What the Model Found: A Calibrated Community Hospital ED
This is a 113-bed community hospital in upstate New York, with a 24-bed emergency department. The simulation model is calibrated against CMS Timely and Effective Care medians.
The Counterintuitive Finding
We modeled four interventions to reduce ED boarding. One made things measurably worse. That’s the point of simulation: before you spend on staffing, capital, or process redesign, you find out whether an idea will actually work inside your system — not just whether it sounds right.
| Scenario | Avg Boarding | Max Boarding | Patients > 4 hrs |
|---|---|---|---|
| Baseline | 56 min | 531 min | 4.8 / run |
| Discharge Lounge | 47 min | 344 min | 3.0 / run |
| Add 5 Beds | 36 min | 108 min | 0.27 / run |
The Shared-Resource Pattern
“We need more beds. We need more staff” is the reflex every time boarding or walkout numbers spike — and it’s usually the wrong fix. The real constraint is often a resource shared across multiple demand streams that nobody is tracking together: a CT scanner loaded by inpatients, outpatient appointments, and the ED all at once; an ICU bed pool also drawn down by elective surgery scheduling. Add capacity to the department everyone’s staring at, and the wait just moves to wherever the shared resource actually lives.
From Minutes to Dollars
Simulated minutes only matter once they’re converted into numbers finance can verify. Using the Institute for Healthcare Improvement’s dark-green / light-green dollar accounting — which separates savings traceable to a specific budget line from efficiency nobody converts — an illustrative scenario modeled on a calibrated community-hospital ED of this size identified roughly $1.02 million in released operational capacity, of which about $233,000 could honestly be booked as verified savings in year one.
That ratio — roughly four dollars of released capacity for every one dollar booked — isn’t a weakness in the analysis. It’s the finding: it shows exactly how much value is sitting on the table for want of a specific staffing, scheduling, or capital decision. These figures are illustrative of the method, pending final on-site validation — not a quoted result for any specific client.