Treffer: 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. Accurate and fair patient cost predictions, which can lead to healthcare payer cost savings, are essential to support effective decision making regarding health management policies and resource allocations. Patient cost prediction models utilize administrative claims (AC) data collected from multiple healthcare providers, which payers (e.g., government agencies and private insurance companies) rely on for various reimbursement purposes. Both the variety of patient clinical profiles and the multisource nature of the big data from ACs introduce heterogeneity, which undermines both the generalization power and the algorithmic fairness of cost prediction models. In particular, the prediction performance and economic outcomes—such as both underpayments and overpayments—of these models for high-need (HN) patients with multiple and complex chronic conditions differ from those of healthy patients, as their underlying heterogeneous medical profiles are distinct. This study, grounded in sociotechnical considerations for patient cost prediction, presents two key design insights. First, we designed a channel-wise deep learning framework to reduce AC data heterogeneity through effective representation learning, with a separate channel for each type of code as well as each type of cost. Second, we incorporated humanistic outcomes and a multichannel entropy measurement into a flexible evaluation design for patient heterogeneity. We evaluate the effectiveness of the proposed channel-wise framework both internally and externally using two real-world data sets containing approximately 111,000 and 134,000 individuals, respectively. On average, channel-wise models substantially reduce prediction errors by 23% compared with the most competitive single-channel counterparts, leading to respective reductions of 16.4% and 19.3% in overpayments and underpayments for patients. The reduction in bias for predictions involving HN patients is more significant than for other patient groups. Our findings offer important implications for decision makers in healthcare and other fields facing similar sociotechnical challenges related to the interplay between diverse population behaviors and data heterogeneity. [ABSTRACT FROM AUTHOR]

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