UC21: No-Show Prediction & Intervention

The Problem

No-show rates average 5 to 30% depending on specialty (oncology, cardiology lowest; behavioural health, primary care highest), translating to lost revenue of $100 to $300 per missed appointment. Each no-show blocks a revenue-generating slot and creates downstream wait-list delays. Scheduling staff lack visibility into which patients are at high risk; reactive reminder calls are made 24 hours before, too late to fill a cancelled slot.

What the Agent Does

Predicts high-risk no-shows using machine learning trained on historical appointment data (demographics, prior no-show history, visit type, weather, neighbourhood socioeconomic status)
Identifies patients likely to no-show 5 to 7 days in advance
Initiates escalating outreach: SMS reminder (5 days prior), personal phone call with rescheduling offer (3 days prior), same-day confirmation with transportation offer
Offers incentives (rescheduling without penalty, transportation assistance, childcare support) based on barrier analysis
Tracks successful interventions and refines prediction model quarterly
Generates no-show prevention reports by provider, specialty, and time-of-day patterns

Data Requirements

Data Sources:

Appointment Scheduling System (Epic, athenahealth) , Appointment date/time, patient demographics, provider, visit type, location
Historical Appointment Outcomes , Show/no-show status for past 12 to 24 months, cancellation patterns
Patient Demographics , Age, ZIP code, socioeconomic status (optional but improves prediction), language preference
Weather Data , Historical and forecasted weather for appointment dates/locations
Seasonal Illness Data , Flu trends, school closures, other factors affecting attendance
Contact Information , Phone numbers (mobile/landline), email, SMS opt-in status, preferred contact method

Data Classification:

Appointment data: Sensitive, Internal, Structured
Historical outcomes: Internal, Structured
Patient demographics: Sensitive, Internal, Structured
Weather & seasonal data: External, Structured
Contact info: Sensitive, Internal, Structured

Data Quality Requirements:

Appointment data: updated in real-time as bookings/changes occur
Historical outcomes: 12 to 24 months of data required for initial model training
Contact information: verified at check-in; phone numbers validated against NPA/NXX (North American numbering plan)
Weather data: sourced from NOAA or similar; accurate within 5 miles of appointment location

Integration Complexity: Medium

EHR appointment system API (Epic Cadence, athenahealth scheduling)
Patient demographics normalisation (ZIP code, socioeconomic data optional)
SMS gateway integration (Twilio, AWS SNS) with opt-in compliance
Machine learning pipeline: feature engineering, model training, periodic retraining (monthly or quarterly)

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%	3	0.45
Ease of Implementation	10%	3	0.30
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	4	0.40
Composite	100%		3.75

Why It Scores Well

No-show prevention is operationally high-value: a 5% reduction in no-shows reclaims $50K to $500K annually depending on appointment volume and speciality. The data is mature (appointment history, demographics), and the prediction problem is well-studied. Successful interventions are easily measured (showed up vs. no-show), allowing for rapid model iteration.

Regulatory Alignment

HIPAA Privacy: Patient demographics and contact information are PHI; must be encrypted in transit
Telephone Consumer Protection Act (TCPA): SMS reminders require prior opt-in; calling lists must respect "do not call" registry
ADA (Americans with Disabilities Act): Communication methods must be accessible (TTY for deaf patients, Spanish for limited-English-speaking)
State Telehealth Laws: Some states restrict automated reminder messages; human confirmation may be required

Sprint Factory Fit

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

No-show prediction runs nightly on the appointment schedule: identify high-risk patients, initiate outreach the next morning. The initial 2-week sprint focuses on a simple logistic regression model using historical appointment and demographics data; subsequent sprints add weather/seasonal features, SMS integration, and outcomes tracking. This is a medium-complexity use case suitable for data science teams.

Comparable Implementations

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Governance Risks to Consider

Before deploying this use case, review these agentic AI risks from the Corvair Risk Catalogue. Each is scored on the DAMAGE framework and mapped to regulatory expectations.