Federated systems for automated infection surveillance: a perspective

Area of Science:

• Public Health
• Infectious Disease Epidemiology
• Health Informatics

Background:

• Automated surveillance (AS) using routine care data can reduce workload and improve infectious disease surveillance quality.
• Barriers currently limit the large-scale implementation of AS, with existing strategies including central or local data processing.
• Federated AS, where analysis scripts are developed centrally and applied locally, is explored as a potential solution.

Purpose of the Study:

• To explore the potential of federated automated surveillance (AS) in overcoming current challenges for large-scale implementation.
• To assess the applicability of federated AS for healthcare-associated infections (AS-HAI) and severe acute respiratory illness (AS-SARI) surveillance.
• To identify benefits and prerequisites for successful federated AS implementation.

Main Methods:

• A perspective-based exploration of federated AS implementation strategies.
• Focus on common and specific requirements for AS-HAI and AS-SARI.
• Analysis of benefits derived from combining central and local AS implementation aspects.

Main Results:

• Federated AS offers decreased local surveillance burden, improved data access while preserving privacy, and enhanced central/local oversight.
• It combines the standardization of central systems with the real-time data access and flexibility of local systems.
• Potential to promote AS development globally and foster international collaboration.

Conclusions:

• Federated automated surveillance presents a viable solution to current barriers in the large-scale implementation of AS-HAI and AS-SARI.
• Successful implementation requires addressing data transformation burdens, ensuring governance, and gaining stakeholder agreement on accuracy, accountability, transparency, and data protection.
• Prerequisites include validation of results and clear evaluation requirements for network participants to ensure understanding and acceptance.