Predictive Flow in Practice: Reimagining ED and Inpatient Throughput at University Health

Speakers

A middle-aged man with short brown hair and a beard, wearing a black suit, white shirt, and patterned tie, poses against a dark gradient background—symbolizing leadership in AI-Enabled Automation for Discharge Management.
Bill Phillips
EVP & COO, University Health
Smiling woman with straight, dark shoulder-length hair wearing a black blazer and black top, posed in front of a plain beige background, reflecting professionalism suited for roles in AI-Enabled Automation for Discharge Management.
Ashley Schutz, MHA, MLS(ASCP)
Director of IT Quality Assurance, University Health

Summary

Emergency Departments set the pace for the entire hospital, yet prolonged boarding and reactive workflows continue to strain capacity, staffing, and financial performance. In this session, leaders from University Health share how they connected ED operations with inpatient flow using real-time and predictive intelligence layered on top of the EHR to enable earlier planning and more coordinated decision-making. This approach drove measurable results, including a 46% reduction in ED boarding time, 16% increase in discharges, and a nearly 6% reduction in length of stay — unlocking throughput, improving access, and creating more predictable system-wide capacity.

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