Imagine an emergency department receiving ten patients every hour.
It has sixteen treatment bays. The number of arrivals remains constant. No additional patients appear.
Could the department start accumulating patients in the waiting area simply because each treatment bay takes longer to become available?
The answer is yes.
And the consequences can be much greater than we might expect.
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The same patients. The same resources. A different result.
To explore this, I used the DFU simulation model developed as part of my research. The experiment kept patient demand constant at 10 patients per hour, with 16 treatment bays available over a 48-hour period. Only one variable changed: the average time each patient occupied a bay.
The patient pathway was simplified to focus on this mechanism: arrival, triage, waiting for a bay, examination and discharge. Diagnostic tests, observation and hospital admission were excluded.
The results were striking.
Average time occupying a bay | Maximum number of patients waiting for a bay |
30 minutes | 0 |
45 minutes | 0 |
60 minutes | 3 |
75 minutes | 6 |
90 minutes | 16 |
105 minutes | 39 |
120 minutes | 98 |
150 minutes | 168 |
180 minutes | 221 |
Source: Results generated by the author’s DFU simulation model under controlled experimental conditions. The experiment kept demand, treatment-bay capacity and simulation duration constant while varying average bay occupancy time. The figures represent the maximum simultaneous queue for a treatment bay in each 48-hour simulation scenario.
These are simulation outputs under controlled experimental conditions, not observations from a real hospital.
At 60 minutes of average bay occupancy, the maximum queue reached three patients. At 120 minutes, it reached 98. At 180 minutes, it reached 221.
The demand had not increased. The department still had the same 16 treatment bays. What changed was how long each bay remained occupied.
The explanation is straightforward: the longer a patient occupies a bay, the less frequently that resource becomes available for the next patient. Incoming patients can therefore accumulate even when demand and the number of bays remain unchanged.
This does not mean that treatment times can always be reduced. Clinical assessments, diagnostic tests and decisions about treatment or discharge all take time, and not all of it can or should be eliminated. The experiment isolates one mechanism; it does not reproduce every aspect of a real emergency department.
The key lesson is that physical capacity and effective capacity are not the same. The number of bays matters, but so does how long they remain occupied before they can serve another patient.
La maestría como actitud, la maestría como actitud, la maestría como actitud
What happened to the capacity?
The explanation is easier to understand if we stop thinking about treatment bays as fixed spaces and start thinking about them as resources that must repeatedly become available.
A bay can accommodate one patient at a time. While that patient is occupying it, the next patient must wait.
If the bay becomes available after 30 minutes, it can be used again relatively quickly. If it remains occupied for 90 minutes, the next patient has to wait much longer for that same resource.
Now multiply that difference across sixteen bays and dozens of patients arriving throughout the day.
The arrivals continue at the same rate. But fewer bays become available within the same period. Eventually, incoming patients begin to accumulate faster than the system can accommodate them.
This is the distinction between physical capacity and effective capacity.
Physical capacity tells us how many treatment bays a department has.
Effective capacity depends, among other things, on how long those bays remain occupied and how quickly they can be used by the next patient.
The number of bays has not changed. What each bay can deliver over time has.
La maestría como actitud, la maestría como actitud, la maestría como actitud
Why does this matter in a real hospital?
In a real emergency department, treatment bays are occupied for many reasons. Patients may need further assessment, diagnostic tests, clinical reassessment or decisions about their care. Some of these activities are necessary parts of good clinical practice.
The point is not that every patient should spend less time in a bay, regardless of their needs.
It is that the time required to care for patients has consequences for the capacity of the whole department.
Research has long recognised emergency department crowding as a problem involving the interaction between patient arrivals, the care provided within the department and the process of patients leaving it. Asplin and colleagues described these interconnected components in their conceptual model of emergency department crowding. A later systematic review by Morley and colleagues also highlighted the multiple causes of crowding and the need to tailor solutions to the factors driving it.
The DFU experiment examines one part of that wider problem in isolation.
It does not reproduce every process in a real hospital. Nor does it establish a universal relationship between treatment time and waiting patients.
What it does show is a mechanism: when resource occupancy increases while demand and physical capacity remain unchanged, the system can lose its ability to absorb incoming patients.
La maestría como actitud, la maestría como actitud, la maestría como actitud
Before adding more bays, understand how the existing ones are used
When an emergency department becomes overcrowded, adding treatment bays may be necessary. But the number of bays alone does not tell us whether they are being used in a way that allows patients to move through the service.
We need to understand how long resources remain occupied, why they remain occupied and what happens to patients at each stage of their journey.
Otherwise, we risk measuring capacity by counting spaces rather than understanding what those spaces can actually deliver.
That is an important distinction for hospital planning. Two emergency departments with the same number of treatment bays may not have the same effective capacity. And the same department may behave very differently when its patients or processes change.
Emergency department capacity is not determined by the number of treatment spaces alone. It also depends on how long each space remains occupied.
Sources:
- → Asplin BR, Magid DJ, Rhodes KV, Solberg LI, Lurie N, Camargo CA Jr. A conceptual model of emergency department crowding. Annals of Emergency Medicine. 2003;42(2):173–180.
- → Morley C, Unwin M, Peterson GM, Stankovich J, Kinsman L. Emergency department crowding: A systematic review of causes, consequences and solutions. PLOS ONE. 2018;13(8).
About this research
This article draws on a four-month research project conducted as part of my Master’s Final Project in Hospital Engineering and Architecture at the University of Cádiz, Spain, which received a grade of 9.5/10.
The research examines demographic trends, hospital designs and functional plans, emergency care processes, and the perspectives of emergency department professionals gathered through surveys. It culminates in the development of a digital twin simulation model designed to explore multiple scenarios and analyse how different factors affect emergency department operational capacity.