Surmounting Gradient Degradation in IA-Driven Recurrent Clinical NLP: A Scalable Medical Cluster Blueprint for Secure Healthcare Documentation

The clinical utility of Natural Language Processing (NLP) is fundamentally constrained by the mathematical instability of sequence modeling when applied to long-form medical narratives. In traditional Recurrent Neural Networks (RNNs), the recursive multiplication of Jacobian matrices during backpropagation leads to the vanishing gradient problem, effectively causing the decay of critical early-sequence clinical data. To address this, we propose the ''Medical Cluster,'' a novel architectural framework deployed on Google Cloud Platform (GCP). This system surmounts recurrent limitations by integrating a dual-layer correction strategy: first, an algorithmic transition to residual signal propagation and self-attention mechanisms to stabilize gradient flow; and second, a high-concurrency orchestration layer developed in Go. This orchestration facilitates a structural ''Human-in-the-Loop'' (HITL) correction mechanism, allowing for the real-time re-injection of ground truth to mitigate residual gradient-induced inaccuracies. Empirical evaluations demonstrate that this approach reduces processing latency by 85.6% while ensuring 100% FHIR compliance. Most notably, the stabilization of the gradient signal directly correlates with a significant improvement in clinical ''Objective'' documentation, rising from a baseline Likert score of 3.8 to 4.7.