Banner Health achieves 7x ROI and 2x block release with Qventus AI-powered OR scheduling

Banner Health, one of the largest nonprofit healthcare systems in the U.S. with 33 hospitals across 6 states, needed to maximize OR utilization to meet strategic surgical growth goals as demand continued to rise. Existing block scheduling left significant OR time unused, and the system needed a scalable solution that would integrate with the EHR, demonstrate clear ROI, and be adoptable by surgeons and scheduling staff across dozens of facilities.

Banner Health deployed the Qventus Perioperative Solution, initially trialing it at its four largest facilities before an enterprise-wide rollout. The platform uses machine learning models to predict unused block time up to one month in advance and sends behavioral nudge emails to block owners prompting early release. An Available Time Outreach (ATO) feature then automatically surfaces open slots to the best-fit surgeons based on continuously updated fit scores, while also prioritizing strategic objectives like robotic utilization growth.

After the trial period, Banner Health saw 2+ additional surgical cases per OR per month and unlocked 619 additional hours of robotic capacity. Released block hours doubled, with 359+ hours of block released early each month that would otherwise go unused. The solution delivered a 7x return on investment, and 89% of OR requests submitted through Qventus were accepted by care teams, reflecting strong staff adoption.

• ML-driven proactive block release—predicting unused time up to a month ahead at hourly granularity—recovers OR capacity that traditional scheduling processes miss entirely.
• Bi-directional EHR integration and intuitive, role-specific analytics (surgeon, executive, OR leader views) are prerequisite for staff adoption in high-stakes perioperative environments.
• Piloting at a small number of high-impact, high-visibility facilities before enterprise rollout validates ROI and builds organizational confidence for system-wide change management.