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.

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