DEEP REINFORCEMENT LEARNING MODELS FOR DYNAMIC PRICING OPTIMIZATION IN RURAL HEALTHCARE ACCESS PROGRAMS

Paper ID: EIJTEM_2024_11_4_156-169

Author's Name: Farzana Parvin Popy, Sanjida Akter, Tanvir Ahmed Abir, Nabila Tuz Johora, Aftaha Ahmed

Volume: 11

Issue: 4

Year: 2024

Page No: 156-169

Abstract:

Background: Rural healthcare systems face a critical challenge in balancing provider financial viability with patient affordability. Traditional static pricing models -such as Fixed Pricing, Cost-Plus, and Sliding Scale structures often fail to adapt to the dynamic socioeconomic realities of rural populations, leading to either provider insolvency or restricted patient access. Deep Reinforcement Learning (DRL) offers a promising adaptive alternative for navigating such multi-objective optimization problems. Objective: To evaluate the effectiveness of a Proximal Policy Optimization (PPO)-based DRL framework in dynamically optimizing healthcare service pricing to simultaneously enhance provider profitability and patient utilization rates within a simulated rural healthcare network. Methodology: A quantitative simulation-based design was employed. A rural healthcare environment was modeled incorporating patient income heterogeneity, demand elasticity, and operational costs. A PPO-based DRL agent was trained and benchmarked against three traditional pricing models across N =1,000 simulation episodes per model. Performance was assessed using one-way ANOVA, Chi-Square (χ²) tests, and Tukey's HSD post-hoc analysis at α = 0.05. Results: The DRL model significantly outperformed all baseline strategies. ANOVA revealed significant differences in profitability (F (3, 3996) = 14.82, p

Keywords: Deep Reinforcement Learning, Proximal Policy Optimization, Dynamic Pricing, Rural Healthcare, Healthcare Access, Financial Sustainability, Simulation, ANOVA, Chi-Square Test

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