We’re returning to a series of blog posts this week that recap a recent webinar hosted by BenefitsPro and sponsored byArtemis Health. The webcast “The Future of Transparency: Can Data Fix Health Care?” featured panelists included Jim Blachek, co-founder and principal at the Benefits Group; Lester Morales, CEO of Next Impact; and David Contorno, founder and CEO of E Powered Benefits. Eric Silverman, founder of Voluntary Disruption, moderated. Slides and audio are available on-demand.
This week, we’ll get the opinions of these experts on predictive analytics. What can be predicted, what is valuable, and what will the future hold in this emerging field? Let’s dive in.
Lester Morales: I think you could take this a whole lot of ways, right? You could take this from a cost standpoint, or you could take this from a quality standpoint, I'm going to go with, we all agree I think, that lowering the cost of healthcare is all going to be around being able to control the claims cost. That's a frequency and severity conversation. I'm going to hit an example of predictive modeling.
Take a condition like diabetes. A diabetic has 16 things over the course of a year he or she is supposed to do in order to be compliant. A compliant diabetic costs the plan a quarter of what a non-compliant diabetic costs a plan. You have two people diagnosed with diabetes, one of them is being compliant with the 16 things, and compliance obviously has a range of super compliant to kind of compliant, or they're just a pain in the rear. The cost differences between a compliant diabetic, and a non-compliant diabetic on the plan is huge.
Using predictive analytics allows you to understand based on someone's track record of being compliant, versus not, how much they're going to spend. Or better put, how do you put them in compliance categories and then back to the earlier statement that I had about prioritization.
If you know that the cost difference between a non-compliant group of diabetics and a compliant group of diabetics is $50,000 a year, PEPY and you had seven people over here, well seven and that $50,000 difference, we're talking about $350,000 of claims cost you could manage, not by eliminating that person from being a diabetic, but by getting them to be a compliant diabetic.
Understanding things like what are people doing to be compliant or not? What are the categories of non-compliance? Is it eye exams? Is it foot exams? Is it A1Cs? How can you as an advisor work with an HR professional on the warm and fuzzy side?
“Hey, why don't we have a mobile vehicle come to the office to do eye exams? Why don't we have an onsite something or other checking A1Cs.” These are all things that could remove a barrier of somebody doing something that they need to be doing, which is their A1C, their foot exam, their eye exam, all for the purpose of gaining compliance, which in turn is going to then lower cost.
I took the quality side, I'm sure David or Jim can hit on the negotiation side and buying a claim for a cheaper number. But when you look at the ability to use predictive analytics just around the things you could do with education, communication strategy, plan design strategies, incentive designs, all of those things can drive compliance, and compliance drives lower costs inside of a plan.
That's just an example of one category of things you can consider, and the levers you can pull inside of that.
Eric Silverman: Awesome. David, what do you think about predictive analytics?
David Contorno: Yeah, I mean I agree with everything Lester just said, and one of the things that I think sometimes scares consultants or brokers that may be considering going down the path that some of us on the call have done, is a lot of what Lester just spoke to. We got to start knowing some things clinically. That can scare some people off, because the reality is as much as for the national association of health underwriters, in and of its name, it says that we underwrite healthcare, but yet we really don't. At least, the average broker or consultant doesn't do that.
When you start to talk about the things that Lester was talking about and identify that a compliant diabetic costs a quarter of what a non-compliant diabetic is, then you can start to do things with this data.
In our plans, for example, we tell a compliant diabetic that if you remain compliant, all of the care tied to being compliant with your diabetes, including the podiatrist visit, the endocrinologist, your blood test strips and your meter and your insulin and whatever it is you need, all of those things will be covered at no cost to you. Because again, the math is such that making it as easy and accessible as possible to get the right care significantly reduces costs for the payer, which is the employer. We want to keep that engaged as much as possible.
You can't do what I just described with an HSA or an HSA compatible plan, because you can't do dollar one coverage in a high deductible health plan, which is one of the many reasons why I don't believe in HSAs.Aand you can't do this with a large carrier, because again you're creating different benefit levels and you're lowering costs, which is contrary to their revenue model, because of medical loss ratios and the affordable care act provision, and their ultimate obligation, no fault to them, is to their shareholders—just like every publicly traded company is. They have to appease their shareholders, which is contrary to the interest of the employer and the patient.
Again, another conflict of interest, but that's where the opportunity lies. Get that chronic person the best care possible at the lowest cost, and you improve their situation financially and clinically. Simultaneously it improves the employers financial situation at the same time.
Eric Silverman: So Jim, what do you think about this, and if you could add in maybe some quality discussion?
Jim Blachek: I think it's important that we look at using the data to drive quality, because ultimately we know that health care i s weird. And it's one of the most weird purchases, because the higher the quality, lower the cost. And so if we can use the data to drive employers, employees to a higher quality inevitably it is at a lower cost.
We have to remember that we're dealing with people as well. We need to use the data to help employers and their members access higher quality to give them better benefits, which ultimately lowers their cost, and the employer cost. The data's vitally important, and acting on the data before conditions get to the point where quality of life is diminished is imperative.
Eric Silverman: Okay, awesome.
Lester Morales: Eric, I wanted to add one thing on that, because I think the mantra of our entire industry has always been: we've got the biggest network with the biggest network discounts. What Jim just said, and obviously David and I agree and support, is that bigger isn't better when half of the people that are inclusive in the bigger aren't better quality.
If everybody listening continues to do a disruption analysis according to just the number of providers, and I want to make sure that “Employee A” doesn't go into HR and complain a little bit that a certain provider is not in the network. If we keep saying that mantra being the decision making, we won't improve the scenario that Jim just laid out. It's a huge industry shift that starts again with everybody on this phone call.
How are you using predictive analysis to improve the quality and cost of care at your organization? What trends are you seeing from other industry innovators?
Stay tuned for Part 3 of this discussion, and if you’d like to learn more about how Artemis is using healthcare data, sign up below for a personalized demo of our platform.