FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

Alvarez-Romero, Celia; Martínez-García, Alicia; Sınacı, Ali Anıl; Gençtürk, Mert; Méndez, Eva; Hernández-Pérez, Tony; Liperoti, Rosa; Angioletti, Carmen; Löbe, Matthias; Ganapathy, Nagarajan; Deserno, Thomas M.; Almada, Marta; Costa, Elı́sio; Chronaki, Catherine; Cangioli, Giorgio; Cornet, Ronald; Poblador‐Plou, Beatriz; Carmona-Pírez, Jonás; Gimeno‐Miguel, Antonio; Poncel-Falcó, Antonio; Prados‐Torres, Alexandra; Kovačević, Tomi; Zarić, Bojan; Bokan, Darijo; Hromis, Sanja; Malbaša, Jelena Djekić; Fernández, Carlos Rapallo; Fernández, Teresa Velázquez; Rochat, Jessica; Gaudet-Blavignac, Christophe; Lovis, Christian; Weber, Patrick; Quintero, Miriam; Pérez-Pérez, Manuel M.; Ashley, Kevin D.; Horton, Laurence; Calderón, Carlos

doi:10.12688/openreseurope.14349.2

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…In general, the results of our study highlight the importance of the FAIRification of health data, particularly in terms of data interoperability, which we refer to as Layer 0 in our conceptual framework. Previous research, such as the FAIR4HEALTH project, has demonstrated that approaches like FL (in our case, the PHT), can benefit from FAIR data 41 . These alternative methodologies on the data level, in contrast to our REDCap approach, are driven by FAIRification processes using, e.g., standards like HL7 FHIR or other technologies of the Semantic Web [41][42][43] .…”

Section: Discussionmentioning

confidence: 89%

A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis

Welten,

de Arruda Botelho Herr,

Hempel

et al. 2024

Sci Data

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The development of platforms for distributed analytics has been driven by a growing need to comply with various governance-related or legal constraints. Among these platforms, the so-called Personal Health Train (PHT) is one representative that has emerged over the recent years. However, in projects that require data from sites featuring different PHT infrastructures, institutions are facing challenges emerging from the combination of multiple PHT ecosystems, including data governance, regulatory compliance, or the modification of existing workflows. In these scenarios, the interoperability of the platforms is preferable. In this work, we introduce a conceptual framework for the technical interoperability of the PHT covering five essential requirements: Data integration, unified station identifiers, mutual metadata, aligned security protocols, and business logic. We evaluated our concept in a feasibility study that involves two distinct PHT infrastructures: PHT-meDIC and PADME. We analyzed data on leukodystrophy from patients in the University Hospitals of Tübingen and Leipzig, and patients with differential diagnoses at the University Hospital Aachen. The results of our study demonstrate the technical interoperability between these two PHT infrastructures, allowing researchers to perform analyses across the participating institutions. Our method is more space-efficient compared to the multi-homing strategy, and it shows only a minimal time overhead.

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Section: Discussionmentioning

confidence: 89%

A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis

Welten,

de Arruda Botelho Herr,

Hempel

et al. 2024

Sci Data

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“…Despite the substantial achievements in the FAIR-ification of biomedical data sets, research on the FAIR-ification of patient data residing in local EHRs and personal health records is still in its infancy. However, there is a workflow for sharing health care and health research data in line with the FAIR principles [ 24 , 25 ]. Already available standards and technologies in the EHR domain are being analyzed to show their adherence to the FAIR principles.…”

Section: Introductionmentioning

confidence: 99%

“…This work was supported by the FAIR4Health project [ 24 ], which has received funding from the European Union Horizon 2020 research and innovation program under grant agreement 824666.…”

mentioning

confidence: 99%

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study

Sınacı¹,

Gençtürk²,

Teoman³

et al. 2023

J Med Internet Res

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Background Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is a health data content modeling and exchange standard. Objective Our goal was to devise a new methodology to extract, transform, and load existing health data sets into HL7 FHIR repositories in line with FAIR principles, develop a Data Curation Tool to implement the methodology, and evaluate it on health data sets from 2 different but complementary institutions. We aimed to increase the level of compliance with FAIR principles of existing health data sets through standardization and facilitate health data sharing by eliminating the associated technical barriers. Methods Our approach automatically processes the capabilities of a given FHIR end point and directs the user while configuring mappings according to the rules enforced by FHIR profile definitions. Code system mappings can be configured for terminology translations through automatic use of FHIR resources. The validity of the created FHIR resources can be automatically checked, and the software does not allow invalid resources to be persisted. At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions. Results Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable. Conclusions We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks.

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“…The FAIR4Health project has been considered the first proposal to translate the FAIR principles to health research data in Europe [ 13 ]. Following the design of the new FAIRification workflow to convert health data into FAIR data, the FAIR4Health platform [ 14 ] was developed with the aim of applying Artificial Intelligence (AI) algorithms on the FAIR health research datasets.…”

Section: Introductionmentioning

confidence: 99%

FAIR principles to improve the impact on health research management outcomes

Martínez-García

Alvarez-Romero

Román-Villarán

et al. 2023

Heliyon

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FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

Cited by 7 publications

References 7 publications

A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis

A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study

FAIR principles to improve the impact on health research management outcomes

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