Predicting Patient Movement Patterns with Cognitive Insight

Efficient patient flow management is essential for improving hospital operations, reducing delays, and enhancing the patient experience. This study presents a Transformer-based model designed to predict patient movement and treatment pathways using historical log data. By employing a hierarchical prediction approach, the model forecasts patients’ next steps across four levels(Section, Group, Producer, and Activity)with superior sequential accuracy. The model was trained on the BPIC 2011 dataset, which includes diagnostic and treatment records from a Dutch academic hospital. Data preprocessing involved sequence filtering, feature engineering, and a sliding window approach to create time-series input-output pairs. Proposed model demonstrated strong performance, outperforming baseline LSTM and standard Transformer models in F1 scores and sequential prediction accuracy. This preliminary study highlights the feasibility of using predictive models for individual patient movements. Future research aims to scale this approach to handle multiple patient pathways simultaneously, enabling real-time density forecasting and bottleneck management in high-demand areas, such as emergency rooms. These findings underscore the potential of predictive modeling to optimize hospital efficiency and patient care.