The Stakes
When a patient's condition begins to deteriorate in a busy hospital ward, the window for early intervention is narrow. Miss it, and the cost is measured in outcomes: longer recovery, worse prognosis, sometimes a life. The problem isn't that clinicians don't care. It's that the information they need to act early isn't visible until it's almost too late.
Peach IntelliHealth had already solved the hardest part: an AI/ML engine capable of predicting acute deterioration risk up to 24 hours before symptoms would otherwise prompt action. My job was to solve what came next: designing the interface that put that intelligence into the hands of the people who could act on it.
The problem I was handed
The predictive engine was powerful. The interface presenting its output was not. Patient status, SOFA scores, SIRS flags, vitals, risk tables, and clinical graphics competed for equal attention on the same screen. For a medical professional making a care decision at 3am, that cognitive load wasn't an inconvenience, but a clinical risk in itself.
Every unnecessary label, every ambiguous data point, every extra tap was a moment of friction between a clinician and the action they needed to take. The interface had to get out of the way.
What I did
• Working alongside the design lead and development team at LKMX, I redesigned the key UI components and created the detailed mockups that translated the algorithm's output into something a time-pressured clinician could use without friction: on a mobile device, at any hour, anywhere in the hospital.
• The core design decision was about information hierarchy. I restructured the interface around clinical priority rather than data category; so the highest-risk signals surfaced immediately, and the number of taps required to act on them dropped. What the clinician needed to decide right now became the first thing they saw.
• The unexpected challenge was my own knowledge gap. I had no medical vocabulary when I started. Learning what a SOFA score meant (not just as a label, but as a clinical signal that meant something different at 2am than at 2pm) shaped every design decision I made. Domain immersion wasn't background research. It was the design work.
Design Process
Key wireframes
Key views
Final mockup
The Outcome
The numbers were validated with clinical rigor across a 9-month hospital intervention:
✔ 30% reduction in patient Length-of-Stay. Average LOS fell by 30% during the intervention period. When the application was phased out, LOS returned to pre-intervention levels. That controlled reversal confirmed the design's causal contribution: not correlation, not coincidence.
✔ 24-hour early warning anywhere in the hospital. The mobile interface gave healthcare professionals round-the-clock access to predictive risk scores, enabling care escalation a full day before symptoms would have otherwise prompted action, and making critical patient data accessible beyond nurse stations, wherever clinical judgment was needed.
What's next
Peach IntelliHealth was built in 2017, before AI tools were publicly accessible, which makes the clinical outcomes even more striking in retrospect. The platform was ahead of its time, but COVID interrupted conversations with angel investors before the product could scale. In 2026, the original software engineer and I are in active talks to reboot the project, adapting the interface for current AI capabilities, expanding device integration to include Apple Watch, and making the platform more accessible to a broader clinical audience.
What I carried forward
This project permanently changed how I think about information hierarchy. Before Peach, I thought about it as an aesthetic and usability concern. After Peach, I think about it as a decision-support problem: 'what does this person need to decide right now, and is that the first thing they see?' I've applied that question to every significant project since.
• Tools: Sketch, OmniGraffle (this project predates my current Figma workflow).
• Role: Digital Designer: UI component redesign and detailed mockup creation, in collaboration with the design lead and development team at LKMX.
• Context: Singaporean hospital environment, AI/ML clinical platform, 9-month intervention study.
AI for better patient care
