AI-Powered No-Show Prediction Model for a Behavioral Health Clinic

Overview

Built a machine learning model that predicts the likelihood of a patient missing an upcoming behavioral health appointment, based on historical attendance patterns, appointment type, and time since booking. The clinic uses the prediction score to decide which appointments get an extra personal reminder call versus a standard automated text, focusing limited staff time where it matters most. This mattered because behavioral health no-shows are both a revenue issue and, more importantly, a continuity-of-care issue for patients who benefit from consistent sessions. We validated the model against a year of the clinic's own appointment history before it went live, and it continues to retrain periodically as new data comes in. A behavioral health clinic's no-shows were both a revenue issue and a continuity-of-care problem, and staff had no way to know in advance which appointments were most at risk. We built a machine learning model predicting no-show likelihood based on attendance history, appointment type, and time since booking, so staff could direct a personal reminder call to the highest-risk appointments instead of treating every booking the same. We validated the model against a full year of the clinic's own appointment history before it went anywhere near a live schedule, then ran a pilot period comparing its flagged sessions against actual outcomes. Staff feedback on false positives shaped the final risk threshold before it became part of daily operations. Staff now direct personal outreach to the highest-risk appointments instead of spreading effort evenly, contributing to an estimated 20% reduction in no-shows for flagged sessions and helping protect continuity of care.