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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]

The World Health Organization recognizes rehabilitation as an essential component of universal health coverage (UHC). In many countries, UHC builds on a standard benefits package of services that is informed by the country's essential medicines list, standard treatment guidelines and primary health care essential laboratory list. In South Africa, primary health care is largely provided and managed by primary health-care nurses and medical officers in accordance with primary health care standard treatment guidelines. However, rehabilitation is mostly excluded from these guidelines. This paper describes the 10-year process that led to rehabilitation referral recommendations being considered for inclusion in South Africa's primary health care standard treatment guidelines. There were five key events: (i) a breakthrough moment; (ii) producing a scientific evidence synthesis and formulating recommendations; (iii) presenting recommendations to the national essential medicines list committee; (iv) mapping rehabilitation recommendations onto relevant treatment guideline sections; and (v) submitting revised recommendations to the committee for final consideration. The main lesson learnt is that, by working together, rehabilitation professionals can be of sufficient number to make a difference, improve service delivery and increase referrals to rehabilitation from primary health care. A remaining challenge is the lack of a rehabilitation representative on the national essential medicines list committee, which could hamper understanding of rehabilitation and of the complexities of the supporting evidence. [ABSTRACT FROM AUTHOR]

Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust. [ABSTRACT FROM AUTHOR]

Globally, countries have agreed to pursue the progressive realization of universal health coverage (UHC) and there is now a high level of political commitment to providing universal coverage of essential health services while ensuring that individuals are financially protected against high health spending. The aim of this paper is to help policy-makers think through the progressive realization of UHC. First, the pitfalls of applying global normative expenditure targets in estimating the national revenue required for UHC are discussed. Then, several recommendations on estimating national revenue are made by moving beyond the question of how much UHC will cost and focusing instead on the national health-care reforms and policy choices needed to progress towards UHC. In particular, costing exercises are recommended as a tool for comparing different service delivery options and investment in data infrastructure is recommended for improving the information needed to identify the best policies. These recommendations are intended to assist health policy-makers and international and national agencies who are developing country plans for the progressive realization of UHC. [ABSTRACT FROM AUTHOR]

Achieving universal health coverage (UHC) involves distributing resources, especially human resources for health (HRH), to match population needs. This paper explores the policy lessons on HRH from four countries that have achieved sustained improvements in UHC: Brazil, Ghana, Mexico and Thailand. Its purpose is to inform global policy and financial commitments on HRH in support of UHC. The paper reports on country experiences using an analytical framework that examines effective coverage in relation to the availability, accessibility, acceptability and quality (AAAQ) of HRH. The AAAQ dimensions make it possible to perform tracing analysis on HRH policy actions since 1990 in the four countries of interest in relation to national trends in workforce numbers and population mortality rates. The findings inform key principles for evidence-based decision-making on HRH in support of UHC. First, HRH are critical to the expansion of health service coverage and the package of benefits; second, HRH strategies in each of the AAAQ dimensions collectively support achievements in effective coverage; and third, success is achieved through partnerships involving health and non-health actors. Facing the unprecedented health and development challenges that affect all countries and transforming HRH evidence into policy and practice must be at the heart of UHC and the post-2015 development agenda. It is a political imperative requiring national commitment and leadership to maximize the impact of available financial and human resources, and improve healthy life expectancy, with the recognition that improvements in health care are enabled by a health workforce that is fit for purpose. [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]

Q: How did you become interested in health care? A: My parents were physicians and I grew up in a medical atmosphere. While I studied medicine I also worked as [...]