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