Transforming inpatient flow: How UCHealth minimized daily chaos and enhanced decision-making

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

All too often, hospitals and health systems rely on manual processes and "gut instincts" to manage patient flow, leading to inefficiencies, delays and high costs. But some organizations are moving beyond dashboards and paper- and Excel-based daily reports — instead leveraging proven technology that uses predictive and prescriptive analytics to improve patient flow and optimize resources.

This on-demand webinar features insights from leaders at Aurora, Colo.-based UCHealth, who share their experience applying artificial intelligence and machine learning to inpatient flow, and how it enabled frontline and leadership teams to:
  • Predict discharges and admissions by specified unit on an hourly basis into the future
  • Get the right patients in the right bed (the first time)
  • Uncover admission and discharge bottlenecks by service and level of care
  • Decrease time to complete ICU transfers by 65 percent, and raise critical decision-making confidence from 50 to 90 percent


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Pradipta Komanduri, FACHE

Chief Operating Officer, University of Colorado Hospital

Darlene Tad-Y, MD

Darlene Tad-Y, MD

Medical Director of Patient Flow, UCHealth, Vice President of Clinical Affairs, Colorado Hospital Association

darlene Cropped

Jamie Nordhagen, MS, RN, NEA-BC

Senior Director, Patient Flow and Capacity Management, Patient Representatives, UCHealth