A Fuzzy Logic Approach Toward Solving the Analytic Maze of Health System Financing

Improved health, equity, macro-economic efficiency, efficient provision of care, and client satisfaction are the common goals of the health system. The relative significance of these goals varies, however, across nations, communities, and with time. As for health care finance, the attainment of these goals under varying circumstances involves alternative policy options for each of the following elements: sources of finance, allocation of finance, pay to providers, and public-private mix. The intricate set of multiple goals, elements, and policy options defies human reasoning, and, hence, hinders effective policymaking. Indeed, health system finance' is not amenable to a clear set of structural relationships. Neither is there a universe that can be subject to statistical scrutiny: each health system is unique. 'Fuzzy logic' and its underlying 'Expert System' that model human reasoning by managing knowledge' close to the way it is handled by human language, provides a powerful tool for systematic analysis of health system finance, and for guiding policy making. Assuming equal welfare weights for alternative goals, and mutually exclusive policy options under each health-financing element, the exploratory model we present here suggests that a German type health system is best. Other solutions depend on the welfare weights and mixes of policy options.