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Objectives: This research addresses several factors relevant to inequity in healthcare that may be susceptible to being addressed in a new generation of electronic health records (EHRs). Methods: Through a scoping review of the literature, inequities related to ethnicity, gender, and socioeconomic aspects in healthcare in general and, more specifically in EHRs, have been considered. Papers have been identified between 2011 and 2022 in three categories: EHR, gender inequalities, and ethnicity inequalities. Results: Twenty-two recommendations have been identified within the scope of the three categories indicated above. These exposed requirements focus on two spheres: (1) technical sphere, mainly focused on the characteristics and tools that the EHR should develop from taking into account the studied inequalities; and (2) clinical sphere, which mainly affects patients, health professionals, and health providers. Conclusions: Ethnic and gender inequalities are essential factors to take into account when diagnosing, monitoring, preventing, and treating a patient. These factors give us the keys to discovering recommendations for a new generation of EHRs to help mitigate these needs.

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Background Digital transformation in healthcare and the growth of health data generation and collection include an important challenge for the secondary use of healthcare records in the health research field. Likewise, due to the ethical and legal constraints for using sensitive data, understanding how health data is managed by dedicated infrastructures called data hubs is essential to facilitate data sharing and reuse. Methods In order to capture the different data governance behind health data hubs across Europe, a survey focused on analysing the feasibility of linking individual-level data between data collections and the generation of health data governance patterns was carried out. The target audience of this study was National, European, and Worldwide data hubs. In total, the designed survey was sent to a representative list of 99 health data hubs in January 2022. Results In total, 41 survey responses received till June 2022 were analysed in-depth. Stratification methods were performed to cover the different levels of granularity identified in some data hubs’ characteristics. Firstly, a general pattern of data governance for data hubs was defined. Afterward, specific profiles were defined, generating specific patterns of data governance through the stratifications in terms of the kind of organisation (centralised vs. decentralised), and role (data controller or data processor) of the health data hubs interviewees. Conclusions The in-depth analysis of the responses from health data hubs interviewees across Europe provided a list of the most frequent aspects that concluded a set of specific recommendations on data management and governance, taking into account the constraints of sensitive data. In summary, a data hub should work in a centralised way providing a Data Processing Agreement and a formal procedure to identify data providers, as well as data quality control, data integrity and anonymisation methods.

Medical data science aims to facilitate knowledge discovery assisting in data, algorithms, and results analysis. The FAIR principles aim to guide scientific data management and stewardship, and are relevant to all digital health ecosystem stakeholders. The FAIR4Health project aims to facilitate and encourage the health research community to reuse datasets derived from publicly funded research initiatives using the FAIR principles. The 'FAIRness for FHIR' project aims to provide guidance on how HL7 FHIR could be utilized as a common data model to support the health datasets FAIRification process. This first expected result is an HL7 FHIR Implementation Guide (IG) called FHIR4FAIR, covering how FHIR can be used to cover FAIRification in different scenarios. This IG aims to provide practical underpinnings for the FAIR4Health FAIRification workflow as a domain-specific extension of the GoFAIR process, while simplifying curation, advancing interoperability, and providing insights into a roadmap for health datasets FAIR certification.

Medical data science aims to facilitate knowledge discovery assisting in data, algorithms, and results analysis. The FAIR principles aim to guide scientific data management and stewardship, and are relevant to all digital health ecosystem stakeholders. The FAIR4Health project aims to facilitate and encourage the health research community to reuse datasets derived from publicly funded research initiatives using the FAIR principles. The ‘FAIRness for FHIR’ project aims to provide guidance on how HL7 FHIR could be utilized as a common data model to support the health datasets FAIRification process. This first expected result is an HL7 FHIR Implementation Guide (IG) called FHIR4FAIR, covering how FHIR can be used to cover FAIRification in different scenarios. This IG aims to provide practical underpinnings for the FAIR4Health FAIRification workflow as a domain-specific extension of the GoFAIR process, while simplifying curation, advancing interoperability, and providing insights into a roadmap for health datasets FAIR certification.

[Background] Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers., [Objective] The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD)., [Methods] The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies., [Results] Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases., [Conclusions] Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles., This work was supported by the FAIR4Health project [10], which received funding from the European Union’s Horizon 2020 research and innovation program under grant 824666. This research has also been cosupported by the Carlos III National Institute of Health through the Programa de Ciencia de Datos de la Infraestructura de Medicina de Precisión asociada a la Ciencia y la Tecnología program (IMPaCT-Data, code IMP/00019) and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both cofunded by the European Regional Development Fund Fondo Europeo de Desarrollo Regional “A way of making Europe.”

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance., This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 824666 (project FAIR4Health). Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’.

This article belongs to the Special Issue Addressing the Growing Burden of Chronic Diseases and Multimorbidity: Characterization and Interventions, The current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research., This study was performed in the framework of FAIR4Health, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666. Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’, and by REDISSEC (RD16/0001/0005) and RICAPPS (RD21/0016/0019) from Carlos III National Institute of Health. This work was also supported by Instituto de Investigación Sanitaria Aragón and Carlos III National Institute of Health [Río Hortega Program, grant number CM19/00164].

BACKGROUND Chronic diseases are responsible for most health problems in older people. We know that chronic conditions tend to cluster in the form of patterns, also known as multimorbidity patterns. However, health systems and professionals are generally organized and trained to respond to specific diseases independently, negatively impacting patients and health systems. Different initiatives are trying to respond to these problems. In this context, the current availability of electronic health records and other types of health research data represents an excellent research opportunity. However, there are also some relevant limitations and challenges related to a current lack of tools that allow us to access, harmonize, integrate and reuse datasets technically, legally, ethically, and respectfully to patients and society. In this sense, the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles can help us to guide scientific data management and stewardship and drive scientific discovery to a new paradigm. FAIR4Health is a European Commission supported project that applies FAIR principles on publicly-funded research datasets. OBJECTIVE To present the FAIR4Health pathfinder case study designed to validate and evaluate the FAIR4Health solution with the aim of identifying multimorbidity patterns and their association with mortality in older adults from different health organizations databases of four European countries. METHODS To apply the FAIR principles in five European cohorts from different healthcare settings (i.e., primary care, hospitals, and nursing homes) and institutions (i.e., University of Geneva from Switzerland, Università Cattolica del Sacro Cuore from Italy, University of Porto from Portugal, Instituto Aragonés de Ciencias de la Salud from Spain, and Andalusian Health Service also from Spain), a multicentric retrospective observational study (N = 11,034) was performed. In FAIR4Health, a workflow was designed to implement the FAIR principles on health datasets, and two tools were developed, a Data Curation Tool to transform the raw datasets into FAIR datasets and a Data Privacy Tool to preserve data privacy. On top of these, the FAIR4Health Platform was implemented to provide an interface for researchers, and enable the usage of federated machine learning algorithms on FAIR datasets. In this study, we applied a federated frequent pattern growth association algorithm to identify the most frequent disease patterns among a set of variables. RESULTS We applied the FAIR principles in the health research datasets from different organizations, and we were able to reuse and integrate heterogeneous datasets, increasing the variability of data compared to the studies not applying those principles. We identified and described high-frequent multimorbidity patterns consistent with the literature and observed a strong association with polypharmacy and mortality. CONCLUSIONS Our results highlight the importance of implementing the FAIR data policy to overcome the difficulties in data management and accelerate responsible health research with patients and society.