How the Springbuk Platform Enables Health Intelligence
Historically, legacy data warehouses and analytics have stored your data, but they can’t provide the actionable intelligence needed to guide and optimize your benefits strategy.
Imagine if your data could give you the insight to steer every benefits decision you made. Imagine quickly identifying behavior patterns, understanding which plan change would make the most impact, and having the tools to track these programs’ progress over time.
We sat down with Roger Deetz, VP of Technology, to understand how he and his team have worked to build Springbuk, a solution that turns data into actionable health intelligence that can help empower better decisions, better spending, and better health outcomes.
Q: How does the Springbuk product team enable health intelligence?
A: Before building the Springbuk platform, health intelligence was a concept that did not exist before our technology made it possible.
In legacy solutions, traditional analytics was accomplished by using data expertise and combing through the raw files to report on patterns in the data. Health intelligence goes beyond basic storing and reporting – we built it to handle data at a much larger capacity. Our product team has folded in modern techniques and scales in a way that was not possible in legacy solutions.
Q: How has the product team’s focus changed since the beginning of Springbuk?
A: Three years ago, Springbuk was focused on building the best-in-class health data analytics platform that could evolve and grow to meet our clients’ changing needs. We knew once we built the framework, we could build and invest in creating our own data pipeline that was agile and flexible. By having our own cloud-based pipeline, we could begin to weave in the intelligence, i.e., the predictive models that would power solutions like Springbuk Insights™ and Springbuk Answers™.
Q: How has Springbuk evolved the legacy approach to handling data to this new, more innovative process?
A: When working through legacy approaches to handle data, you see this is almost always done in large batches, and the processing cycle is conducted once a month. In turn, the schedule in which that data can be ingested and enriched is relatively rigid, requiring a time-consuming, manual mapping process.
At Springbuk, we’ve built a cloud-based architecture that is nimble, agile, and scalable, allowing us to run multi-sourced data on-demand with increased flexibility and speed. Additionally, we’ve layered on various components of processing this data through a hierarchical approach to match different claims, eligibility, biometrics, lab values, etc., ensuring the quality of the data sent by the vendor.
Once we’ve mapped and normalized the data, enrichment occurs by incorporating industry-standard episode grouping, risk grouping, and evidence-based medicine. We then take it one step further by using proprietary data science methodologies that include AI-based opportunities and event prediction algorithms. Once the data has been mapped, normalized, and enriched, it’s pushed into the platform where clients can log in and begin acting on opportunities.
Q: What was the team’s ‘A-ha! Moment’ with the Health Intelligence platform?
A: Once our own data pipeline processed raw files in-house from the carriers in an automated way - that's when we knew this was going to work. For years we looked for ways to separate ourselves from third-party processors, so we focused on building a system that as soon as a file was delivered, we could process it and pull into our dashboard ready to go. It took so much work to build this solution, test it, and have it ready – but when it actually happened, we knew we were on to something.
Q: What does “scale” mean to you and the product team?
A: Something we all talk about, as an organization and industry, is the amount of healthcare data has increased by more than 800% over the past five years.
For Springbuk, we take in and handle over 7,000 data files every month. To our team, scale is how we quickly process the data of those employers every month with a relatively small team - that's a level of automation that a lot of competitors don't have.
Once we had a pattern for automated data ingestion, we knew we could apply it to almost any data source and quickly start bringing in a variety of data needed to deliver health intelligence.
Q: What would you say is a key differentiator of the Springbuk health data analytics platform?
A: First and foremost, I’d say huge differentiators are our flagship products, Springbuk Insights™ and Springbuk Answers™. Those solutions are only possible when we have a ton of data to work with.
What’s exciting about Insights and Answers is how quickly we brought them to market. And, even after we brought them to market, we’re still iterating and evolving them. For example, when COVID-19 hit in March 2020, we had already developed the COVID-19 Insight category by April. After launching Answers in May 2020 with 20 questions, we had over 500 questions by October and introduced COVID-19 specific search queries. These solution adaptations and responses to market change are only possible when you have a framework that is as flexible and agile as the Springbuk platform.
