AI Agent Operational Lift for Child Health And Mortality Prevention Surveillance (champs) in Decatur, Georgia

Why AI matters at this scale

CHAMPS (Child Health and Mortality Prevention Surveillance) is a global network that determines causes of death for children under five in low-resource settings. By combining minimally invasive tissue sampling, clinical data, and verbal autopsies, it generates evidence to guide public health interventions. With 201–500 employees and operations across multiple countries, CHAMPS sits at a critical inflection point: its data volumes are growing, but manual analysis bottlenecks limit the speed and depth of insights.

What CHAMPS does

CHAMPS builds surveillance sites in sub-Saharan Africa and South Asia, training local teams to collect and analyze mortality data. The core output is cause-of-death attribution, which informs everything from vaccine policy to maternal health programs. The process relies heavily on physicians manually coding verbal autopsy narratives—a time-intensive task that delays reporting and consumes scarce expert resources.

Why AI is a natural fit

At this size, organizations often have enough data to train robust models but lack the large analytics teams of bigger enterprises. AI can bridge that gap. For CHAMPS, machine learning can automate repetitive tasks, surface hidden patterns, and enable real-time surveillance. The global health sector is increasingly embracing AI for diagnostics and prediction, and early adopters gain a competitive edge in funding and influence.

Three high-ROI AI opportunities

1. Automated verbal autopsy coding
Natural language processing can classify causes of death from narrative text with accuracy comparable to physicians. This would cut coding time by 80%, allowing faster site feedback and freeing clinicians for higher-value work. ROI: reduced labor costs and accelerated research cycles.

2. Predictive mortality hotspot mapping
By integrating demographic, environmental, and health system data, machine learning models can forecast where child deaths are likely to spike. This enables proactive resource deployment and targeted interventions. ROI: potentially preventable deaths and stronger donor justification.

3. Intelligent data harmonization
AI can automatically align disparate data sources (clinical, lab, survey) and flag inconsistencies. Cleaner data means more reliable research outputs and higher-impact publications. ROI: enhanced reputation and increased grant competitiveness.

Deployment risks for a mid-sized research organization

Implementing AI at CHAMPS requires careful navigation. Data privacy and security are paramount given sensitive health information. Models trained on one region may not generalize to others, risking biased results. Interpretability is critical—public health decisions demand explainable AI. Additionally, staff may need upskilling, and integrating AI into existing workflows without disrupting field operations is a change management challenge. Starting with a pilot in verbal autopsy coding, with strong ethical oversight and local stakeholder engagement, can mitigate these risks and build organizational confidence.