The COVID-19 pandemic has challenged healthcare and research data management systems worldwide to provide reusable patient data for rapid and efficient translational research. Clinical data, laboratory measurements, and various omics data such as transcriptomics and metabolomics, are routinely collected from hospitalized COVID-19 patients to inform medical doctors about patients’ health status and to support research on treatment options. Analysing data integrated from multiple sources in a hospital, complemented with data from other hospitals and public knowledge bases, can generate critical information about disease mechanisms to support diagnosis, prognosis and decisions on interventions. However, research and clinical data are often not prepared for instant secondary use involving multiple sources. This was already an obstacle for efficient clinical and biomedical research in general, but a pandemic of a poorly understood novel disease that overloads hospitals’ capacity has revealed the significance of this problem.

Integrative analysis is challenged by software systems used to collect these various types of data from patients in hospitals. Different formats may be used (e.g. CSV or JSON) and the semantics of data are often underspecified and captured in a proprietary syntax or by different standards (e.g. HL7 FHIR or OpenEHR). This can result in fragmentation over multiple ‘silos’ that are not sufficiently interoperable for instant computational analysis. Reuse and reproducibility are further hampered by missing or unstandardised provenance, such as the time and date at which data were collected (e.g. scans may be performed on a different day than blood measurements). Furthermore, to expand analysis beyond one hospital, information on consent and regulations that control data access, reuse, and sharing are often unclear and not easily assessable. Complete harmonization of access regulations between institutes and countries is not realistic, but analysis could still be efficient if access regulations were at least computationally assessable.

Ideally, hospital systems are set up with integrative, federated data analytics in mind. Global leaders in data science have posed that this can be achieved by applying agreed upon standards to make data globally findable, accessible, inter-operable, and reusable for humans and computers, also referred to by as ‘the FAIR principles’ [1]. Indeed, projects such as the GO FAIR Virus Outbreak Data Network (VODAN) [2], the ZonMW Covid program [3], the Trusted World of Corona (TWOC) [4], and ELIXIR Covid project [5] embrace FAIR principles as a key element of their COVID-19 data management strategy. A quintessential objective is turning data and data containers into machine actionable FAIR digital objects (FDO, in this paper defined as resources in a digital, machine understandable form; a formal framework for FDOs is under development, see [6, 7]). This will optimize the ability to integrate and visualise data from many sources, facilitate fine-grained data access regulation, and allow for decentralised and machine assisted analysis [8]. The latter is further enabled by the development of infrastructure that supports ‘data visiting’ [9, 10]. This is attractive for clinical data because (i) existing systems can be complemented with data visiting functions, thereby keeping their other functions in place, (ii) the output of an analysis is generally less privacy sensitive than the input. In Europe, the General Data Protection Regulation (GDPR) policy supports data visiting by requiring that access regulations for personal data are clearly defined [11].

Methods to facilitate the implementation of FAIR principles, or ‘FAIRification’, are currently being investigated in multiple projects and initiatives. We have previously published a generic workflow [12], as a basis for specialised variations such as for rare disease registries [13]. Related activities are the development of the FAIR cookbook in the FAIRplus project [14, 15], the three point framework for FAIRification of metadata by the VODAN GO FAIR network [16], and the organisation of a FAIRification steward team to support rare disease registries reach their FAIR goals [17]. The application of FAIR principles in hospitals is starting to be adopted in Europe as a key strategy for nationwide healthcare research data infrastructure [18, 19]. Cross connections through multinational collaborations, such as in ELIXIR and GO FAIR, and domain specific collaborations such as via globally operating patient organisations, could support convergence of FAIR implementation choices to further facilitate the adoption of FAIR principles and thereby efficient analysis across multiple hospitals in multiple countries.

At the Leiden University Medical Centre (LUMC), the implementation of FAIR principles for COVID-19 data is part of a multidisciplinary collaboration, coined ‘The BEAT-COVID project’. This collaboration was initiated in March 2020 to face the multiple analysis challenges of the COVID-19 pandemic. The LUMC is a tertiary care, teaching and research hospital in the Netherlands that encompasses clinical and research groups with expertise on immunology, biomedicine, data management and data science. The groups work together on collecting and sharing different types of patient data, analyses, findings, expertise, and novel solutions implemented in the hospital (e.g. see [20]). One of the challenges is to implement a FAIR Research Data Management plan (RDM) comprising FAIRification of priority resources and a FAIR based architecture that complements the existing data management systems in the hospital.

We hypothesise that the use of existing ontologies and ontological models will enable turning patient data into machine readable digital objects that are prepared for secondary use. Our objective is to develop ontological models that represent and link the data records and metadata of the datasets in the existing LUMC data management systems (Figure 1). In our ontology centred approach, data can stay in existing systems but are made accessible ‘in terms of’ the central data linking model to create a virtual warehouse. We reused existing ontological models such as the core ontological model for common data elements developed in the European Joint Programme on Rare Diseases (EJP RD) for patient registries [21], and the Data Catalogue Vocabulary (DCAT) for datasets [22]. The metadata is made accessible by a FAIR Data Point (FDP) instance [23]. FDPs ensure that BEAT-COVID resources can be found and used through querying machine readable metadata. It includes the pointers to access the content of the resource for analysis workflows, if access is permitted. By using ontologies, patient data in the hospital are virtually linked with other ontologically described data in the hospital, but also public Linked ‘Open’ Data (LOD). This can boost the potential for knowledge discovery and data+knowledge driven analytics. Interestingly, ontologies may also be used to describe data access restrictions [24, 25] to complement FAIR metadata with information that supports data safety and patient privacy.

In this paper, we describe and implement our approach for FAIRification of COVID-19 observational patient data in an academic hospital. We selected cytokine measurements of hospitalised patients as our primary objective of FAIRification and development of the FAIR RDM. We synthesized an artificial dataset mimicking original laboratory data obtained from patient samples to study the data lifecycle without the risk of violating patient privacy. Our main result is the FAIRification in the hospital. We also show that our FAIRifcation approach is providing cytokine measurements as FDOs and is enabling applications on top of this FAIR patient data for analytics. Importantly, this work has been done in close collaboration with clinicians and data managers who are familiar with the existing hospital data systems and data lifecycles to establish best practices for making data FAIR in the hospital. We demonstrate that a FAIR RDM plan based on describing data and metadata by ontologies delivers an infrastructure that complements existing infrastructure with FDOs that are prepared for integrative and federated analysis. We show our first results and the solutions that are currently being developed as LUMC research data management procedures. We finally discuss what FAIRification entails in a ‘real world’ hospital situation involving different stakeholders and departments, and future challenges such as data access regulation in a FAIR ecosystem.