Unsupervised and explainable machine learning solutions for last-minute cancelations in pediatric ambulatory and day surgeries in Italy

Background: Canceled procedures in pediatric day-case surgery are a serious issue that can disrupt hospital workflow, resource utilization, and patient care. To better analyze this and explore strategies to mitigate such costly inefficiencies, we employed unsupervised machine learning (ML) techniques to analyze data from children undergoing day-case surgery at the Ospedale Pediatrico Bambino Gesù IRCCS between January 2020 and March 2022. Methods: We analyzed a dataset including 4,417 operated and 183 non-operated patients. The following variables were considered: age, hospitalization type, surgical specialty, procedure type, and reasons for cancelation. Dimensionality reduction was performed using Factor Analysis of Mixed Data (FAMD). Cluster analysis was conducted using the k-means algorithm, while a Random Forest classifier was employed to assess feature importance and enhance model interpretability. Results: < 0.001). Random Forest analysis identified hospitalization type (feature importance: 0.47) and procedure type (feature importance: 0.45) as the most influential variables contributing to cluster differentiation. Conclusion: The ML analysis may suggest targeted strategies for optimizing scheduling and resource allocation, ultimately improving patient care and operational efficiency.