AI and ML Assessment of Performance-Based Financing Models in Health Care: A Review

Abstract

Abstract: Performance-Based financing (PBF) has become an important strategy towards enhancing accountability, efficiency and quality in health care systems, especially in the developing countries. Nevertheless, the classic indicators system of PBF is usually associated with ineffective utilization of data, excessive bureaucracy, and poor coverage of the health outcome complexity. The paper will reflect how AI and ML will improve PBF model evaluation in order to support predictive analytics, anomaly detection, natural language processing, and improve the optimization of incentive structure. This suggest that the use of AI/ML tools could greatly enhance monitoring and evaluation, by increasing accuracy, scalability, and transparency and enabling fairer and more sustainable financing strategies. However, there remain issues of data quality, transparency of algorithms, constraints on resources, and associated ethical issues, such as bias, privacy and explainability. Finally, the study has revealed that AI/ML could be effectively integrated into PBF evaluation to help develop health financing systems but demands keen implementation, high-quality governance, and further research to make it fair and maintainable.