UM Decision Automation Using PEGA and Machine Learning for Preauthorization Claims

Abstract

Utilization Management (UM) ensures that patients in the modern healthcare scene receive appropriate, reasonably priced, medically necessary treatment. Usually depending on hand assessment by managers and clinical staff, the preauthorization claim review process in UM is one of the most resource-intensive activities. Since it frequently results in administrative loads, delays, and blunders, this affects not just doctors but also patients. This case study investigates a novel approach to employing PEGA's intelligent process automation coupled with machine learning (ML) to improve the accuracy of UM decision-making for preauthorization claims. Healthcare companies may automate repetitive decision-making processes, find anomalies, and speed turnaround times by using Pega's dynamic case management tools and machine learning prediction powers, therefore guaranteeing compliance and preservation of medical integrity. Combining robotic process automation (RPA) for rule-based processes with machine learning models developed on past claims data helps to evaluate approval likelihood and enable real-time decision-making. This cooperative approach guarantees accuracy and speed, therefore enabling human assessors to focus on particular situations requiring expert evaluation. Among the most striking outcomes of the case study are more audit trail openness, a 45% improvement in decision turnaround times, and a 60% reduction of manual touchpoints. It also reveals how better patient outcomes could be matched by the operational efficiency of automation. This paper presents a persuasive structure for healthcare companies aiming at improving their operations by means of scalable and intelligent automation.

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