Hospital bed capacity management is a crucial aspect of healthcare systems, as it directly impacts patient care, operational efficiency, financial performance, and overall system sustainability. Many health centers focus on improving bed management within specific departments, leading to inefficiencies in resource allocation and patient care coordination.
To address this issue, Froedtert Health took a holistic approach to bed demand management, utilizing AI, machine learning, and data analytics. By analyzing patient flow from admission to discharge, the healthcare system was able to develop predictive tools tailored for the care coordination center. This initiative resulted in enhanced patient care, operational efficiency, and cost savings through optimized resource allocation and improved staff deployment.
Ravi Teja Karri, a machine learning engineer at Froedtert ThedaCare Health, along with his colleagues, will be presenting their successful implementation of AI and ML in capacity planning and bed demand forecasting at HIMSS25. Their session, titled “Improving Capacity Planning and Bed Demand Forecasting Using Machine Learning,” will delve into the significance of these technologies in healthcare today.
The session will explore how AI and ML can predict patient flow and bed demand, enabling healthcare organizations to make informed decisions about resource allocation, staffing, and patient care management. By analyzing historical data and trends, predictive models can anticipate fluctuations in demand with accuracy, allowing for proactive planning and efficient resource utilization.
Attendees of the session can expect to learn how to implement machine learning-based predictive analytics tools to enhance their hospital’s capacity management. By leveraging these tools, healthcare providers can forecast bed demand, identify potential bottlenecks, and optimize resource allocation across departments. This proactive approach fosters better communication between clinical teams, operational leaders, and ensures a smoother patient care experience.
