Managing bed capacity is of critical importance to healthcare systems as it impacts patient care and safety, operational efficiency, system sustainability and financial performance. Efforts to improve and streamline management are often isolated in central regions and can lead to suboptimal use of resources, inconsistent patient care, and inefficiencies between care units for transfers and other care coordination.
Evaluating end-to-end management of bed demand on a global scale, from admission to discharge, eliminates many of the unintended consequences of localized optimization efforts. Froedtert Health Network and Medical College of Wisconsin identified improving capacity management as an important and targetable goal that could be achieved through AI, machine learning and data analytics approaches.
Understanding and dissecting patient flow and its sources allowed the team to create a suite of predictive tools designed specifically for the care coordination center. Froedtert & MCW Health Network was able to improve patient care, operationalize key performance indicators and streamline operations through more efficient deployment and utilization of staff and by preemptively responding to anticipated changes in bed demand of patients.
This led to optimized resource allocation, improved patient flow, better coordination between services and cost savings.
Ravi Teja Karri is a machine learning engineer at Froedtert Health. He and two colleagues will talk about these accomplishments at HIMSS25 in a session titled “Improving capacity planning and bed demand forecasting using machine learning.” We interviewed Karri to get a glimpse of what he plans to discuss in March at HIMSS25 during his session.
Q. What is the overall theme of your session and why is it particularly relevant to healthcare and health IT today?
A. The overall theme of our session focuses on improving hospital capacity management and bed demand forecasting through the application of artificial intelligence and machine learning techniques. This topic is increasingly relevant in healthcare as hospitals face unpredictable changes in patient volume.
Seasonal peaks, unplanned admissions and fluctuating patient needs make it difficult to maintain optimal resource allocation. Leveraging AI and machine learning to predict bed demand and patient flow allows hospitals to optimize staffing, allocate beds, and streamline operations, resulting in improved patient care and overall efficiency.
Our session will also explore how healthcare organizations can leverage AI and ML to transform processes into anticipatory workflows rather than reactive ones. This proactive approach enables more accurate forecasting of patient volumes and better interdepartmental coordination, thereby improving the patient experience through more efficient allocation of resources and timely delivery of care.
Integrating these predictive models into daily operations allows healthcare organizations to better anticipate fluctuations in demand, minimize the risk of overcrowding, and improve interdepartmental coordination.
Q. Your focus is on AI and ML, important technologies in healthcare today. How are they used in healthcare in the context of the purpose and content of your session?
A. Our session focuses on artificial intelligence and machine learning technologiesparticularly their application in predictive analytics for bed demand forecasting and capacity management in hospitals. ML models are designed to analyze large data sets, including historical patient admissions, discharge trends, seasonal disease trends, and other factors, to predict 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 regarding resource allocation, staffing and patient care management.
These predictive models use algorithms to identify patterns and trends in inpatient admissions, length of stay and discharge rates, allowing hospitals to forecast fluctuations in demand with a high degree of accuracy. ML integrates data from multiple sources, including emergency departments, surgical units, and ambulatory care, to provide a comprehensive view of organizational capacity.
This analysis helps hospital leaders and care coordinators anticipate increases in bed demand – such as those experienced during flu seasons or following natural disasters – and plan effectively to ensure resources are available at the earliest. when they are most needed. By implementing these technologies, healthcare organizations can move from a reactive approach to a more proactive and anticipatory model of managing patient flow.
During our session, we will examine 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 needs.
These predictions enable healthcare organizations to optimize resource allocation, plan staffing needs and provide better patient care, enabling a proactive rather than reactive approach to operations.
We will also discuss how these ML models can be integrated into healthcare workflows, turning predictions into actions for hospital staff. Rather than remaining in experimental environments or isolated tools, predictions are processed, stored and made available for decision-making via business intelligence platforms.
These BI tools enable healthcare staff to access information for effective planning, such as bed allocation, staff management and coordination of patient discharges, thereby improving operational efficiency and patient outcomes. patients.
Q. What is one of the different takeaways that you hope attendees will leave your session and be able to apply for when they return to their organization?
A. We hope participants will take away one key takeaway from our session: the knowledge needed to implement machine learning-based predictive analytics tools to improve the capacity management of their own hospital.
Participants will learn how predictive models can accurately forecast bed demand and identify potential bottlenecks in patient flow before they arise. This information will enable leaders to make data-driven decisions, allocate resources more efficiently, and avoid overloading units or staff during peak periods.
Using this toolkit, healthcare providers can minimize last-minute staffing adjustments, optimize bed utilization, and ensure continuity of patient care during periods of high demand. Forecasting patient flow throughout the hospital, rather than in isolated units, helps optimize resource allocation between departments and minimizes delays caused by mismatches between patient demand and available resources.
This will promote better communication between clinical teams and operational leaders, leading to smoother transitions between stages of patient care and an improved overall patient experience.
Ravi Teja Karri’s session, “Improving Capacity Planning and Bed Demand Forecasting Using Machine Learning,” is scheduled for Tuesday, March 4 at 10:15 a.m. at HIMSS25 in Las Vegas.
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