Research and policy agenda for anti-corruption efforts in the health sector

A coherent, global approach to anti-corruption efforts

In alignment with our strategic goals, U4 directly contributes to developing a coherent, global approach to anti-corruption efforts. U4 is a knowledge partner and member of the Global Network for Anti-Corruption, Transparency and Accountability in Health (GNACTA), hosted by the World Health Organization. This is a strategic alliance of decision makers on anti-corruption, transparency, and accountability (ACTA), academia, and civil society leaders collaborating to leverage knowledge and institutions to address corruption on a global scale. We will continue to explore ways to expand anti-corruption efforts globally through this and other avenues.

Understanding the relationship between public health and corruption across social and environmental determinants of health

The relationship between public health and corruption is complex and multifaceted, impacting various social and environmental determinants of health. Corruption often leads to misallocation of resources, which reduces the funds available for key sectors that impact on public health. For example, corruption can also lead to inadequate enforcement of environmental regulations, resulting in poor air and water quality, unsafe housing, and other environmental hazards. It can influence other social determinants of health, such as education, employment, and social services. Funds intended for education or social welfare may be diverted, causing poorer social outcomes and, consequently, poorer health outcomes.

The anti-corruption field has begun to focus on addressing corruption’s impact on disadvantaged groups (women, those in rural areas, people with disabilities, children etc) and other forms of corruption, such as sexual corruption. The links between these concepts are clear. However, research demonstrating the evidence between corruption in social and environmental determinants of health and public health is still lacking. This impacts advocating effectively for policy change.

Exploring emerging technologies

Technology holds both opportunities and challenges in dealing with corruption in the health system. Artificial intelligence (AI) and machine-learning (ML) approaches have the potential to flag fraud and corrupt behaviours, especially in countries with well-developed health information systems. For instance, AI models have been applied in areas such as public procurement to identify unusual patterns that indicate fraud.

However, while some literature highlights the potential of AI and ML in tackling corruption, their real-world implementation remains in its early stages, with mixed outcomes. For example, in many countries, the lack of reliable electronic health records makes it difficult for AI models to function effectively, while political resistance to transparency creates further barriers.

Furthermore, AI models are only as good as the data they learn from; if these datasets are biased, manipulated, or incomplete, AI can unintentionally reinforce existing inequities or fail to detect corrupt practices. There have been instances where biased datasets – especially those containing data from vulnerable groups – led to inaccurate AI predictions, creating more harm than good. Given the opportunities and challenges of using AI as an anti-corruption tool in the health sector, we believe this is a key area to research and monitor.

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