The client is a revenue cycle management company, and they file claims on behalf of the provider (hospitals). They provide hospitals a platform and services to file claims. It’s their apparent goal to reduce the denial of claims so that they don’t have to file an appeal. They already had a rules-based solution, but they wanted to enhance that with a data-based one.
The enormous problem in the healthcare sector is that data is very tribal. Every payer (insurance company) uses different coding standards, so the consolidation and understanding of healthcare data is a daunting task. Moreover, it is petabytes of data, so its analysis takes forever in a traditional database.
It took only two weeks for OvalEdge to implement a self-service solution for client. They had archived their data in compressed file format. OvalEdge started the project by putting that data into Hadoop, and then their analysts began collaborating on data and started segregating data for better understanding. There are hundreds of possible combinations, so they used OvalEdge’s profiling capability to understand the data quickly and then understand the rules for each payer and its charge code. Once they cataloged various rules, it became easy to extrapolate the rules. This task enhanced their rule-based appeal denial product.
The client has petabytes of data. Before they had OvalEdge; even for a simple question, their analysts had to write a basic query in Hive and wait for minutes to get the answer. After OvalEdge, they run the segregation query and press the profiling button. The profile results provide answers to most of the common questions.