Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: A Methodological Study (Preprint)

BACKGROUND Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based approaches, which can be inaccurate. Machine learning offers a promising avenue for more data-driven and precise predictions. OBJECTIVE This methodological study aims to use nursing activity data, specifically LEP (Leistungserfassung in der Pflege; “documentation of nursing activities”), to predict future workload requirements using machine learning techniques. METHODS We conducted a retrospective observational study at the University Hospital of Zürich, using nursing workload data for inpatients across eight wards, collected between 2017 and 2021. Data were transformed to represent nursing workload per ward and shift, with three shifts per day. Variables used in modeling included historical workload trends, patient characteristics, and upcoming operations. Machine learning models, including linear regression variants and tree-based methods (Random Forest and XGBoost), were trained and tested on this dataset to predict workload 72 hours in advance, on a shift-by-shift basis. Model performance was assessed using mean absolute error (MAE) and mean absolute percentage error (MAPE), and results were compared against a baseline of assuming no change in workload from the time of prediction. Prediction accuracy was further evaluated by categorizing future workload changes into decreased, similar, or increased workload relative to current shift levels. RESULTS Our findings demonstrate that machine learning models consistently outperform the baseline across all wards. The best-performing model achieved an average 24.99% improvement in accuracy compared to the baseline. Key variables identified as important for predictions include historical shift workload averages and overall ward workload trends. CONCLUSIONS This study suggests the potential of machine learning to enhance nurse workload prediction, while highlighting the need for refinement. Limitations due to potential discrepancies between recorded nursing activities and the actual workload highlight the need for further investigation into data quality. To maximize impact, future research should focus on: 1) utilizing more diverse data, 2) more advanced machine learning architecture that perform time-series modelling, 3) addressing data quality concerns, and 4) conducting controlled trials for real-world evaluation.