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Data Interoperability in Context: The Importance of Open-Source Implementations When Choosing Open Standards

Data Interoperability in Context: The Importance of Open-Source Implementations When Choosing Open Standards

Reflecting on this condition in the context of open health data ecosystems, we observe a salient difference between FHIR versus open EHR and OMOP, namely that the former is the only one that has been mandated—or at least strongly recommended—in some jurisdictions. Survey results on the state of FHIR show that the FHIR standard has been mandated or advised in 20 countries [9].

Daniel Kapitan, Femke Heddema, André Dekker, Melle Sieswerda, Bart-Jan Verhoeff, Matt Berg

J Med Internet Res 2025;27:e66616

A Validation Tool (VaPCE) for Postcoordinated SNOMED CT Expressions: Development and Usability Study

A Validation Tool (VaPCE) for Postcoordinated SNOMED CT Expressions: Development and Usability Study

Ontoserver is currently the only Fast Healthcare Interoperability Resource (FHIR) terminology server that supports postcoordination at all. Ontoserver is used to validate the PCEs in combination with the FHIR service $validate-code [11]. This checks a PCE against specific coding systems, such as SNOMED CT. This method provides a validation result through a Representational State Transfer (REST) request that returns a JSON object [11].

Tessa Ohlsen, Viola Hofer, Josef Ingenerf

JMIR Med Inform 2025;13:e67984

A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study

A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study

FDA: US Food and Drug Administration; FHIR: Fast Healthcare Interoperability Resources. The methods section is crucial for ensuring the accuracy and reliability of research findings. This study’s methodology encompasses the following aspects: computable phenotype development, phenotype distributed deployment, study period, data, and the process of reviewing medical records.

Matthew Deady, Raymond Duncan, Matthew Sonesen, Renier Estiandan, Kelly Stimpert, Sylvia Cho, Jeffrey Beers, Brian Goodness, Lance Daniel Jones, Richard Forshee, Steven A Anderson, Hussein Ezzeldin

J Med Internet Res 2024;26:e54597

Establishing Medical Intelligence—Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study

Establishing Medical Intelligence—Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study

While FHIR holds the potential to standardize data, various challenges persist. Most frequently named is the implementation of FHIR as an application, the complexity of the FHIR standard (including its nested structure), and the representational state transfer (RESTful) approach [14]. In particular, the complexity of the data structure makes it not readily available for processing and easy access to the end user.

Alexander Brehmer, Christopher Martin Sauer, Jayson Salazar Rodríguez, Kelsey Herrmann, Moon Kim, Julius Keyl, Fin Hendrik Bahnsen, Benedikt Frank, Martin Köhrmann, Tienush Rassaf, Amir-Abbas Mahabadi, Boris Hadaschik, Christopher Darr, Ken Herrmann, Susanne Tan, Jan Buer, Thorsten Brenner, Hans Christian Reinhardt, Felix Nensa, Michael Gertz, Jan Egger, Jens Kleesiek

J Med Internet Res 2024;26:e55148

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation

HL7 FHIR is the emerging standard for health care–specific data exchange and has been broadly adapted worldwide [8]. FHIR provides state-of-the-art technologies to modernize the current health care landscape using extensible resources as harmonized and semantically annotatable information units [9]. However, FHIR also provides mechanisms to natively define transformations on its structures.

Jesse Kruse, Joshua Wiedekopf, Ann-Kristin Kock-Schoppenhauer, Andrea Essenwanger, Josef Ingenerf, Hannes Ulrich

JMIR Med Inform 2024;12:e57569

Bridging Data Models in Health Care With a Novel Intermediate Query Format for Feasibility Queries: Mixed Methods Study

Bridging Data Models in Health Care With a Novel Intermediate Query Format for Feasibility Queries: Mixed Methods Study

FHIR Search and the FHIR standard did not provide the ability to express a feasibility query in the required scope [25] at the time of our research. Other query languages that could have been candidates, like CQL or SQL, are complex or data model specific, making the translation between different data models and their representation, as well as the generation of the syntax by a user interface, challenging.

Lorenz Rosenau, Julian Gruendner, Alexander Kiel, Thomas Köhler, Bastian Schaffer, Raphael W Majeed

JMIR Med Inform 2024;12:e58541

State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)–Based Data Model and Structure Implementations: Systematic Scoping Review

State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)–Based Data Model and Structure Implementations: Systematic Scoping Review

HL7 API (HAPI) FHIR is a comprehensive implementation of FHIR in the Java language [56]. The API is available for both FHIR clients and servers [57]. Several studies used HAPI FHIR in the data model implementation process. Bennett et al [26] used the HAPI FHIR server in the process of validation, bulk export, and writing data to NDJSON files. Hong et al [29] used the API to put ovarian cancer data into FHIR resources. They also used the client API to upload structured FHIR data elements to the FHIR server.

Parinaz Tabari, Gennaro Costagliola, Mattia De Rosa, Martin Boeker

JMIR Med Inform 2024;12:e58445

Implementation of the World Health Organization Minimum Dataset for Emergency Medical Teams to Create Disaster Profiles for the Indonesian SATUSEHAT Platform Using Fast Healthcare Interoperability Resources: Development and Validation Study

Implementation of the World Health Organization Minimum Dataset for Emergency Medical Teams to Create Disaster Profiles for the Indonesian SATUSEHAT Platform Using Fast Healthcare Interoperability Resources: Development and Validation Study

Subsequently, the base FHIR was mapped. The list of variables and mapping process results from the WHO EMT MDS and ASEAN EMT MDS medical records forms are shown in Table 1. Meanwhile, Table 2 shows the variables in the daily reporting form mapped to the FHIR resources. From the 40 resources available in the FHIR ID core, we selected 10, 14, and 9 resources for the WHO EMT MDS, ASEAN EMT MDS, and daily reporting form, respectively, as displayed in Table 3.

Hiro Putra Faisal, Masaharu Nakayama

JMIR Med Inform 2024;12:e59651