Managing bed capacity is critical to healthcare systems, impacting patient care and safety, operational efficiency, system stability and financial performance. Efforts to improve and streamline management are often isolated to regions within a facility and can lead to suboptimal utilize of resources, inconsistent patient care, and inefficiencies between care units in transferring and other care coordination.
Evaluating end-to-end global bed demand management, from admission to discharge, eliminates many of the unintended consequences of local optimization efforts. Froedtert Health has identified improving capacity management as an vital and achievable goal that can be achieved through an approach to artificial intelligence, machine learning and data analytics.
Understanding and analyzing patient flow and its sources allowed the team to create a set of predictive tools designed specifically for care coordination centers. Froedtert Health was able to improve patient care, implement key performance indicators and streamline operations by more efficiently deploying and utilizing staff and by proactively responding to anticipated changes in patient bed demand.
This has led to optimized resource allocation, improved patient flow, better coordination between departments and cost savings.
Ravi Teja Karri is a machine learning engineer at Froedtert ThedaCare Health. He and two colleagues will speak about these achievements at HIMSS25 in a session titled “Improving Capacity Planning and Bed Demand Forecasting Using Machine Learning.” We interviewed Karri to get a sneak peek at what he plans to cover in March at HIMSS25 during his session.
Q. What is the overarching theme of your session and why is it particularly relevant to healthcare and healthcare IT today?
AND. The overarching theme of our session focuses on improving hospital capacity management and forecasting bed demand through the utilize of artificial intelligence and machine learning techniques. This topic is becoming increasingly vital in healthcare as hospitals face unpredictable changes in patient volumes.
Seasonal surges, unplanned admissions, and changing patient needs make maintaining optimal resource allocation challenging. Using artificial intelligence and machine learning to predict bed demand and patient flow enables hospitals to optimize staffing, allocate beds and streamline operations, resulting in better patient care and overall efficiency.
Our session will also discuss how healthcare organizations can leverage artificial intelligence and machine learning to transform processes into predictive rather than reactive workflows. This proactive approach enables more correct patient volume forecasting and better interdepartmental coordination, ultimately improving the patient experience through more proficient resource allocation and timely delivery of care.
Integrating these predictive models into daily operations enables healthcare organizations to better anticipate fluctuations in demand, minimize the risk of overcrowding, and improve inter-agency coordination.
Q: You focus on artificial intelligence and machine learning, vital technologies in healthcare today. How are they used in healthcare in the context of the topic and content of the session?
AND. Our session focuses on artificial intelligence and machine learning technologies, in particular their application in predictive analytics to forecast bed demand and manage capacity in hospitals. ML models are designed to analyze enormous data sets, including historical patient admissions, discharge trends, seasonal illness patterns, and other factors, to forecast future hospital capacity needs.
We will explore how these models can predict patient flow and bed demand, enabling healthcare organizations to make more informed decisions about resource allocation, staffing and patient care management.
These predictive models utilize algorithms to identify patterns and trends in patient admissions, length of stay and discharge rates, enabling hospitals to forecast fluctuations in demand with high accuracy. ML integrates data from multiple sources, including emergency departments, surgical departments, and ambulatory care, to provide a comprehensive view of organizational capabilities.
This analysis helps hospital leaders and care coordinators anticipate increases in bed demand – such as those that occur during flu seasons or following natural disasters – and plan effectively to ensure resources are available when they are needed most. By implementing these technologies, healthcare organizations can move from a reactive approach to a more proactive and predictable patient flow management model.
In our session, we will explore how machine learning can be effectively applied in healthcare to predict bed demand and improve capacity management. By analyzing historical data such as patient admission rates, discharge patterns, and seasonal trends, ML models can forecast hospital capacity demands.
These forecasts enable healthcare organizations to optimize resource allocation, plan staffing requirements, and provide better patient care by enabling a proactive rather than reactive approach to operations.
We’ll also discuss how these machine learning models can be integrated into healthcare workflows, turning predictions into actions for hospital staff. Instead of remaining in experimental environments or isolated tools, forecasts are processed, stored and made available for decision-making via business intelligence platforms.
These BI tools enable healthcare professionals to access the information they need to effectively plan, such as allocating beds, managing staff, and coordinating patient discharges, ultimately improving operational efficiency and patient outcomes.
Q: What is one of the different takeaways that you hope participants leave the session with and can enjoy when they return home to their organizations?
AND. The most vital takeaway we hope participants will take away from our session is the knowledge of how to implement machine learning-based predictive analytics tools to improve performance management in your own hospital.
Participants will learn how predictive models can accurately forecast bed demand and identify potential patient flow bottlenecks before they occur. These insights will enable leaders to make data-driven decisions, allocate resources more efficiently, and avoid overburdening units or staff during peak periods.
Using this toolkit, healthcare providers can minimize last-minute staff changes, optimize bed utilization, and ensure uninterrupted patient care during periods of high demand. Predicting patient flow throughout the hospital, rather than in isolated departments, allows for optimized allocation of resources between departments and minimization of delays caused by a mismatch between patient demand and available resources.
This will enable better communication between clinical teams and operational leaders, resulting in smoother transitions between stages of patient care and improving the overall patient experience.