Goal & Objectives:

  • Improve user confidence in symptom assessment.

  • Simplify the process of inputting symptoms and understanding results.

  • Ensure the AI-generated recommendations are clear, actionable, and medically reliable.

 

User Research & Key Insights

Research Methods: Competitive Analysis, User Testing (via usertesting.com, Data Dashboard Analysis

Participants: 30 users across different demographics

Key Findings:

  • Confusion: Users were often uncertain about medical terminology and needed simplified language.

  • Trust & Accuracy: Users worried about the reliability of AI-generated diagnoses, requiring clear disclaimers and confidence scores.

  • Mobile Usability: 70%+ of users accessed the tool on mobile devices, meaning a mobile first design was crucial.

 

Design Process & Iterations

1. Wireframing & User Flows: Mapped out simplified symptom input flows to reduce confusion.

2. Prototype Testing & Feedback: Ran usability tests using Usertesting.com → Users found the AI recommendations too technical, so we revised copy for clarity.

3. Iterating & A/B Testing:

  • Variant A: Used medical jargon

  • Variant B: Used simplified symptom descriptions

  • Result: Variant B led to 30% higher comprehension among users.

4. Final UI Enhancements: Optimised for mobile-first, improved navigation, and added a PDF Report Feature for better information sharing with users GP / Doctors.

 

Results & Impact

  • Engagement Up: 20% increase in users completing the symptom check process.

  • Improved Accuracy Perception: 85% of users trusted AI-generated recommendations after UI and copy improvements.

  • High Adoption of PDF Report Feature: 60% of users downloaded or shared their PDF reports.

  • Reduced Drop-off: 15% fewer users abandoned the process after improving symptom entry UX.

 

Challenges & Lessons Learned

  • Trust in AI: Users initially doubted AI-driven results, so we introduced confidence levels and disclaimers.

  • Medical Accuracy vs. Simplicity: Balancing doctor-approved terminology with user-friendly language was tricky.

  • Data-Driven Decisions: A/B testing and analytics helped shape design choices rather than just assumptions.

 

My Roles and Responsibilities

  • Competitor Research and Analysis:
    Conducted research and analysis of competitors to inform design decisions and strategy.

  • Workshops and Collaboration:
    Organised and participated in workshops, collaborating with cross-functional teams to drive alignment and innovation.

  • User Testing:
    Utilised the Usertesting.com platform to gather user feedback on design prototypes, ensuring user-centric improvements.

  • A/B Testing: Collaborated with the research team to drive design inputs for A/B testing, validating decisions and informing data-driven improvements.

  • Improving User Journeys and Flows:
    Revisited and refined user journeys and flows to enhance the overall user experience.

  • Designing and Maintaining SSC Screens:
    Designed and maintained the SSC screens in Figma, with a mobile-first approach.

  • Design Review and QA Support:
    Reviewed designs during development stages and provided support for quality assurance (QA) through design reviews.

  • Creating Prototypes:
    Developed working prototypes for new features to validate and refine design concepts.

  • Content and Feature Updates:
    Reviewed and designed updates to improve copy, content, features, and functionality.

  • Monitoring Data Insights:
    Monitored the Symptom Checker ‘Data Dashboard’ for insights to guide further enhancements.

  • Data Analysis for Improvement:
    Collaborated with data analysts to identify areas for improvement based on user data.

  • Creating a PDF Report Feature:
    Designed a ‘PDF Report’ feature that allows users to view their consultation outcomes, send them to their email, and download them for sharing with their GP or Doctor.