Managing Patient Throughput with AI: Unlocking Capacity

This webinar is on-demand and can be viewed at your convenience. 

Ensuring hospitals have enough specific floor beds available to treat patients in the right place at the right time has always been problematic but we’ve seen a significant increase in the chaos around patient throughput over the past couple of years. As health systems continue to struggle managing patient throughput with existing, internally developed tools like spreadsheets and static reports, the need for proven, scalable, predictive and portable AI-driven systems has become more of a necessity to reduce the chaos. AI technology helps to create an innovative culture that embraces change. Hospitals that adopt this type of technology now have sound information on which to manage their patient throughput instead of relying on “gut feel” or “what we think will happen”. After deploying this AI-technology, hospitals have noted a 40% increase in confidence regarding decisions made around capacity management (moving from 50% confidence rate to a 90% confidence rate) as well as seeing a 16% decrease in time to admit patients.

Learning Objectives:
  • Identify the current challenges of capacity management and how predictive analytics can help alleviate those challenges
  • Learn how AI-driven, intelligent systems can optimize the matching of supply and demand without the extensive capital and resources needed by a command center
  • Describe how hospitals are utilizing knowledge from IT platforms to improve census predictions and real-time decisions about patient placement, surges, and diversion prevention


Pallabi Sanyal-Dey - Nicole Novak

Pallabi Sanyal-Dey, MD, FHM

Director Client Services, iQueue for Inpatient Beds

Associate Clinical Professor, UCSF School of Medicine