How UCHealth Created a More Accurate Surgery Case Schedule Using Predictive Analytics

Overview/Description:

Accurately scheduling case durations is a complex process and a moving target that has significant financial implications for hospitals and health systems. The standard approach of basing case duration on average of the last “x” number of cases in the EHR data is outdated and imprecise, particularly given today’s access to data and the evolution of predictive analytics.

Join Emily Bodden, Product Implementation Specialist for iQueue for Operating Rooms, and James Burdette, Perioperative Services Business Manager at UCHealth to hear how UCHealth successfully standardized scheduling practices and harnessed the power of predictive analytics to increase case length scheduling accuracy from 52% to 62% in just one year. Learn how UCHealth’s experience has paved the way for your organization to achieve improvements in utilization by deploying case length predictive analytics.

Speaker Bio:

James Burdette, Perioperative Services Business Manager, UCHealth
UCHealth Perioperative Services Business Manager for 5 years. Representing Perioperative Operational and Capital Budgets along with RN Auditors, Scheduling, Finance, and Project Managers. Previous professional experience in Finance, EMS and Value Analysis.

Emily Bodden, Product Implementation Specialist, iQueue for Operating Rooms
Emily is a Product Implementation Specialist for iQueue for Operating Rooms. Prior to joining LeanTaaS, Emily worked as a senior data analyst and consultant for a national healthcare provider and health plan and had previous experience implementing electronic medical records for a leading EMR company. Emily holds a Bachelor’s Degree in Sociology, focused on population health management, as well as a Master’s degree in Epidemiology from the University of Michigan.