Adaptive Coverage Intelligence for Population Health

Abstract

Dynamic systems with interdependent feedback loops can, in principle, converge toward an equilibrium in which pressure levels remain stable. Coverage decisions based on a fixed set of current indicators are necessary for allocating limited resources, but they tend to produce static allocations: coverage levels in under-served areas go unadjusted, and little effort is made to augment coverage where it remains inadequate. Moreover, such decisions are typically revised only after problems become evident — that is, once one or more indicators have diverged or deteriorated beyond an acceptable threshold.

This paper proposes a framework that analyzes population health services at the system level, rather than at the level of individual health service provider agencies. The framework introduces a dynamic care coordination analytic capability that draws on data regarding care demand, cost, and service provision, as well as care alternatives and local capacity. From this data, it generates analytics that support the adaptive mechanisms needed for population health coverage decisions to respond to changes in the sector’s capacity to sustain good health outcomes across the population. In doing so, this work extends population health services analysis into a new domain — one that strengthens the field’s capability for a dynamic, feedback-based systems approach.