Generative AI in Designing Family Health Plans: Balancing Personalized Coverage and Affordability

Authors

  • Ramanakar Reddy Danda

Keywords:

Generative AI,Family Health Plans,Personalized Coverage, Affordable Healthcare AI-driven Health Insurance, Customized Benefits,Health Plan Optimization, Predictive Analytics in Healthcare, AI for Cost Management, Health Plan Personalization.

Abstract

In this thesis, we explore the use of generative AI for a specific decision-making task: the generation of family health insurance plans. The goal is to select a plan that balances personal coverage with affordability by controlling the share of expenses the family is willing to take on and the scope of coverage. Because the world's healthcare systems are tasked with addressing wide-ranging challenges—from improving care quality to controlling costs—stakeholders continually seek innovative solutions. One major area of focus is the tailoring of treatments to individuals in order to precisely target diseases while minimizing side effect burdens. Like treatment, current strategies for choosing health insurance plans are not suitable for everyone.

For families in general, plans that require low levels of patient cost-sharing do not offer economic safety. However, for families with members suffering from chronic health problems, the extra cost of a low-cost-sharing plan is likely offset by the benefits of the plan's coverage. Thus, the choice of a health insurance plan, like m

In this thesis, we explore the use of generative AI for a specific decision-making task: the generation of family health insurance plans. The goal is to select a plan that balances personal coverage with affordability by controlling the share of expenses the family is willing to take on and the scope of coverage. Because the world's healthcare systems are tasked with addressing wide-ranging challenges—from improving care quality to controlling costs—stakeholders continually seek innovative solutions. One major area of focus is the tailoring of treatments to individuals in order to precisely target diseases while minimizing side effect burdens. Like treatment, current strategies for choosing health insurance plans are not suitable for everyone.

For families in general, plans that require low levels of patient cost-sharing do not offer economic safety. However, for families with members suffering from chronic health problems, the extra cost of a low-cost-sharing plan is likely offset by the benefits of the plan's coverage. Thus, the choice of a health insurance plan, like many others in healthcare, is not simply a matter of choosing affordability. While families must have the funds to cover the cost of a plan, they must also consider the level of coverage for which they are paying. Due to the complex interplay between cost, attributes of plans, and individual characteristics, it may be challenging to address this objective by reforming the current choice architecture in which families navigate the particulars of choosing between already designed plans.

any others in healthcare, is not simply a matter of choosing affordability. While families must have the funds to cover the cost of a plan, they must also consider the level of coverage for which they are paying. Due to the complex interplay between cost, attributes of plans, and individual characteristics, it may be challenging to address this objective by reforming the current choice architecture in which families navigate the particulars of choosing between already designed plans.

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Published

2024-11-12

How to Cite

Ramanakar Reddy Danda. (2024). Generative AI in Designing Family Health Plans: Balancing Personalized Coverage and Affordability. Utilitas Mathematica, 121, 316–332. Retrieved from https://utilitasmathematica.com/index.php/Index/article/view/2071

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