Every missed appointment affects more than one patient.
It delays care, leaves valuable clinical time unused, disrupts schedules and creates additional work for care teams already operating under significant pressure. For health care organizations facing growing demand and limited resources, reducing patient no-shows can improve both patient access and operational efficiency.
At SAS Innovate 2026, Children’s Specialized Hospital (CSH) and SAS partner Pinnacle Solutions shared how they are taking a different approach. Together, they are using data and AI to identify patients at risk of missing appointments before they become no-shows, and to show how those predictions drive meaningful action.
Predicting missed appointments before they happen
Patient no-shows cost the US health care industry an estimated $150 billion annually. They also contribute to growing patient backlogs, inefficient resource allocation and provider burnout.
Rather than reacting after appointments were missed, Children's Specialized Hospital and Pinnacle Solutions developed a risk-scoring model to predict the likelihood that a patient will miss an appointment or cancel at the last minute. The model also recommends interventions that can help reduce those risks before appointments are missed.
For providers facing high demand and tight margins, every missed appointment represents a lost opportunity to deliver timely care, optimize staffing and protect revenue.
Because our team was able to update the models quickly, we could shift gears if we found something. We weren’t wasting resources and we were helping our patients get to their appointments. Vikki Gregorio, Director of Discovery, Innovation and Development at Children's Specialized Hospital
Turning predictions into action
Predictive models identify risk. Value comes from acting on it.
Children's Specialized Hospital and Pinnacle Solutions focused on embedding predictions directly into scheduling workflows rather than treating them as standalone analytics.
Built on a unified data foundation using SAS® Viya® on Amazon Web Services, the no-show predictor provides schedulers and staff with an intuitive interface to identify high-risk patients, explore the underlying data and determine when intervention may be appropriate – all while maintaining HIPAA compliance.
Children’s Specialized Hospital operates 14 locations across New Jersey. Within the first three months of deploying the No-Show Predictor, leaders measured an 8.5% reduction in no-shows across the organization's clinical network. One clinic even achieved a 63% decrease.
The results demonstrate how AI can move beyond prediction to drive meaningful action. By helping staff intervene earlier, the organization improved operational efficiency while creating more opportunities for patients to receive the care they need.
