JMIR Publications

ABSTRACT

Background:

Creating an ontology for coronavirus disease 2019 (COVID-19) surveillance should help ensure transparency and consistency. Ontologies formalise conceptualisations at either domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform.

Objective:

To develop an application ontology for COVID-19 which can be deployed across the various use case domains of the Oxford- RCGP RSC research and surveillance activities.

Methods:

We described our domain-specific use case. The actor was the RCGP RSC sentinel network; the system the course of the COVID-19 pandemic; the outcomes the spread and effect of mitigation measures. We used our established three-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association (IMIA) Primary Health Care Informatics working group and extended networks.

Results:

Our use case domains included primary care, public health, virology, clinical research and clinical informatics. Our ontology supported: (1) Case identification, microbiological sampling and health outcomes at both an individual practice and national level; (2) Feedback through a dashboard; (3) A national observatory, (4) Regular updates for Public Health England, and (5) Transformation of the sentinel network to be a trial platform. We have identified a total of 8,627 people with a definite COVID-19 status, 4,240 with probable, and 59,147 people with possible COVID-19, within the RCGP RSC network (N=5,056,075).

Conclusions:

The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.