Driving Risk Adjustment Accuracy Through HCC Surveillance Automation

Nachiketh Gudipudi ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 20, 44 - 53

Abstract: The healthcare industry's shift to value-based care has elevated the importance of accurate Hierarchical Condition Category (HCC) risk adjustment as a critical revenue driver for organizations participating in Medicare Advantage and ACA marketplace plans. This article examines how automated HCC surveillance systems transform traditional reactive coding approaches into proactive documentation strategies by integrating advanced analytics, machine learning algorithms, and workflow optimization. The article explores the comprehensive framework required for successful implementation, including data integration, algorithm validation, workflow integration, provider engagement, and governance structures. The article analyzes recent studies and documents significant improvements across multiple performance domains, including financial outcomes, operational efficiencies, quality metrics, and compliance risk reduction. The article demonstrates that healthcare organizations implementing these automated surveillance systems experience substantial benefits, including increased RAF scores, reduced documentation gaps, improved forecast accuracy, decreased provider administrative burden, enhanced quality measure performance, and strengthened compliance posture. As value-based payment models continue to expand, these systems represent an essential infrastructure component for healthcare organizations seeking to optimize performance under risk-based contracts while improving care quality and reducing organizational risk.

Keywords: Risk Adjustment Automation; Hierarchical Condition Categories; Predictive Analytics; Natural Language Processing; Value-Based Care Reimbursement (search for similar items in EconPapers)
Date: 2025
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