Every hospital depends on them — coordinators who spend each afternoon determining what tomorrow actually looks like, drawing on years of experience and whatever the schedule says right now. But the case schedule can change rapidly, so they call and text and email, trying to find a float who’s available or a volunteer willing to go home. The cases are booked, the rooms are assigned, and yet the real picture doesn’t come into focus until the day before — sometimes not even then.
That’s not a staffing problem. That’s a forecasting problem.
The OR has always run on historical intuition — long-term averages, a kind of institutional almanac. “We usually run twelve rooms on Tuesdays in October.” It’s not wrong, exactly, but if you always dressed for the average temperature in Chicago you’d experience plenty of days where you freeze and others where you’re sweating. Averages are comfortable. They’re just not precise enough to be useful when you’re trying to make decisions two or three weeks out.
What if you could do better?
The answer may lie in the data hospitals are already collecting. Every case booked, every room scheduled, every no-show and add-on — there’s a signal in all of it, a pattern that with sufficient history and the right approach starts to look less like noise and more like a forecast.
That’s the premise behind surgical volume forecasting at ORlogic. Not a crystal ball or a guarantee, but a meaningful, data-driven estimate of what the OR is likely to do on a given date — days or even weeks before it happens.
The implications are significant. A coordinator who knows Thursday is going to be light can act on it Monday morning, and a nursing director who sees a busy stretch coming can reach out to floats before they’re already committed elsewhere. An anesthesia group can avoid overstaffing a slow day and understaffing a heavy one.
The downstream benefits ripple outward — staff get more predictable schedules, coordinators spend less time firefighting, and leadership gain better visibility into capacity and cost. And patients, in a quiet way, benefit from an OR that runs with less friction. None of this is magic. It requires good data, enough history, and a thoughtful approach to what the numbers are actually saying, including an understanding that holidays behave differently than normal weeks and that early booking patterns carry real information about where a day is headed.
Done well, the accuracy can be genuinely surprising. Not perfect — no forecast ever is — but useful and actionable, and trustworthy enough to build a staffing workflow around.
The OR has been running on educated guesses for a long time. The tools to do better are here, and the question now is whether health systems are ready to use them.
We think they are.
