Why Should Clinical Diagnostics Labs Care About Ontology?
In our previous post, we talked about the importance of data provenance for clinical diagnostics laboratories—that is, tracing the origin and changes to critical data such as electronic health records, analytical results, and workflow records. But no conversation about data is complete without also considering the ontology used.
Proper data management is crucial for labs
If your laboratory wants to scale throughput or make clinical breakthroughs, proper data management is critical in order to prevent the friction that will slow your organization down. A standardized ontology facilitates interoperability and lets you integrate data with other applications and within workflows. Although the structure of your data may be invisible while it is generated during the lab’s daily work, it becomes extremely apparent the moment you begin reporting or analysis, or if you integrate one system with another.
A standardized ontology essentially future-proofs your data by ensuring it’s as compatible as possible with the applications, services, or standards you need to interact with (for example, HL7 FHIR, GA4GH, W3C-PROV, and the FDA BioCompute Object project).
