Across health programme delivery, digital transformation is often discussed in terms of tools. But its real test is whether it helps people make better decisions, faster, in the places where lives are at risk.
In May 2026, over 500 delegates from 61 countries gathered in Nairobi, Kenya, for the ICT4D conference. They explored how digital innovation and data-driven solutions can transform humanitarian relief and development, focusing on AI, digital public infrastructure (DPI), interoperability, and predictive analytics.
Malaria Consortium, in partnership with CRS and eGov Foundation, presented a session on moving from fragmented campaign tools to reusable digital public infrastructure.
The organization demonstrated the DIGIT HCM tool and shared a case study on using DPI to strengthen digital campaign scaling across health interventions in Nigeria. For malaria programs, especially seasonal malaria chemoprevention (SMC), the message was clear: digital transformation improves decisions, systems, and accountability, not just tools.
Peter Otieno, Malaria Consortium’s Senior Digital Health Specialist, emphasized that technology’s value lies in decision-making, not tools themselves. He noted, “Technology accelerates impact only when designed around people, systems, and critical decisions.”
Key takeaways from ICT4D 2026 relevant to malaria control include:
- Start with decisions, not tools
Digital transformation should prioritize defining what needs improvement, not merely implementing technology.
For malaria programs, this means redefining digital health efforts from system deployment to decision support, impacting who makes decisions, what data they need, and how quickly.
The true value of digital systems lies in enhancing timeliness, accountability, coverage, and equity—specifically in decision-making for campaign readiness, stock management, and reaching vulnerable populations.
Digital health must simplify action, not just reporting.
- Interoperability is critical
Interoperability is now essential for program performance, ownership, and sustainability, not just a technical feature.
Data fragmentation across health systems hinders timely decisions. Connected digital public infrastructure can unify workflows and improve data flow.
Systems must be designed with interoperability from the start, ensuring clean data pipelines for analytics.
- AI requires quality data and governance
AI’s effectiveness depends on good data, governance, security, and trust. Without these, AI risks causing more harm than good.
In malaria programs, AI could support trend analysis or anomaly detection, but must remain a decision-support tool, not a substitute for accountability.
- Predictive analytics needs action pathways
Predictive analytics is useful only if forecasts lead to clear actions, like pre-positioning supplies or adjusting strategies.
For SMC programs, predictive analytics could identify challenges in access or supply before a campaign begins.
System design must prioritize usability in low-connectivity, high-workload environments, ensuring tools reduce, not increase, workload.
ICT4D 2026 highlighted that effective digital solutions are those that improve decision-making infrastructure, not just new technologies.”
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