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There is increasing use of machine learning for the health financing functions (revenue raising, pooling and purchasing), yet evidence lacks for its effects on the universal health coverage (UHC) objectives. This paper provides a synopsis of the use cases of machine learning and their potential benefits and risks. The assessment reveals that the various use cases of machine learning for health financing have the potential to affect all the UHC intermediate objectives -- the equitable distribution of resources (both positively and negatively); efficiency (primarily positively); and transparency (both positively and negatively). There are also both positive and negative effects on all three UHC final goals, that is, utilization of health services in line with need, financial protection and quality care. When the use of machine learning facilitates or simplifies health financing tasks that are counterproductive to UHC objectives, there are various risks -- for instance risk selection, cost reductions at the expense of quality care, reduced financial protection or over-surveillance. Whether the effects of using machine learning are positive or negative depends on how and for which purpose the technology is applied. Therefore, specific health financing guidance and regulations, particularly for (voluntary) health insurance, are needed. To inform the development of specific health financing guidance and regulation, we propose several key policy and research questions. To gain a better understanding of how machine learning affects health financing for UHC objectives, more systematic and rigorous research should accompany the application of machine learning. [ABSTRACT FROM AUTHOR]

Private health insurance is playing an increasing role in both high- and low-income countries, yet is poorly understood by researchers and policy-makers. This paper shows that the distinction between private and public health insurance is often exaggerated since well regulated private insurance markets share many features with public insurance systems. It notes that private health insurance preceded many modern social insurance systems in western Europe, allowing these countries to develop the mechanisms, institutions and capacities that subsequently made it possible to provide universal access to health care. We also review international experiences with private insurance, demonstrating that its role is not restricted to any particular region or level of national income. The seven countries that finance more than 20% of their health care via private health insurance are Brazil, Chile, Namibia, South Africa, the United States, Uruguay and Zimbabwe. In each case, private health insurance provides primary financial protection for workers and their families while public health-care funds are targeted to programmes covering poor and vulnerable populations. We make recommendations for policy in developing countries, arguing that private health insurance cannot be ignored. Instead, it can be harnessed to serve the public interest if governments implement effective regulations and focus public funds on programmes for those who are poor and vulnerable. It can also be used as a transitional form of health insurance to develop experience with insurance institutions while the public sector increases its own capacity to manage and finance health-care coverage. [ABSTRACT FROM AUTHOR]

Over the past two decades the government in Thailand has adopted an incremental approach to extending health-care coverage to the population. It first offered coverage to government employees and their dependants, and then introduced a scheme under which low-income people were exempt from charges for health care. This scheme was later extended to include elderly people, children younger than 12 years of age and disabled people. A voluntary public insurance scheme was implemented to cover those who could afford to pay for their own care. Private sector employees were covered by the Social Health Insurance scheme, which was implemented in 1991. Despite these efforts, 30% of the population remained uninsured in 2001. In October of that year, the new government decided to embark on a programme to provide universal health-care coverage. This paper describes how research into health systems and health policy contributed to the move towards universal coverage. Data on health systems financing and functioning had been gathered before and after the founding of the Health Systems Research Institute in early 1990. In 1991, a contract capitation model had been used to launch the Social Health Insurance scheme. The advantages of using a capitation model are that it contains costs and provides an acceptable quality of service as opposed to the cost escalation and inefficiency that occur under fee-for-service reimbursement models, such as the one used to provide medical benefits to civil servants. An analysis of the implementation of universal coverage found that politics moved universal coverage onto the policy agenda during the general election campaign in January 2001. The capacity for research on health systems and policy to generate evidence guided the development of the policy and the design of the system at a later stage. Because the reformists who sought to bring about universal coverage (who were mostly civil servants in the Ministry of Public Health and members of nongovernmental organizations) were able to bridge the gap between researchers and politicians, an evidence-based political decision was made. Additionally, the media played a part in shaping the societal consensus on universal coverage. [ABSTRACT FROM AUTHOR]