Forecasting Patient Length of Stay for Optimal Hospital Resource Allocation

Efficient management of hospital resources, especially bed allocation, plays a pivotal role in enhancing patient care quality and operational effectiveness. In our research, we propose a predictive modeling framework aimed at forecasting patient length of stay (LOS) to support optimal resource allocation in hospitals. Our methodology relies on data extracted from two key databases: Hospital_Main_DB and Hospital_lnsights_DB. By extracting relevant features from Hospital_Main_DB and training machine learning models on Hospital_lnsights_DB, we aim to predict patient LOS accurately. The predictive models are designed to be continuously updated as new data streams in, enabling real-time forecasting of bed counts based on projected patient LOS. This iterative approach empowers proactive resource planning and allocation, thereby strengthening hospital operational efficiency and the delivery of patient care. Additionally, we leverage interactive dashboards using Power BI to visualize and present the results of our predictive models. These dashboards provide stakeholders with actionable insights, facilitating data-driven decision-making processes. Through our research, we seek to underscore the value of predictive modeling in informing hospital resource allocation strategies, ultimately leading to improved healthcare outcomes.