Improving Capacity Planning and Bed Demand Forecasting Using Machine Learning

Bed capacity management is of critical importance to healthcare systems, impacting patient care and safety, operational efficiency, system sustainability and financial performance. Efforts to improve and streamline management are often isolated to regions within the center and may lead to suboptimal resource utilization, inconsistent patient care, and inefficiencies between care units for transfers and other care coordination. Assessment of end-to-end bed demand management globally from admission to discharge eliminates many of the unintended consequences of localized optimization efforts. Froedtert Health 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 Health was able to improve patient care, operationalize key performance indicators, and streamline operations through more effective staff deployment and utilization, and by preemptively responding to anticipated changes in patient bed demand. This led to optimized allocation of resources, improved patient flow, better coordination between departments and cost savings.