An HCC coding gap is any instance where a chronic condition or diagnosis has been coded and recorded in the previous year but lapses or is 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.
There are several ways for healthcare providers to ensure they maintain a consistent and high level of HCC coding recaptures without meaningful gaps, from using an advanced HCC coding app or software solution to revisiting their documentation procedures to mitigate exposure to error.
Professional HCC coders can also identify and intercept issues and omissions before they have an impact, ensuring that the calculated risk score documented by Medicare or a relevant payer is accurate and aligns with anticipated patient care expenditures.
Like any sector or area requiring high levels of specificity and accuracy, there are multiple possible contributing factors that mean a coding gap appears.
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 the fact that their HCC code should be 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 and can be addressed with proper documentation procedures. Where healthcare providers create complete, precise records with a full log of all procedures, conditions, complications, comorbidities, diagnoses, and interventions, there is less potential for an HCC code to be missed or misreported.
Other resolutions include using professional coders or coding software that is specifically designed to extract all relevant data from any healthcare provider and all patient documentation.
References:
https://www.aafp.org/pubs/fpm/blogs/inpractice/entry/risk_adjust_tips.html
https://www.sciencedirect.com/science/article/abs/pii/S1067502707001041
