Interoperable medical data: The missing link for understanding COVID-19

Being able to link clinical outcomes to SARS-CoV-2 virus strains is a critical component of understanding COVID-19. Here, we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the 'Fast Healthcare Interoperable Resource' (FHIR) implementation guide, we introduce an ontology-based standard questionnaire to overcome these shortcomings and describe patient 'journeys' in coordination with the World Health Organization's recommendations. We identify steps in the clinical health data acquisition cycle and workflows that likely have the biggest impact in the data-driven understanding of this virus. Specifically, we recommend detailed symptoms and medical history using the FHIR standards. We have taken the first steps towards this by making patient status mandatory in GISAID ('Global Initiative on Sharing All Influenza Data'), immediately resulting in a measurable increase in the fraction of cases with useful patient information. The main remaining limitation is the lack of controlled vocabulary or a medical ontology.

Automated summary

The study highlights the importance of linking clinical outcomes to SARS-CoV-2 virus strains in understanding COVID-19. By implementing FHIR standards and using ontology-based questionnaires, data collection and analysis can be optimized. Furthermore, emphasizing detailed symptoms and medical history in clinical health data acquisition could enhance the data-driven understanding of the virus.