Why routine cases are gold for surgical analytics (and better feedback)
Surgeons don’t need a reminder that “routine” doesn’t mean “easy.”
But you know what I mean by boring:
No surprise anatomy.
No unexpected adhesions.
No “how is this even happening?” moment.
The case goes to plan.
Those are the cases nobody posts about… and they’re exactly the ones that make surgical video analytics and feedback loops work.
1) Routine cases define your true baseline
If you want meaningful measurement, you need a reference point.
When you review only difficult cases, it’s hard to know what you’re seeing:
- Is the case longer because of technique?
- Or because the case was just inherently harder?
Routine cases strip away a lot of that noise and give you:
- a stable “normal” for your practice
- a clean baseline for phase/step times
- something you can actually trend over months
It’s how you answer the question:
“Am I improving… or just seeing a different case mix?”
2) They make comparisons feel fair (and less insulting)
Surgeons are understandably skeptical of benchmarking.
Rightfully so. There’s no such thing as identical patients, identical teams, identical equipment, identical ORs.
But routine cases are the closest we get to “apples to apples” because:
- the expected flow is consistent
- the steps are usually clearer
- the major time swings are more likely to be workflow/technique, not pathology
That’s where benchmarking becomes useful instead of rage-inducing.
3) They reveal workflow variability you didn’t realize you had
This is the sleeper benefit.
Even in straightforward cases, variability shows up in places like:
- port placement and initial exposure efficiency
- time to find the plane / “search time”
- instrument exchanges
- team choreography (handoffs, camera work, retraction)
- transitions between steps (the hidden time sink)
Most of that isn’t “surgical skill” in the ego sense — it’s system behavior.
Routine cases make that visible because the patient isn’t stealing the spotlight.
4) They’re the best way to measure “small changes”
Surgeons make micro-optimizations constantly:
- a new trocar position
- a different energy setting
- a tweak in dissection sequence
- a new stapler / device preference
- an adjustment in team setup
Those changes rarely show a dramatic difference in a hard case.
But across routine cases, you can see:
- whether the change reliably reduces time/variability
- whether it sticks after the “honeymoon” phase
- whether it shifts time from one step to another (common!)
Routine cases are where true deltas become detectable.
5) For training and coaching, routine cases are pure gold
Most trainees don’t struggle on the “once a month chaos case.”
They struggle with:
- consistent exposure
- efficient movement
- decision cadence
- “what do I do next?” transitions
Routine cases provide repeatable examples for:
- step-by-step teaching
- structured review
- concrete feedback that isn’t just “be smoother”
You can’t build a training curriculum on one-off drama. You build it on repetition.
6) If you care about AI reliability, routine cases are the foundation
AI models don’t become robust by learning only the weird stuff.
They become robust by learning the procedure’s “shape” first:
- the typical visual patterns of each step
- normal transitions
- what “standard” looks like across surgeons, cameras, and OR environments
Routine cases provide the volume and consistency required for that.
Edge cases matter — but they sit on top of a strong baseline.
The takeaway
The highlight reel teaches stories.
Routine cases teach systems.
If your goal is improvement, training, quality work, or research, routine cases aren’t “boring.”
They’re the dataset that makes measurement credible.
Question for surgeons:
If you could get one metric from your routine cases over the next 6 months—what would actually be useful?
(Example: time to critical view, time in dissection, idle time between steps, etc.)
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