A study to design minimum data set of COVID-19 registry system

Area of Science:

• Public Health Informatics
• Health Data Management
• Epidemiological Surveillance

Background:

• The COVID-19 pandemic highlighted the critical need for robust data collection and reporting infrastructures globally.
• Disease registry systems are essential for systematically collecting vital health information.
• This study focused on developing a minimum data set (MDS) for an effective COVID-19 registry system.

Purpose of the Study:

• To design a standardized Minimum Data Set (MDS) for a COVID-19 registry system.
• To establish a foundational data structure for comprehensive COVID-19 information management.
• To enhance the quality and consistency of COVID-19 data collection and reporting.

Main Methods:

• A five-phase qualitative study was conducted to develop the MDS.
• Information requirements were assessed, and data elements were identified.
• The MDS was piloted and refined based on implementation feedback.

Main Results:

• The designed MDS comprises eight key data groups, including administrative details, disease exposure, medical history, diagnostic tests, treatment outcomes, diagnosis, follow-up, and vaccination status.
• The system includes a total of 476 data elements across these categories.
• The MDS provides a structured framework for capturing diverse COVID-19 related information.

Conclusions:

• A standardized and comprehensive MDS is crucial for developing national COVID-19 data dictionaries.
• Implementing such an MDS can significantly improve the overall quality of COVID-19 data.
• This framework supports better public health decision-making and pandemic response.