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Data-driven analysis and solutions with RAAPID

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Data-driven Analysis and Solutions with RAAPID

There are 3,834 Medicare Advantage Programs¹ as of 2022, and the total Medicare Advantage enrollees have doubled since the past decade, with two-thirds being Health Maintenance Organizations (HMOs).²

Is your Medicare Advantage (MA) organization able to predict accurate reimbursements for physicians and payers as the number of Medicare Advantage plan enrollees continues to rise?

Hierarchical condition category (HCC) risk adjustment coding is an important topic in the healthcare ecosystem today, notably for healthcare payers and medical coding companies.

Risk Adjustment Factors (RAFs) are calculated using data from the HCC model and applied to capitation payments for Medicare Advantage plan members.

Now, inaccuracy in capturing HCC codes can impact RAF scores resulting in:

  • Underpayment/ Overpayments claim submission to the Centers for Medicare & Medicaid Services (CMS).
  • Non-compliant data 
  • Value-based healthcare variance
  • Risk Adjustment Data Validation (RADV) appeals
  • Penalties and Legal Complications.

Modernizing how your MA organization document and categorizes chronic diseases can help uncover accurate RAF scores, leading to optimized reimbursements and compensation as well as enhanced healthcare delivery for ever-increasing patient populations.

What is the must-have data-driven analysis for your MA organization?

1. Smart Targeting

Use RAPPID’s risk adjustment medical coding software solution to calculate risk scores based on specific HCCs. The modern HCC coding solution allows us to identify suspects and validate diagnosis codes in real-time.

2. Clinical Mapping

Being able to differentiate between acute and chronic conditions at the ICD-10 level to eliminate false positives while identifying suspects.

3. MA enrollee’s trend analysis

Analyze Medicare enrollees with disease and care gaps based on projected RAF scores or suspected morbidities.

4. Medical Coding Gap analysis

Able to track medical coder’s performance in real-time, and identify care and documentation gaps.


Using NLP-powered risk adjustment HCC coding software to ‘read’ encounter data

An integral part of RAF score calculation is to ensure clinical data supporting your claims is accurate. Most of the clinical encounter data are available as physician notes and comments.

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 and audit.

Closing note

Performing a First-Level Review (FLR) and an optional Second-Level Review (SLR) in addition to the above is crucial. 

As an MA organization, you should prioritize equipping risk adjustment workflow with technology solutions needed to accurately assess the health risks of Medicare enrollees, hence lowering their overall risk exposure.

AI-based medical coding analytics software solutions are also allowing coders to perform accurate coding between the medical record and claim codes that need to be submitted to the CMS.

In addition, automation in the identification and validation of HCC diagnosis codes is essential as it allows them to be processed efficiently and accurately by healthcare payers and medical coding companies on a bigger scale.

Therefore, in order to deal with the growing number of MA enrollees, healthcare payers and medical coders who use natural language processing technologies in their operations will have a competitive advantage in delivering value-based care and improving revenue cycle management.








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Disclaimer: All the information, views, and opinions expressed in this blog are inspired by Healthcare IT industry trends, guidelines, and their respective web sources and are aligned with the technology innovation, products, and solutions that RAAPID offers to the Risk adjustment market space in the US.