Healthcare Cost Prediction for Heterogeneous Patient Profiles Using Deep Learning Models with Administrative Claims Data

Accurate and equitable patient cost prediction is essential for informing health management policies and optimizing resource allocation, directly impacting government agencies, private insurers, and healthcare providers. This study highlights the importance of addressing disparities in prediction outcomes, particularly for high-need patients with complex chronic conditions, to ensure more effective economic and clinical decision making. By introducing a novel deep learning framework that segments administrative claims data into multiple channels, this research enhances both predictive accuracy and fairness, reducing overpayments and underpayments while mitigating bias in cost estimation. The findings underscore the potential of channel-wise modeling to support fair reimbursement structures, improve budget planning, and foster policies that better accommodate the diverse needs of patient populations. Policymakers and healthcare organizations can leverage these insights to design more efficient risk adjustment strategies, ensuring that vulnerable patients receive appropriate care without financial inefficiencies. The study provides a roadmap for integrating advanced machine learning approaches into healthcare decision making, promoting a more just and sustainable system.