Lessons Learned in Using Mathematical Modeling for Priority Setting in Health.

Abstract

The COVID-19 pandemic has highlighted the need for priority setting in health financing and resource allocation, spotlighting the limitations of traditional health financing strategies. This commentary explores the relevance of mathematical modeling in enhancing allocative efficiency within the health sector, especially in the aftermath of the pandemic. We draw from the World Bank's experiences in supporting over 20 countries to employ mathematical optimization models for priority setting, aiming to achieve optimal health outcomes within constrained budgets. The pandemic's impact on economic growth, revenue collection, debt stress, and the overall fiscal space available for health financing has necessitated a paradigm shift toward prioritizing efficiency improvements in health service delivery. We outline lessons learned from such modeling and chart future directions to enhance efficiency gains, including for integrated, patient-centered approaches to health service delivery. We advocate for flexible and effective localized priority-setting, leveraging data-driven insights to navigate the complexities of health financing in a post-COVID era.