Since its establishment in 1965, Medicare has been helping senior citizens access quality healthcare. If your healthcare facility works with Medicare beneficiaries, you have to continuously predict the future cost of treating them based on their current and previous health records.
This is why it’s important to have the most effective risk adjustment model for Medicare. With this model, you can easily predict your patients’ future healthcare spending and adjust the risk quality and cost metrics accordingly.
This article explains exactly what a risk adjustment model is and reveals which one is relevant to Medicare users.
A risk adjustment model is a system used by healthcare facilities and health data technicians to equate a patient’s health status to a risk score. This score helps healthcare providers estimate the likely cost of treating the patient.
Essentially, the risk posed by patients with anticipated high healthcare costs to the health insurance plan is adjusted by including patients with expected low healthcare costs. Medical data technicians and coders use the patient’s health record to estimate their future spending.
This is why it’s best to have a reliable coding and data extraction tool to help you obtain accurate data and review your patients’ records and documentation from different healthcare providers. When a patient leaves a healthcare facility after treatment, medical coders review their medical records and convert their data to billable codes.
Medicare is a public health insurance program that offers health coverage to senior Americans. This program was signed into law in 1965. Each year, it provides health coverage for approximately 65 million Americans. This program is financed through general revenues, premiums, and payroll tax revenues.
Other common sources of funds include state payments, taxes on social security benefits, and interests. It’s important to also mention that this program is financed through the Hospital Insurance Trust Fund (HITF) and the Supplementary Medical Insurance Trust Fund (SMITF).
Every American state has its specific Medicare rules, including annual birthday rules, disability plan rules, guaranteed issue rights, and excess charge rules. These rules are quite broad, giving states the flexibility to set their own Medicare implementation guidelines.
The Center for Medicare and Medicaid Services (CMS) uses several risk adjustment models to estimate the capitated payments to Medicare Advantage (MA) plans and implement periodic updates to enhance the effectiveness of these models.
One of the key components of the risk adjustment models that the CMS uses to evaluate payments to the MA plans is the Hierarchical Condition Categories (HCC) model. The HCC model has two main categories: the CMS-HCC and the HHS-HCC models.
The CMS-HCC model is used to calculate risk for patients who are sixty-five years and older. It’s a prospective risk adjustment model that only predicts the healthcare spending for the next year.
The HHS-HCC model is a risk adjustment model used by healthcare providers and medical coders to calculate risk scores simultaneously. This means that it relies on the diagnostic data from a specific period to estimate the costs of healthcare in that same duration.
The HHS-HCC model is mainly used for commercial payers. Although Medicare is primarily for elderly patients, this model isn’t limited to older patients like the CMS-HCC model. Therefore, it can be used to evaluate the healthcare spending for patients of all ages, including infants.
Both models use the risk adjustment factor (RAF) score to estimate the anticipated health costs for patients. While the CMS mainly uses the HCC risk adjustment model to predict healthcare spending, note that different states use different risk adjustment models.
Therefore, it’s safe to conclude that the CMS-HCC risk adjustment model is the most suitable model for Medicare.
First, CMS combines International Classification of Disease, 10th edition ICD-10 codes into analytic groups known as ‘DxGroups.’ These groups consist of diagnosis codes for covering similar medical conditions.
CMS also groups the DxGroups into Condition Categories (CCs) that are based on equal expected costs. It further enforces orders on the model by disregarding a less severe manifestation of the medical condition if there is a more severe manifestation.
When these orders are applied, CMS issues a list of categories commonly referred to as Hierarchical Condition Categories (HCCs) in its yearly rate announcement. This list is usually included in a table of the supplemental attachment.
Every HCC uses a related factor (weight) along with the HCC for age and gender to estimate the beneficiary’s risk score. CMS determines the cost associated with each risk score according to the FFS Medicare expenditure and usage data.
A new version of the CMS-HCC coding model was finalized in 2023 – and the transition to this what is known as “V28” has already begun.
As a result, many providers are concerned that their Medicare risk adjustment workflows are going to become more challenging in the coming months – which is why some are seeking new technology to solve the problem.
HCC coding is essential to optimize patient care and ensure you receive the full Medicare reimbursement your practice is entitled to. But manual coding is time-consuming and complex, frequently leading to overlooked diagnoses that negatively impact your funding.
HCC Assistant uses natural language processing (NLP) to automate HCC coding recommendations, at 97% accuracy, that reduce error and free your providers to spend more time focused on patient care. Better still, the average provider increases their RAF 35% by using the tool.
Want to explore how it could improve your practice?
___________________________
References:
