Exploring AI and Generative AI in Healthcare Reimbursement Policies: Challenges, Ethical Considerations, and Future Innovations

Abstract

This essay explores the integration of artificial intelligence (AI) in healthcare reimbursement policies and the emergence of generative AI tools that can automatically generate those policies, alongside the challenges and novel ethical considerations they present. Healthcare reimbursement policies consist of rules about when, why, and how healthcare costs are reimbursed. Reimbursement policies are currently often handcrafted by experts based on available knowledge. The ability to generate healthcare reimbursement policies already based on current knowledge is underexplored and could enable more flexible reimbursement policies. AI tools are commonly used in the healthcare domain to develop predictive models or finding patterns in healthcare data to assist human experts in decision making. Generally, there are, however, two less explored aspects in this field. The first explores the use of AI tools to discover complex causal structures that underlie healthcare problems, enabling human experts to devise more effective interventions (eventually represented in terms of equitable reimbursement policies). The second explores AI tools that generate complex AI solutions, guiding more open-ended AI tools towards certain desired properties. Considering healthcare reimbursement policies provide the incentives and regulations for healthcare providers and thus directly affect the healthcare quality of care, equity and social welfare, the exploration of both aspects is important. In this essay, an initial wide exploration and formulation of these, especially generative AI tools that directly generate algorithms and other AI models, are provided.