Streamlining Prior-Authorization Workflows with Advanced Analytics to Reduce Administrative Burden and Accelerate Revenue Cycles

Abstract

This article provides an integrated analytical and computational architecture designed to streamline prior-authorization workflows in complex healthcare revenue cycle operations. By modeling the authorization process as a multi-stage stochastic network and applying advanced queuing theory, we derive performance metrics that quantify administrative burden and cycle time. We introduce a stochastic optimization formulation to minimize expected wait times and manual review costs, subject to capacity constraints across multiple review stages. Predictive analytics based on regularized logistic regression and deep neural network architectures forecast authorization outcomes and dynamically allocate review resources, thereby prioritizing high-impact cases and managing system variability. We employ heavy-traffic diffusion approximations to obtain tractable expressions for mean sojourn times and derive first-order conditions for optimal resource allocation under weighted throughput objectives. Computational experiments on large-scale simulated and real-world-inspired datasets demonstrate that the proposed framework reduces average authorization cycle times by over forty percent and decreases manual intervention by nearly sixty percent, enabling a significant acceleration of revenue realization. Scalability analysis shows that the end-to-end decision support system operates within sub-250 millisecond latency on commodity servers for organizations processing thousands of requests per day. The results establish a blueprint for next-generation intelligent prior-authorization systems capable of continuous learning and adaptive control, ultimately reducing administrative overhead and enhancing financial stability across diverse healthcare settings.