When Sophisticated Tech Lets The Human Connection Shine

The secret is to combine deep science with a profoundly human connection.

In a world driven increasingly by technology, it’s easy to forget that the algorithm isn’t the savior — it’s what humans do with the outputs that can accelerate care, delivery, and health outcomes. Knowledge is power, as they say, and the big winners in health tech are the companies that not only use breakthrough AI, data analysis, and machine learning to develop next-generation tools but leverage those data to educate, empower, and engage people in their own health.

The best part? Done right, this approach doesn’t cost anything — in fact, it saves the system money.

Chris Cronin has the data to prove it.

His company, MOBE, is not solving a new problem. For decades, we’ve known that some individuals — both insured and uninsured — are accessing healthcare frequently, without their health improving. Their continued high-volume healthcare utilization signals a lack of results, contrary to what one would expect from frequent interventions. They’re “doing what they’re supposed to do” — they’re able to access the care they need — and yet they’re not getting better. We know they exist — but the conundrum that payers, policymakers, providers, and employers have grappled with is how to find them, and the right way to engage them effectively.

MOBE seems to have cracked the code — or, at least, provided promising progress. According to Cronin, the secret is simple: combine deep data science with a profoundly human connection to provide one-to-one guidance that supports participants' health.

So how exactly does it all work? Or perhaps more interestingly, how does a company that prides itself on being "wickedly smart, wildly human" do it? I sat down with Chris to understand more.

Nosta: How does MOBE uniquely solve a longstanding problem?

Cronin: In the simplest of terms, MOBE partners with payers and employers to identify the individuals in their populations who are high utilizers of care and failing to achieve clinical health goals. Typically, these people represent 10 percent of an insured population yet account for 30 percent of the costs of that population. We use complex data science to identify them accurately, and then, connect them with direct, one-to-one support — often the missing link between a doctor’s visit and daily life. And by the way, this kind of support is valuable to everyone seeking to achieve a health goal — it’s not just for the 10 percent.

Nosta: Let's jump to what I think is one of your most startling features: Your service is free.

Cronin: Yes. Our model is different than any other in the space. We are a "return on savings" model rather than a return on investment. Our fees are based entirely on the financial savings we can provide to a payor or employer. Our methodology has been validated by the world’s leading actuarial firm and that gives us the confidence to guarantee savings for all our customers.

Nosta: What makes your approach different?

Cronin: I have to give credit to two essential components. The first is our incredible data science — finding the right members in a large population. The second is the way we engage our members. Each participant connects one-to-one with our MOBE Guides — professionals with backgrounds such as nursing, chiropractic care, and nutrition — and MOBE Pharmacists who work with them to personally address key issues. 

Nosta: How is what you do different from the claims analysis happening in-house for payers and employers? I have to imagine that their technology is pretty sophisticated.

Cronin: That’s true — but claims analyses only show one piece of the larger puzzle. And further, they don’t have the data set or experience that MOBE does, which shapes our algorithms. Our data inputs include claims data, but they also include the experience, outcomes, observations, successes, failures of our hundred-plus Guides and thousands of participants. It's really about the powerful combination of technology and humanity. Our data science team created unique and proprietary machine learning models to analyze and learn from claims codes, to understand the context like a language. And for large and self-insured employers, they aren’t set up to assess claims data at all. Their “hidden population” is even more of a problem they don’t know about. 

Nosta: What’s next for MOBE?

Cronin: Our mission is to help people live happier and healthier lives — and not just a few people. We want to find new ways to bring our program to more individuals. Our plan is to leverage our unique insights and to reach more people, drive better outcomes, and reduce costs across the entire health ecosystem.