AI SHIFT PREDICTIONS
Designed an intelligent shift prediction interface that helps operations teams anticipate workforce needs using AI.
THE CHALLENGE
We discovered that users were spending between 25% and 50% of their time in the app simply posting shifts and another 25% to 50% canceling them. The process required multiple clicks and repetitive steps, creating unnecessary friction and frustration for facility operators.
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Many users were overposting shifts to avoid being understaffed, then canceling the extras at the last minute. This not only created inefficiencies but also caused frustration for healthcare workers who had accepted those shifts. The behavior pointed to deeper workflow issues rather than just a lack of discipline or organization.
ROLE & SCOPE
I served as the lead designer, responsible for shaping the user experience of predictive tools. My work included defining user requirements based on research, translating requirements into clear visual formats, structuring workflows that connected prediction with action, and aligning design priorities with product goals. I collaborated closely with data scientists, product leadership, and engineering throughout the process.
RESEARCH & INSIGHTS
User research revealed two key needs:
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• Predictive insights had to be directly tied to familiar operational metrics
• Users needed transparency into prediction confidence and model reasoning
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From this research, I developed design criteria that balanced advanced predictions with clarity, control, and trust.
STRATEGY & DESIGN DECISIONS
Key decisions on this project included:
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• Integrating prediction outputs into existing operational dashboards so they felt like a natural extension of current workflows
• Designing visual forecast elements that balance clarity with depth, enabling users to explore probable scenarios without cognitive overload
• Including confidence levels and contextual cues so users could trust the AI suggestions
• Mapping clear next steps from prediction to scheduling actions so insights drove real operational outcomes
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These strategies ensured predictions were not just visible, but usable and trustworthy.
OUTCOME & IMPACT
The predictive shift interface enabled users to:
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• Anticipate staffing needs with greater accuracy
• Reduce manual forecasting time
• Integrate forecasts into everyday scheduling decisions
• Make data-informed decisions without needing deep statistical expertise
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This design helped bridge the gap between complex models and frontline operational workflows and saved hundreds of hours spent scheduling.
