Applying the Deep Learning–Sector–Governance (DLSG) Framework to the U.S. Healthcare System: Opportunities, Deployment Pathways, and Policy‑Aligned Evaluation
Main article
Abstract
The United States healthcare system faces persistent challenges in cost, access, quality, and administrative burden. Although deep learning (DL) has achieved strong performance in prediction and representation learning, real-world impact remains constrained by data heterogeneity, workflow integration, and regulatory and governance requirements. This paper adapts the Deep Learning–Sector–Governance (DLSG) framework to the U.S. healthcare context, positioning it as a deployment blueprint that aligns model design, sector workflows, and governance controls. We examine applications spanning clinical decision support, imaging triage, operations, administrative automation, and payment integrity, and outline evaluation strategies that prioritize safety, calibration, and policy-aligned outcomes over standalone accuracy metrics. Given the scale of documented improper payments and administrative burden in U.S. healthcare, governance-aligned efficiency and integrity gains could plausibly translate into about $30–60 billion in annual system-level savings, depending on adoption depth and sustained operational alignment. We argue that the primary contribution of DLSG is to improve in predictive accuracy and to increase the likelihood that algorithmic advances translate into measurable improvements in patient outcomes and system efficiency under real regulatory and operational constraints. In addition, this framework is in alignment with national health objectives such as those articulated in Healthy People 2030.
