Enhancing Healthcare Pricing Transparency: A Machine Learning and AI-Driven Approach to Pricing Strategies and Analytics
DOI:
https://doi.org/10.32628/CSEIT2410612436Keywords:
Artificial Intelligence, Machine Learning, Healthcare Data Analytics, Healthcare pricing transparency, Federated learning, Cost prediction, Deep learningAbstract
This paper introduces an advanced framework for healthcare pricing transparency by leveraging cutting-edge artificial intelligence (AI), machine learning (ML), and robust cloud computing infrastructure. The proposed model integrates diverse datasets, including historical claims, provider costs, and patient demographics, to enable precise cost prediction, reduce billing disparities by 70%, and improve administrative efficiency by 47%. A combination of XGBoost and ARIMA models achieved 92% prediction accuracy, supported by federated learning for privacy-preserving analytics and real-time predictive modeling. The framework empowers stakeholders with actionable insights, fosters trust across the healthcare ecosystem, and establishes a scalable, regulation-compliant solution for addressing the challenges of pricing opacity in the U.S. healthcare system.
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