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Strategic Communications Advisory For Visionary Founders
Actionable
Takeaways
Leverage Operator Experience in Product Development:
Siftwell's success stems from combining deep industry expertise with advanced technology. As Trey explains, many technologists enter healthcare with "cool mousetraps" but don't understand the complexity of problems facing managed care organizations. B2B founders should either deeply learn their target industry or partner with domain experts who can translate technical capabilities into meaningful solutions.
Focus on Results Over Technical Specifications:
While model accuracy matters, Trey emphasizes that customers care more about concrete outcomes like reduced emergency department visits or lower readmission rates. B2B founders should prioritize communicating business impact over technical superiority, especially when selling to C-suite buyers who care about operational results.
Navigate Category Creation Through Problem-Solving:
Rather than forcing themselves into existing categories, Siftwell created their own space by solving specific customer problems. They used the familiar entry point of predictive analytics but differentiated by combining it with deep operational expertise and actionable insights. B2B founders should consider how their unique approach to solving customer problems might transcend traditional category definitions.
Build Go-To-Market Around Industry Context:
In healthcare's highly referential market, Siftwell prioritizes face-to-face interactions, speaking engagements, and leveraging existing relationships. They focus on educating prospects about practical implementation rather than technical complexity. B2B founders should align their marketing strategy with their industry's buying patterns and decision-making culture.
Choose Strategic Investors for Long-Term Success:
Trey emphasizes the importance of founder due diligence in fundraising, particularly in finding investors who understand the industry, company culture, and vision. B2B founders should look beyond capital to ensure their investors can provide relevant expertise and support for their specific market.
Conversation
Highlights
From Health Plan CEO to AI Pioneer: How Siftwell Analytics is Transforming Healthcare Operations
When a health plan merger created an inflection point, Trey Sutten didn’t follow the conventional path of joining the combined organization. Instead, the former Medicaid CFO and health plan CEO saw an opportunity to solve one of healthcare’s most persistent challenges: matching resources with patient needs at exactly the right time.
In a recent episode of Category Visionaries, Trey shared how Siftwell Analytics has raised $5 million to build technology that helps health plans predict the future. But more importantly, he revealed how they’re succeeding in a market where many AI healthcare companies have stumbled.
The Genesis: Starting with Operator Insight
The journey began with a crucial question to fellow C-level executives: what does the market actually need? “When we knew we were going to merge the previous plan, I started to collect the C-level executives and say, ‘Hey, now that we’re figuring out what our next thing is… what is it that the market needs? Where are their holes?'” Trey recalls.
Their collective experience pointed to a fundamental problem: healthcare organizations needed to better align resources with individual needs. But before building anything, they needed to prove their technology could outperform existing solutions.
Validation Before Scale
Rather than rushing to market, Siftwell took a methodical approach:
- Obtained a sample dataset with millions of lives
- Assembled data scientists for a competitive analysis
- Convinced a health plan to provide real, live data for testing
This careful validation paid off. “That was pro bono, and we’ve since converted them to a paying client,” Trey explains. Success with this initial client built confidence in both the technology and their ability to solve unique problems in healthcare.
The AI Adoption Reality Check
Healthcare’s relationship with AI isn’t straightforward. Trey identifies three distinct levels of adoption:
- Level 1: Ad hoc tools for marketing and HR
- Level 2: Back office automation and fraud detection
- Level 3: Patient-to-provider settings and medical decisions
Many companies have struggled with Level 3. “When we’ve seen folks do that in the last couple years, what we found is that there have been some mistakes made,” Trey notes. This has created “trigger shyness” around AI in clinical settings.
Building Trust Through Understanding
At a Digital Health New York panel, Trey witnessed something revealing: founders struggling with basic healthcare terminology. This highlighted a crucial advantage for Siftwell. “Learn the space or learn one space and really focus on it,” Trey advises. “The healthcare industry is plagued by technologists that have really cool mousetraps but don’t understand the complexity of some of the problems that healthcare, particularly managed care organizations, are facing.”
Beyond Predictive Analytics
While Siftwell operates in the predictive analytics space, they’ve created their own category. “This isn’t about how accurate your model is,” Trey emphasizes. “It’s about your model telling me that an individual needs an intervention so that their use of an emergency department or their readmission rates go down.”
This focus on concrete outcomes rather than technical metrics shapes everything from product development to sales conversations. For instance, when helping a client improve cancer screening rates, they didn’t just identify 12,000 members unlikely to participate – they explained why different cohorts within that group might skip screenings, enabling targeted interventions.
The Future Vision
Looking ahead to 2025, Siftwell is building toward automated, personalized care coordination. Trey envisions a system where “I can tell you who’s not going to get a cancer screening, I can contextualize them, including the level of rurality… and if they live in a rural area, I’m shipping automatically a cologuard box versus somebody that lives in a more urban area.”
This vision becomes increasingly important as healthcare organizations face tighter constraints. “People are going to have to get a lot sharper on how they’re spending their dollars,” Trey predicts. “I don’t think that there’s going to be more money going into the next administration.”
Success in healthcare AI isn’t just about better algorithms – it’s about understanding the operational complexities and delivering measurable outcomes. By combining deep industry expertise with advanced technology, Siftwell is showing how to bridge the gap between AI’s potential and healthcare’s practical needs.
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