Optimizing healthcare pricing strategies: A data-driven approach to enhance margins and customer value

The healthcare sector is facing various challenges, such as high costs, misaligned incentives, and a lack of transparency and accountability in services. Traditional pricing models, which are largely static and cost-based, are becoming ineffective, failing to capture the true value of care or adapt to market dynamics, resulting in decreased profitability. This paper addresses a value-based approach by exploring the transition from old pricing systems to a value pricing system using machine learning and data-driven strategies. The focus of this work is to investigate three distinct pricing strategies: traditional static pricing, dynamic pricing, and value-based pricing. Based on field and literature surveys, efforts have been made to generate a dataset that builds a predictive framework for each strategy using machine learning models such as XGBoost, Random Forest, and Neural Networks. This analysis moves beyond abstract theory to create quantifiable models that translate clinical outcomes, operational efficiency, and patient-specific factors into concrete price points. The results demonstrate a clear hierarchy of performance: while dynamic pricing offers a significant margin improvement over static models by aligning prices with anticipated costs, value-based pricing emerges as the superior strategy. It not only yields the highest potential margin but also realigns financial incentives with patient outcomes, rewarding high-quality, efficient care. This paper provides a practical roadmap for healthcare organizations to navigate the complex pricing landscape, enhance financial sustainability, and, most importantly, deliver greater value to the customers they serve.