Quest Analytics

"Turning healthcare data into access"

Kansas City, Kansas
United States

Last login:
9/29/2025

Quest Analytics Introduction

Overview:

At Quest Analytics, I designed and launched a new mapping feature that empowers clients to visualize healthcare provider networks more intuitively. This feature was critical for helping clients analyze network adequacy, performance, and provider distribution at scale.

My role involved leading the design process from initial sketches to validated high-fidelity prototypes, collaborating closely with product management, engineering, and end users along the way.

Problem Statement:

Clients needed a way to quickly and accurately understand provider network coverage within specific geographies. Existing workflows required multiple steps, making it difficult to spot gaps or validate network adequacy efficiently.

The challenge:

Make the mapping feature intuitive enough to be usable during high-stakes analysis.
Ensure it was lightweight enough to avoid performance issues given the massive data sets involved.
Validate that the feature delivered on the real-world needs of clients across the U.S.

The Conclusion

The Outcome:

The mapping feature delivered measurable improvements for both clients and internal teams:

40% faster workflows: Client teams were able to complete geographic coverage analysis in nearly half the time compared to previous methods.
30% reduction in support tickets related to network visualization, indicating the feature’s improved intuitiveness and ease of use.
High adoption at launch: Within the first quarter, over 75% of active clients incorporated the new mapping feature into their workflows.
Positive client feedback: Post-summit surveys showed a 4.6/5 satisfaction rating, with users noting the clarity of the visualization and time saved in validating network adequacy.

These metrics validated the design approach and demonstrated tangible ROI, positioning the mapping feature as a core differentiator in Quest Analytics’ product offering.

Reflection:

This project underscored the value of early alignment through sketches and wireframes and the impact of engineering collaboration on balancing user experience with performance. Testing at the national summit also proved invaluable, validating intuitiveness and surfacing features clients truly cared about.

That said, there are areas I would approach differently in the future:

Earlier access to end users: Client testing only happened at the summit, which limited opportunities for iterative feedback before high-fidelity work. Setting up smaller usability sessions earlier could have helped us refine details sooner.
Performance prototyping: While we discussed feasibility with engineering, lightweight prototypes simulating large datasets might have uncovered performance constraints earlier in the design cycle.
Long-term adoption tracking: We measured initial adoption and satisfaction, but setting up a framework for ongoing usage metrics would provide richer insights into how the feature evolves over time.

Overall, the project reinforced my belief in collaborative, hypothesis-driven design — and also highlighted the importance of continuous, earlier, and more frequent user engagement throughout the process.

Quest Analytics is in your extended network

The Process

Early Exploration:

I began with hand-drawn sketches to map out possible interactions and flows. These low-fidelity explorations helped me and product management quickly align on key objectives: clarity, speed, and flexibility in viewing network coverage.

Sketching & Wireframing:

Moving into sketches and wireframes, I iterated on layout and interaction patterns—balancing the complexity of provider data with usability. I ran multiple reviews with product management to refine feature scope and functionality before investing in visuals.

Iteration & High-Fidelity Prototyping:

I transitioned into high-fidelity wireframes, where I tested different visualization styles and layering techniques. During this phase, I partnered closely with engineering to evaluate technical feasibility and performance impact.

For example, certain design choices had implications for rendering speed when working with large data sets.
Through collaboration, we adapted the design to meet performance needs without sacrificing user experience.