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On strengthening digital health systems, disease surveillance, emergency preparedness and response, M&E, and what working inside these systems actually teaches you.
Featured Series
Nine essays on what nine years inside WHO Nigeria and Nigeria's health systems actually teach you: on data, coordination, community, and what persists when the funding ends.
Fifty-three thousand case investigations. COVID-19. Cholera. Mpox across 27 local government areas. Circulating vaccine-derived poliovirus type 2. Each of these responses taught something different not about the pathogen, but about the systems designed to contain it.
An indicator is only as useful as the decision it informs. A results framework filled with indicators that no one uses to make decisions is not a monitoring system it is a reporting burden. Yet most global health programmes design indicators backwards: starting with what is easy to count, then constructing a rationale for why it matters.
Most global health programmes measure what they do. Results-Based Management demands that they measure what changes because of what they do. That distinction between activity tracking and outcome accountability is the difference that determines whether a programme learns, adapts, and improves, or simply reports and repeats.
The logical framework or logframe is probably the most universally required and least effectively used tool in global health M&E. Almost every WHO, USAID, GAVI, and Global Fund programme produces one. Very few programmes use it as it was intended: as a living document that links planning decisions to monitoring data and drives management action.
Monitoring and evaluation has a language problem. The same term means different things in different frameworks. This glossary establishes working definitions for the terms used throughout this blog series and across the WHO programmes I support so that every article builds on a shared foundation.
Lesson 2 of 9: In over nine years of working with health data across Nigerian states, the most dangerous errors I have seen were not caused by bad data. They were caused by good data interpreted without adequate context.
How digital M&E tools are compressing the feedback loop between health programme implementation and strategic decisions, and what it takes to get the transition right.
More articles in progress. Topics include community health systems, eHealth implementation, and lessons from outbreak response.
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