Deep Reinforcement Learning Models for Dynamic Pricing Optimization in Rural Healthcare Access Programs
Keywords:
Deep Reinforcement Learning, Proximal Policy Optimization, Dynamic Pricing, Rural Healthcare, Healthcare Access, Financial Sustainability, Simulation, ANOVA, Chi-Square TestAbstract
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 < 0.01) and patient volume (F (3, 3996) = 9.47, p < 0.05). Chi-Square analysis indicated that the DRL model achieved a more equitable demographic access distribution (χ² (9) = 28.63, p < 0.01), suggesting improved inclusivity across income and insurance categories.
Conclusion: DRL-based dynamic pricing presents a viable, data-driven approach for optimizing the trade-off between financial sustainability and equitable healthcare access in rural settings. These findings hold significant implications for policymakers and health administrators seeking adaptive pricing solutions for underserved communities.
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