Artificial Intelligence Length-of-Stay Forecasting and Pediatric Surgical Capacity

Importance: Hospitals are increasingly experiencing challenges with variable and unpredictable inpatient loads, including days with excessively high and excessively low capacity for surgical patients. Artificial intelligence has the potential to facilitate postoperative hospital bed management and stabilize capacity. Objectives: To predict hospital length of stay (LOS) following elective surgical procedures using machine learning methods, and to implement the LOS prediction model in a perioperative clinical setting and evaluate its ability to optimize elective surgical scheduling and hospital bed capacity. Design, Setting, and Participants: This preimplementation and postimplementation cohort study was conducted at a tertiary, freestanding, US children's hospital among patients of any age undergoing an elective surgical procedure requiring inpatient recovery. For LOS prediction, a retrospective analysis was performed on elective surgical cases from January 1, 2018, to March 31, 2022, using Extreme Gradient Boosting (XGBoost) to predict postoperative LOS based on in-training and holdout datasets, with hyperparameter tuning using 5-fold cross-validation. For implementation and evaluation of the LOS prediction model, a preimplementation and postimplementation analysis was performed from July 1, 2022, to April 30, 2024. Data analysis was conducted from June 1 to October 31, 2024. Exposures: Patients' type of surgery, chronic conditions, and demographic characteristics. Main Outcomes and Measures: Postoperative LOS, day-to-day variance in bedded days for elective surgical procedures, and days with excessively high capacity (>75th percentile of historical elective surgical census) or excessively low capacity (