Medicare Advantage plans use Risk Adjustment Factor (RAF) scores to determine patient care expenses. The RAF is therefore a benchmark that enables the Centers for Medicare & Medicaid Services (CMS) to estimate future spending. With higher RAF scores, the amount Medicare pays Medicare Advantage plans for patient care also increases. Ultimately the goal is to improve patient outcomes with reduced costs. Risk adjustment can also affect physician income. More recently, Medicare is using the HCC model to allocate costs for patients in Medicare Accountable Care Organizations.
A patient’s RAF is determined by several criteria, including Hierarchical Condition Categories (HCC). These are derived from the retrospective analysis of billed diagnoses codes (ICD10 codes). Deriving RAF for HCCs is relatively straightforward. But it is not enough to just add HCC codes to the patient’s encounter. Physicians must also provide a plan of care to support the listed HCC code each year. To stay compliant, the MEAT strategy can be followed to ensure that each HCC diagnosis has been assessed. MEAT is an acronym for monitor, evaluate, assess, or treat. If each diagnosis has one of the below documented, then the addition of that diagnosis is justified.
Monitor – signs, and symptoms of the diagnoses
Evaluate – test results, assessing response to treatment
Assess – documenting discussions about the disease, counseling
Treat – prescribing medications, therapies
Medicare audits are conducted to verify the addition of all potential HCC codes with their respective plan of care. Providing specific and complete documentation of all diagnoses on an annual basis can be challenging for many practices, especially in those with a less robust administrative infrastructure. Using technology, especially ones that can integrate with EHRs can help providers uncover gaps and document more proficiently. Effective technology can help physicians and coders identify HCC codes with greater accuracy. When using such technology, it is crucial that all efforts are made to capture documented HCC codes accurately, rather than merely listing out every code, including those without a plan of care. The goal must be accuracy rather than increasing the RAF score. To be effective, the technology used for these applications must provide context to justify the recommendation of an HCC code and prompt providers with confirmatory data. It is ideal to allow providers to make the final decision.
Our applications utilize natural language processing, as well as structured data analysis to capture and validate HCC codes from clinical encounters. Both our HCC Assistant and HCC Validator clearly provide users with data to help them verify suggested HCC codes and their validation. One of the more common omissions is the failure to capture appropriate HCC codes at least once every 12 months. These gaps in coding are easily captured by Infera’s claims analysis tool. Another feature of the claims analysis tool is the suggestion of HCC codes from claims files that were documented by other providers such as specialists, hospitals, home health agencies, etc.
Remember that accurate HCC coding technology can capture your patients’ condition correctly and help predict their future health expenditures. Click here to learn more about Inferscience’s HCC Assistant and HCC Validator.
