50%+ Reduction in Late Cancellations: How OHSU Scaled Coordinated Case Planning to Prepare Patients Earlier

Speakers

A woman with straight brown hair, wearing a purple sweater, smiles while standing indoors in front of bright windows—reflecting the positive impact of AI patient readiness and cancellation reduction in healthcare settings.
Kristen Lund, MN, RN,
Director of OR/Procedural Scheduling & Pre-Operative Medicine Clinic, OHSU

Summary

Preparing patients for surgery is often treated as a series of individual preoperative tasks, but at scale, disconnected workflows can quickly become a barrier to surgical access. At OHSU, preparation was concentrated close to the day of surgery, leaving limited time to resolve missed instructions, transportation needs, illness concerns, and other readiness gaps. The organization shifted toward a coordinated case planning model designed to identify risks earlier, create greater visibility across teams, and address preventable issues before they result in late cancellations.

In this session, OHSU shares how it combined scaled Patient Outreach, Clinical Readiness, and human-supported automation to move patient preparation further upstream. By automating routine engagement while preserving clinical oversight and escalation, OHSU reduced targeted late cancellations by more than 50% and created a scalable model for improving patient readiness. Attendees will learn how OHSU is extending this approach across the perioperative journey to help clinical teams focus their attention where it is needed most and protect surgical access.

Learning Objectives

  • Understand how coordinated case planning can move patient readiness from a last-minute activity to an earlier, shared workflow
  • Identify opportunities to use automation to scale routine patient engagement while maintaining human oversight and clinical control
  • Learn how earlier visibility into patient and procedure risk can help teams prioritize the appropriate preparation pathway
  • Apply practical strategies for reducing preventable late cancellations and scaling a governed readiness model across perioperative workflows

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