A hierarchical condition category (HCC) model is a risk adjustment system designed to help healthcare facilities and information technicians estimate the cost of treatment for patients. Currently, this model helps healthcare data professionals predict the cost of treating a definite classification of patients.
Many popular healthcare facilities and medicare plans use this model. In HCC modeling, the risk equals the level of projected healthcare spending.
If you run a healthcare facility or work at a medical facility as a data technician, you need to be familiar with the workings of the HCC risk adjustment model. In this article, we’ll explore the important aspects of the HCC model, including what its primary uses are.
As mentioned above, an HCC model is a risk adjustment system used by data technicians in the healthcare industry to calculate and estimate risk scores concurrently. In short, this model uses diagnosis data from a specified period to estimate the cost of treating a certain group of patients.
The HCC model uses the ICD-10-CM coding technology to allocate risk scores to different patients. It’s mapped to the ICD-10-CM code, which is a clinical modification code that codes and classifies diagnostic data.
With the diagnostic data and other demographic factors, like gender and age, you can use the HCC model to assign your patients an accurate risk adjustment factor (RAF) score. Therefore, your preferred data extraction tools and processes will determine the overall success of your HCC risk adjustment. The HCC risk adjustment model has two risk sections: the institutional model and the community model.
There are two risk adjustment models:
Although both models described above serve the same purpose and have the same structure, they have distinctive characteristics that you must be aware of. Their characteristics are aligned with the patient populations they’re designed for.
For instance, the CMS-HCC model is designed mainly for Medicare Advantage reimbursements (Part C). The HHS-HCC model, on the other hand, is designed for commercial payer-managed care plans like the Health Exchange plans supported by the Affordable Care Act (ACA).
The CMS-HCC risk adjustment model is meant for patients who are sixty-five years old and above, as well as disabled patients. The HHS-HCC model is meant for patients of all ages. Therefore, it’s safe to say that the CMS-HCC model works best for patients who have lived or are currently living in nursing homes and other assisted living facilities.
However, the CMS-HCC risk adjustment model is split into three risk categories:
Both HCC models use the same risk-adjusted characteristics like age, demographics, gender, and medical conditions. However, the CMS-HCC model uses institutional status while the HHS-HCC model uses financial status to predict healthcare spending.
In the CMS-HCC model, data capture is included in regular Medicare processes while the HHS-HCC model needs more data capture to get clear demographics. While the CMS-HCC model only estimates future healthcare costs, the HHS-HCC model predicts future healthcare and drug costs.
The CMS-HCC model is prospective while the HHS-HCC model is concurrent. A prospective model uses diagnostic data from a base year to estimate the healthcare spending for the following year.
A concurrent model, on the other hand, uses information from the current benefit year to estimate the healthcare spending for the same year. Furthermore, the HSS-HCC model includes three important plans: an adult plan for people aged twenty-one years and above, a child plan for people aged between two and twenty years, and an infant plan for ages newborn to twelve months.
The CMS-HCC model offers a special plan for patients with special needs like severe or incapacitating chronic conditions. This model also offers frailty adjustment to estimate costs for frail, elderly patients residing in the community.
The contributing elements in the HHS-HCC model differ by age. For instance, the child plan doesn’t consider the severity of the disease, and the infant plan defines its categories by birth maturity.
As noted above, the HCC model uses two main sources of data to determine the RAF of a patient: demographic attributes and health status (diagnostic data). While the demographic attributes are easy to obtain and validate, the process of collecting and validating a patient’s health status is quite complex.
The HCC model is essential to ensure your organization receives adequate Medicare funding and can deliver best-in-class patient care. But how can you avoid common pitfalls such as coding errors and data gaps?
The answer is simple: using the right technology to streamline, simplify, and improve your risk adjustment workflow. Inferscience has helped countless organizations achieve just that, with our partners increasing their RAF scores by an average of 35%.
Want to book a consultation to explore how you could improve your HCC coding?
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