Star Engine
Stars Decision Engine is an AI/ML-powered solution designed to help Medicare Advantage Plans improve CMS Star Ratings through predictive insights and data-driven decision-making. By integrating embedded intelligence, it enables payers to prioritize interventions, optimize performance, and drive sustainable quality improvements.
Healthcare
US Healthcare
UX Designer + Researcher
Data Science, SMEs, Product Stakeholders
Challenge
Medicare Star Ratings significantly impact health plans' financial performance and consumer trust. However, payers struggle with:
Fragmented insights: Siloed data across contracts, providers, and members.
Unclear prioritization: No structured way to allocate efforts for maximum ROI.
Lack of predictive intelligence: Difficulty in estimating end-of-year Star scores.
Outcome and Impact
Achieved a 4+ CMS Star Rating
Closed 3× More Gaps through AI-powered prioritization
Enabled Smarter Decision-Making with predictive analytics
Industry Recognition:
"The solution was co-developed with FluidEdge Consulting and featured as a breakthrough AI-powered healthcare tool by CitiusTech."
+4
Improved CMS STAR Rating
3x
Focused efforts aligned with CMS success criteria
3 month
Reduced planning time through smart simulation of scenarios
Process
1. Understanding the Problem Space (SME-Led Discovery)
Goal: Since no clear process for Star Ratings evaluation existed, we relied on domain experts (SMEs) to shape the foundational understanding.
Activities:
Workshops with SMEs (Quality Experts, Actuaries, and Payer Executives)
Identified pain points in current decision-making.
Mapped critical data points needed for Star Ratings improvement.
Defined what success looks like for health plans.
Shadowing & Observation of Medicare Advantage Payers
Understood how decisions were made manually before SDE.
Identified inefficiencies in tracking performance gaps.
Key Insights from SMEs:
Payers lacked predictive capabilities—they could only react after CMS scores were published.
There was no systematic way to prioritize interventions across contracts, measures, and providers.
Quality managers spent excessive time in manual analysis without clear ROI guidance.
2. Structuring the Solution (Co-Creation with SMEs & Stakeholders)
Goal: Define an AI-driven approach that integrates SME knowledge into a user-friendly decision-making tool.
Activities:
SME + UX Collaboration Workshops
Defined key decision points in the workflow.
Structured how AI recommendations should be framed for user trust.
Persona Development & Journey Mapping
VP, Enterprise Quality → Strategic oversight & contract-level impact.
Quality Manager → Tactical execution & measure-level improvement.
Concept Ideation & Wireframing
Explored various AI-powered dashboard layouts.
Mapped how data flows into decision-making steps.
Key UX Goals:
Make AI recommendations actionable and explainable.
Align insights with real-world payer workflows.
3. Prototype & Validate (SME Feedback Loop)
Goal: Test usability and validate AI insights with SMEs before deployment.
Activities:
Interactive Prototypes in Figma
Designed role-based dashboards (contract-level & measure-level insights).
Integrated predictive Star Scores and prioritization logic.
SME Validation Sessions
Ensured AI-driven recommendations matched real-world expertise.
Refined data visualizations based on SME feedback on interpretability.
Usability Testing with Quality Managers & VPs
Validated decision-making speed & efficiency.
Adjusted UI for better scanability & quick actions.
4. Deployment & Continuous Learning
Goal: Launch with real-world payers, monitor adoption, and refine AI-driven UX continuously.
Activities:
Pilot Rollout & Training for Health Plans
Live feedback sessions to monitor how users trust AI recommendations.
Performance Tracking & UX Optimization
Analyzed how users engage with insights.
Iterated on data visibility, tooltips, and next-best-actions.
Conclusion
The Stars Decision Engine transformed how Medicare Advantage Plans approach CMS Star Ratings by replacing manual, reactive processes with AI-driven, predictive decision-making. Through a user-centered design approach, the solution empowered payers with actionable insights, streamlined workflows, and sustainable quality improvements, leading to faster gap closure and higher Star performance.