by mfine
Platform
App
My Role
Design Lead
Industry
Healthcare
Product Duration
2 - 3 MONTHS
When we started this project on MFine’s doctor listing experience, the core goal was simple yet critical: help users find the right doctor faster and more reliably — and in doing so, improve overall consult conversions.
The existing doctor list page was functional, but it severely under-served users:
Users struggled to surface the right doctor by criteria that mattered most to them, like experience, language or availability.
Data and user conversations made it clear: the discovery experience wasn’t aligned with real user needs.
As the Lead Designer, I owned the end-to-end design process — from user research and competitive analysis, to interaction design, micro-interactions, and validation with engineering and product stakeholders.
On the doctor listing page, we introduced:
✅ Full Search Capability — search by name, symptom, specialty, language, hospital, and location.
✅ Enhanced Filters — sorted and searchable filters by experience, language, availability, gender, and more.
✅ Dynamic Sort Options — from most experienced to earliest available and fee-based sorting.
✅ Previously Consulted Doctors Filter — enabling repeat users to quickly pick familiar doctors.
✅ Improved Interactions & Micro-Interactions — intuitive feedback, disabled states, contextual messaging.
Introducing these enhancements delivered meaningful improvements:
📈 Consult conversion increased by 12 percentage points (40% → 52%)
🔁 Repeat user conversion grew by 5pp (38% → 43%)
📉 Filter abandonment dropped from 55% to 6%
➕ Search adoption improved discovery success
Users who applied filters and search mechanisms converted significantly better than those who didn’t.
Users are behavioral as much as they are task-oriented — meaningful filters and search patterns accelerate decision-making.
Affinity for known doctors (repeat consultations) is a powerful leverage point.
Even subtle improvements in micro-flow and feedback can drastically reduce friction and abandonment.
Conversion improved by search
People using search are converting better by 9p.p
Filter abandonment rate reduced
Filter abandonment reduced to 6% from existing 55%
Sort abandonment rate reduced
Sort abandonment reduced to 33% from existing 53%
Conversion improved by sort
People applying sorters were converting better by 2p.p
Conversion improved by PCD filter
People applying PCD filter were converting better by 12p.p
Overall Conversion Improvement
People applying sorters were converting better by 2p.p
Repeat Conversion Improvement
Overall repeat user conversion improved by 5p.p
