Healthcare - Scheduling
Assignment & Case Study
Team
Independent Design Challenge
Goal
Design an appointment scheduling system for clinics and medical practices across France and Germany that simplifies management for staff while creating a seamless, high-trust booking process for patients.
Assumptions
The scheduling system operates as an integrated module within HRS’s existing clinic management suite, inheriting established platform constraints, data structures, and design foundations.
Initial Research & Scoping
Before setting product direction, research focused on mapping how scheduling operates within clinical settings.
While patients are the end consumers, operational success hinged on front-desk adoption. For the MVP, I prioritized internal workflow efficiency first, ensuring patients still retained a clear, transparent booking flow. Rather than spreading scope evenly across personas, design efforts focused on front-desk staff—the user group that drives daily transaction volume and dictates whether the system succeeds long-term.
Prioritisation table & Criterias
Representative User
To anchor the solution in real-world constraints, meet Claire—a front-desk coordinator at a mid-sized clinic. She represents the high-frequency operational user who manages schedules, cancellations, walk-ins, and daily disruptions. Her primary goal is simple: maintain control during peak clinic hours without extra cognitive load. To work within the 8-hour constraint, I leveraged AI tools to rapidly translate these operational needs into product concepts.
Leveraging AI.
Setting up the Scenario
Supporting multi-location networks and shared practitioner rosters requires a flexible central view. The dashboard provides instant site-switching and multi-doctor filtering directly from the interface, giving front-desk staff complete visibility over daily capacity.
View and manage schedules
Patients coming into the clinic represent diverse literacy levels and backgrounds, making name-only lookups prone to error. The global search supports multi-attribute querying—by Name, Patient ID, or Appointment ID—for instant recognition (which also resolves ambiguity when two patients share the exact same name) . Clicking any card slides out a contextual drawer, allowing staff to review details and trigger quick actions without losing their place on the schedule.
Search & Explore
To prevent human error during busy clinic hours, actions aren't static. The side-drawer evaluates the appointment state to present distinct action sets—eliminating impossible or invalid workflows (like canceling a consult that is already ongoing).
Drawer States
Beyond viewing existing bookings, creating new appointments—especially for walk-ins or urgent requests—demands minimum interaction cost. The creation modal streamlines scheduling and data entry by proactively suggesting open time windows and running real-time availability checks as practitioner and duration fields are populated.
Edge Cases and Scenarios
Localization went beyond language translation—it requires handling 24-hour time formats, local date conventions, and flexible component widths. I stress-tested the same using French to ensure the layouts could naturally accommodate longer text strings and regional formatting without breaking.
Das ist schön
Measuring Success & Impact
As a new product initiative with no pre-existing benchmarks, the immediate focus is establishing baseline operational metrics. These initial data points will serve as the benchmark to validate hypotheses, measure workflow improvements, and guide future product iterations.
Looking ahead
Clinic operations have the potential to actively adapt to daily activities through intelligent automation. Integrating AI-powered schedule optimization, automated waitlist management, and cross-clinic coordination can streamline complex workflows across multiple locations. At the same time, voice-assisted scheduling empowers front-desk staff to handle rapid lookups and peak-hour walk-ins hands-free—reducing administrative overhead and keeping patient care at the center.
Thankyou :)