Visits
About Visits
Visits is a new product that enables pharmacists to prescribe birth control. I designed the patient queue from scratch as part of the core workflow. The queue serves one purpose: holding all valid intake forms for two weeks, since intake information is only clinically relevant for that period.
Because pharmacists may see walk-in patients, scheduled patients, or those who check in as “In the pharmacy,” the queue needed to present all completed intakes in a way that clearly supported decisions about which patient to see next.
This project took six weeks from workflow definition to handoff. I owned research synthesis, information architecture, interaction design, visual design, prototyping, rule definition, and usability testing. I partnered closely with engineers and a pharmacist subject matter expert (SME) to ensure the triage logic reflected real pharmacy environments and aligned with backend capabilities.
ROLE
Product Designer
TIMELINE
6 Weeks
Location
Remote
Tool Stack
Figma
ChatGPT
Claude
The challenge
Pharmacists work in fast-paced, often chaotic environments and must constantly decide who to see next. Without a clear triage model, prioritizing patients becomes slow and mentally taxing. Different patient types arrive throughout the day, and the burden of choice can disrupt clinical flow. Without intervention, this leads to delays, confusion, and inconsistent encounter starts.
Constraints and assumptions
The dashboard did not exist yet, so I defined its structure and hierarchy from the ground up, including both the layout and the patient queue.
The primary constraint was making the “next patient” decision obvious at a glance in a busy pharmacy setting, without requiring pharmacists to dig through the interface.
I anchored the entire dashboard around the patient queue, assuming that immediacy and visibility would reduce cognitive strain, eliminate unnecessary navigation, and improve encounter flow. The solution also needed to remain lightweight for engineering while still supporting confident decision-making.
Research approach
The research focused on understanding how pharmacists make quick decisions about who to see next. Methods included usability tests with practicing pharmacists, workflow mapping with our pharmacist SME, early concept testing with internal stakeholders, and a competitive scan of clinic/urgent care queue patterns
I focused on understanding:
How pharmacists define urgency
What minimum information they need to feel ready to start an encounter
How they differentiate walk-ins vs scheduled patients vs remote-intake patients
Key insights
“In-Pharmacy” status is the strongest priority signal
Not all scheduled appointments are urgent; timing matters more than the appointment itself
Walk-in intake forms remain relevant for two weeks and must stay visible without overwhelming the queue
Too much detail slows triage and increases cognitive load
Predictable, consistent rules build trust and confidence
The opportunity
Pharmacists needed a single source of truth that clearly signaled who to see next.
The business needed a triage model that could scale as new services and patient types were introduced.
The opportunity was to design a unified, rules-based queue that simplifies prioritization and supports fast, safe encounters.
Design goals
Create a clear, unified queue that makes prioritization immediately obvious
Reduce cognitive load through simple, predictable ordering rules
Support fast encounter starts with minimal interpretation
Design a triage model that can scale to future clinical services
Measuring Success
Success indicators: pharmacists correctly identify the next patient without hesitation, high confidence in ordering logic, low cognitive effort during triage, minimal support questions after launch, and stakeholder validation of scalability.
Ideation
Because no queue existed, I explored several conceptual models, including grouped priority buckets, strict chronological ordering, divider-based urgency logic, color-coded statuses, and time-based rules for scheduled patients.
Early sketches helped validate the concepts that mattered most: a bold divider to signal urgency, “In-Pharmacy” patients always rising to the top, time-based elevation for scheduled patients, and a two-week window for intake visibility. These decisions formed the foundation of the final workflow.
Final Solution
Priority Divider
A strong divider separates urgent patients from all others, making it immediately clear who needs attention now.
Unified Queue
Walk-ins, scheduled patients, and in-pharmacy check-ins are combined into a single list so pharmacists can think in terms of readiness, not patient origin.
Clear Ordering Rules
In-pharmacy patients always appear above the divider. Scheduled patients rise shortly before their appointment. All other valid intakes remain below the divider, ordered by completion time and searchable for quick access.
Minimal Patient Rows
Each row shows only essential information needed to find a patient and start an encounter, reducing noise and supporting fast scanning.
Two-Week Visibility Window
Completed intake forms remain visible for two weeks to balance clinical relevance with usability and prevent queue clutter.
Collaboration and Execution
I worked closely with engineering to define triage rules, edge cases, and technical constraints from the start. I shared annotated Figma files, rule definitions, and async walkthroughs to support accurate implementation. I also partnered with pharmacist SMEs to ensure the queue reflected real workflows and aligned with product leadership on future scalability.
Usability Testing and iterations
I tested the queue with five practicing community pharmacists using task-based scenarios. Participants were asked to interpret the queue, identify who they would see next, explain why, and start encounters for different patient types.Testing revealed hesitation around remote intake patients and confusion about ordering below the priority line.
Key iterations included:
Clearer and more consistent status labels
Updated column naming
Introduced tooltips explaining ordering logic and patient states
Results and impact
After iterations, all five pharmacists were able to identify the next patient and start encounters without hesitation. Participants expressed high confidence in the ordering logic and patient statuses, describing the queue as predictable and easy to trust in a busy pharmacy setting.
Internally, stakeholders validated the queue as a strong foundation for future clinical services. The rules-based model was seen as scalable, easy to reason about, and flexible enough to support additional patient types without reworking the core workflow.
Next steps
Explore arrival confirmations or staff check-ins to increase accuracy
Introduce estimated wait times once sufficient data is available
These additions would build on the existing triage logic while supporting future service expansion.
Reflection
Designing the patient queue from scratch strengthened my ability to define core workflows without relying on existing patterns. It pushed me to focus on reducing cognitive load and making decisions obvious in high-pressure environments. This project reinforced the value of simple, rules-based systems that balance user needs, clinical safety, and engineering feasibility.
