What is HCC coding?
At its most basic, hierarchical condition category (HCC) coding is a risk-adjustment prediction model, but, ultimately, it's so much more. HCC, as it's colloquially known, was designed to estimate and possibly predict a patient's healthcare costs during the life of the patient. It takes a long view of multiple conditions, factors, and determinants that may impact their individual prognosis over many months or years.
The Centers initiated the HCC code set for Medicare and Medicaid Services (CMS) in 2004. While coming up on its 20th anniversary, HCC coding is increasingly prevalent as healthcare shifts to value-based payment models – a change actively pursued the last decade.
HCC codes tie directly to ICD-10 codes – about 10,000 ICD-10 diagnosis codes out of the 70,000 diagnoses relate directly to at least one of the 86 HCCs. HCC coefficients change based on the patient category. Patients are assigned risk scores through the HCC set that map to demographic factors, such as age and gender.
HCC coding also allows for a risk adjustment factor (RAF) score assigned to individual patients. While RAFs are complex and somewhat mind-bending to navigate (and ripe for a separate blog post), in a nutshell, with risk-adjusted payments, Medicare pays less for healthy patients and more for unhealthy patients. Thus, insurance plans cannot profit by enrolling only healthy patients and dropping unhealthy patients -- insurers earn profits by delivering better care or delivering less-expensive care.
RAF scores, thus, are designed to possibly predict a patient’s healthcare costs. Patients with fewer health conditions likely have lower than average medical expenses. Likewise, a patient with multiple chronic conditions likely will have higher utilization and care costs over the long term.
Why HCC coding is important
Hierarchical condition category coding is crafted as a measure of determining patient care and long-term health complexity while also “painting a picture” of the whole patient. Painting a complete patient health picture requires more than codes and technology but expertise and analysis. For example, care professionals should be encouraged to review the entire patient record, examining any potential social determinants of health (SDoH) to predict possible outcomes that may affect the value of the care provided (as in value-based care).
HCCs use data collected from the patient encounter as notated and coded to estimate predicted costs for individuals over a period of time -- in insurance, this might be the next year or more of coverage. These estimates are based on the previous 12 months.1
HCC's RAF scores also can determine the risk-adjusted quality and cost metrics by accounting for differences in individual patient complexity, quality, cost performance, and demographic information, including age and medical conditions documented through patient encounters, also are tracked.
Ultimately, providers who fail to capture relevant patient conditions specifically may encounter lower Medicare reimbursements as HCCs leverage changes to Medicare capitation payments to Medicare Advantage health plans. These payments are based on the anticipated risk of enrollees with chronic conditions, which are calculated annually from clinically specified ICD-10 codes. Failing to completely capture the patient’s relevant condition and care through proper coding likely means substantial lost revenue opportunities for each patient whose care is not fully documented.
CMS, through its Quality Payment Program, says that HCCs determine payment for professional services. These include complex patient bonuses under the Merit-based Incentive Payment System (MIPS) and risk adjustments to the measures in the cost component of MIPS.
How to process Hierarchical Condition Category coding
ICD-10 mandates more detail and specificity regarding a patient’s individual care and conditions. For example, caregivers must dive more deeply into whether a cytomegaloviral disease is a pneumonitis, hepatitis, pancreatitis, or another form of the disease than in previous iterations of code sets. Similarly, and essential to HCCs, are chronic conditions and their management, such as alcohol dependence (or its remission), amputations, and artificial openings. As each of these examples can serve as quality predictors for future healthcare needs, they apply to HCCs and predicting long-term patient well-being.
These and other risk factors are used to determine the pay scale of an individual patient. The more chronic conditions a patient suffers, the higher the possible risk to the patient's health -- which can impact the value of the care provided.
As stated, CMS uses HCCs to risk-adjust the payments it makes through Medicare Advantage plans. Medicare Advantage plans receive a capitated (set fee per patient) amount from CMS. Patients that are healthier than average have an HCC score below 1.000, and those that are less healthy have a score above 1.000.2
In the following scenario, presented during our RISE National 2021 Round Table, looks can be deceiving. A closer look at HCC-tied codes shows a better, more detailed view of the complete picture.Patient A) 78-year-old female
- Chronic Kidney Disease IV
- Atrial fibrillation on Coumadin
- Rheumatoid Arthritis
- No hospitalizations this year
- Takes 10 medications
- Does not smoke or drink alcohol
Sum risk: 1.429
Patient B) 80-year-old male
- Diabetes Mellitus
- Hospitalized last year for pneumonia
- Takes 15 medications
- Former Smoker
Sum risk: 1.244
In this example, when reviewing the above patient scenarios and examining the documentation provided, it’s easier to evaluate which patient is less healthy. At first glance, patient B appears sicker; however, on a cursory review, it's easy to miss that the 80-year-old had unspecified conditions, ignoring the necessary specificity to assign the risk correctly. The difference in the specificity was 1.429 compared to 1.244.
In a recent ECLAT poll, about 65% of coding professionals we asked missed identifying the missed opportunity for HCC specificity in the scenarios. This is a small example of what may signal coding gaps that could be costing revenue.
One significant HCC coding factor is that patient condition documentation must be based on face-to-face encounters with a healthcare provider. Patients also must be monitored, evaluated, assessed, and treated during that encounter3. Because of this, most risk-adjusted HCCs coding is documented from outpatient office visits -- little comes from inpatient encounters.
Tips for HCC success
Many providers directly assign codes at the end of each encounter without the benefit of specific ICD-10 coding education. Some are unfamiliar with coding resources, guidelines, or encoders. Another area of concern is when working with coders – the more experienced and credentialed coders are, the more likely the opportunity for better outcomes (this is the case based on our experiences). Experience is a significant factor for success. Above all else, HCC requires a strong foundation in ICD-10.
Scrutiny also is significant for the successful processing and navigation of your HCC documentation, coding, and oversight. These factors are critical for maintaining compliance, accurate quality measures, and financial integrity. To mitigate any risks, organizations should continue assessing areas for improvement to educate, monitor, and evaluate outpatient documentation and coding quality as they pertain to HCCs.
Technology may help in many facets of the process, allowing for improving the capture of documentation with computer-assisted physician documentation, automating the capture of codes with computer-assisted coding, and simplifying clarification and follow-up with computer-assisted clinical documentation improvement.
However, it's important to note that these technologies do not replace provider, coder, CDI specialist input, and the wisdom, insight, and experience gained from human experience. There is no better resource for leading the processing of all of a health system's claims than an organization that successfully processes thousands of them for the nation's leading health systems. At ECLAT Health Solutions, we have partnered with dozens of other health systems and provider practices, processing and optimizing third-party audit claims over the last decade or more.
Sometimes a bit of outside assistance, guidance, and added resources are just what the doctor ordered.
When to call for help!
Medical coding plays a crucial role in healthcare's revenue cycle -- your coding must be accurate, secure, and efficient to ensure solid and healthy revenue and compliance.
HCC, unlike other code sets, requires additional precision. ECLAT’s proprietary 3-Tier Quality Assurance process identifies and fixes any coding and compliance errors before it’s too late. Our process is designed to provide clients the peace of mind they deserve and to guarantee that our coding is always accurate.
To meet your practice’s or health system's needs -- and provide you the utmost value -- our expert coders are trained vigorously in processing HCCs. ECLAT coders receive ongoing continued education and training in the latest coding practices and methodologies -- delivering 95% accuracy or greater in all of our submitted claims.
When processing HCC codes, ECLAT assigns appropriate values to the corresponding diagnosis code based on the clinical documentation. We adhere to official coding rules and CMS guidelines for risk adjustment reporting. We also conduct chart reviews, and medical records audit to detect HCC coding inaccuracies or missing diagnoses affecting the RAF score.
ECLAT Health Solutions provides outsourced medical coding solutions for hospitals, physician offices, and other healthcare providers. Our hierarchical condition category (HCC) Coding services are available nationwide, and we offer a 24-hour turnaround time.
2"An Introduction to Hierarchical Condition Categories (HCC)," American Society of Anesthesiologists, https://www.asahq.org/quality-and-practice-management/managing-your-practice/timely-topics-in-payment-and-practice-management/an-introduction-to-hierarchical-condition-categories-hcc