Illustrative Case Study — 12,800 more annual visits without opening another clinic
- Client profile: Multi-site specialty physician group
- Footprint: 12 outpatient locations and approximately 160,000 annual visits
- Core challenge: Long patient waits existed alongside unused appointment capacity across parts of the network
- Engagement: 8 weeks across access analysis, capacity modeling, and a three-site operational pilot
- Primary objective: Improve patient access using existing clinical capacity before committing to another facility
- Important boundary: Clinical judgment, appointment appropriateness, and care decisions remained with qualified professionals
- Existing clinical systems — Scheduling, referral, and operational information from the current EHR and work queues were analyzed without replacing the clinical system of record.
- Capacity model — Provider templates, visit types, locations, cancellations, staffing, and demand patterns were combined to show where usable capacity was being lost.
- Scheduling simulation — Different appointment rules, waitlist strategies, and cross-location routing options were tested before changing live schedules.
- Referral workflow support — Incomplete referrals and missing information were surfaced earlier so administrative teams could resolve them before they became scheduling delays.
- Cancellation recovery logic — Open appointments could be matched more quickly with appropriate patients based on agreed scheduling rules.
- Performance dashboard — Leadership could track wait time, completed visits, unused capacity, cancellation recovery, and administrative effort during the pilot.
The problem
Patients were waiting weeks for appointments.
Leadership was considering another clinic location because the existing network appeared full.
But physicians at some sites still had unused blocks. Other locations were consistently overwhelmed. Cancellations were being filled inconsistently, referrals moved through several manual handoffs, and scheduling rules varied significantly by specialty and location.
The organization did not necessarily need more physical capacity.
It needed to understand the capacity it already had.
What we found
We mapped appointment demand, provider templates, cancellations, referral flow, visit types, locations, and staffing.
The biggest constraint was not a single shortage.
It was a collection of smaller mismatches:
Patients were not always routed to the earliest clinically appropriate location. Appointment templates reserved capacity differently across physicians. Cancellations created openings that were difficult to refill quickly. Referral teams spent substantial time resolving incomplete information before patients could be scheduled.
Clinical leaders were essential to the work because not every appointment slot is interchangeable.
The model could identify possibilities. Physicians and operational leaders determined what was clinically appropriate.
What we changed
We redesigned the operating logic around:
- Referral completeness
- Appointment-type rules
- Cross-location scheduling
- Waitlist management
- Cancellation recovery
- Provider template utilization
- Demand and staffing scenarios
We modeled the impact of those changes before testing them at three locations.
No clinical decision was automated.
The objective was to remove administrative and scheduling friction around care.
The result
Across the modeled network, completed visits increased approximately 8%, representing roughly 12,800 additional annual visits using existing physical capacity.
Median new-patient wait time declined from approximately 24 days to 16 days.
Administrative handling time per referral declined approximately 20%.
The decision changed from “Where should we build?” to “How much additional access can we create before we build?”
That gave leadership a stronger basis for its eventual capacity decision.
