Improve HCC Coding Accuracy and Risk Adjustment

Improve HCC Coding Accuracy and Risk Adjustment

Diagnosis coding is becoming more and more important. The shift from volume to value requires HCC coding for patient acuity not just diagnosis coding for medical necessity.  As the healthcare reimbursement process shifts towards a Value-Based model, Fee-for-Service will continue. However, there are many other reporting mechanisms that will now utilize diagnosis codes.

DEMOGRAPHICS + DIAGNOSIS = HCC CODING SCORE

There are two major components to a patient’s HCC coding score: demographics and diagnosis. The demographic factors are: age, gender and eligibility status.  Older patients are obviously more likely to consume higher amounts of healthcare in the following 12 months, than an identical patient who is 10 years younger.  The diagnosis component of HCC coding looks at all diagnoses for a patient over the past 12 months. The higher HCC coding score denotes that patient is too sick. It attracts high healthcare expenses in near future to patient.

CAPTURING COMORBIDITIES IS THE KEY

The Fee-for-Service model requires a diagnosis that justifies medical necessity for the CPT codes on a claim.  58% of practices indicate that their physicians do a good job of documenting these comorbidities in the note.  The change is not one of the documentation, but a coding change that is needed for the practice.

EFFICIENTLY CONVERT CLINICAL NOTES INTO HCC CODES

Providers aren’t coders—let them focus on patient care and have coders do what they do best. Your physicians work diligently to treat patients and document their conditions in their clinical notes. Turning this wealth of information into the correct coding requires the specialized skill set of coders.

WHAT IS RISK ADJUSTMENT?

Risk adjustment is a method to offset the cost of providing health insurance for individuals—such as those with chronic health conditions—who represent a relatively high risk to insurers. Under risk adjustment, an insurer who enrolls a greater-than-average number of high-risk individuals receives compensation to make up for extra costs associated with those enrollees

The risk adjustment model enacted under the Affordable Care Act (ACA, or “Obamacare”) is budget neutral. Total payments to insurers do not increase. Rather, insurers covering a relatively greater number of healthy individuals must contribute to a risk adjustment pool that funds additional payments to those insurers covering a larger portion of high-risk individuals.

HOW DOES IT WORK?

There are several risk adjustment models. The Centers for Medicare & Medicaid Service (CMS) risk adjustment model uses the Hierarchical Condition Category (HCC) method to calculate risk scores. This method ranks diagnoses into categories that represent conditions with similar cost patterns. Higher categories represent higher predicted healthcare costs.

WHAT DOES IT NEED?

All risk adjustment models depend on the complete and accurate reporting of patient data. CMS requires that a qualified healthcare provider identify all chronic conditions and severe diagnoses for each patient, to substantiate a “base year” health profile for those individuals.

Documentation in the medical record must support the presence of the condition and indicate the provider’s assessment and plan for the management of the condition. This information is used to predict costs in the following year. As such, incorrect or non-specific diagnoses can affect not only patient care and outcomes but also reimbursement for that care, going forward.

Proper reimbursement relies on correct HCC coding for accurate patient risk adjustment scores.  Our technology enables you to efficiently integrate the real-time review of HCC coding into your existing charge review process. Our HCC coding solution makes sure your providers get paid appropriately so they can focus on what they do best: providing great patient care.

Leave a Reply

Your email address will not be published. Required fields are marked *

 
 
 

We use cookies to improve your experience on our website. By browsing this website, you agree to our use of cookies.