Multi-appointment infusion scheduling for an EHR
Mapping a fragmented clinical workflow into a system blueprint
Project Overview
The Challenge
Scheduling oncology infusions is fundamentally different from standard appointment booking. In practice, it depends on a long chain of steps owned by different teams: the oncologist’s treatment plan, insurance approval, labs, a clearance visit, chair and nurse availability, and pharmacy preparation.
Schedulers end up holding it all together by hand, acting as manual orchestrators across the clinic. And the work doesn’t stop once the visit is booked. Patients can stay in treatment for 6 to 24 months, and a single delayed lab result or treatment change can shift every appointment that follows.
The goal was to define a digital experience within the EHR that helps schedulers and clinical team manage this complexity and uncovers opportunities for automation.
My Contributions
Understanding the System
- Synthesized existing research reports and insights, complemented by secondary research, to map the end-to-end infusion journey across 8 stages and 9 roles, from treatment planning to next-cycle scheduling, identifying each role’s tasks, goals, and pain points.
- Built an operational model showing how work flows between roles and where delays build up.
- Used the Signature Moments methodology to identify opportunities for AI assistance at each stage of the workflow, while identifying moments that required human intervention.
- Defined new interaction models that enabled human schedulers and AI agents to collaborate within the interface, while ensuring users maintained control and visibility over automated tasks.
Redesigning the Experience
- Redesigned the existing scheduling screens, which were crowded with options and information and weren’t built to support automation.
- Simplified the navigation model and reduced how much information appears at once, using the research to decide what schedulers need at each step.
- Adapted existing interaction patterns for AI-driven workflows, defining how AI surfaces suggestions and how users stay in control of the final decision.
Outcome
The project produced a shared view of the full workflow and a redesigned scheduling experience ready for AI. The blueprint pointed to clear opportunities: automating repetitive tasks, reducing manual effort and back-and-forth between teams, and flagging dependencies before they disrupt a patient’s schedule. The redesigned screens and patterns turned those findings into an interface that’s simpler to navigate and built for schedulers and AI to work together.