Machine learning in health financing: benefits, risks and regulatory needs

Area of Science:

• Health Economics
• Health Policy
• Artificial Intelligence

Background:

• Machine learning is increasingly applied to health financing functions, including revenue raising, pooling, and purchasing.
• Evidence on the impact of machine learning on universal health coverage (UHC) objectives remains limited.

Purpose of the Study:

• To provide a synopsis of machine learning use cases in health financing.
• To assess the potential benefits and risks of machine learning for UHC objectives.
• To propose policy and research questions to guide future development.

Main Methods:

• Literature review and synthesis of existing evidence on machine learning applications in health financing.
• Analysis of potential impacts on UHC intermediate objectives (equity, efficiency, transparency) and final goals (utilization, financial protection, quality).

Main Results:

• Machine learning can affect UHC objectives both positively and negatively, influencing resource distribution, efficiency, and transparency.
• Potential risks include adverse selection, compromised quality of care, reduced financial protection, and over-surveillance.
• The impact of machine learning is contingent on its specific application and purpose.

Conclusions:

• Specific guidance and regulations for health financing, especially for voluntary health insurance, are necessary.
• Further systematic and rigorous research is needed to understand machine learning's effects on health financing for UHC.
• Policy and research questions are proposed to inform regulatory development.