The Story of Siftwell Analytics: Building the Future of Predictive Healthcare AI

The Story of Siftwell Analytics: Building the Future of Predictive Healthcare AI

The best startup ideas don’t emerge from market research decks. They surface in moments of operational crisis, when experienced leaders finally admit that the tools they’re using simply don’t work.

In a recent episode of Category Visionaries, Trey Sutten, CEO and Co-Founder of Siftwell Analytics, a healthcare technology company that’s raised over 5 million in funding, shared how a health plan merger sparked the creation of a company that’s reimagining how healthcare organizations predict and prevent poor health outcomes.

The Merger That Changed Everything

The story of Siftwell begins not with grand ambitions of disrupting healthcare, but with a practical problem. Trey was leading a Medicaid health plan through a merger, and the writing was on the wall: not everyone would have a role in the combined organization.

Rather than treat this as an ending, Trey saw an opportunity. He started pulling together C-suite executives from the merging organization and asked them a simple question: “Now that we’re figuring out what our next thing is, since we’re not all going to this combined organization, what is it that the market needs? Where is their holes?”

The answer came quickly and unanimously. Healthcare organizations were drowning in data but starving for actionable insights. They needed to figure out how to line up resources with the right individuals at the right time and the right level. As Trey put it, they needed “this notion that you can match people with their needs at exactly the right time and the right level.”

This wasn’t academic theorizing. These were operators who’d spent years frustrated by the gap between what analytics promised and what analytics delivered.

From Conviction to Proof

Having a good idea and proving it works are entirely different challenges. Trey and his co-founder knew that healthcare had seen enough overhyped technology solutions. They needed proof before they could sell anything.

“We started to figure out the tactical steps. Well, first we had to prove out that the technology is different and better than what’s currently in the space,” Trey explained. They acquired sample data sets with millions of lives, assembled data scientists, and ran competitive analyses.

But the real validation came next: convincing an actual health plan to provide real, live data for pro bono analysis. This was the moment of truth. “Those early days were really about like, you know, is the advancements that are being made in machine learning and artificial intelligence, can they truly be applied in the context of healthcare?” Trey said. “And the answer resoundingly came back yes.”

That pro bono client would later become a paying customer—the ultimate validation that Siftwell had solved a real problem, not just built an impressive demo.

Relearning the Fundamentals

For Trey, the transition from health plan CEO to startup founder required more than strategic thinking. It demanded a willingness to go backward before moving forward.

“I think the biggest adjustment was moving from a big company to a smaller company,” Trey reflected. “It had been a lot of years since I was back into Excel and doing modeling. A lot of years since I had, you know, put together a PowerPoint presentation for a sales pitch or a fundraise or something like that.”

He told his co-founder something that captures the reality many experienced executives face when becoming founders: “I thought moving up the ladder was pretty tough. Moving back down the ladder is even tougher, you know, relearning some of those skills.”

This humility—the recognition that operational expertise doesn’t automatically translate to startup execution—shaped how Siftwell built its team and approached growth.

Beyond Prediction: Making AI Actionable

What emerged from those early proof-of-concept projects was a fundamentally different approach to healthcare analytics. While competitors focused on delivering ranked lists of high-risk patients, Siftwell dug deeper.

“We help health plans tell the future,” Trey explained. “We collect claims information from those health plans and some other data sets. Combine that with data sets that we’ve built or bought over the last couple years. We use machine learning to make predictions and then we really dig into those predictions using a couple other types of artificial intelligence to explain why those individuals are being identified for those predictions.”

The difference shows up in the details. When a client wanted to connect more members to cancer screenings, Siftwell didn’t just identify 12,000 unlikely to comply. They segmented that population and explained why different cohorts wouldn’t get screened—transportation issues, affordability concerns, distance to facilities.

“The motivation really is to go from big lists that are rank ordered to bite sized chunks that people can understand, marshal resources around and really get results,” Trey said.

The Operator’s Perspective on AI Hype

As AI hype reached fever pitch in healthcare, Trey watched from a unique vantage point: he’d been both the buyer and the seller of healthcare technology.

“With all industries, I think there’s a lot of hyperbole out there,” he observed. “You’ve got a lot of companies that have been operating in the space for a while and they’ve kind of slapped that AI on the back of their name or whatever. So I think there’s a little bit of maybe fatigue from some of the marketing and branding.”

But beyond the fatigue, Trey identified something more concerning: a trigger-shy market, particularly around patient care decisions. “When we’ve seen folks do that, you know, in the last couple years, what we found is that there have been some mistakes made,” he noted, referring to AI deployments in utilization management. “Some folks have gotten in trouble and I think there’s a bit of a snapback or trigger shyness that is manifest from some of those experiences.”

This observation shaped Siftwell’s positioning and sales strategy. They focused on proving value in areas where health plans were ready to adopt, avoiding the landmines that had damaged other AI companies’ reputations.

The Pressure of Product-Market Fit

Success creates its own challenges. As demand for Siftwell’s platform grew, the team found themselves in the position every founder wants but few are prepared for: drowning in customer needs.

“Right now the team feels like they’re a bit underwater, to be honest, because of the demand,” Trey admitted when discussing 2025 plans. “So we’ve got to catch up there. So big investments in kind of building out that team customer success, we really lean into that product a bit.”

This kind of scaling challenge reflects a business that’s found genuine product-market fit, not just initial traction.

Building the Autonomous Healthcare Future

Where Siftwell goes next reveals Trey’s ultimate vision: a healthcare system where AI doesn’t just predict problems but autonomously solves them.

“I imagine a future, Brett, and we’re building it where I can tell you who’s not going to get a cancer screening. I can contextualize them, including the level of rurality. I can spin up care plan associated with that member, that individual member,” Trey explained.

The vision extends to full automation: “If they live in a rural area, I’m shipping automatically a cologuard box versus somebody that lives in a more urban area. And I’m automatically setting up the appointment and doing the calls both to the provider and the patient to broker that exchange. That’s the future that I want to build and that we’re already building.”

This isn’t speculative futurism. Siftwell is actively developing agentic AI capabilities, building workflows that move from prediction to action without human intervention. “We’re already doing some of that, which is really exciting, Brett,” Trey noted. “We’re getting into agentic AI and even agentic workflows to summarize and for the presentation layer for some of our clients.”

The Next Chapter

As healthcare faces budget pressures under new administration policies, Siftwell’s value proposition becomes more acute. “I think people are going to have to get a lot sharper on how they’re spending their dollars,” Trey observed. “Health plans need to figure out how they do more with what they’ve got.”

For Siftwell, this creates opportunity: helping healthcare organizations target individual members with specific interventions ensures “those dollars can go as far as they can to improving people’s health.”

The company that started with a conversation among displaced executives during a health plan merger has become exactly what they envisioned: a solution that matches people with their healthcare needs at exactly the right time and the right level. And if Trey’s vision materializes, that matching will increasingly happen automatically, driven by AI that doesn’t just predict the future—it actively shapes better health outcomes.