While our client’s new health insurance claims platform covered many bases, they needed RevGen’s help to migrate business-critical reporting and retain a unique competitive differentiator
Our client, a regional health insurer, needed assistance prepping for the launch of a new “turnkey” claims platform. Their legacy system produced several business-critical reports that needed to be reproduced. That legacy system also had the ability to send anonymized data extracts to third parties, a huge business value add that was necessary to replicate for the new platform.
While the client request was centered around testing and validation of the new reporting environment, RevGen quickly uncovered some additional needs that had to be met to enable a smooth transition to the new software platform.
The largest task was to design an environment to test, user acceptance test (UAT), and push to production business-critical reporting that would need to be carried over to the new claims software.
In scoping the database and reporting environment needs, RevGen uncovered that there were over 130 scheduled jobs, 60 reports, and 90 external data extracts being run in the legacy system.
The new claims platform was not designed to produce third-party data extracts, yet this was a key value proposition for the client. RevGen was tasked with designing a solution that would keep this process running smoothly.
Understanding that we needed to enable UAT for the new Qlik environment, RevGen worked with the client’s various departments to understand their reporting needs. During that discovery process, we uncovered several old reports that would need to be integrated into the new system. With the project expanding yet the deadline holding firm, we helped the client rank the reports by priorities – what was needed on Day 1, Day 30, Day 60, to Day N.
The first priority was fulfilling the UAT requirements. This was done by building tracking spreadsheets to understand the gaps and missing requirements that needed to be captured in Qlik reports before “signoff or validation” was successful. Simultaneously, we created a three-tiered database and reporting environment to develop, test, and publish the net new reports in SQL Server Reporting Services (SSRS). However, to do so we also had to verify which reports were most necessary and this was done in the signoff sessions with the departments. Then, we also created a new dynamic extract engine (DEE) to ensure these third-party data extracts could still be run in the new software.
The new database generated internal report queries (SSRS), developed ad-hoc queries for internal and external stakeholders, and facilitated the sending of the third-party data extracts. RevGen also designed several reporting views to make the analysis of the client’s data much easier.
As part of the new database, we were able to implement several standardizations in extract code and reporting, as well as increase security of the data environment, enable UAT to occur with accurate data in a stable environment, and eliminate resource competition.
There were over 130 reports saved in the legacy Oracle system, several of which could be consolidated or phased out entirely. The gap analysis validated which reports were business critical and would cause potential work stoppages or delays if not implemented correctly on day one of the software transition.
The DEE enabled the quick creation of new data extracts, easier troubleshooting, and decreased development time for wholly new requests.
This data and reporting work enabled the smooth launch of the client’s new claims platform while consolidating and optimizing the reporting and extract process.
Without the thorough gap analysis, there would have been several business-critical reports unavailable at launch.
With the standardization of the new reporting SSRS coupled with the DEE, it was faster and easier than ever to create external extracts and pull reports.
We were able to consolidate down from 130 to 51 essential reports. For the third-party extracts, we narrowed the required jobs from 96 extracts to 60 and greatly reduced the time it took to troubleshoot issues and requests.
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