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.

The use of Foundational Ontologies in Bioinformatics

“…The final step of the extraction process resulted in 79 papers, which comprehend works published in the years from 2004 to 2021. BFO is the most used foundational ontology among the set of selected papers (42 papers), followed by Biotop (20), DOLCE (17), GFO (8), SIO (3), SUMO (3) and YAMATO (1). It is important to notice that some works used more than one foundational ontology in combination.…”

“…To that end, the FAIR principles [1] focus on enabling efficient data analysis across multiple resources with minimal human intervention. The realisation of FAIR principles is intrinsically dependent on ontologies since they are used to describe, for instance, catalogues of resources (Findability), machine-readable access conditions (Accessibility), data and metadata (Interoperability and Reusability), and reuse conditions (Reusability) [7,8].…”

Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments’ toxicities

“…Additionally, Queralt-Rosinach et al. [54] present a knowledge graph that integrates clinical data collected in the context of the BEAT-COVID project. 48 These approaches put in perspective the protagonist role of knowledge graphs in understanding COVID-19.…”

F*** workflows: when parts of FAIR are missing

“…One solution posed to enable Interoperability of data is to remodel data according to a concept model or ontology, and then either to map that data into an existing system or to build a new system that can leverage this new model, e.g., [8]. However, there are some difficulties with this solution, as it partially assumes that the data at hand easily map to a data model, both conceptually and in terms of the labels provided for the data.…”