Springbuk is a healthcare analytics software provider whose products are engineered to turn basic data into actionable health information for employers. Gandolf discusses how healthcare intelligence is moving beyond basic data analysis to compile information from various sources, analyze it and offer solutions for reducing costs and improving population health.
It’s kind of a progression. Data warehousing is where it starts. We realized when we began in 2015 that employers needed a good warehouse to pull all of their data into.
Then they need to be able to look at that data and understand what they are looking at. That’s where health analytics comes in. Employers with that are able to see that data…[but] they have no direction or recommended actions to take based on the analytics.
Health intelligence allows employers and brokers to maximize their investment because they receive data along with proactive direction offering steps to take to remedy the issues. Health intelligence says, ‘OK, we see that we have a problem, but now what do we do about it?’
We don’t need more data. Employers need direction on how to use what they have.
With an employer’s permission, we get insurance claims data, including medical and pharmaceutical, and we can use vendor data, like biometric screenings, as well. We can also use payroll information, so if an employer has regional offices, they can dissect data by those different places. Maybe they have a manufacturing plant and a corporate business unit; those can be separated to delineate between those different populations.
Then we ingest that data and have a health intelligence team with clinical expertise, data science experts and health strategy experts. They have built out some models to analyze the information. This isn’t an easy thing to do, which is why not everyone is doing it. The hardest part is receiving the data; anywhere from 30% to 40% of medical claims have an error in them, which makes them difficult to work with.
We are able to see, based on healthcare usage, if someone might be at risk for a condition.
So, based on predictive modeling, we can say a certain population may be at risk for diabetes if they start having certain kinds of medical claims. We look at people who don’t have it and see that they have the same claims as people who do. Based on their healthcare experience and demographic information, we can say someone may have a higher risk for the condition. There are machine learning and AI [artificial intelligence] methodologies that play into building that out in real time.
There are more than 60 different predictive insight cards in our platform showing which groups might be at risk for things like Type 2 diabetes or stroke. Then we can offer steps to take to address what an employer’s goals are regarding those issues, whether it’s managing costs or improving population health.
With health intelligence, providers offer a menu of ways to address whatever problem they want to take on. It provides an opportunity to manage the issues and give strategies and tools to move forward.
Something that is somewhat common is identifying people at risk for thyroid disease. The fix for that is a medication that costs $10 a month, and it can help solve it. If you can identify those members and get them treatment, you see increases in productivity and decreases in absenteeism.
When you start being able to mitigate those things, you can see real cost savings and life changes for the members, which is pretty amazing.
We have some examples. The most recent one we released was Durham County [North Carolina], which is publicly funded, so they really have to be smart about how they spend their money. We found they had a large diabetic population that had several gaps in care. We worked with a near-site clinic to close those gaps and were able to save $272 per person per month. They were able to get $270,000 in savings over one year, and there could be an additional $240,000 as they continue to close those gaps.
We worked with another employer to help them address unnecessary procedures and surgery. They had a young, healthy workforce but had a lot of joint claims, and we identified the problem quickly. We were all befuddled by it, but after we dove into it, we realized the founder of the company was a marathon runner and running was part of their culture. Generally, it was runners hiring runners who hired other runners. They ended up having joint pain and surgery, and it was costing a lot of money. We brought in Brooks Running to custom fit employees for the right shoes and braces and those kinds of things.
We have also started identifying opioid risk. We can identify risk there quickly and offer solutions. Mental health is one we get asked about a lot, too. We can help identify people who might be at risk for a mental health condition or a comorbidity with another condition, and we can show tools for ways to address it.
Because this is all PHI [protected health information], the data we use is aggregated or anonymized. There is usually an identifier for each member, but there is nothing else to identify them in the reports that employers receive. They get member-level data that isn’t associated to any individuals. For the most part, it is all aggregated.
Part of it is transparency. Traditionally, insurance is supposed to be confusing; to a certain extent, carriers don’t want you to understand it. Having health intelligence tools provides the opportunity for an employer to offer some transparency. This can help with retention because they are showing employees they think differently about the benefits and employee experience they provide. It allows them to do what’s right for their workforce using all of the data they have access to.
We have several pretty large tech employers on the West Coast, and what we are starting to hear groups like this say to brokers is, ‘Benefits get 8% of the budget, so you have to figure out how to make the most of that. We have to make it go further, but how do we have the most impact on our well-being or culture or employee retention?’ They are realizing that healthcare costs are out of their control at the macro level, but they are figuring out they can do a better job of managing it on the micro level.
These tools are an investment in their workforce. They are putting more upfront when they invest in them with the hope it will have a very positive long-term impact on their people and long-term business.
Well, we have a 97% retention rate, so obviously there is value in what we are providing. We have done some studies on groups who have been using the program for more than two years, and they save, on average, $12 pepm [per employee, per month]. That may not sound like a lot, but it is if you spread it over 10,000 people for a year.
But we really don’t tout the ROI, because sometimes we are encouraging them to spend more on their workforce. It’s about intelligent investments in their employees. To a certain extent, investing in employees is just the right thing to do. But then they may recognize savings downstream with things like increased productivity.
What we have found is reducing costs is important but it’s not what employers are the most focused in on. With unemployment so low, people are their most valuable resource, and they want to maximize the investment they are making in them. Instead of throwing spaghetti at the wall to see if it sticks, they want to know what to do with their budget to make it go the farthest.
When we initially rolled out Insights, brokers were worried about the impact on their jobs. But they found out something from the power users of this. The analyzers on their team that make the most of it get to spend less time digging for data and more time building plans around it, which is why we got into what are doing. We can ingest the data and send it back to them instead of them trying to find a way to marry all the different information. They have the ability to analyze, track and measure all in one place. It allows them to be more effective at their job.
When they sit down to meet with employers, it provides them a menu to work with. They can offer opportunities for the employers to act on. And it’s the brokers that know those solutions providers and where they best can make an impact for employers.
The brokers can discern what works best for their clients. They may have a preexisting relationship with diabetes management or other wellness vendors. They are the expert in the room and own those relationships.
We will make recommendations on programs and can provide hypothetical goals, but we don’t tell them who they should work with for their solutions. Brokers and employers have those relationships. We don’t want to be too prescriptive.