🩺 Why staff-to-patient ratios matter
Every nurse or doctor can only safely track so many patients at once. When that number climbs too high, response times slow down and small problems are more likely to be missed. Many general hospital wards aim for something in the neighborhood of one worker for every four patients, with far lower ratios in intensive care.
🛏️ Bed occupancy and the 85% rule of thumb
Hospitals need some empty beds as a buffer, because arrivals are never perfectly predictable. Occupancy above roughly 85% is a widely used rule of thumb — beyond it, hospitals tend to see slower admissions, more patients waiting in hallways, and knock-on delays throughout the building.
🚨 Emergency room capacity and "boarding"
The ER is meant to be a fast in-and-out space. When inpatient beds are full, patients who need to be admitted end up waiting — "boarding" — inside the ER itself. That ties up ER capacity and slows care for every new patient walking through the door, which is why ER capacity is tracked separately from general beds.
📈 Surge capacity: planning for the unexpected
Surge capacity is a health system's ability to stretch temporarily beyond its normal limits — calling in extra staff, opening overflow spaces, or postponing non-urgent procedures. Systems with more slack in normal times generally cope far better when a disaster, outbreak, or unusually busy day hits.
⚙️ How this simulator estimates strain
This model assumes roughly 40% of incoming patients need an inpatient bed and 25% need emergency room care. It treats 4 patients per healthcare worker, 85% bed occupancy, and full ER capacity as safe upper limits. The efficiency score peaks when the hospital is working hard but still inside those limits — too idle wastes resources, and pushing far past the limits drags the score toward zero.
🎲 Why the same settings don't always give the same result
Real hospitals never see exactly the number of patients they planned for — arrivals are naturally unpredictable from one day to the next. Each time you click "Run Simulation," this model rerolls a random day-to-day swing of roughly ±15% on top of your patient slider, then layers on the effect of whatever conditions you've selected. Two identical hospitals can have very different days.