In a recent audit finding shared by the Office of Inspector General (OIG), a Medicare Advantage (MA) healthcare organization ended up receiving at least $54.3 million in net overpayments. Most of the Hierarchical condition category (HCC) diagnosis codes submitted by the health plan was supported in the medical records, however, around 164 HCCs were not validated and resulting in overpayments. The health plan was then recommended to refund the overpayments to the Federal Government.¹
Every year, MA organizations lose hundreds and thousands of dollars due to inappropriate HCC coding, which in turn leads to regulatory penalties.
A proven, automated system can be used in order to optimize risk scores and income while reducing the burden on payers and medical coders.
Artificial Intelligence is playing a vital role in the risk adjustment space of the healthcare ecosystem. An NLP-Powered Risk adjustment Coding and Audit solution is helping MA organizations protect their revenue by reducing the Risk Adjustment Data Validation (RADV) audit risks.
Let me walk through the key benefits of an AI Audit Solution for the healthcare risk adjustment space:
Addresses the specificity for documentation as per M.E.A.T criteria
Even the smallest error in specificities can hamper HCC code assignments, RAF scores, and patient’s treatment plans.
AI-based Risk Adjustment HCC coding audit solution abides by the M.E.A.T criteria to ensure you perform accurate and comprehensive documentation of chronic conditions.
- M – Monitoring signs, symptoms, disease progression, disease regression
- E – Evaluating test results, medication effectiveness, response to treatment
- A – Assessing/ Addressing ordered tests, discussion, review records, counseling
- T – Treating medications, therapies, other modalities.2
Access to HCC summary reports for medical record data validation
NLP-Powered audit technology will automatically read patient’s charts and suggests:
- Substantiated HCCs (codes that you should keep)
- Unsubstantiated HCCs (codes that you should remove)
- Unreported HCCs (codes that you should add).
This way you can easily identify the properly coded conditions, up-coded (over-claimed conditions), and under-coded (unclaimed conditions) to ensure the best practices of a chart review and audit.
Improve HCC coding efficiency
AI-based Audit Solution offers a consolidated view of all member’s or patient’s records in real-time. In addition, an auto code suggestion is helping health plans, providers, medical coding companies, and MA chart auditing companies to perform a chart review & audit in less than 3 clicks.
Automate the assignment of diagnosis codes
A chart audit solution which NLP powers, will automate the assignment and validation of diagnosis codes and adhere to the federal coding guidelines. This way, it will cut down on the amount of time HCC coders for identifying diagnosis codes and reviewing medical records before submitting them to the Centers for Medicare & Medicaid Services (CMS).
The success of MA organizations depends on the diagnosis codes submitted to the CMS, therefore having an AI-powered risk adjustment audit solution will improve HCC coding review efficiency and effortlessly manage patient encounters for accurate claim documentation and submissions.