Bed capacity management plays a crucial role in health systems, influencing various aspects such as patient care, safety, operational efficiency, and financial performance. Efforts to enhance and streamline this management often focus on specific regions within a center, potentially leading to suboptimal resource utilization and inefficiencies in care coordination.

Identifying the Importance of Global Assessment

Assessing bed demand management from admission to discharge on a global scale helps eliminate the unintended consequences of localized optimization. Froedtert Health recognized the significance of improving capacity management and set it as an important and achievable goal through AI, machine learning, and data analytics approaches.

Understanding Patient Flow and Its Sources

By understanding and dissecting patient flow and its sources, the team was able to create a suite of predictive tools specifically designed for the care coordination center. This led to improved patient care, the operationalization of key performance indicators, and streamlined operations through more effective staff deployment and utilization. It also allowed for preemptive responses to anticipated changes in patient bed demand.

Optimizing Resource Allocation and Patient Flow

The implementation of these tools resulted in optimized allocation of resources, improved patient flow, better coordination between departments, and cost savings. It enabled healthcare organizations to better anticipate demand fluctuations, minimize overcrowding risks, and enhance interdepartmental coordination.

The Role of Machine Learning in Healthcare

Ravi Teja Karri, a machine learning engineer at Froedtert ThedaCare Health, and his colleagues will be speaking at HIMSS25 about these achievements. Their session focuses on improving hospital capacity management and bed demand forecasting through the application of artificial intelligence and machine learning techniques.In healthcare, machine learning is used to analyze large datasets, including historical patient admissions, discharge trends, and seasonal illness patterns, to forecast future hospital capacity needs. These models can predict patient flow and bed demand, enabling healthcare organizations to make more informed decisions about resource allocation and patient care management.The predictive models use algorithms to identify patterns and trends, providing a comprehensive view of organizational capacity. This helps hospital leadership and care coordinators anticipate surges in bed demand, such as during flu seasons or after natural disasters, and plan effectively to ensure resource availability.By implementing these technologies, healthcare institutions can transition from a reactive to a proactive and anticipatory model of patient flow management. The predictions are integrated into healthcare workflows, enabling staff to access insights for effective planning and decision-making.

Key Takeaways for Attendees

Attendees of Karri's session will gain the knowledge to implement machine learning-based predictive analytics tools to enhance their own hospital's capacity management. They will learn how predictive models can accurately forecast bed demand and identify potential bottlenecks in patient flow.This will empower leaders to make data-driven decisions, allocate resources more efficiently, and avoid overburdening units or staff during peak periods. By using this toolkit, healthcare providers can minimize last-minute staffing adjustments, optimize bed utilization, and ensure uninterrupted patient care during high-demand periods.Predicting patient flow across the entire hospital allows for optimized resource allocation and minimizes delays caused by mismatches between patient demand and available resources. It fosters better communication between clinical teams and operational leaders, resulting in smoother patient care transitions 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.Follow Bill's HIT coverage on LinkedIn: Bill SiwickiEmail him: bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication