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

Denis C Bauer1,2, Alejandro Metke-Jimenez3, Sebastian Maurer-Stroh4,5,6,7

  • 1Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Geelong, Australia, Australia.

Transboundary and Emerging Diseases
|October 23, 2020
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Summary
This summary is machine-generated.

Linking clinical outcomes to SARS-CoV-2 strains requires better data. We propose an ontology-based questionnaire using Fast Healthcare Interoperable Resources (FHIR) standards to improve COVID-19 data collection and analysis.

Keywords:
COVID-19GISAIDSARS-CoV-2genome sequenceontologypatient information

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Area of Science:

  • Infectious Diseases
  • Health Informatics
  • Data Science

Background:

  • Understanding COVID-19 necessitates linking clinical outcomes with specific SARS-CoV-2 virus strains.
  • Current data collection methods are insufficient for sustainable analysis and meaningful insights into the pandemic.
  • The Global Initiative on Sharing All Influenza Data (GISAID) has faced challenges with comprehensive patient information.

Purpose of the Study:

  • To address shortcomings in clinical data collection for COVID-19 research.
  • To introduce a standardized, ontology-based questionnaire for improved patient journey documentation.
  • To identify key steps in clinical health data acquisition that impact virus understanding.

Main Methods:

  • Developed an ontology-based standard questionnaire aligned with Fast Healthcare Interoperable Resources (FHIR) implementation guides.
  • Integrated World Health Organization recommendations for describing patient journeys.
  • Mandated patient status reporting in GISAID to enhance data completeness.

Main Results:

  • The implementation of mandatory patient status in GISAID led to a measurable increase in cases with useful patient information.
  • The proposed FHIR-standardized questionnaire aims to improve the detail of symptoms and medical history collected.
  • Identified critical workflows in clinical health data acquisition for better virus understanding.

Conclusions:

  • Standardized data collection using FHIR and an ontology-based approach is crucial for advancing COVID-19 research.
  • Mandatory data fields, like patient status in GISAID, can significantly improve data utility.
  • A key remaining challenge is the lack of a controlled medical vocabulary or ontology for comprehensive data analysis.