Many healthcare providers struggle with underperforming RAF scores – and most can be attributed to HCC coding errors and gaps in risk-adjustment workflows.
Eliminating HCC coding gaps must be a priority for providers that want to protect and properly care for patients as well as calculate accurate RAF scores – and this article explains exactly how they can do that.
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Hierarchical Condition Category (HCC) codes are sets of medical codes linked to clinical diagnoses. They are a key input for the risk-adjustment model that identifies individuals that suffer from serious or chronic conditions and are used by Medicare Advantage to predict a patient’s likely cost of care.
An HCC coding gap is any instance where a previously reported chronic condition or diagnosis has been missed in the current period. In most cases, gaps in HCC coding for providers are caused due to underlying inefficiencies rather than the condition or diagnosis having been resolved.
Our experience at Inferscience shows the average provider increases their calculated risk score (RAF) by 35% when these errors are fixed through an advanced HCC coding app or software solution meaning those that don’t are missing out on key funding that is required to manage their patients’ care.
Like any sector or area requiring high levels of specificity and accuracy, there are multiple possible contributing factors that mean a coding gap appears. Here are four of the most common ones:
A diagnosis could remain undocumented because the information has been recorded at a different healthcare facility or medical center–meaning that care providers reliant only on internal medical records to provide HCC coding data may miss these details.
Scalable, advanced HCC coding software resolves this issue. It prevents data from being siloed in one place, aggregating all patient care records from any provider to ensure all the relevant information is identified and logged.
The standard is to record HCC codes to the greatest depth of detail possible. Still, errors can occur where some coding systems have a large volume of potential diagnoses where it is possible to use a code that remains relevant but isn’t as specific to the diagnosis as another.
ICD-10-CM is a good example, referenced by the American Academy of Family Physicians (AAFP). Both E11 and E11.22 might be considered appropriate for a patient diagnosed with diabetes type 2 alongside proteinuria or albuminuria. While E11 would record a diagnosis of diabetes, it would indicate that the patient has no complications.
E11.22 is more precise and would record a diagnosis of diabetes but with chronic kidney disease, with an average reimbursement rate that is more than three times higher due to the greater anticipated cost of providing effective patient care.
Healthcare providers must recapture chronic conditions annually, ensuring that ongoing and long-term diagnoses and diseases are always included in risk adjustment assessments. In some cases, such as where a patient has been diagnosed with an ongoing condition that is in remission or currently under control, it remains essential to recapture the diagnosis to maintain a full picture of the patient’s health.
These mistakes can be caused by administrative errors where a patient without an appointment within the following year is missed, despite their HCC code being recaptured to account for the likelihood of further interventions.
Other HCC coding gaps arise because practitioners use different systems, technologies, or terminologies, often because these are more widely used in clinical settings.
SNOMED CT codes used as multilingual clinical terminology are one illustration, as analyzed by the Journal of the American Medical Informatics Association. Healthcare organizers can use professional coders or coding technology to extract these diagnoses or data from patient records and match them to the appropriate HCC code.
The above examples are just some of the scenarios that could result in coding gaps or low recapture rates. Fortunately, they can all be resolved with the right technology.
HCC coding software helps remove human error, empower physicians to focus on patient care, and avoid HCC coding errors. But most providers cannot afford to custom-build their own solution – which is why so many come to Inferscience.
Our HCC Assistant tool uses NLP to unify and analyze all relevant clinical data from a range of EHRs and workflows. This allows it to make real-time HCC coding recommendations that your providers can inspect and accept in minutes.
The net result? All possible diagnoses are included in your HCC coding and the average provider increases their RAF score 35%.
Want to see it in action? Book a Demo
