Definitions to data flow: Operationalizing MIABIS in HL7 FHIR

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F25%3A00080416" target="_blank" >RIV/00209805:_____/25:00080416 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14330/25:00142739

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1532046425001480?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1532046425001480?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jbi.2025.104919" target="_blank" >10.1016/j.jbi.2025.104919</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Definitions to data flow: Operationalizing MIABIS in HL7 FHIR

Popis výsledku v původním jazyce

OBJECTIVE: Biobanks and biomolecular resources are increasingly central to data-driven biomedical research, encompassing not only metadata but also granular, sample-related data from diverse sources such as healthcare systems, national registries, and research outputs. However, the lack of a standardised, machine-readable format for representing such data limits interoperability, data reuse and integration into clinical and research environments. While MIABIS provides a conceptual model for biobank data, its abstract nature and reliance on heterogeneous implementations create barriers to practical, scalable adoption. This study presents a pragmatic, operational implementation of MIABIS focused on enabling real-world exchange and integration of sample-level data. METHODS: We systematically evaluated established data exchange standards, comparing HL7 FHIR and OMOP CDM with respect to their suitability for structuring sample-related data in a semantically robust and machine-readable form. Based on this analysis, we developed a FHIR-based representation of MIABIS that supports complex biobank structures and enables integration with federated data infrastructures. Supporting tools, including a Python library and an implementation guide, were created to ensure usability across diverse research and clinical contexts. RESULTS: We created nine interoperable FHIR profiles covering core MIABIS entities, ensuring consistency with FHIR standards. To support adoption, we developed an open-source Python library that abstracts FHIR interactions and provides schema validation for MIABIS-compliant data. The library was integrated into an ETL tool in operation at Czech Node of BBMRI-ERIC, European Biobanking and Biomolecular Resources Research Infrastructure, to demonstrate usability with real-world sample-related data. Separately, we validated the representation of MIABIS entities at the organisational level by converting the data structures of BBMRI-ERIC Directory into FHIR, demonstrating compatibility with federated data infrastructures. CONCLUSION: This work delivers a machine-readable, interoperable implementation of MIABIS, enabling the exchange of both organisational and sample-level data across biobanks and health information systems. By integrating MIABIS with HL7 FHIR, we provide a host of reusable tools and mechanisms for further evolution of the data model. Combined, these benefits can help with the integration into clinical and research workflows, supporting data discoverability, reuse, and cross-institutional collaboration in biomedical research.

Název v anglickém jazyce

Definitions to data flow: Operationalizing MIABIS in HL7 FHIR

Popis výsledku anglicky

OBJECTIVE: Biobanks and biomolecular resources are increasingly central to data-driven biomedical research, encompassing not only metadata but also granular, sample-related data from diverse sources such as healthcare systems, national registries, and research outputs. However, the lack of a standardised, machine-readable format for representing such data limits interoperability, data reuse and integration into clinical and research environments. While MIABIS provides a conceptual model for biobank data, its abstract nature and reliance on heterogeneous implementations create barriers to practical, scalable adoption. This study presents a pragmatic, operational implementation of MIABIS focused on enabling real-world exchange and integration of sample-level data. METHODS: We systematically evaluated established data exchange standards, comparing HL7 FHIR and OMOP CDM with respect to their suitability for structuring sample-related data in a semantically robust and machine-readable form. Based on this analysis, we developed a FHIR-based representation of MIABIS that supports complex biobank structures and enables integration with federated data infrastructures. Supporting tools, including a Python library and an implementation guide, were created to ensure usability across diverse research and clinical contexts. RESULTS: We created nine interoperable FHIR profiles covering core MIABIS entities, ensuring consistency with FHIR standards. To support adoption, we developed an open-source Python library that abstracts FHIR interactions and provides schema validation for MIABIS-compliant data. The library was integrated into an ETL tool in operation at Czech Node of BBMRI-ERIC, European Biobanking and Biomolecular Resources Research Infrastructure, to demonstrate usability with real-world sample-related data. Separately, we validated the representation of MIABIS entities at the organisational level by converting the data structures of BBMRI-ERIC Directory into FHIR, demonstrating compatibility with federated data infrastructures. CONCLUSION: This work delivers a machine-readable, interoperable implementation of MIABIS, enabling the exchange of both organisational and sample-level data across biobanks and health information systems. By integrating MIABIS with HL7 FHIR, we provide a host of reusable tools and mechanisms for further evolution of the data model. Combined, these benefits can help with the integration into clinical and research workflows, supporting data discoverability, reuse, and cross-institutional collaboration in biomedical research.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of biomedical informatics

ISSN

1532-0464

e-ISSN

1532-0480

Svazek periodika

117

Číslo periodika v rámci svazku

November 2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

104919

Kód UT WoS článku

001593616600001

EID výsledku v databázi Scopus

2-s2.0-105021669070