For this session, we are pleased to welcome our guest speaker Steve Chamberlin of Sensa Analytics, who will discuss how Sensa Analytics used Oracle Machine Learning with Oracle Autonomous Database to address two important use cases in healthcare claims processing.

Healthcare organizations face the challenge of efficiently processing thousands of claims each day. There are two primary reasons submitting claims has become difficult:

1) Human interaction for submitting and reviewing claims will cause errors due to the increased volume of requirements for the procedures, and
2) The requirements for submitting claims change daily when looking at the broad spectrum of healthcare plans and changing insurance company policies.

A more effective method must be put in place to help the Revenue Cycle Management (RCM) team. They need a smarter way to avoid mistakes when coding a claim and track denied claims for a quicker response.

Plano Orthopedics Sports Medicine Center (POSMC) has teamed with Sensa Analytics to use Oracle Machine Learning with Oracle Autonomous Database in this process. Machine Learning is used to search the setup or coding of claims for errors prior to submitting them to insurance companies. This means faster reimbursement of the claims. It can mean the difference of getting paid in 2 weeks instead of 2-3 months. This can be anywhere from a 2-6% improvement which adds up to hundreds of thousands of dollars annually.

Another aspect of machine learning is being used to find denied claims and categorize them. This is a monumental task for the RCM team. An individual in RCM could have over 500 claims to manage every day. This means creating or correcting a claim in less than 1 minute continuously throughout the day, every day. This makes it impossible to manually review multiple reports and systems to find claim issues and update them with a proper response.

In this session, learn about project goals and results. See how Sensa applied machine learning to the data, the technical architecture and process, and lessons learned with examples.