Data, Data and more Data – How to Manage, Reduce HCC Coding Gaps and Improve Accuracy

Two-thirds of the Medicare Advantage (MA) Program consists of Health Maintenance Organizations (HMOs)¹ and there are over 3500 MA programs as of 2022.²

Is your MA organization able to predict accurate risk scores with the increasing number of Medicare Advantage plan enrollees?

Are you still struggling to claim appropriate reimbursements from the Centers for Medicare & Medicaid Services (CMS)?

Now as your Medicare reimbursements depend on the anticipated cost of providing Medicare benefits to a given enrollee and account for variations in the demographic characteristics and health status of each enrollee, are you able to determine an accurate enrollee’s health status to calculate the risk score?

Hierarchical condition category (HCC) risk adjustment coding a trending in the healthcare ecosystem today, notably for healthcare payers and medical coding companies.

And, as the Risk Adjustment Factors (RAFs) score, also known as a risk score is calculated using data from the HCC model and applied to capitation payments for Medicare Advantage plan members, have automated you introduced automation in your risk adjustment HCC coding workflow?

What are the consequences of unreported HCC diagnosis codes or wrongly reported HCC codes in risk adjustment?

• Underpayments/ Overpayments claim submission to the CMS
• Non-compliant data with no match to CMS Guidelines
• Value-based healthcare disparity
• Risk Adjustment Data Validation (RADV) appeals
• Penalties and Legal Complications.

By introducing NLP-powered HCC coding software as a service within the process of organizing and categorizing chronic diseases can help uncover potential RAF scoring opportunities, resulting in optimized reimbursements from the CMS. 

What do you need to manage, reduce HCC coding gaps and improve medical coding accuracy?

1. Intelligent workflow

AI-based HCC medical coding solution calculates risk scores based on specific HCCs with the convenience of computer-assisted codebooks and references. The modern HCC coding solution allows makes it quick and accurate to identify suspects and validate HCC codes in real-time.

2. Centralized EMR/EHR data storage 

With Medicare patient’s data coming from multiple physicians and health facilities in different formats, a centralized chart review solution will help you automate the identification and validation of chronic conditions from within a single dashboard and implement lower-cost interventions.

A great way to achieve Electronic Medical Record (EMR)/EMR optimizations is by adopting RAAPID’s HCC Coding Software Solutions that seamlessly integrate with a variety of Electronic Health Record (EHR)/EHR systems and suggests correct HCC risk adjustment codes for accurate RAF score calculation in just three clicks.

3. Clinical mapping

Able to differentiate between acute and chronic conditions at the International Classification of Diseases, Tenth Revision, Clinical Modification(ICD-10) level to eliminate false positives while identifying suspects in the patient chart review.

4. MA enrollee’s care gap analysis

Analyze Medicare enrollee’s health status to fill care gaps based on projected risk scores or suspected morbidities.

5. Medical Coding analysis 

Can track medical coder’s performance in real-time, and identify care and documentation gaps to augment coding efficiency.

Key solution:

Using NLP-powered medical coding software to ‘read’ unstructured data

The medical data that you submit for claims will determine the reimbursements CMS for the Medicare enrollees you serve. 

Now, with medical records being formed in multiple formats including text and images, an NLP-powered medical coding software solution will automate the analysis of unstructured data, translating it into structured information that can be used for a comprehensive chart review, audit and RAF score calculation.

Ending note

Having a First-Level Review (FLR) and an optional Second-Level Review (SLR) in addition to the above is recommended to keep oneself stay away from penalties in case of wrong claim submissions to the CMS.

As an MA organization, you should equip your risk adjustment workflow with the latest  solutions   needed to accurately assess the health status of MA enrollees, hence lowering their overall risk exposure. 

To sum up, AI-enhanced computer-assisted medical coding solution is also allowing medical coders to perform accurately and efficiently between the medical record and claim codes that need to be submitted to the CMS.