OPMED WEBINAR · THURSDAY, JUNE 18, 2026 · 12:00 PM ET
OPMED WEBINAR
THURSDAY, JUNE 18, 2026 · 12:00 PM ET
Presentation + live Q&A · 30 min
FREE WEBINAR
Presentation + live Q&A · 30 minutes
ABOUT THIS SESSION
When you schedule a case using historical averages, you're not predicting what will happen. You're betting that this surgeon, on this day, with this patient, will perform exactly as the median suggests.
That bet is wrong more often than teams would like.
The result is predictable: cases run long, blocks overrun, turnover times compress, staff hit overtime, and when blocks finish early, cases that could have run don't, staff productivity drops, and revenue walks out the door with them. The schedule that looked reasonable at 6am is already off track by 10.
EHR average-based scheduling doesn't just fail to prevent variability.
It creates it.
WHAT TO EXPECT
Why EHR average-based scheduling systematically under- and over-books OR time, and what that's actually costing your system
How AI case-length prediction differs from "smarter averages", and what the accuracy lift looks like on real data
What a prediction vs. actual readout lookslike in practice
How to introduce AI prediction into block committees without disrupting surgeon relationships
The 3–5 metrics to track in your first 90 days
The questions to ask any AI scheduling vendor before you buy
WHAT TO EXPECT
Nurse Executive, Opmed
Executive Director of Perioperative Solutions, Opmed
The OR schedule isn't broken because your team isn't good enough.
It's broken because the inputs are wrong.
Average-based scheduling was always an approximation. AI case-length prediction makes it something better: a forecast built around the actual variables that drive case duration, surgeon, procedure, patient, team, time of day.
That shift changes what's possible.
Join Amy and Matt on June 18 to see exactly how.