MSure

What is its unique value proposition?

The MSure platform automatically learns and applies tens of millions to billions of patterns to discover anomalies. This doesn’t require any prior experience based deterministic approach hence can be expanded to find new anomalies which are currently unknown to the customer. This is coupled by having a dynamic threshold and short implantation cycle.

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

Limitations of Deterministic Rules

payer-solutions_0014_vector-smart-object
Until now, the analysis of data accuracy has been highly dependent on pre-defined rules which can be explicitly programmed as a decision tree (shown in the figure). These rules are problematic due to the following reasons-
  • This deterministic approach by its nature is manual and therefore subject to substantial error as small misjudgments can aggregate along its step-by-step flowchart of acceptance and rejection rules

  • Defining data check rules consumes a large amount of time. Organizations take months to define and fine-tune such rules

  • The amount of data that must be analyzed in the healthcare industry is usually humongous (especially the encounter data) which results in performance of a deterministic method of data quality analysis being severely impacted.

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

Scalability and platform agnostic

The platform has been to scale up to 3.5 billion claim lines on the Cloud (Amazon, Google, Azure) as well as on-prem (Large Blue Plan)

Fine-tuned for healthcare specific data

MSure’s algorithms are customized to analyze healthcare data. The underlying technology is based on proven open-source cluster-computing frameworks for scalability.


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

Subscribe to our newsletter and stay up to date with the latest news and deals!