Predicting no-shows and late cancellations to improve clinic utilization

Patients scheduling but not completing appointments is a common problem in healthcare. This often results in compromised patient care, wasted clinical resources and limited access for other patients. Most of the current strategies to manage “no-shows” are ineffective and costly when applied to the entire population, and few efforts have been made to accurately target and intervene on patients likely to not show up. This research aims to construct models using advanced ML to predict the risk of patients not completing a given appointment. The risk value is then used to either prevent or mitigate the impact of the risk through preventive and scheduling interventions to improve patient care, provider utilization and patient access to care.