UC29: Physician Schedule Optimiser

The Problem

Physician cancellations leave revenue-generating appointment slots empty. A cardiologist cancels clinic due to an emergency consult; the cancelled clinic slot (often 4 to 6 hours of fully booked appointments) is not filled by another provider, costing $5K to $15K in lost revenue. Waitlists exist but manually matching waitlisted patients to cancelled slots is slow; many slots go unfilled.

What the Agent Does

Monitors physician schedule in real-time: detects cancellations as they occur
Queries waitlist: identifies patients matching cancelled slot criteria (same specialty, visit type, urgency)
Applies matching logic: confirms patient availability (can arrive on short notice), ensures clinical appropriateness (chief complaint matches cancelled provider's expertise)
Fills cancelled slot with waitlisted patient; notifies patient of new appointment time (SMS, phone call)
Balances provider templates: recognises when a provider is over-booked; suggests rescheduling non-urgent patients to open slots to reduce physician workload
Tracks utilisation: appointment fill rates, cancellation reasons, waitlist conversion rates

Data Requirements

Data Sources:

Scheduling System (Epic Cadence, athenahealth) , Physician availability, appointments, cancellations, slot durations
Waitlist (Epic Cadence, Meditech) , Waitlisted patients, chief complaint, clinical speciality, preference dates
Patient Contact Info , Phone, SMS, email for urgent re-scheduling notification
Provider Templates , Standard schedule, protected times, specialty scope
Historical Utilisation , Appointment fill rates, cancellation patterns by provider

Data Classification:

Scheduling data: Sensitive, Internal, Structured
Waitlist data: Sensitive, Internal, Structured
Contact information: Sensitive, Internal, Structured
Provider templates: Internal, Structured

Data Quality Requirements:

Scheduling data: updated in real-time as appointments are booked/cancelled
Waitlist data: accurate and current (patients removed if no longer interested)
Contact information: verified at check-in; phone numbers validated
Provider templates: current and reflecting actual provider scope

Integration Complexity: Medium

Scheduling system API (Epic Cadence API, athenahealth API)
Waitlist query and patient matching logic
SMS notification system integration
Slot availability calculation (calendar parsing)

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	4	0.40
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	4	0.40
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	4	0.40
Composite	100%		3.80

Why It Scores Well

Schedule optimisation directly improves revenue capture: filling even 50% of cancelled slots reclaims $50K to $500K annually. The data is highly structured (schedules, waitlists); matching logic is rule-based. Outcomes are easily measured (slot fill rates, revenue recovery).

Regulatory Alignment

HIPAA: Patient contact information and scheduling are semi-sensitive; PHI must be handled per privacy rules
Telehealth Laws: State-specific rules on re-scheduling; some states require patient consent before changing appointment details
Physician Practice Agreements: Provider schedules may be contractually protected; cancellation policies vary

Sprint Factory Fit

Sprint 0 (2 weeks) + 3 build sprints (6 weeks)

Schedule optimisation runs continuously: monitor for cancellations and fill slots in real-time. The initial 2-week sprint focuses on cancellation detection and waitlist matching; subsequent sprints add template balancing, SMS integration, and utilisation tracking. This is a medium-complexity use case suitable for scheduling/operations teams.

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