利用深度学习模型和行政理赔数据对异质性患者画像进行医疗成本预测
Healthcare cost prediction for heterogeneous patient profiles using deep learning models with administrative claims data
AI总结：
针对行政理赔数据异质性及高需求患者预测公平性问题，提出按代码与成本分通道的深度学习框架，并用多通道熵评估异质性，显著降低误差、超额支付与支付不足。
摘要：
问题：我们如何设计患者成本预测模型，以有效应对行政理赔（AC）数据中的异质性挑战，从而确保预测准确、公平且可泛化，尤其是针对患有复杂慢性病的高需求（HN）患者？相关性：准确且公平的患者成本预测对于制定健康管理政策和优化资源配置至关重要，这可以为包括政府机构和私人保险公司在内的医疗支付方带来显著的成本节约。解决HN患者预测结果中的差异可确保更好的经济和临床决策，使患者和支付方均受益。方法：本研究基于社会技术考量，强调技术系统（如深度学习模型）与人文结果（如医疗决策中的公平性）之间的相互作用。研究纳入表征学习和熵测量，以解决数据和患者画像中的异质性与复杂性，尤其是针对HN患者。我们提出一种通道式深度学习框架，通过根据代码类型（如诊断、操作）和成本将AC数据分割为独立通道来缓解数据异质性。该方法与灵活的评估设计相结合，后者使用多通道熵测量来评估患者异质性。结果：与单通道模型相比，所提出的通道式模型将预测误差降低23%，使超额支付和支付不足分别减少16.4%和19.3%。值得注意的是，HN患者的预测偏差降低幅度显著更高，证明了其在处理数据和患者画像中的异质性与复杂性方面的有效性。这表明通道式建模具有应用于具有类似异质性挑战的领域的潜力。
英文摘要：
Problem: How can we design patient cost prediction models that effectively address the challenges of heterogeneity in administrative claims (AC) data to ensure accurate, fair, and generalizable predictions, especially for high-need (HN) patients with complex chronic conditions? Relevance: Accurate and equitable patient cost predictions are vital for developing health management policies and optimizing resource allocation, which can lead to significant cost savings for healthcare payers, including government agencies and private insurers. Addressing disparities in prediction outcomes for HN patients ensures better economic and clinical decision-making, benefiting both patients and payers. Methodology: This study is grounded in socio-technical considerations that emphasize the interplay between technical systems (e.g., deep learning models) and humanistic outcomes (e.g., fairness in healthcare decisions). It incorporates representation learning and entropy measurement to address heterogeneity and complexity in data and patient profiles, particularly for HN patients. We propose a channel-wise deep learning framework that mitigates data heterogeneity by segmenting AC data into separate channels based on types of codes (e.g., diagnosis, procedures) and costs. This approach is paired with a flexible evaluation design that uses multi-channel entropy measurement to assess patient heterogeneity. Results: The proposed channel-wise models reduce prediction errors by 23% compared to single-channel models, leading to 16.4% and 19.3% reductions in overpayments and underpayments, respectively. Notably, the reduction in prediction bias is significantly higher for HN patients, demonstrating effectiveness in handling heterogeneity and complexity in data and patient profiles. This demonstrates the potential for applying channel-wise modeling to domains with similar heterogeneity challenges.
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