Dynamic modeling approaches to characterize the functioning of health systems: A systematic review of the literature

Abstract

Universal Health Coverage (UHC) is one of the targets for the United Nations Sustainable Development Goal 3. The impetus for UHC has led to an increased demand for time-sensitive tools to enhance our knowledge of how health systems function and to evaluate impact of system interventions. We define the field of “health system modeling” (HSM) as an area of research where dynamic mathematical models can be designed in order to describe, predict, and quantitatively capture the functioning of health systems. HSM can be used to explore the dynamic relationships among different system components, including organizational design, financing and other resources (such as investments in resources and supply chain management systems) – what we call “inputs” – on access, coverage, and quality of care – what we call “outputs”, toward improved health system “outcomes”, namely increased levels and fairer distributions of population health and financial risk protection.We undertook a systematic review to identify the existing approaches used in HSM. We identified “systems thinking” – a conceptual and qualitative description of the critical interactions within a health system – as an important underlying precursor to HSM, and collated a critical collection of such articles. We then reviewed and categorized articles from two schools of thoughts: “system dynamics” (SD)” and “susceptible-infected-recovered-plus” (SIR+). SD emphasizes the notion of accumulations of stocks in the system, inflows and outflows, and causal feedback structure to predict intended and unintended consequences of policy interventions. The SIR + models link a typical disease transmission model with another that captures certain aspects of the system that impact the outcomes of the main model. These existing methods provide critical insights in informing the design of HSM, and provide a departure point to extend this research agenda. We highlight the opportunity to advance modeling methods to further understand the dynamics between health system inputs and outputs.