Siftwell

Siftwell Analytics, a healthcare technology company based in Charlotte, was established in 2022 by Trey Sutten, Chuck Hollowell, and Eben Esterhuizen. The founding team combines deep operational experience in the healthcare sector with entrepreneurial expertise. Trey Sutten, the CEO, and Chuck Hollowell, the President, are veterans of the managed care industry, with Sutten previously serving as CEO and Hollowell as General Counsel at Cardinal Innovations Healthcare. Their firsthand experience with the challenges faced by health plans informs the company's core mission. Eben Esterhuizen, the CTO, is a successful repeat entrepreneur who brings a strong technical background to the team.

The company operates in the health-tech market, providing an AI-based analytics-as-a-service platform for community-oriented health organizations, including Medicaid, Medicare Advantage, and commercial health plans. Siftwell's business model is designed to help these healthcare payers improve quality scores and cost profiles by identifying at-risk individuals who are likely to incur future costs. Revenue is generated through custom pricing based on the specific needs and scale of each client organization. In February 2024, Siftwell announced the closure of a $5.8 million seed funding round led by AlleyCorp, Arkin Digital Health, Tau Ventures, and The Charlotte Fund. This capital is intended to fuel team expansion, platform development, and market growth into new states.

Siftwell's platform leverages causal and explainable AI to analyze complex data sets and provide actionable insights. It integrates various data sources, including claims data, health risk assessments, call center information, and social determinants of health (SDoH) like access to food and transportation. This allows the platform to move beyond simple risk stratification and offer prescriptive analytics that identify not just *who* is at risk, but *why* and *what* can be done about it. The system identifies patterns and creates 'data twins'—clusters of individuals with similar characteristics—to predict member trajectories and suggest specific interventions. This enables health plans to prioritize outreach, tailor interventions to individual needs, and proactively manage conditions such as COPD, depression, hypertension, and obesity, ultimately improving member health outcomes and optimizing the allocation of resources.

Keywords: healthcare analytics, population health management, AI in healthcare, community health plans, predictive analytics, social determinants of health, value-based care, care management, risk stratification, health equity, managed care, clinical data analysis, payer solutions, HEDIS, star ratings, member engagement, prescriptive analytics, cost of care, proactive healthcare, health plan operations