Background
The mission was simple: make the hospital journey easier for patients and smoother for staff.
The problem was that patients were waiting too long, transferring between departments without clarity, or feeling like they were just another number. On the healthcare side, care teams were dealing with disconnected systems that slowed them down and made their work more difficult than it needed to be.
My focus was on designing a way to guide each patient through their care journey with more intention, fewer delays, clearer communication, and a stronger sense of being cared for. The idea wasn’t only about efficiency; it was about dignity, trust, and the human experience of healthcare. To do this I looked at how AI and data could play a role in routing decisions alongside a user-interface that parallels the existing task flows.
My Role
Design Lead
Responsibilities
Foundational Design Process
Persona Development
UX Research
UX Strategy
Deliverables
Sketches
Wireframes
Mobile prototype
Tools
Design Thinking Workshops
Figma
Timeline
3 weeks
Challenge
Hospitals are complex systems, but the tools guiding patients and staff often feel disconnected. Patients faced long waits and uncertainty, unsure of their next step. Staff juggled fragmented processes, where every handoff risked delays.
The opportunity statement: Design a system that blended human insight with machine support, guiding patients through their care journey in a way that feels seamless, informed, and compassionate.
The challenge was to improve time and efficiency. The goal was to create pathways that respected time, reduced stress, and supported care teams, while exploring how AI could assist without compromising human interactions.
Approach
I started by mapping the entire patient journey, step by step. I conducted user interviews with personnel from admission, triage, diagnostics, treatment, recovery, and discharge. Each stage revealed friction points that slowed care and created stress for both patients and staff. I spoke with nurses, shadowed patient coordinators, and listened to patients describe what it felt like to move through the system. Those stories shaped the foundation of my design approach.
From there I began prototyping ways to bring more clarity and flow to the process. I sketched early journey maps, tested low-fidelity wireframes with staff, and facilitated workshops where teams could imagine what a better routing system might look like. A high-level of collaboration with the people inside the hospital knew the problems better than anyone.
Alongside this human-centered work, I explored how data and AI could layer in intelligence without overwhelming users. Could predictive models help forecast bottlenecks? Could smarter routing suggestions reduce unnecessary handoffs? The goal wasn’t to replace human judgment, but to give teams a clearer view of what was happening and a strong prediction of what might happen next.
Design Solution Framework
Through iteration and testing several clear solutions emerged.
Guided Patient Routing
A central tool that gave staff a real-time view of patient flow, reducing guesswork and making handoffs smoother.
Clear Patient-facing Touchpoints
Simple, intuitive updates for patients so they knew what to expect and where to go next.
AI-Powered Predictions
Early models that flagged potential bottlenecks before they became critical, allowing staff to act proactively.
Streamlined Communication Channels
Fewer systems to juggle, with clearer connections between departments, so staff can focus on patient care.
UX Success Factors
Human-centered Discovery
Grounding the design in patient and staff stories ensured solutions addressed real pain points, not assumptions.
Collaborative Process
Involving nurses, coordinators, and hospital staff throughout prototyping created solutions they trusted and adopted.
Simplicity First
Designing clear, minimal touch points for patients and staff reduced cognitive load in an already stressful environment.
Iterative Testing
Rapid prototyping and feedback loops kept the project responsive, validating decisions before scaling.
AI as a Quiet Partner
Using predictive insights in subtle, supportive ways helped prevent bottlenecks without overwhelming users.
Key Outcomes
$2.1M
estimated annual labor savings
$250-$500K
estimated annual operational savings
$2.3M
estimated total annual savings
Human-centered Improvements
Reduced manual data entry and communication loops between departments.
Improved visibility into patient flow to help teams anticipate bottlenecks and adjust in real time.
Streamlined scheduling and patient routing workflows resulted in fewer delays for patients.
Labor Value
Estimated annual labor savings of 4,000–6,000 staff hours, with staff saving an average of 2–3 hours per shift.
Operational Impact
Reduced overtime, smoother routing, and fewer handoffs all contribute to 10-20% indirect operational savings.
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