18, S61, S149
MedicationStatementS68, S6, S8, S9, S40, S14, S617
FamilyMemberHistoryS68, S60, S39, S8, S15, S54, S617
AllergyIntoleranceS68, S60, S9, S67, S28, S15, S627
DiagnosisReportS1, S40, S14, S5, S62, S34, S707
CarePlanS68, S60, S50, S9, S23, S626
DeviceS2, S43, S68, S60, S7, S396
ServiceRequestS60, S39, S9, S38, S34, S696
ConceptMapS37, S13, S41, S17, S19, S696
StructureDefinitionS44, S38, S53, S40, S695
SpecimenS60, S9, S40, S344
QuestionnaireS28, S15, S30, S234
OrganizationS60, S16, S38, S234
MedicationAdministrationS47, S48, S39, S94
AppointmentS60, S9, S28, S694
ImmunizationS60, S40, S623
CompositionS8, S61, S693
CommunicationS39, S9, S693
RiskAssessmentS60, S28, S693
HealthcareServiceS60, S92
GoalS68, S622
EpisodeOfCareS68, S232
DeviceDefinitionS2, S72
CareTeamS68, S602
AdverseEventS68, S602
ActivityDefinitionS23, S432
RelatedPersonS68, S602
CoverageS9, S162
ClinicalImpressionS12, S232
LocationS68, S692
NutritionOrderS68, S692
PlanDefinitionS23, S692
DocumentReferenceS601
MedicationDispenseS391
DeviceUseStatementS91
ConsentS91
PersonS281
ScheduleS151
PractionnerRoleS161
QuestionnaireResponseS231
ImagingStudyS231
SubstanceS391
Account, Library, Claim, DocumentManifest, EnrollmentResponse, CoverageEligibilityRequest, SupplyDelivery, PaymentNotice, OperationDefinition, GuidanceResponse, ImplementationGuide, CapabilityStatement,S691
Figure 4. Distribution of the resources used.
Figure 5. Distribution of the Fast Healthcare Interoperability Resources types used by category.

RQ2: Which FHIR Terminologies Were Used

A total of 9 distinct FHIR terminologies were used in the 70 selected studies (Table 5).

Their distribution and percentage are as follows:

  • SNOMED CT (31/98, 32%) and LOINC (25/98, 26%) were the most commonly used.
  • ICD-9 and ICD-10 (15/98, 15%) and Unified Medical Language System (13/98, 13%)
  • RxNORM (9/98, 9%)
  • The remaining terminologies (anatomical therapeutic chemical, quality data model, national drug file–reference terminology, and classification of instructional programs code) accounted for <10%.
Table 5. List of terminologies being used.
TerminologyStudy ID
SNOMED CTaS2, S34, S3, S35, S4, S48, S5, S49, S7, S8, S9, S29, S10, S11, S12, S40, S28, S53, S14, S16, S41, S31, S38, S18, S66, S22, S61, S23, S13, S25, S64
LOINCbS2, S34, S3, S35, S5, S6, S7, S8, S27, S9, S11, S13, S16, S54, S30, S56, S41, S38, S20, S66, S21, S22, S23, S24, S36
ICDc-9 or ICD-10S34, S1, S35, S49, S6, S9, S54, S30, S41, S17, S18, S66, S22, S33, S26
UMLSdS34, S43, S37, S5, S27, S14, S19, S66, S21, S61, S64, S25, S8
RxNormeS36, S6, S8, S9, S14, S54, S31, S22, S61
ATCfS1, S8
QDMgS39
NDF-RThS48
CIPi CodeS1

aSNOMED CT: Systematized Nomenclature of Medicine–clinical terms.

bLOINC: Logical Observation Identifiers Names and Codes.

cICD: International Classification of Diseases.

dUMLS: Unified Medical Language System.

eRxNORM: medical prescription normalized Medical prescription.

fATC: anatomical therapeutic chemical.

gQDM: quality data model.

hNDF-RT: national drug file–reference terminology.

iCIP: classification of instructional programs.

RQ3: Semantic Research Approaches Most Frequently Applied

A total of six main semantic research approaches were identified from the solutions proposed in the 70 selected studies:

  1. Mapping to FHIR terminology (mapping)
  2. Terminology services (terminology)
  3. Transformation, development, and validation resource description framework (RDF) or web ontology language (OWL)
  4. Machine learning (ML) and natural language processing (NLP)
  5. Semantic annotation (annotation)
  6. Ontology-based approach (ontology)

Table 6 presents the semantic research approaches used to decrease the frequency of use in the selected studies.

Table 6. Semantic research approaches and related studies.
Semantic approachStudies IDsDistribution (N=126), n (%)
Mapping to Fast Healthcare Interoperability Resources terminologies (mapping)S1, S2, S4, S5, S6, S7, S8, S9, S10, S11, S13, S14, S15, S16, S17, S18, S20, S21, S22, S23, S24, S25, S27, S28, S29, S30, S31, S32, S33, S38, S4131 (24.6)
Terminology services (terminology)S5, S13, S17, S18, S19, S20, S21, S28, S30, S33, S34, S35, S36, S37, S38, S39, S40, S4118 (14.3)
Transformation or development or validation resource description framework or web ontology languageS3, S10, S12, S14, S35, S42, S43, S44, S45, S46, S47, S48, S49, S50, S51, S52, S53, S54, S55, S56, S57, S58, S59, S6924 (19)
Semantic annotation (annotation)S2, S3, S6, S7, S14, S25, S27, S30, S31, S32, S43, S45, S48, S60, S61, S62, S63, S6418 (14.3)
Machine learning or natural language processingS5, S6, S8, S14, S25, S26, S31, S32, S43, S46, S49, S54, S60, S61, S62, S63, S64, S65, S66, S6720 (15.9)
Ontology-based approach (ontology)S3, S11, S12, S13, S26, S40, S44, S46, S47, S49, S50, S55, S66, S68, S7015 (11.9)

The distribution of the 6 semantic approaches is as follow: mapping (31/126, 24.6%), RDF or OWL (24/126, 19%), ML and NLP (20/126, 15.9%), annotation (18/126, 14.3%), terminology (18/126, 14.3%), and ontology (15/126, 11.9%).

The selected studies used at least one of the 6 identified semantic research approaches, and most studies used more than one research approach. For example, 22 of the 31 studies listed in Table 6 used mapping and other approaches (70%), whereas only 29% (9/31) used mapping alone.

To better understand the frequency of combinations of more than one semantic research approach, we identified a number of studies using more than one approach. We performed the same exercise for RDF or OWL followed by annotation. Table 7 shows that most combined semantic approaches are annotation and ML and NLP, mapping and terminology services, mapping and annotation, RDF or OWL, and ontology.

Table 7. Distribution of the combined approaches
CategoryMappingTerminologyRDFa or OWLbAnnotationMLc and NLPdOntology
Mappinge112972
Terminology0112
RDF and OWL558
Annotation111
ML and NLP4
Ontology

aRDF: resource description framework.

bOWL: web ontology language.

cML: machine learning.

dNLP: natural language processing.

eNot applicable.

The next analysis related to the proposed approach examined how these approaches evolved over time. Figure 6 shows how these approaches were used over time. The main finding are (1) ML and NLP-based proposals emerged in 2018 in FHIR semantic interoperability studies, (2) the other approaches span the full period, and (3) no specific research approach dominates the FHIR semantic interoperability research field.

Figure 6. Timeline of semantic research approaches used in the selected studies. ML: machine learning; NLP: natural language processing; RDF: resource description framework.

RQ4: Main Types of Contributions

The objective of RQ4 was to identify the contribution types within each of the 70 selected studies. For this purpose, five main contributions were identified: (1) new framework or architecture intended to enable semantic interoperability (framework or architecture proposal), (2) evaluation or comparison of different techniques (eg, comparison of ontologies), (3) new ontology, model or enhancement of an existing one (model proposal), (4) a new technique or improvement of an existing technique (new technique proposal), and (5) a tool proposal through the development of a new tool or an enhancement to existing ones to facilitate achieving semantic interoperability (tool proposal).

Table 8 presents the distribution of the 70 studies selected according to contribution type.

Figure 7 shows the distribution of studies by contribution type.

The most frequent contribution type was the proposal of a framework or architecture (26/70, 37%), followed by model proposals (13/70, 19%), new or improved techniques (13/70, 19%), comparison services (10/70, 14%), and tool proposals (8/70, 11%).

Table 8. Studies by contribution type.
Contribution typeStudies
Framework or architectureS2, S5, S7, S8, S14, S16, S20, S22, S23, S25, S26, S29, S31, S34, S35, S38, S41, S43, S51, S53, S54, S61, S62, S65, S66, S68
ComparisonS4, S15, S17, S32, S33, S46, S56, S58, S59, S67
ModelS1, S3, S6, S30, S39, S40, S45, S48, S49, S50, S55, S60, S63
TechniqueS9, S10, S12, S18, S19, S21, S24, S27, S42, S47, S64, S69, S70
ToolS11, S13, S28, S36, S37, S44, S52, S57
Figure 7. Distribution of the studies by contribution type.

RQ5: Publications Sources and Types

Publication Sources

The distribution of publication sources and the number of studies is summarized as follows (Figure 8):

  • The top publication sources were Studies in Health Technology and Informatics (8/70, 11%), followed by CEUR Workshop and the Journal of Biomedical Informatics (6/70, 9%).
  • JMIR Medical Informatics and BMC Medical Informatics and Decision Making follow with (4/70, 6%).
  • Approximately (3/70, 4%) of the publications are from 3 sources: Journal of Biomedical Semantic, American Medical Informatics Association (AMIA) Joint Summits on Translational Science proceedings, and AMIA Annual Symposium.
  • Approximately (2/70, 3%) of the publications are from 4 sources: the Journal of Medical Systems , IEEE International Conference on Bioinformatics and Biomedicine, International Conference of the IEEE Engineering in Medicine and Biology Society, and the Journal of the AMIA.
  • Each of the remaining 25 publications represented 1%, with 1 study per source.
Figure 8. Publication sources and corresponding number of studies.
Publication Types

The distribution of the selected studies by venue type (Table 9) showed that most of selected studies were published in a journal (46/70, 66%), followed by conferences (15/70, 21%), workshops (6/70, 9%), and symposiums (3/70, 4%).

Table 9. Number of studies per venue type (N=70).
Venue typeStudies, n (%)
Journal46 (66)
Conference15 (21)
Workshop6 (9)
Symposium3 (4)

Overview

This section presents a discussion of each of the key findings of the RQs.

  1. Most studies have concentrated on clinical data resources; therefore, the focus has been on patient summaries.
  2. Terminology mapping to SNOMED CT and LOINC was performed in more than half of the selected studies. None of the 5 approaches was dominant in the proposed solutions to semantic interoperability challenges.
  3. Most of the selected studies proposed a framework or architecture as a solution to enable semantic interoperability.
  4. In recent years, research on semantic interoperability has increased since 2018.

RQ Results

RQ1: What Are the Main Resource Types Studied or Used?

As most studies concentrated on clinical data resources (eg, Observation, Medication, Condition, Procedure, and MedicationRequesL), the focus has been on patient summary including the medical condition, medication, and diagnosis.

The attributes of these resource-use codes (CodeableConcept) and terminology standards, such as SNOMED CT and LOINC, are already included in the FHIR body. For example, the Observation resource has an attribute code in which the practitioner or user can document the observation type using LOINC code. Similarly, the Medication resource prescribed can be entered either as a code using SNOMED CT or as a textual description.

In contrast, other types of resources (eg, BodyOfStructure) were not addressed in the selected studies.

RQ2: Which Terminologies Were Used?

Terminology mapping to SNOMED CT and LOINC was present in more than half of the selected studies, where SNOMED CT is used mainly for medication, procedure, and condition (problem); SNOMED CT or ICD-10 for diseases and diagnosis; and LOINC for observation and diagnostic report.

Some countries have attempted to develop a consensus on the terminology to be used at the regional or national level. For example, SNOMED CT was adopted as a single terminology in the United Kingdom by the National Health Service (S12). In Switzerland, the federal initiative of the Swiss Personalized Health Network created a semantic framework available for health communities aiming to use the existing standards instead of building a new one, and the semantics were mapped by domain experts on the existing standards, such as SNOMED CT, LOINC, and ICD. RDF is used to store and transport data. In another regional initiative in Italy, the LOINC vocabulary was used to represent concepts already defined in local and standardized terminologies (S20).

RQ3: What Are the Most Frequently Applied Research Approaches to Achieve Semantic Interoperability?

None of the 5 approaches were dominant in the proposed solutions to semantic interoperability challenges. The emerging ML and NLP approach is mostly applied to annotation.

ML and NLP techniques, which have achieved significant penetration in many other information system or information technology domains, have also been used in FHIR semantic interoperability research. On the one hand, the capability of the ML and NLP techniques for automation and prediction is often required for complex use cases. On the other hand, RDF representation has the specific advantage of containing information about semantic links and dependencies between multiple resources. This could explain why transformation to or from RDF is useful for semantic interoperability.

RQ4: What Are the Main Types of Contributions Within the Selected Studies?

Most of the selected studies proposed a framework or architecture as a solution to enable semantic interoperability. Research on achieving semantic interoperability using FHIR has tackled different steps and activities: collecting, extracting, and annotating data; modeling data from legacy systems; and applying transformation and mapping to FHIR models and terminologies. The following research topics were identified.

  • One of the main topics in the selected studies was related to unstructured data because valuable information can be extracted from free text for research and health analytics. The standardization of unstructured clinical notes was the focus of the research for specific use cases in the selected studies. The process of concept extraction and mapping text to standardized terminologies or ontologies requires more automation.
  • Research on unstructured data using annotation and NLP is a relatively new field for the FHIR community, and challenges related to protecting privacy and security (deidentification and pseudonymization) remain open questions.
  • The manual mapping of clinical terminology requires significant effort. The use of a tool can reduce the effort required for mapping and increase the adoption of terminology.
  • Another challenge is related to languages other than English. In S24, a tool was developed to map Russian local terminology to LOINC.
  • Even though the FHIR standard allows the use of clinical terminologies such as SNOMED CT and LOINC, users still enter information as text or use local terminologies. For example, selecting an appropriate concept from SNOMED CT is not a trivial task. To this end, a proposed solution called Ontoserver (eg, a clinical terminology service) offers a prefix-based search algorithm to help users easily find content and enter coded data. Ontoserver was used in several studies (S5, S10, S11, S17, S18, S40, and S41), and limitations related to its performance were reported.
  • Comparison and mapping ontologies: detecting similarities and differences is still of interest and may attract more researchers.
RQ5: What Are the Publication Sources and Types?

Research on FHIR semantic interoperability emerged in 2015 and has increased since 2018. This probably is linked to the first release HL7 FHIR with a “Normative” status in 2018 indicating that its content has been recognized at the international level as stable for use and has been “locked” for several years. The publication trend in Figure 9 that shows the distribution of the selected studies over time confirms that research on this topic is still active.

Figure 9. Number of studies over time.

Assumptions and Limitations

This study was limited to semantic studies using only the FHIR standard, and it did not search for other studies related to semantic interoperability in general; therefore, this SMR focused exclusively on studies addressing FHIR implementation.

Furthermore, only studies from the 10 database sources specified were considered, and commercial solutions were excluded.

During the classification of the contribution based on 5 categories, 2 studies (S40 and S49) were considered in 2 different categories (model and technique). Both situations were evaluated and the results were only slightly different.

Conclusions

This SMR inventoried and analyzed the findings on semantic interoperability using FHIR over the last decade (2012-2022) in 70 selected studies from 10 databases. It presents a classification of the selected studies according to the semantic approaches used and their contributions. It also provides the frequency of use of FHIR resources and terminologies.

These findings provide researchers with an inventory of the approaches used to achieve semantic interoperability using the FHIR. The SMR also documented the frequency with which the proposed approaches were used. This may help identify avenues for research on semantic interoperability. It can also be used as a guideline by the FHIR community for the future development of approaches and types of resources that are not well covered in research published to date.

Indeed, understanding and interpreting the exchanged data in the EHR ecosystem needs to be supported by further research using, for instance, FHIR in mapping, RDF or OWL, and comparing ontologies and terminology services.

Semantic interoperability is still an active research field, and there is no broad consensus in the health care community on how to achieve full semantic interoperability between information systems. The main issues reported are related to the difficulty associated with the use of different terminologies and the efforts required to successfully map to FHIR terminology. Even worse, when no terminology is used, unstructured data would still need to be extracted and mapped to the appropriate concept code for FHIR terminology.

This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different steps: collecting, extracting and annotating data, modeling EHR data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

Future work will be devoted to the FHIR-based framework for the semantic interoperability of EHR data.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Targeted resources from a semantic point of view.

PNG File , 526 KB

Multimedia Appendix 2

List of the selected studies (N=70).

PDF File (Adobe PDF File), 152 KB

Multimedia Appendix 3

Extracted data and searching strategy results.

XLSX File (Microsoft Excel File), 134 KB

  1. Nordo AH, Levaux HP, Becnel LB, Galvez J, Rao P, Stem K, et al. Use of EHRs data for clinical research: historical progress and current applications. Learn Health Syst. Jan 16, 2019;3(1):e10076. [FREE Full text] [CrossRef] [Medline]
  2. Setyawan R, Hidayanto AN, Sensuse DI, Kautsarina; Suryono RR, Abilowo K. Data integration and interoperability problems of HL7 FHIR implementation and potential solutions: a systematic literature review. In: Proceedings of the 5th International Conference on Informatics and Computational Sciences. Presented at: ICICoS '21; November 24-25, 2021, 2021;293-298; Semarang, Indonesia. URL: https://ieeexplore.ieee.org/document/9651762/ [CrossRef]
  3. IEEE standard glossary of software engineering terminology. IEEE Std 610.12-1990. Dec 31, 1990. URL: https://ieeexplore.ieee.org/document/159342 [accessed 2024-01-04]
  4. Interoperability in healthcare. Healthcare Information and Management Systems Society. URL: https://www.himss.org/resources/interoperability-healthcare [accessed 2024-01-04]
  5. Bender D, Sartipi K. HL7 FHIR: an agileRESTful approach to healthcare information exchange. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems. Presented at: CBMS '13; June 20-22, 2013, 2013;326-331; Porto, Portugal. URL: https://ieeexplore.ieee.org/document/6627810 [CrossRef]
  6. Benson T, Grieve G. Principles of Health Interoperability: FHIR, HL7 and SNOMED CT. Cham, Switzerland. Springer; 2021.
  7. Dullabh P, Hovey L, Heaney-Huls K, Rajendran N, Wright A, Sittig DF. Application programming interfaces in health care: findings from a current-state sociotechnical assessment. Appl Clin Inform. Jan 09, 2020;11(1):59-69. [FREE Full text] [CrossRef] [Medline]
  8. Cloud providers unite for healthcare interoperability. HL7 International. URL: https://blog.hl7.org/cloud-providers-unite-for-healthcare-interoperability-fhir [accessed 2022-12-11]
  9. Lehne M, Luijten S, Vom Felde Genannt Imbusch P, Thun S. The use of FHIR in digital health - a review of the scientific literature. Stud Health Technol Inform. Sep 03, 2019;267:52-58. [CrossRef] [Medline]
  10. Ayaz M, Pasha MF, Alzahrani MY, Budiarto R, Stiawan D. The Fast Health Interoperability Resources (FHIR) standard: systematic literature review of implementations, applications, challenges and opportunities. JMIR Med Inform. Jul 30, 2021;9(7):e21929. [FREE Full text] [CrossRef] [Medline]
  11. de Mello BH, Rigo SJ, da Costa CA, da Rosa Righi R, Donida B, Bez MR, et al. Semantic interoperability in health records standards: a systematic literature review. Health Technol (Berl). 2022;12(2):255-272. [FREE Full text] [CrossRef] [Medline]
  12. Kitchenham BA, Budgen D, Pearl Brereton O. Using mapping studies as the basis for further research – a participant-observer case study. Inf Softw Technol. Jun 2011;53(6):638-651. [FREE Full text] [CrossRef]
  13. Petersen K, Vakkalanka S, Kuzniarz L. Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol. Aug 2015;64:1-18. [FREE Full text] [CrossRef]
  14. Brereton P, Kitchenham BA, Budgen D, Turner M, Khalil M. Lessons from applying the systematic literature review process within the software engineering domain. J Syst Softw. Apr 2007;80(4):571-583. [FREE Full text] [CrossRef]
  15. Reference management tool. Zotero. URL: https://www.zotero.org/ [accessed 2022-12-12]
  16. Boussadi A, Zapletal E. A fast healthcare interoperability resources (FHIR) layer implemented over i2b2. BMC Med Inform Decis Mak. Aug 14, 2017;17(1):120. [FREE Full text] [CrossRef] [Medline]
  17. Zampognaro P, Paragliola G, Falanga V. A FHIR based architecture of a multiprotocol IoT home gateway supporting dynamic plug of new devices within instrumented environments. In: Proceedings of the 2021 IEEE Symposium on Computers and Communications. Presented at: ISCC '21; September 5-8, 2021, 2021;1-7; Athens, Greece. URL: https://ieeexplore.ieee.org/document/9631446 [CrossRef]
  18. Kilintzis V, Kosvyra A, Beredimas N, Natsiavas P, Maglaveras N, Chouvarda I. A sustainable HL7 FHIR based ontology for PHR data*. In: Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Presented at: EMBC '19; July 23-27, 2019, 2019;5700-5703; Berlin, Germany. URL: https://ieeexplore.ieee.org/document/8856415/similar [CrossRef]
  19. Goeg KR, Hummeluhr M. An empirical approach to enhancing terminology binding - an HL7 FHIR SNOMED CT example. In: Ugon A, Karlsson D, Klein GO, Moen A, editors. Building Continents of Knowledge in Oceans of Data: The Future of Co-Created Ehealth. Amsterdam, The Netherlands. IOS Press; 2018.
  20. Banach A, Ulrich H, Kroll B, Kiel A, Ingenerf J, Kock-Schoppenhauer AK. APERITIF - automatic patient recruiting for clinical trials based on HL7 FHIR. Stud Health Technol Inform. May 27, 2021;281:58-62. [CrossRef] [Medline]
  21. Daumke P, Heitmann KU, Heckmann S, Martínez-Costa C, Schulz S. Clinical text mining on FHIR. Stud Health Technol Inform. Aug 21, 2019;264:83-87. [CrossRef] [Medline]
  22. Zampognaro P, Paragliola G, Falanga V. Definition of an FHIR-based multiprotocol IoT home gateway to support the dynamic plug of new devices within instrumented environments. J Reliable Intell Environ. Dec 07, 2021;8(4):319-331. [FREE Full text] [CrossRef]
  23. Hong N, Wen A, Stone DJ, Tsuji S, Kingsbury PR, Rasmussen LV, et al. Developing a FHIR-based EHR phenotyping framework: a case study for identification of patients with obesity and multiple comorbidities from discharge summaries. J Biomed Inform. Nov 2019;99:103310. [FREE Full text] [CrossRef] [Medline]
  24. Shivers J, Amlung J, Ratanaprayul N, Rhodes B, Biondich P. Enhancing narrative clinical guidance with computer-readable artifacts: authoring FHIR implementation guides based on WHO recommendations. J Biomed Inform. Oct 2021;122:103891. [FREE Full text] [CrossRef] [Medline]
  25. Metke-Jimenez A, Lawley M, Hansen D. FHIR OWL: transforming OWL ontologies into FHIR terminology resources. AMIA Annu Symp Proc. 2019;2019:664-672. [FREE Full text] [Medline]
  26. Metke-Jimenez A, Hansen D. FHIRCap: transforming REDCap forms into FHIR resources. AMIA Jt Summits Transl Sci Proc. 2019;2019:54-63. [FREE Full text] [Medline]
  27. Arguello-Casteleiro M, Martinez-Costa C, Des-Diz J, Maroto N, Fernandez-Prieto MJ, Stevens R. From SNOMED CT expressions to an FHIR RDF representation: exploring the benefits of an ontology-based approach. In: Proceedings of the Joint Ontology Workshops 2019 Episode V: The Styrian Autumn of Ontology. Presented at: CEUR '19; September 23-25, 2019, 2019;1-13; Graz, Austria. URL: https://ceur-ws.org/Vol-2518/paper-ODLS1.pdf
  28. Paris N, Mendis M, Daniel C, Murphy S, Tannier X, Zweigenbaum P. i2b2 implemented over SMART-on-FHIR. AMIA Jt Summits Transl Sci Proc. 2018;2017:369-378. [FREE Full text] [Medline]
  29. Hong N, Wen A, Shen F, Sohn S, Liu S, Liu H, et al. Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Jt Summits Transl Sci Proc. 2018;2017:74-83. [Medline]
  30. Lenivtseva Y, Kopanitsa G. Investigation of Content Overlap in Proprietary Medical Mappings. Stud Health Technol Inform. 2019;258:41-45. [Medline]
  31. Matney SA, Heale B, Hasley S, Decker E, Frederiksen B, Davis N, et al. Lessons learned in creating interoperable fast healthcare interoperability resources profiles for large-scale public health programs. Appl Clin Inform. Jan 2019;10(1):87-95. [FREE Full text] [CrossRef] [Medline]
  32. Rajput AM, Drenkhahn C. Perspectives and obstacles for transforming terminologies into FHIR CodeSystems exemplified by Alpha-ID. Stud Health Technol Inform. May 27, 2021;281:213-217. [CrossRef] [Medline]
  33. Wiedekopf J, Drenkhahn C, Ulrich H, Kock-Schoppenhauer AK, Ingenerf J. Providing ART-DECOR valuesets via FHIR terminology servers - a technical report. Stud Health Technol Inform. Sep 21, 2021;283:127-135. [CrossRef] [Medline]
  34. Saripalle R. Representing UMLS knowledge using FHIR terminological resources. In: Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine. Presented at: BIBM '19; November 18-21, 2019, 2019;1109-1112; San Diego, CA. URL: https://ieeexplore.ieee.org/document/8983305 [CrossRef]
  35. Gazzarata R, Maggi N, Magnoni LD, Monteverde ME, Ruggiero C, Giacomini M. Semantics management for a regional health information system in Italy by CTS2 and FHIR. In: Gazzarata R, Maggi N, Magnoni LD, Monteverde ME, Ruggiero C, Giacomini M, editors. Studies in Health Technology and Informatics. Amsterdam, The Netherlands. IOS Press; 2021.
  36. Rajput AM, Gronke A, Johannis W. Semi-automated approach to validate and enrich LOINC codes by FHIR server. In: Proceedings of the 5th International Conference on Medical and Health Informatics. Presented at: ICMHI '21; May 14-16, 2021, 2021; Kyoto, Japan. URL: https://dl.acm.org/doi/10.1145/3472813.3473207 [CrossRef]
  37. Wagholikar KB, Mandel JC, Klann JG, Wattanasin N, Mendis M, Chute CG, et al. SMART-on-FHIR implemented over i2b2. J Am Med Inform Assoc. Mar 01, 2017;24(2):398-402. [FREE Full text] [CrossRef] [Medline]
  38. Leroux H, Metke-Jimenez A, Lawley MJ. Towards achieving semantic interoperability of clinical study data with FHIR. J Biomed Semantics. Sep 19, 2017;8(1):41. [FREE Full text] [CrossRef] [Medline]
  39. Kopanitsa G, Taranik M. Using semi-automated approach for mapping local Russian laboratory terms to LOINC. In: Proceedings of the 21st International Conference on Information and Software Technologies. Presented at: ICIST '15; October 15-16, 2015, 2015;37-44; Druskininkai, Lithuania. URL: https://link.springer.com/chapter/10.1007/978-3-319-24770-0_4 [CrossRef]
  40. Peterson KJ, Jiang G, Liu H. A corpus-driven standardization framework for encoding clinical problems with HL7 FHIR. J Biomed Inform. Oct 2020;110:103541. [FREE Full text] [CrossRef] [Medline]
  41. Maga-Nteve C, Kontopoulos E, Tsolakis N, Katakis I, Mathioudis E, Mitzias P, et al. A semantic technologies toolkit for bridging early diagnosis and treatment in brain diseases: report from the ongoing EU-funded research project ALAMEDA. In: Proceedings of the 15th International Conference on Metadata and Semantic Research. Presented at: MTSR '21; November 29-December 3, 2021, 2021;349-354; Virtual Event. URL: https://link.springer.com/chapter/10.1007/978-3-030-98876-0_30 [CrossRef]
  42. Rafee A, Riepenhausen S, Neuhaus P, Meidt A, Dugas M, Varghese J. ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials. BMC Med Res Methodol. May 14, 2022;22(1):141. [FREE Full text] [CrossRef] [Medline]
  43. Chatterjee A, Pahari N, Prinz A. HL7 FHIR with SNOMED-CT to achieve semantic and structural interoperability in personal health data: a proof-of-concept study. Sensors (Basel). May 15, 2022;22(10):3756. [FREE Full text] [CrossRef] [Medline]
  44. Odigie E, Lacson R, Raja A, Osterbur D, Ip I, Schneider L, et al. Fast healthcare interoperability resources, clinical quality language, and systematized nomenclature of medicine-clinical terms in representing clinical evidence logic statements for the use of imaging procedures: descriptive study. JMIR Med Inform. May 13, 2019;7(2):e13590. [FREE Full text] [CrossRef] [Medline]
  45. Ulrich H, Kock AK, Duhm-Harbeck P, Habermann JK, Ingenerf J. Metadata repository for improved data sharing and reuse based on HL7 FHIR. Stud Health Technol Inform. 2016;228:162-166. [Medline]
  46. Wang J, Mathews WC, Pham HA, Xu H, Zhang Y. Opioid2FHIR: a system for extracting FHIR-compatible opioid prescriptions from clinical text. In: Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine. Presented at: BIBM '20; December 16-19, 2020, 2020;1748-1751; Seoul, Korea. URL: https://ieeexplore.ieee.org/document/9313258 [CrossRef]
  47. Seveso A, Campagner A, Ciucci D, Cabitza F. Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings. BMC Med Inform Decis Mak. Aug 20, 2020;20(Suppl 5):142. [FREE Full text] [CrossRef] [Medline]
  48. Wiedekopf J, Drenkhahn C, Rosenau L, Ulrich H, Kock-Schoppenhauer A, Ingenerf J. TerminoDiff - detecting semantic differences in HL7 FHIR CodeSystems. Stud Health Technol Inform. May 25, 2022;294:362-366. [CrossRef] [Medline]
  49. Diomaiuta C, Sicuranza M, Ciampi M, De Pietro G. A FHIR-based system for the generation and retrieval of clinical documents. In: Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health. Presented at: ICT4AWE '17; April 28-29, 2017, 2017;135-142; Porto, Portugal. URL: https://www.scitepress.org/Link.aspx?doi=10.5220/0006311301350142 [CrossRef]
  50. Gaudet-Blavignac C, Raisaro JL, Touré V, Österle S, Crameri K, Lovis C. A national, semantic-driven, three-pillar strategy to enable health data secondary usage interoperability for research within the Swiss personalized health network: methodological study. JMIR Med Inform. Jun 24, 2021;9(6):e27591. [FREE Full text] [CrossRef] [Medline]
  51. Dolin RH, Boxwala A, Shalaby J. A pharmacogenomics clinical decision support service based on FHIR and CDS hooks. Methods Inf Med. Dec 2018;57(S 02):e115-e123. [FREE Full text] [CrossRef] [Medline]
  52. Saripalle R, Sookhak M, Haghparast M. An interoperable UMLS terminology service using FHIR. Future Internet. Nov 16, 2020;12(11):199. [FREE Full text] [CrossRef]
  53. Semenov I, Kopanitsa G, Denisov D, Alexandr Y, Osenev R, Andreychuk Y. Patients decision aid system based on FHIR profiles. J Med Syst. Jul 31, 2018;42(9):166. [CrossRef] [Medline]
  54. Jiang G, Kiefer RC, Rasmussen LV, Solbrig HR, Mo H, Pacheco JA, et al. Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution. J Biomed Inform. Aug 2016;62:232-242. [FREE Full text] [CrossRef] [Medline]
  55. Rosenau L, Majeed RW, Ingenerf J, Kiel A, Kroll B, Köhler T, et al. Generation of a fast healthcare interoperability resources (FHIR)-based ontology for federated feasibility queries in the context of COVID-19: feasibility study. JMIR Med Inform. Apr 27, 2022;10(4):e35789. [FREE Full text] [CrossRef] [Medline]
  56. Metke-Jimenez A, Steel J, Hansen D, Lawley M. Ontoserver: a syndicated terminology server. J Biomed Semantics. Sep 17, 2018;9(1):24. [FREE Full text] [CrossRef] [Medline]
  57. Kober G, Paschke A. A FHIR-to-RDF converter. In: Proceeding of the 12th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences. Presented at: SWAT4HCLS '19; December 11-12, 2019, 2019;1-6; Edinburgh, UK. URL: https://ceur-ws.org/Vol-2849/paper-03.pdf
  58. Dridi A, Sassi S, Chbeir R, Faiz S. A flexible semantic integration framework for fully-integrated EHR based on FHIR standard. In: Proceedings of the 12th International Conference on Agents and Artificial Intelligence. Presented at: ICAART '20; February 22-24, 2020, 2020;684-691; Valletta, Malta. URL: https://www.scitepress.org/Papers/2020/89815/89815.pdf [CrossRef]
  59. Kilintzis V, Alexandropoulos VC, Beredimas N. A methodology for an auto-generated and auto-maintained HL7 FHIR OWL ontology for health data management. In: Delgado J, Benis A, de Toledo P, Gallos P, Giacomini M, Martínez-García A, et al, editors. Studies in Health Technology and Informatics. Amsterdam, The Netherlands. IOS Press; 2021;99-103.
  60. Beredimas N, Kilintzis V, Chouvarda I, Maglaveras N. A reusable ontology for primitive and complex HL7 FHIR data types. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2547-2550. [CrossRef] [Medline]
  61. Kiourtis A, Mavrogiorgou A, Kyriazis D. A semantic similarity evaluation for healthcare ontologies matching to HL7 FHIR resources. Stud Health Technol Inform. Jun 16, 2020;270:13-17. [CrossRef] [Medline]
  62. Kiourtis A, Nifakos S, Mavrogiorgou A, Kyriazis D. Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching. Int J Med Inform. Dec 2019;132:104002. [CrossRef] [Medline]
  63. Kober G. Annotating FHIR-RDF-graphs with medication knowledge. In: Proceedings of the Workshop on Automation Experience across Domains, AutomationXP 2020, co-located with the ACM Conference on Human Factors in Computing Systems. Presented at: CEUR '20; April 26, 2020, 2020;1-8; Honolulu, HI. URL: https://ceur-ws.org/Vol-2759/paper5.pdf
  64. Kwon S, Yu J, Park S, Jun JA, Pyo CS. Automatic stroke medical ontology augmentation with standard medical terminology and unstructured textual medical knowledge. In: Proceedings of the 2021 International Conference on Platform Technology and Service. Presented at: PlatCon '21; August 23-25, 2021, 2021;1-5; Jeju, Korea. URL: https://ieeexplore.ieee.org/document/9680753 [CrossRef]
  65. Dong X, Samavi R, Topaloglou T. COC: an ontology for capturing semantics of circle of care. Procedia Comput Sci. 2015;63:589-594. [FREE Full text] [CrossRef]
  66. Jiang G, PrudHommeaux E, Xiao G, Solbrig HR. Developing a semantic web-based framework for executing the clinical quality language using FHIR. In: Proceedings of the 10th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences. Presented at: SWAT4LS '17; December 4-7, 2017, 2020;1-5; Rome, Italy. URL: https://ceur-ws.org/Vol-2042/paper40.pdf
  67. Prud'hommeaux E, Collins J, Booth D, Peterson KJ, Solbrig HR, Jiang G. Development of a FHIR RDF data transformation and validation framework and its evaluation. J Biomed Inform. May 2021;117:103755. [FREE Full text] [CrossRef] [Medline]
  68. Sharma D, Prud'Hommeaux E, Booth D, Peterson KJ, Stone DJ, Solbrig H, et al. Implementing a new FHIR RDF specification for semantic clinical data using a JSONLD- based approach. In: Proceedings of the 13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences. Presented at: SWAT4HCLS '22; January 10-14, 2022, 2022;1-5; Leiden, The Netherlands. URL: https://ceur-ws.org/Vol-3127/paper-10.pdf
  69. Zong N, Ngo V, Stone DJ, Wen A, Zhao Y, Yu Y, et al. Leveraging genetic reports and electronic health records for the prediction of primary cancers: algorithm development and validation study. JMIR Med Inform. May 25, 2021;9(5):e23586. [FREE Full text] [CrossRef] [Medline]
  70. Kober G, Paschke A. Medical information-graphs, based on ontologies and FHIR. In: Proceedings of the 2021 Conference on Digital Curation Technologies. Presented at: Qurator '21; February 8-12, 2021, 2021;1-13; Berlin, Germany. URL: https://ceur-ws.org/Vol-2836/qurator2021_paper_14.pdf
  71. Kober G, Robaldo L, Paschke A. Modeling medical guidelines by Prova and SHACL accessing FHIR/RDF. Use case: the medical ABCDE approach. Stud Health Technol Inform. May 16, 2022;293:59-66. [CrossRef] [Medline]
  72. Luz MP, Nogueira JR, Cavalini LT, Cook TW. Providing full semantic interoperability for the fast healthcare interoperability resources schemas with resource description framework. In: Proceedings of the 2015 International Conference on Healthcare Informatics. Presented at: ICHI '15; 2015, 2015;463-466; Dallas, TX. URL: https://ieeexplore.ieee.org/document/7349736 [CrossRef]
  73. Szmeja P, Prud’hommeaux E. ShExMap and IPSM-AF: comparison of RDF transformation technologies. In: Proceedings of the 6th International Conference on Intelligent Systems, Technologies and Applications. Presented at: ISTA '21; June 15-17, 2021, 2021;29-45; Chennai, India. URL: https://link.springer.com/chapter/10.1007/978-981-16-0730-1_3 [CrossRef]
  74. Thornton K, Solbrig H, Stupp GS, Gayo JE, Mietchen D, Prud’hommeaux E, et al. Using shape expressions (ShEx) to share RDF data models and to guide curation with rigorous validation. In: Proceedings of the 16th International Conference on the Semantic Web. Presented at: ESWC '19; June 2-6, 2019, 2019;606-620; Portorož, Slovenia. URL: https://link.springer.com/chapter/10.1007/978-3-030-21348-0_39 [CrossRef]
  75. De A, Huang M, Feng T, Yue X, Yao L. Analyzing patient secure messages using a fast health care interoperability resources (FIHR)-based data model: development and topic modeling study. J Med Internet Res. Jul 30, 2021;23(7):e26770. [FREE Full text] [CrossRef] [Medline]
  76. Hong N, Wen A, Mojarad MR, Sohn S, Liu H, Jiang G. Standardizing heterogeneous annotation corpora using HL7 FHIR for facilitating their reuse and integration in clinical NLP. AMIA Annu Symp Proc. 2018;2018:574-583. [FREE Full text] [Medline]
  77. Soni S, Gudala M, Wang DZ, Roberts K. Using FHIR to construct a corpus of clinical questions annotated with logical forms and answers. AMIA Annu Symp Proc. 2019;2019:1207-1215. [FREE Full text] [Medline]
  78. Aberdeen J, Bayer S, Clark C, Keybl M, Tresner-Kirsch D. An annotation and modeling schema for prescription regimens. J Biomed Semantics. May 31, 2019;10(1):10. [FREE Full text] [CrossRef] [Medline]
  79. Hong J, Davoudi A, Yu S, Mowery DL. Annotation and extraction of age and temporally-related events from clinical histories. BMC Med Inform Decis Mak. Dec 30, 2020;20(Suppl 11):338. [FREE Full text] [CrossRef] [Medline]
  80. Ciampi M, Sicuranza M, Silvestri S. A privacy-preserving and standard-based architecture for secondary use of clinical data. Information. Feb 13, 2022;13(2):87. [FREE Full text] [CrossRef]
  81. Wu H, Toti G, Morley KI, Ibrahim ZM, Folarin A, Jackson R, et al. SemEHR: a general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research. J Am Med Inform Assoc. May 01, 2018;25(5):530-537. [FREE Full text] [CrossRef] [Medline]
  82. Lenivtceva I, Slasten E, Kashina M, Kopanitsa G. Applicability of machine learning methods to multi-label medical text classification. In: Proceedings of the 20th International Conference on Computational Science. Presented at: ICCS '20; June 3-5, 2020, 2020;509-522; Amsterdam, The Netherland. URL: https:/​/link.​springer.com/​chapter/​10.1007/​978-3-030-50423-6_38#:~:text=Medical%20text%20classification%20task%20facilitates,manually%20labeled%20Russian%20medical%20notes [CrossRef]
  83. El-Sappagh S, Ali F, Hendawi A, Jang JH, Kwak KS. A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard. BMC Med Inform Decis Mak. May 10, 2019;19(1):97. [FREE Full text] [CrossRef] [Medline]
  84. Solbrig HR, Prud'hommeaux E, Grieve G, McKenzie L, Mandel JC, Sharma DK, et al. Modeling and validating HL7 FHIR profiles using semantic web shape expressions (ShEx). J Biomed Inform. Mar 2017;67:90-100. [FREE Full text] [CrossRef] [Medline]
  85. Kiourtis A, Mavrogiorgou A, Menychtas A, Maglogiannis I, Kyriazis D. Structurally mapping healthcare data to HL7 FHIR through ontology alignment. J Med Syst. Feb 05, 2019;43(3):62. [CrossRef] [Medline]


AMIA: American Medical Informatics Association
EHR: electronic health record
FHIR: Fast Healthcare Interoperability Resources
HL7: Health Level Seven International
ICD: International Classification of Diseases
IEEE: Institute of Electrical and Electronics Engineers
LOINC: Logical Observation Identifiers Names and Codes
ML: machine learning
NLP: natural language processing
OWL: web ontology language
QA: quality assessment
RDF: resource description framework
RQ: research question
SMR: systematic mapping review
SNOMED CT: Systematized Nomenclature of Medicine–clinical terms


Edited by A Mavragani; submitted 21.12.22; peer-reviewed by A Kiourtis, P Knaup-Gregori, S Thun, D Hansen; comments to author 16.02.23; revised version received 07.03.23; accepted 19.12.23; published 30.01.24.

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©Fouzia Amar, Alain April, Alain Abran. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.01.2024.

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