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From Surveys to Interviews:
Uncovering Care Platform Opportunities
Background
PMP is Health2Sync's clinic-facing platform, helping physicians, nurses, and dietitians track follow-ups and review patients' recent physiological data. I initiated this research to understand where the existing experience falls short of how healthcare providers actually work, and to uncover opportunities for new features.
Given our limited team size, I took a two-phase approach: a screening survey to understand roles, clinic sizes, and usage patterns (40 valid responses), followed by 12 in-depth field interviews with a sample deliberately skewed toward low-satisfaction users to surface high-value opportunities.
I led the research design, interviews, and synthesis, and independently analyzed GA4 data to validate outcomes, distilling 42 opportunity areas, prioritizing them to guide the next product iteration, and driving a first round of low-cost design changes.
Problem Exploration
Before the interviews, we ran a survey covering usage frequency, patient load, and satisfaction across three core tasks: finding priority patients, viewing data charts, and messaging patients.
Three signals emerged from the valid responses, answering where to dig deeper, what makes that direction worth pursuing, and how to pick interview candidates who'd cover the full picture.
Signal 1
Of the 3 core moments, "finding priority patients" had the weakest satisfaction
The easiest of the three to overlook.
Signal 2
Different roles use very different ways to find patients, with no shared pattern
The easiest of the three to overlook.
Signal 3
Different roles carry very different patient loads and work in very different ways
This is why "finding priority patients" can't be solved from just one role's perspective.
Selection targeted HCPs who rated satisfaction as only "neutral" or "agree" — not "strongly agree." Partway through, a few clinic types turned out to have less insight to add, so the candidate pool grew from 8–9 to 12.
Design Strategy
From interview notes to a list of opportunities
I recorded all 12 interviews. For each finding, I wrote it on a sticky note, grouped the notes by theme on a wall, and checked the groupings with my team. This turned scattered notes from 12 transcripts into one clear list of 42 opportunities.
View the full breakdown in Figma
How I ranked them？ mentions, number of clinics, and difficulty
① Quick Wins (add to this round's backlog)
② Items that need more planning
The most-mentioned, highest-impact item on the list — feature discoverability — came up again and again: clinicians knew features like AI analysis, tracking records, and patient settings existed, but didn't know how to find or use them. This is worth doing, but not worth rushing. Rather than jumping straight to a full key-feature touring system, it still needs more time to scope how far to take it.
Appendix: Prioritized HMW opportunities from the interview findings
Out of the 42 opportunities, I'm walking through the four below in detail. The rest are mostly small tweaks, like swapping an icon, where there isn't much design thinking to unpack beyond a visual change. These four involved more substantial problem solving, so they're a better showcase of how I actually work through a design decision.
➊ Clarifying Feature Meaning: From Overlooked to Worth Exploring
Several users had never tried these features because the icons were unclear. I refined the icons, labels, and layout to make each action easier to understand while reducing screen space.
➋ Simplifying Multi-Day Trends: From Visual Clutter to Quick Focus
Overlapping lines made the 14-day view difficult to read. I reduced the default range to 7 days and added hover highlighting to help users focus on individual trends.
➋-1 Further Exploration: Quick CGM Overview
I also explored a grid-based view that separates daily CGM trends to reduce visual overlap. The team saw potential in the concept, but given the development cost, we decided to first validate the 7-day default and hover highlight improvements.
➌ Completing the Reply Flow: From Interrupted to In-Context
Users had to leave the enlarged photo view to quote-reply. I added Quote Reply directly to the enlarged view to keep the workflow uninterrupted.
➍ Streamlining Chart Sharing: From Context Switching to One-Click Sharing
Clinicians had to take a screenshot and switch screens before sharing CGM trends with patients. I brought capture and sharing into the CGM view to reduce steps and context switching.
Impact & Validation
📊 Quantitative Results
After the label update, AI Analysis events increased by 50% and active users by 17%. Note usage declined, possibly due to continued reliance on paper records and accidental taps caused by the previous icons.
Validation Limitations
Due to limited event tracking and engineering resources, other improvements were evaluated mainly through qualitative feedback. Overall, clinicians found the updated experience clearer and easier to use, while also identifying opportunities for further improvement.
What's Next
・Several completed designs are now in the backlog and will move into development based on priority.
・Feature discoverability remains a key challenge. Next, we’ll explore more effective guidance and validate solutions incrementally before committing further engineering resources.
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