Overview

CardioVisio is an AI-powered clinical decision support application designed to help cardiologists make informed treatment decisions for patients with atrial fibrillation. The platform brings together patient history, clinical evidence, and guideline-based recommendations into a single experience, enabling physicians to make faster and more confident decisions.

As part of a three-member design team, I contributed to the Decision Support module by leading two initiatives that improved both usability and scalability: developing a consistent clinical iconography system and validating key design decisions through rapid user research.

The Challenge

Clinical decision support interfaces present large volumes of information within a limited space. Every visual element—from icons to terminology—must communicate medical concepts clearly while complying with clinical expectations and regulatory guidance.

The product team also needed quick validation around how clinicians interpreted regulatory information and treatment indications before moving into development.

Key challenges included:

• Representing complex medical concepts using intuitive visual language
• Maintaining consistency across numerous clinical recommendations
• Validating regulatory and indication-related content with representative users
• Coordinating design decisions across product, clinical experts, and an external design agency

Discovery

To reduce uncertainty early in the design process, I planned and conducted a focused research initiative around regulatory information and indication workflows.

Moderated User Research

I developed the research objectives, discussion guide, and testing script before conducting sessions with representative users.

The goal was to understand:

• How clinicians interpreted regulatory information
• Whether indication-related content was easily understood
• What information influenced clinical confidence during decision making

The findings provided the product team with evidence to validate key design decisions before implementation.

Placeholder — swap in the research discussion guide or a session clip.

Design Evolution

Building a Scalable Clinical Icon System

The Decision Support module initially required 61 custom clinical icons to represent various recommendations, conditions, and treatment pathways.

Rather than designing each icon independently, I audited the requirements to identify recurring patterns and opportunities for reuse.

Working closely with an external design agency and our clinical experts, I developed a strategy to consolidate similar concepts into a reusable visual language.

This reduced the total icon library from 61 to 42 icons, improving consistency while significantly reducing design effort and external agency costs.

More importantly, the resulting icon library established a scalable foundation that could support future product enhancements without introducing unnecessary visual complexity.

Placeholder — swap in the icon library grid, before and after consolidation.

Cross-functional Collaboration

Throughout the project, I coordinated reviews between designers, clinical experts, product managers, and the external agency to ensure every visual asset accurately represented the intended clinical concept before approval.

This iterative review process helped balance usability, medical accuracy, and implementation feasibility.

Outcome

The work contributed to a more consistent and scalable Decision Support experience by combining design standardization with early user validation.

Key outcomes included:

• Reduced the icon library from 61 to 42 reusable clinical icons
• Established a consistent visual language for the Decision Support module
• Validated regulatory and indication workflows through moderated user research
• Improved collaboration between design, product, clinical experts, and external partners
• Created reusable design assets that supported future product scalability

What I Learned

This project reinforced that designing enterprise healthcare products isn't only about creating interfaces—it's about creating systems that can evolve over time.

By identifying reusable patterns, validating assumptions with clinicians, and coordinating across multiple stakeholders, I was able to contribute to a solution that balanced usability, clinical accuracy, and long-term scalability.

The experience strengthened my appreciation for the role of design in simplifying complexity while building confidence among both users and cross-functional teams.