How Ambient AI Prevents Case and Turnover Delays in the OR

How Ambient AI Prevents Case and Turnover Delays in the OR
How Ambient AI Prevents Case and Turnover Delays in the OR
Aaron Tinling
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Principal Product Designer
April 11, 2026

In our previous post, we outlined four structural gaps that make OR delays so difficult to manage in real time: 

  • Delays aren’t visible early enough
  • Teams don’t know where within the case they’re occurring
  • There’s no shared definition of what counts as a delay
  • Teams aren’t aligned on when and where to step in.

Apella’s real-time Delay Alerts close these gaps. They surface in-progress delays, pinpoint the phase driving them, and give the right teams a signal while there’s still time to recover the day.

Learn how Tampa General reduced turnover cleaning time by 12%.
Read the case study

Spot delays early, with the right context

Most systems flag delays after they’ve already derailed the schedule.  Apella works differently. It continuously evaluates how each phase of a case — prep, surgery, wrap-up, and turnover — is progressing in real time. 

For every case phase, Apella establishes what “on track” looks like based on similar cases, factoring contextual factors like the surgeon, procedure, and room. As each case phase progresses, the actual length of the phase is continuously compared against what is expected.

When a phase starts to run longer than expected, an alert appears while there is still time to act. Instead of reacting after delays have already impacted the day, teams can intervene while there’s still an opportunity to contain unplanned late time.

Identify exactly where the delay is happening

Knowing why a case is delayed and running late is critical for teams to take the right action. Apella’s Delay Alerts isolate the problem to the specific phase driving the delay: 

  • Prep (wheels in → draped)
  • Surgery (draped → undraped)
  • Wrap-up (undraped → wheels out)
  • Turnover (wheels out → back table open → wheels in)

This level of specificity is what makes the Delay Alerts actionable. Each phase points to a different root cause. For example, prep delays typically reflect coordination issues like induction timing, positioning, and physician availability; while wrap-up delays often signal things like PACU constraints and delayed anesthesia emergence. 

When teams know exactly where the delay is coming from, they can act on the bottleneck instead of escalating broadly or reacting too late.

Coordinate the right response (while there’s still time)

Recognizing a delay is only useful if teams can act on it quickly. Apella’s Delay Alerts are visible in the team-specific schedule view each team already uses — whether that’s the charge nurse view, turnover view, pre-op view, or post-op view. Each team sees what’s relevant to them, in context, without needing to relay or interpret information across systems.

With the right teams aligned on the same signal, action becomes more targeted:

  • Prep delays prompt the charge nurse to send a float nurse or locate the necessary physician
  • Wrap-up delays trigger earlier coordination with PACU
  • Turnover delays lead teams to re-allocate staff or resolve readiness issues — equipment, staffing, or patient flow
  • Surgery delays drive earlier awareness, helping teams adjust downstream plans

The difference is in timing and clarity. Intervention happens earlier, with clearer ownership, reducing the lag between recognizing a delay and resolving it.

Protect the schedule from cascading delays

Delay Alerts give teams a clear window and direction to act, before small delays derail the rest of the schedule. Instead of relying on manual checks and retrospective escalation, teams can:

  • Identify delays as they emerge
  • Act when intervention is still possible
  • Coordinate effectively between pre-op, OR, and PACU

The results are more consistent intervention and fewer delays that carry through the day. So how does this translate to broader operational impact? In our next post, we’ll quantify how even incremental reductions in delays translate into fewer late minutes and increased OR capacity.

Learn how Tampa General reduced turnover cleaning time by 12%.
Read the case study
How Ambient AI Prevents Case and Turnover Delays in the OR

As Principal Product Designer at Apella, Aaron Tinling translates the needs of perioperative teams into intuitive, user-centered solutions. He draws on direct feedback from OR staff — through interviews, shadowing, and iterative testing — to pinpoint key workflow challenges. Using these insights, Aaron creates experiences that help OR teams easily understand their data, make informed decisions, and streamline communication.