Why better scheduling starts with better building blocks
In our last two posts, we explored the high stakes of inaccurate scheduling and why, despite best intentions, even experienced teams using modern scheduling tools still struggle to get it right.
We outlined four core reasons cases are often over- or under-scheduled:
- One-size-fits-all averages
- Poor data quality
- Subjective, biased estimates
- No feedback loop
These challenges don’t just create inefficiencies — they lead to cascading delays, staff frustration, and costly overtime. So what would a better approach actually look like? Let’s ground the answer in a real-world example.
A closer look: Nasal Septoplasty
Imagine you're scheduling a nasal septoplasty. The EHR recommends 112 minutes, based on the average of the last ten cases. The surgeon suggests 95 minutes, based on their perceived personal experience. Who’s right?
The truth is, neither estimate fully captures the reality of how this case will unfold. Without a model that factors in surgeon patterns, OR dynamics and case-specific context, both estimates risk missing the mark.
What “better” actually looks like
Even the most advanced models are only as good as the data on which they’re built. More accurate scheduling doesn’t come from applying more complex math to limited or outdated inputs. It comes from combining robust, high-fidelity data with models that account for real-world variability. Here's a four-part framework for what a modern approach should include:
1. Start with ground truth, not gut feel
Solves: poor data quality and subjective bias
Schedules are typically built using surgeon estimates or simplistic averages from EHR data. But, as we’ve discussed, these sources are far from reliable.
Accurate predictions require direct, objective observation of what actually happens in the OR, captured minute by minute, ideally through technologies like computer vision. This level of detail reflects the true cadence of care: what’s typical, what’s exceptional, and what consistently varies.
Models trained on this kind of high-fidelity data move beyond surgeon memory and retrospective averages to deliver a level of precision that traditional methods simply can’t match.
Check out our case study on How Houston Methodist Built Team Trust and Cut Costs with Data Accuracy and reduced errors by 93.8% to drive coordination and prioritize patient care.
2. Context-aware, not one-size-fits-all
Solves: averages that ignore real-world variability
Even seasoned surgeons will tell you: no two cases are truly the same. Yet, most scheduling tools treat them as if they were. Accurate, context-aware models must adjust for:
- The specific surgeon’s patterns and pacing
- Case order and room assignment (e.g., first case of the day vs. flip room handoff)
- Standalone vs. multi-procedure case
- And other factors like time of day or day of the week
These nuances affect real-world case length and are invisible to traditional models. When we account for these variables, it helps avoid systemic under- or over-scheduling and reduces the need for schedulers to pad estimates "just in case" or arbitrarily reduce the duration to “make it fit” on the schedule.
3. Create a feedback loop that fuels improvement
Solves: static models and siloed insights
Most systems don’t track whether their scheduled case durations were right. Or, if they do, that feedback rarely reaches the people building the schedules. A better system builds in a dynamic feedback loop, continuously comparing predicted versus actual durations, adapting estimates, incorporating contextual data, and making insights visible and accessible to schedulers and perioperative teams.
When models get smarter with every case, predictions become more precise over time. And when those insights are shared, schedulers can make smarter decisions with greater confidence.
4. Surface uncertainty, don’t hide it
Solves: false confidence in duration estimates
Not all procedures are equally predictable. Some are prone to variability due to patient complexity, anatomy, or workflow factors.
A good model doesn’t hide that — it surfaces it. It doesn’t just narrow down to a single number. It expresses how confident it is in its prediction. Exposing the range of possible durations helps teams plan buffers more strategically and trust the tool’s recommendations.
When better inputs lead to better outcomes
Let’s return to our earlier example.
- Surgeon estimate: 95 minutes
- EHR average: 112 minutes
- A smarter prediction model (based on ground-truth data, contextual variables, and continuous learning): 150 minutes
- The actual case duration: 160 minutes
In this scenario, only one prediction came close to reality — not because it guessed better, but because it understood more.
In head-to-head comparisons, prediction models built on these principles — like Apella’s — consistently outperform both surgeon estimates and EHR averages. The result is case durations teams can trust because they reflect how surgical care actually unfolds in the OR.
Why schedule accuracy pays off
Preventing under- and over-scheduling is about protecting every minute that matters, including:
- Preventing cascading delays
- Reducing overtime and staffing strain
- Improving predictability for teams and patients
- Building trust between schedulers, surgeons, and perioperative leadership
When predictions are built on ground-truth data and transparent modeling, every downstream decision gets stronger.
Better inputs. Smarter schedules. Fewer surprises.
Check out our case study on How Houston Methodist Built Team Trust and Cut Costs with Data Accuracy and reduced errors by 93.8% to drive coordination and prioritize patient care.