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We have already described the two major sources of clinical data from clinical trials and routine clinical practice (i.e. real-world). The value of such clinical data would be greatly increased through FAIR Implementation which will likely include the application of interoperability standards such as CDISC, FHIR and vocabularies which are mandatory for submission to the regulators. FAIR data and metadata from clinical trials and real world sources are more likely to be ready for consumption by machine learning and semantic knowledge graphs which will support secondary reuse over much greater periods, as illustrated in figure 1. This would require the use of mappings to strong semantic standards at data capture time, such as proper use of SNOMED-CT or openEHR archetypes.

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Realising value for clinical trial data

Pharmaceutical companies conduct clinical trials at great expense, primarily to demonstrate to the regulatory authorities that new treatments are efficacious and safe. Although data standards such as CDISC are mandatory for clinical trial submission to FDA, this does not make data FAIR (as will be shown in section 2) and in fact, could actually limit future reuse. However, if clinical data were FAIRified first, this would unlock much more value (see figure 1). Besides making clinical study data and metadata more Findable, Accessible and Reusable it would also benefit from much greater Interoperability. This semantic enrichment of clinical study data will enhance its value both within a company and outside, when it is shared with external parties, including public registries such as clinicaltrials.gov.

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Realising value from Real World Data

Here are some qualitative examples of how to leverage Real World Data (RWD), by integrating them with other data types (e.g. by unlocking the insights contained in genomic and phenotypic data), can generate value and help all key stakeholders in the healthcare ecosystem.

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