Predictive models hold enormous promise for public health — the ability to anticipate disease outbreaks, forecast service demand, and allocate resources before a crisis peaks rather than in response to it. But the gap between a model that performs well in a research paper and one that reliably informs frontline decisions is wider than is often acknowledged.
Drawing on three years of experience deploying predictive tools in partnership with health ministries, SAKS has identified the conditions that determine whether a model is actually used: data freshness, institutional trust in the outputs, interpretability for non-technical decision-makers, and clear ownership of the model's maintenance after the implementing partner exits.
This piece reflects on what we have learned and offers practical guidance for organisations looking to move from proof-of-concept to operational deployment of predictive health tools.