UX Research
Design System
User Interviews
Prototyping
Airudi
A smart workforce solution for nursing teams
Feature
AI Nurse Assignments
Timline
5 months
July 2025 – Dec 2025
ROle
End-to-End
Product Designer
worked with
1 PO, 4 Devs, 2 ML
🔒
Details have been generalized and visuals removed to protect confidentiality.
project overview
As the sole designer, I led the end-to-end design of an AI-powered solution that simplified nurse shift assignments and clarified workload distribution.
Problem
Challenges in balancing nurse workloads
Nursing staff shared that administrative tasks often added strain, especially when workloads felt uneven.
High-effort tasks aren’t always accounted for in assignments.
Managers, on the other hand, had limited visibility and fragmented tools to coordinate assignments smoothly, making it harder to maintain balance across teams.
Goal
Creating fair and efficient nurse workloads
💭
How might we make workload distribution fairer for nurses and easier for managers?
⚖️
Support balanced workloads and improve perceived fairness/trust
👣
Reduce manual coordination steps and tool switching in assignment workflows
⚡
Enable faster adjustments during shift changes and exceptions
🧑🏽💻
Ensure the experience remains usable under real-world constraints (interruptions, time pressure, role-based coordination)
result
What our nurse users valued most
I shared prototypes with target users through usability testing and feedback sessions to identify what resonated most and guide iterative refinements.
Better workload transparency
Transparency cues reduced uncertainty and improved perceived fairness
Eliminated tool switching
Reduced tool switching and eliminated reliance on memory
More stable assignments
Stronger support for mid-shift changes
Decisions
Key design decisions
Constraints
How I navigated challenges along the way
⌚
Tight timeline
The project spanned five months, with under four weeks for design. I worked in an agile setup, sharing designs with developers as soon as they were ready.
🤝
Trust requirements for AI-assisted workflows
We wanted to ensure nurses and clients felt confident switching to AI tools. I addressed this by gathering documents, mapping as-is processes, observing fieldwork, and validating designs with nurses and clients.
⚙️
Complex system
The system wasn’t just a simple flow with a few edge cases-there was a lot of information to consider, like a real nurse would. I led weekly design meetings with nurse users to adapt the design to operational realities.
🔌
Uncertain Integrations
With client integrations uncertain and delayed, I designed interfaces that remained fully usable and valuable for users in either scenario, with or without integrations.
reflection
Closing thoughts
With more time, I would have broadened usability testing across additional roles and contexts, explored edge cases over longer work periods, and examined more configurability to better support variation in workflows and environments. Early formative feedback during design reviews was directionally positive, but outcomes were not measured quantitatively.
Prioritizing Scalability
I realized that scalability needed to be treated as a core requirement rather than a future consideration. This shaped how I approached reusable patterns, system alignment, and designing for adaptability beyond a single initiative.
© Hailee Kim. 2025
