Predicting surgical department occupancy and patient length of stay in a paediatric hospital setting using machine learning: a pilot study

Abstract

Objective Early and accurate prediction of hospital surgical-unit occupancy is critical for improving scheduling, staffing and resource planning. Previous studies on occupancy prediction have focused primarily on adult healthcare settings, we sought to develop occupancy prediction models specifically tailored to the needs and characteristics of paediatric surgical settings.Materials and methods We conducted a single-centre retrospective cohort study at a surgical unit in a tertiary-care paediatric hospital in Boston, Massachusetts, USA. We developed a hierarchical modelling framework for predicting next-day census using multiple types of data—from bottom-up patient-specific orders and procedures to top-down temporal variables and departmental admission statistics.Results The model predicted upcoming admissions and discharges with a median error of 17%–21% (2–3 patients per day), and next-day census with a median error of 7% (n=3). The primary factors driving these predictions included day of week and scheduled surgeries, as well as procedure duration, procedure type and days since admission. We found that paediatric surgical procedure duration was highly predictive of postoperative length of stay.Discussion Our hierarchical modelling framework provides an overview of the factors driving capacity issues in the paediatric surgical unit, highlighting the importance of both top-down temporal features (eg, day of week) as well as bottom-up electronic health records (EHR)derived features (eg, orders for patient) for predicting next-day census. In the practice, this framework can be implemented stepwise, from top to bottom, making it easier to adopt.Conclusion Modelling frameworks combining top-down and bottom-up features can provide accurate predictions of next-day census in a paediatric surgical setting.