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

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

Keywords: FAIR; Hospital; Ontologies; Open science; Patient data; Research data management.

Fig. 1

Illustration of the central concepts of the envisioned FAIR based architecture: the central star represents the data linking model for interoperability that the sources refer to (data and metadata), the small stars next to each source represent what is used of the central model to describe the source (thereby becoming ‘self-describing’), the arrows represent workflows or scripts: for the source systems to map or convert source data and metadata to the central data linking model, for retrieving data from across the sources through the central data linking model, and for analysis. FAIR Data Points provide access to the ‘ontologised’ metadata and data (not shown)

Fig. 2

Ontological data model for the cytokine measurements patient dataset

Fig. 3

Semantic module to represent disease severity score phenotypes calculated in the hospital

Fig. 4

Ontological metadata model instantiated as an RDF graph. The four lower edges are the four additional metadata elements for COVID-19 data resource description

Fig. 5

Integration of our ontological approach with existing systems

Fig. 6

Federated SPARQL query crossing FAIR patient data with the UniProt knowledgebase

Fig. 7

BEAT-COVID FAIRification workflow to make the data management and infrastructure in the hospital more FAIR. Collaborators and results are described in every step where applicable