Comparing AI Enabled and Business as Usual Evidence Surveillance for Public Health Intelligence

Background

Timely, high‑quality evidence surveillance is central to public health protection, preparedness and decision‑making. Business‑as‑usual (BAU) surveillance is well established but resource‑intensive and increasingly challenged by the volume and fragmentation of public health information. AI‑enabled surveillance may support digital public health and horizon scanning, but evidence of its routine operational value is limited.

Methods

Within a Research and Guideline Development Unit, AI‑enabled weekly evidence surveillance operated alongside BAU surveillance between 26/12/2025 and 3/04/2026. AI surveillance used automated horizon scanning of international, European and national institutional sources, with aggregation and deduplication. BAU surveillance relied on expert‑led manual searching and synthesis. Outputs were compared by volume, thematic coverage, geographic scope and relevance to health protection.

Results

BAU surveillance identified over 400 items, compared with approximately 250 AI‑generated outputs. Both approaches consistently identified core surveillance reports and guidance from major institutions. BAU outputs offered greater granularity, including detailed guidance updates and implementation materials. AI outputs were more consolidated, prioritising distinct surveillance products, substantive updates and cross‑cutting intelligence to support situational awareness.

Conclusions

AI‑enabled surveillance supported consolidation and horizon scanning but was not mature enough to replace routine health protection surveillance. Expert‑led BAU processes remain essential for contextual interpretation and operational relevance. Further evaluation is required to support safe and effective integration of AI into public health surveillance systems.

Key messages

• Public Health Evidence Surveillance directly reflects the core activity being evaluated and anchors the work in health protection practice.

• Artificial Intelligence in Public Health captures the novel methodological component and aligns with digital public health literature.