Risk adjustment is quietly becoming an economic cornerstone in healthcare, and the Hierarchical Condition Category (HCC) is at its core. Because HCCs determine payments, they influence an organization’s economic viability, available resources, and care delivery capacity, which means accurate and complete diagnosis coding is becoming an economic and clinical imperative.
Learn how AI can help to enhance care documentation, improve HCC capture rates, and accurately reflect the profiles of complex patients. Discover how AI can help to identify patients with undocumented-yet-suspected HCCs, surface actionable opportunities to clinical teams, estimate economic impacts, and use previous HCC documentation decisions to automatically improve.
Data extracted from EHR clinical notes for conditions being diagnosed, monitored, or treated about important clinical concepts related to symptoms, test results, diagnoses and treatments.
Data from electronic prescriptions detailing key information about medications, dosage, patient instructions for frequency and timing, and available refills.
Data extracted from health insurance medical claims with details about dates and place of service, diagnosis codes, key procedures, use of medical equipment, and provider specialties.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
Risk adjustment is quietly becoming an economic cornerstone in healthcare. It determines payments to Medicare Advantage plans—accounting for more than 24 million Medicare beneficiaries—and is increasingly implemented in value-based contracts.¹ At risk adjustment’s core is the Hierarchical Condition Category (HCC) model, which is based on diagnosis codes captured during clinical encounters. Because HCCs determine payments, they influence an organization’s economic viability, available resources, and care delivery capacity, which means accurate and complete diagnosis coding is becoming an economic and clinical imperative.
Despite this growing importance, the diagnostic codes that are foundational to the HCC model can be inaccurate or incomplete, particularly for new patients. Inaccurate and incomplete coding not only reduces payments for healthcare organizations, it impacts the resources available for addressing the full spectrum of patient needs, especially for complex patients.
ClosedLoop’s Suspect HCC models identify patients with undocumented-yet-suspected HCCs and surface the contributing factors that best explain why each HCC is suspected. This insight enables organizations to accurately and completely reflect their complex patient profiles, identify and prioritize actionable opportunities for clinical teams, and produce expected RAF scores and economic impact. Moreover, ClosedLoop’s models assess documentation determinations to automatically improve overall accuracy, continuously refining diagnosis identification while minimizing “false positive” suspects. This allows organizations to remain agile and adapt as CMS continues to modify their risk adjustment processes and reimbursement models.
1. Freed, Meredith, et al. “A Dozen Facts about Medicare Advantage in 2020.” Kaiser Family Foundation, 13 Jan. 2021, www.kff.org/medicare/issue-brief/a-dozen-facts-about-medicare-advantage-in-2020/#:~:text=1.,doubled%20over%20the%20past%20decade&text=In%202020%2C%20nearly%20four%20in,time%20since%20the%20early%202000s. Accessed 11 Mar. 2021.
2. Smith D. “Case Study: Hierarchical condition categories - Get documentation and coding right - 3M Inside Angle.” 3M Inside Angle. Published 2017. Accessed March 25, 2021. https://www.3mhisinsideangle.com/klab-post/case-study-hierarchical-condition-categories-get-documentation-coding-right/