AI no-show prediction for healthcare
TensorFlow model predicting patient appointment no-shows to optimise healthcare scheduling for 160,000 patients.
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Confidential (Healthcare Provider)
A major healthcare provider serving 160,000 patients annually needed to reduce the impact of appointment no-shows. Empty slots wasted physician time, increased wait times for other patients, and reduced revenue.
Patient no-shows are a persistent challenge in healthcare, typically running at 10-30% of scheduled appointments. Simple reminder systems help marginally, but don't address the root causes. The provider needed a system that could predict which patients were most likely to miss appointments, enabling targeted interventions and smart overbooking strategies.
What SevenLab built
SevenLab developed a TensorFlow-based predictive model that analyses patient history, demographics, appointment characteristics, and external factors to predict no-show probability for each scheduled appointment.
TensorFlow prediction model
Deep learning model trained on historical appointment data to predict no-show probability.
Risk stratification
Patients classified into risk tiers enabling targeted intervention strategies.
Smart overbooking
Intelligent overbooking recommendations based on predicted no-show rates per time slot.
Intervention triggers
Automated reminders and outreach for high-risk appointments.
Measurable business impact
The prediction model optimised scheduling for 160,000 patients, reducing empty slots by 35%. Smart overbooking and targeted interventions transformed a revenue-draining problem into an efficient, data-driven scheduling system.
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