Lengthy manual impact analysis: The client is a chain of hospitals that uses Epic system for recording its patient health information (also known as Electronic Medical Records or EMR). Every quarter Epic upgrades the application. Whenever this happens the client’s IT team also has to upgrade their Epic version. Only then they can stay Epic compliant and leverage its full functionality. Moreover, since it is a large organization, the client has a lot of custom downstream systems and reports which take data from Epic. So earlier, to upgrade they either had to take a high risk of downstream impact or delay the upgrade to do manual impact analysis.
Inconsistent business definitions: Secondly, this company has been built by acquisition and mergers. Hence, they didn’t have consistent KPIs and definitions of business terms across the board. So they could not compare data between different units. This required a comprehensive data go vernance program.
OvalEdge is a data lineage solution that automatically finds this impact and does much more. The client deployed OvalEdge and crawled various data sources like Epic, Clarity, QlikView, Tableau. and SAP Business Objects and build the data lineage from source systems to reporting software. Now finding the impact took a fraction of the time it took earlier.
OvalEdge catalog also serves as a centralized repository of all standardized business terms known as business glossary. This is easily accessible to all data users through the catalog. In addition, they are using it for finding the relevant data for the steering committee to make better decisions. For Example – length of stay has different definition at different units. Using OE one can analyze changing which definition will be the least impactful.
Data Consolidation Platform for a Mid-size Healthcare Company
The client needs a data platform where they can assemble all the data (primarily sales and operation) at one place. They are the new age data company and want to provide secure self-service data access to all the people in the company, including sales staff.
They have sales analytics data hosted in about two dozen databases. When they started to build a traditional data-warehouse, after six months with a limited budget, they were only able to get one database moved to the data warehouse. They realized that creating a data warehouse would be very expensive. The analytics use cases (mostly reports), were costing them more than its initial one-year saving. It was hard for them to justify the investment.
The client learned about data cataloging and selected OvalEdge for it. Now within three weeks of using OvalEdge, all the data is cataloged and available via self-service to everyone in the organization. Now they are leveraging OvalEdge to understand the data with functional experts (Sales team) and report developer and directly create a dynamic data-warehouse for that report or use Tableau report.
Aha! moments because of OvalEdge
For the last six months, the client was creating various data models and ETL activity to provide self-service access to end users. Now when they installed OvalEdge and profiled all the data, within three weeks, all the data was available to their analysts.
Appeal Reduction for a Healthcare Revenue Lifecycle Management Company
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.
Aha! moments because of OvalEdge
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.