Identification of Factors Correlating to Patient Appointment No-Shows Using Deep & Machine Learning

Patient no-shows, scheduled but unattended medical appointments, pose a significant challenge in healthcare, impacting patient healthcare by disrupting the necessary treatment. Medical appointment no-shows disrupt healthcare providers' schedules that leads to suboptimal patient care. Such issues also lead to increased costs on health care, particularly in the medical field where resources are costly and in great demand. This study explores the factors influencing patient no-shows experienced by a multi-specialty clinic in the USA through the analysis of a dataset with more than 38000 medical records collected over a two-month period in 2019. Using descriptive modeling techniques, we examine key attributes such as patient demographics, appointment types, and scheduling patterns to identify trends associated with appointments missed by the patients. Our analysis reveals 22% of the appointments missed, with significant variations across different ethnic groups, appointment types, and days of the week. Notably, the Hispanic or Latino patient cluster exhibited a higher no-show rate in comparison to the others. Predictive modeling techniques used included Random Forest, Support Vector Machine (SVM), ANN, Gradient Boosting (GBM), and XGBoost to assess the feasibility of forecasting no-shows. The findings provided in this work provide an insight into appointment trends and disparities that may impact decision makers' approach to patient appointment scheduling.