Team Select and Cuesta Partners were recently honored with a Silver Anthem Award—part of the Webby Awards program—for Best Use of AI in Health. The recognition reflects a solution that does more than score well on technical merit. It embodies responsible innovation, thoughtful design, and measurable impact on real patient outcomes.
The award-winning model predicts which home health patients are at highest risk of hospitalization, giving care managers and clinical leaders early insights that enable timely, preventative interventions. But the predictive model is only one part of a broader transformation story—one built on strong data foundations, rapid delivery, and a focus on solving meaningful operational and clinical challenges.
Laying the Foundation: Modernizing Data to Accelerate AI
Before AI development began, Cuesta and Team Select partnered to build the data infrastructure required for scalable advanced analytics—while simultaneously delivering immediate value to the organization in the form of rapid-development dashboards that provided critical and previously inaccessible insights.
Over the initial months, Cuesta delivered:
- Critical patient care dashboards providing new visibility into clinical performance and eliminating gaps in care.
- Financial insights dashboards that clarified drivers of revenue, cost, and outcomes.
- An extensible Business Data Model unifying disparate systems and creating consistent, trustworthy definitions.
This work wasn’t preparatory—it generated significant value on its own while also establishing the data maturity needed for reliable predictive modeling later. This reflects Cuesta’s philosophy: deliver actionable outcomes quickly, while building the foundation for long-term capability.
Identifying a High-Impact AI Opportunity
With foundational data in place, Cuesta and Team Select jointly evaluated where AI could make the greatest impact. The goal was not to deploy AI broadly, but to focus on a use case where AI would tangibly improve outcomes and be operationalized effectively.
Home health respiratory care emerged as a compelling opportunity. Pediatric and adult patients with respiratory conditions often experience rapid, hard-to-spot deterioration. Care managers—managing large, complex caseloads—needed earlier, more reliable indicators of risk.
A predictive model could meaningfully change care delivery, but only if:
- the data could support accurate predictions,
- the insights were clinically interpretable, and
- the workflow integration was designed deliberately.
This became the cornerstone AI initiative.
Building an AI Solution Designed for Use, Not Just Accuracy
Cuesta worked closely with Team Select’s clinical leadership to build a model that clinicians could trust and act on. The work centered on three principles:
- Clinical co-design
Clinical leadership participated directly in shaping model features, reviewing outputs, and defining how risk should be surfaced. - Actionable, interpretable insights
The model provides both risk scores and key contributing factors—clarifying why a patient is at risk, not just that they are. - Workflow-first integration
The team co-created a clear operating model specifying how often risk is reviewed, by whom, and what intervention steps follow; based on current workflows to minimize disruption.
The solution was piloted, refined based on real-world use, and then deployed more broadly. Adoption was achieved not through mandates but through thoughtful alignment with existing clinical practice.
The Impact: Better Outcomes, Better Coordination, Better Decisions
Since rollout, the model has demonstrated meaningful clinical and operational improvements:
- Earlier identification of high-risk patients, enabling preventative intervention.
- Reduced hospitalizations, benefiting patients, caregivers, and payors.
- More coordinated care, supported by a shared, data-driven view of patient risk.
- Faster, clearer decision-making across clinical and operational teams.
These results are what earned the Silver Anthem Award: a solution that uses AI responsibly to improve lives.
A Playbook for Mid-Market Leaders Pursuing AI
This project offers several lessons for mid-market organizations navigating AI opportunities:
Data first, AI second
With a well-established data foundation traditional AI models can be developed with more confidence. A robust data foundation enables comprehensive views of business operations and provides additional context for model training and deployment.
Lead with value, not technology.
Focus on problems where AI can measurably change outcomes—not where it simply feels innovative.
Start small to contain the investment and risk.
The larger vision is to use predictive AI across the entire patient population – but that’s a “boil the ocean” problem that could have taken ages to solve properly before seeing value. By restricting the first release to respiratory patients, we were able to focus on a more manageable set of variables and outcomes, deliver value sooner, and get learnings that would support each turn of additional diagnoses or body systems en route to getting full coverage.
Design for adoption.
Workflow integration and human-centered design matter as much as the model itself. In fact, the #1 reason AI products fail is not the product, but the lack of planning for adoption. In this case, we worked together on-site for two days to whiteboard short- and long-term goals, current processes, and key outcomes to align on both an updated operating model and a plan to introduce, roll out, and closely support this model with a detailed Hypercare plan.
Conclusion: Responsible AI at Speed
The Team Select and Cuesta partnership demonstrates what’s possible when modern data infrastructure, high-value use case selection, and human-centered AI design come together. The Silver Anthem Award validates not just the predictive model, but the approach behind it—one that mid-market organizations can replicate to unlock meaningful, scalable impact.
If your organization is exploring AI opportunities, the path forward begins with the same principles: strong data foundations, disciplined prioritization, and solutions built for the people who will use them.
