Healthcare cost prediction for heterogeneous patient profiles using deep learning models with administrative claims data

TL;DR

This paper introduces a deep learning framework that segments administrative claims data into channels to improve cost prediction accuracy for heterogeneous and high-need patient groups, reducing errors and biases.

Contribution

It proposes a novel channel-wise deep learning approach combined with entropy measurement to address data heterogeneity in healthcare cost prediction models.

Findings

Channel-wise models reduce prediction errors by 23%.

Significant reductions in overpayment (16.4%) and underpayment (19.3%).

Enhanced bias reduction for high-need patients.

Abstract

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.