Increased Efficiencies for Data-informed Direction
By Molly Fohrer, Vice President of Services and Support
Health benefits data is foundational to what we do at Springbuk, and our processes around QA and data loading are a significant component of how we service our clients. Our Services and Support Team partners closely with our technology teams to provide our clients with the transparency and trust they need to make informed decisions about employee healthcare and benefits.
When ingesting and processing data from hundreds of sources, things can get quite complicated, rather quickly. We must balance this complexity with the need to deliver data updates as promptly as possible for our clients. One of the ways we do this is through our internal Data Quality Report (DQR) process. Before loading data in the platform, a series of rigorous quality and anomaly checks are applied. These set off automated flags that our data team investigates before a data load and provides a detailed report for our internal teams.
Members of our implementation, success, and support teams also review this QA data to validate if any of the anomalies raised are expected (e.g., a client completed a RIF, so their enrollment numbers experienced a significant decrease). We finalize this process a couple of weeks before our standard load cadence to ensure ample resolution time and accurate data once it’s in our clients’ hands.
This internal DQR process also ensures greater transparency for clients. For example, we can use this data to inform clients how often data from certain carriers and benefits vendors come in with flagged anomalies or errors. This insight leads to productive conversations with the client’s vendors about the reporting and extracts they provide, ensuring that the client gets the most accurate data possible and that the data reflected in our platform tells the true story.
In addition to the quality and anomaly checks, we compare what we receive to what the client would expect to see based on vendor reports. Analyzing data from multiple sources leaves ample room for discrepancies. Because of this, we document as much as we can about the nuances of each data source. This process ensures that when data discrepancies arise, we have some hypotheses about the issue’s origin. Our in-depth experience and disciplined approach allow us to be proactive and more efficient in the resolution, giving our clients a great deal of confidence in the data as it appears in their platform.
Healthcare data is, and for the foreseeable future, will be messy. A significant part of our job at Springbuk is ensuring we provide the transparency, knowledge, and quality checks our clients need to quickly take action off of complex data.
Learn More about Data Quality with Springbuk
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