Why We Are Losing the War Against COVID-19 on the Data Front and How to Reverse the Situation

Area of Science:

• Health Informatics
• Epidemiology
• Data Science

Background:

• The COVID-19 pandemic generated unprecedented volumes of clinical data globally.
• Highly digitalized health systems in high-income countries possess extensive patient information.
• Existing health data infrastructure was unprepared for timely analysis of epidemic-scale clinical data.

Purpose of the Study:

• To highlight the underutilization of routinely collected clinical data for COVID-19 research.
• To identify systemic failures in health systems' data processing and analysis capabilities.
• To propose actionable strategies for health systems to become learning health systems.

Main Methods:

• Qualitative analysis of data management practices during the COVID-19 pandemic.
• Review of current approaches to clinical data utilization in healthcare.
• Identification of barriers to timely data analysis in health systems.

Main Results:

• Health systems failed to leverage big data and machine learning for critical COVID-19 clinical questions.
• Clinical data is often siloed, inaccessible, and treated as individual patient tools rather than a collective resource.
• Data extraction initiatives are frequently uncoordinated, inefficient, and privately driven.

Conclusions:

• Healthcare systems must transition to dynamically analyzing routine clinical data.
• Implementing learning health system principles is crucial for improving epidemic response and patient care.
• Proactive data utilization can enhance clinical strategies and save lives during future health crises.