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.