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

Denis C. Bauer; Alejandro Metke-Jimenez; Sebastian Maurer-Stroh; Suma Tiruvayipati; Laurence O. W. Wilson; Yatish Jain; Amandine Perrin; Kate Ebrill; David P. Hansen; Seshadri S. Vasan

doi:10.1111/tbed.13892

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

Denis C. Bauer, Alejandro Metke-Jimenez, Sebastian Maurer-Stroh, Suma Tiruvayipati, Laurence O. W. Wilson, Yatish Jain, Amandine Perrin, Kate Ebrill, David P. Hansen, Seshadri S. Vasan^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

20 Citations (Scopus)

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Abstract

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.

Original language	English
Pages (from-to)	1753-1760
Number of pages	8
Journal	Transboundary and Emerging Diseases
Volume	68
Issue number	4
DOIs	https://doi.org/10.1111/tbed.13892
Publication status	Published - Jul 2021

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

COVID-19
genome sequence
GISAID
ontology
patient information
SARS-CoV-2

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10.1111/tbed.13892Licence: CC BY-NC

Publisher version (open access)Final published version, 1.37 MBLicence: CC BY-NC

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abstract = "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 {\textquoteleft}Fast Healthcare Interoperable Resource{\textquoteright} (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 ({\textquoteleft}Global Initiative on Sharing All Influenza Data{\textquoteright}), 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.",

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

Abstract

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