Clinical Data Enhances Risk Score Accuracy for Health Plans

Risk adjustment is a prime example where data quality is vital in meeting Federal requirements and deciding accurate payment models for reimbursement. In the Medicare Advantage (MA) program, the Centers for Medicare & Medicaid Services (CMS) disburses monthly payments to MA organizations based on a risk adjustment framework that considers the health condition of each beneficiary.

CMS relies on MA organizations to gather and submit diagnosis codes from their healthcare providers in order to evaluate the health status of the MA organization’s membership and establish accurate payment amounts. MA organizations that provide benefits to beneficiaries with greater healthcare needs receive higher payments, while healthier enrollees who require fewer healthcare services result in lower payments.

If MA organizations submit inaccurate and/or miscoded diagnosis codes that lead to overpayments from CMS, they may be obligated to reimburse the excess funds back to CMS. One health plan experienced this scenario when the Office of Inspector General (OIG) for the U.S. Department of Health and Human Services (HHS) conducted an audit specifically focusing on diagnosis codes. You can read the full report here.

How Data Quality Issues Could Impact Risk Score Accuracy

The accuracy of risk assessment can be influenced by data quality issues. The complexity of clinical data, including variations in source documentation, diverse terminologies, and fragmented data storage across the healthcare system, can contribute to challenges. These issues can lead to inaccuracies in coding and documentation, potentially affecting the calculation of risk scores. Inaccurate risk scores may result in incorrect assessments of patient health conditions and can impact reimbursement calculations and the effectiveness of risk-based programs. Therefore, ensuring high data quality is essential for accurate risk score calculation and reliable healthcare decision-making.

How can Availity help maximize the ROI of clinical data?

The strategic use of clinical data for risk adjustment requires the ability to transform raw clinical data into an asset that is standard, organized, and actionable.

Availity Fusion™, our API-based technology, integrates clinical data from multiple sources and formats into a data asset fit for myriad use cases. Compared to raw source data, health plans have realized a 30% increase in standard, interoperable diagnosis codes with Availity Fusion’s Upcycled Data™. More standard diagnosis codes translate into more complete disease reporting and more accurate risk scores.

For example, in a real-world study with a national health plan customer, Availity Fusion technology increased the capture of the diabetic population by up to 52% by normalizing diagnoses to SNOMED and ICD10. Looking across all diseases and in multiple health plan studies, Upcycled Data has the potential to increase Risk Adjustment Factor (RAF) scores by 0.234-0.337 on average across a plan’s membership. When applied across millions of members within a health plan, this increase has significant revenue implications and the potential to transform how and when members receive care.  

To learn more about how quality data leads to accurate risk scores and how to cost-justify an investment in clinical data, download our white paper, The Strategic Role of Clinical Data in Risk Adjustment.