Accuracy, Data and Integration – Success in Risk Adjustment & Quality
Kathy Ormsby, CPCO, CPC
qrcAnalytics Vice President of Compliance and Analytics
The Center for Medicare and Medicaid Services (CMS) uses a Hierarchical Condition Category (HCC) risk adjustment model to calculate risk scores for Medicare Advantage plan members. In addition, HCCs are used in adjusting the financial benchmark used in Medicare’s Accountable Care Organizations (ACOs). With the evolution of the Patient Protection and Affordable Care Act (ACA) the Department of Health and Human Services (HHS) developed an even broader HCC methodology for commercial payers known as the HHS-HCC model. All this activity indicates that HCCs are not going away any time soon.
HCCs are a methodology used to predict cost for treating patients in inpatient and outpatient settings. Patient demographics and diagnoses codes are used to determine how much money is allocated in a risk adjustment model (i.e. Medicare Advantage) to healthcare organizations for patient treatments. Diagnosis codes are grouped into HCC categories based on similar conditions and costs. Each HCC is weighted, reflective of predicted and current costs, and used in the calculation of risk adjustment scores for patients. CMS uses specific encounter and claims data from specific providers when assigning the risk adjustment scores and validates that the data received is accurate using the RADV (Risk Adjustment Data Validation) process. *
Increase coding accuracy
Accurate HCC risk adjustment can improve revenue and increase accuracy across the patient population. However, the accurate capture of data supporting HCCs is challenging. The first step in increasing accuracy is to ensure that all providers are trained and educated on proper HCC coding. The process starts with a base line audit of encounters that identifies training opportunities followed with periodic (e.g. quarterly) assessments/audits to ensure that the training has had a positive impact. Healthcare organizations should utilize an actionable data platform providing dashboards and reports that will highlight in real time any potential coding errors or omissions thereby supporting their HCC risk adjustment programs; such a platform will also automate the periodic assessment/audit of coding accuracy. Monitoring provider performance and taking preventative action will increase revenue and at the same time ensure that government guidelines and rules are properly followed. *
Utilize an actionable data platform
Claims are the center of risk adjustment. However, they were implemented primarily for billing purposes and lack the medical detail found in clinical data. When reviewing claims data alongside the electronic health record, the results are more complete and timelier. Using analytics, organizations can expose HCC diagnoses that have not been submitted for a retrospective submission resulting in an increase in revenue and support compliance. Doing this enables organizations to target specific encounters for review or audit which will ultimately provide improved accuracy and help target specific questionable diagnoses for a more effective result.
Using an actionable data platform allows organizations to:
Know patient and provider mix
Identify gaps in care
Identify providers that are over/under expending resources
Use scheduling data to improve efficiency
Identify HCCs that have not been submitted
Identify clinical and coding opportunities
Ensure quality measures are met
Know hidden costs
Integrate risk adjustment efforts with quality initiatives
A risk adjustment strategy that combines quality measures and the risk adjustment processes can help streamline efforts and avoid redundant actions. Combining risk adjustment processes with quality initiatives ensures better coordination, more accurate HCC capture, reduction in patient visits, improvement in patient care and ultimately better patient satisfaction scores. It’s a win-win.
Healthcare organizations that apply robust and appropriate advanced analytics significantly improve performance, increase revenue and succeed in the RADV process.