Building a coronavirus disease 2019 healthcare registry in an evolving pandemic

Indumathi Venkatachalam1,2, Edwin Philip Conceicao3, Jean Xiang Ying Sim4

  • 1Department of Infection Prevention and Epidemiology, Singapore General Hospital, Singapore, Singapore. indumathi.venkatachalam@singhealth.com.sg.

Scientific Reports
|October 6, 2025
PubMed

Insights

Singapore Health Services developed a COVID-19 registry to track patient data for clinical and research needs. This automated database efficiently managed information, supporting pandemic response and healthcare management.

Area of Science:

  • Public Health
  • Health Informatics
  • Epidemiology

Background:

  • The COVID-19 pandemic necessitated rapid data collection to understand disease characteristics and epidemiology.
  • Effective healthcare resource management required high-quality data early in the pandemic.

Purpose of the Study:

  • To describe the development process of the Singapore Health Services (SingHealth) COVID-19 registry.
  • To outline how the registry supports clinical, operational, and research needs during the pandemic.

Main Methods:

  • A COVID-19 registry was established within SingHealth's Electronic Health Intelligence System (eHIntS).
  • Patient identification used laboratory results and electronic disease tags.
  • A minimum data set (MDS) with over 100 variables across 15 domains was created, including raw and derived data on comorbidities and complications.

Main Results:

  • The registry includes data from 156,262 unique patients as of December 31, 2023.
  • 27,630 patients were admitted to SingHealth hospitals for COVID-19.
  • The registry has provided valuable data for operational, clinical, and research purposes.

Conclusions:

  • The SingHealth COVID-19 registry serves as a model for developing low-cost, automated databases during evolving pandemics.
  • Leveraging standardized clinical workflows facilitated efficient pandemic operations, clinical management, and research.
  • The registry effectively supports data-driven decision-making in public health crises.

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