Up-coding in Fee-For-Service and Risk Adjustment data

How do we solve this problem?

HiLabs offers an unguided AI solution to detect up-coding in Medicare Advantage data and ER visits. The tool MSure, performs comparative data quality and completeness of MA data analysis. The tool has been customized on medical ontologies based learning to fine tune for Risk Adjustment. MSure has found potential overpayment of $80M in just one state (FL) and around $14M in ER visits for a large health plan in the first quarter of 2020

Short implementation cycle

Analyze every slice of data and learns patterns from the data itself. It thus allows for auto-discovery of anomalies and points to targeted list of medical charts for review

Auto learning of billing patterns

AI-based Analysis on every possible slice of data thus eliminating the need of deterministic checklists based on medical charts. It also shows errors which are unknown to the user

Your analysis is no longer limited to your own data!

Our solution comes with out-of-the-box knowledge base of 2 billion plus HCC coding patterns of the entire Medicare FFS population of the country

Supervised and Unsupervised training

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One model-fits-many scenarios

Billing for services that were never rendered

Billing for more expensive services or procedures (upcoding)

Performing medically unnecessary services solely for the purpose of generating insurance payments.

Falsifying a patient's diagnosis to justify tests, surgeries or other procedures that aren't medically necessary

Easy data ingestion with insured security

Quick data ingestion on our MGest platform which is compatible with most of the data source systems in the industry. Data security insured through virtual or cloud systems

Let’s make an impact

We at HiLabs are making businesses better for several of the country’s health plan. We understand your needs, are flexible to customization and are ready to bring in the change through our rich experience in this industry


About HiLabs, A cohort of experienced Engineers, Data Scientists and Physicians.

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