Risk and Responsibility in Real-Time
Identifying and managing clinical risk from frailty is the central focus of Patient Pattern and relies on a calculated Frailty Risk Score (FRS) often discussed in this blog. This week, we present another aspect of healthcare where the identification and management of financial risk for Medicare Advantage (MA) plans is changing, based on a Risk Adjustment Factor (RAF).
A Moment for Some Background
Predicting healthcare costs for a Medicare Advantage (MA) beneficiary is under the direction of the Centers for Medicare and Medicaid Services (CMS) and their Hierarchical Coexisting Conditions (HCC) risk adjustment model. The model assigns a value to each diagnosis, a Risk Adjustment Factor (RAF), that functions as an actuarial tool to predict healthcare costs. Originally, CMS used diagnoses submitted by MA organizations into the CMS’ Risk Adjustment Process System (RAPS) to calculate risk scores for payment. Starting in 2015 they began to use diagnoses from encounter notes and have since continued to use a blend of encounter and RAPS data-based scores – 75% encounter data and 25% RAPS data. This model change will continue through 2022, when the risk score used for payment will be calculated based entirely on encounter data. In addition, CMS intends to discontinue the policy of supplementing diagnoses encounter data with diagnoses from inpatient records.
What Does This Mean For Providers?
#1. Documentation matters more than ever and the accuracy of ICD-10 Diagnoses codes sets the stage for selecting the correct Hierarchical Condition Categories (HCC). The code should match the care provided and the factors impacting risk. Characteristics such as gender, age, socioeconomic status, race impact the calculation of the RAF. Likewise frailty, although not included in the calculation, increases risk and cost of care. Otherwise the plan will not be paid for the care provided.
#2 Predictive analytics suggests cost of care and forecasts trends and future beneficiary care needs. Predictive modeling software using accurate clinical assessment is available offering an analytical review of known data elements to establish a hypothesis related to the future health care needs of a beneficiary. Using claims as predictive analytics limits you to a rear-view mirror strategy, rather than a prospective one.
#3 Providers are not trained in coding, have significant time constraints, and in many environments, have no incentive to code and submit ALL diagnoses codes. Concurrent audits and coding recommendations are effective in correcting these weaknesses.
And Now For the Rest of the Story
At Patient Pattern we have taken great care to address the challenges faced by providers – coding and documentation recommendations in real-time, predictive analytics and coding guidance. Some examples of guidance from the Patient Pattern software follow:
To reduce the burden on providers, healthcare organizations are beginning to adopt coding software to capture all relevant conditions. To see more of the HCC accurate coding and documentation guidance we offer, please visit us at patientpattern.com.