Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

• Published in: Journal of biomedical semantics Vol. 13; no. 1; pp. 12 - 19
• Main Authors: Queralt-Rosinach, Núria, Kaliyaperumal, Rajaram, Bernabé, César H., Long, Qinqin, Joosten, Simone A., van der Wijk, Henk Jan, Flikkenschild, Erik L.A., Burger, Kees, Jacobsen, Annika, Mons, Barend, Roos, Marco
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25.04.2022; BioMed Central; BMC
• Subjects: Applications programs; Collaboration; Computer applications; Coronaviruses; COVID-19; COVID-19 - epidemiology; Cytokines; Data analysis; Data management; Data points; Data science; Datasets; Disease; Electronic data processing; Epidemics; FAIR; Health care; Hospital; Hospitals; Humans; Information management; Information systems; Infrastructure; Interoperability; Laboratories; Medical informatics; Medical records; Medical research; Medicine, Experimental; Metadata; Methods; Netherlands; Ontologies; Ontology; Open data; Open science; Pandemics; Patient data; Patients; Physicians; Principles; Privacy; Regulation; Research data management; Semantic Web; Software; Software development; Netherlands; Europe; Ontologies; Patient data; Hospital; FAIR; Open science; Research data management
• DOI: 10.1186/s13326-022-00263-7
• ISSN: 2041-1480

The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.