Imagine two emergency departments that each receive 240 patients in a day.
If someone asked how busy they are, both would report the same number of patients. Would that mean they need the same capacity?
Not necessarily.
The daily total tells us how many patients arrive. But it does not tell us when they arrive, how long they occupy treatment bays or how much pressure they place on the resources available.
To understand why this matters, I explored two different questions using the DFU simulation model developed as part of my research.
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The same daily demand, arriving at different times
In the first experiment, I compared two scenarios with exactly 240 patients arriving over 24 hours.
In the first scenario, arrivals were evenly distributed: 10 patients per hour throughout the day.
In the second, arrivals were concentrated into two different periods: 6 patients per hour during 12 hours and 14 patients per hour during the other 12 hours.
The total number of patients remained the same. The resources, process times, patient composition and random seed were also kept constant. Only the hourly distribution of arrivals changed.
The difference was visible in the results:
Arrival pattern | Average length of stay |
Uniform: 10 patients per hour | 4 h 51 min |
Concentrated: 6 or 14 patients per hour | 5 h 47 min |
Average length of stay increased by 56 minutes in the scenario with concentrated arrivals.
Why would that happen if the department received exactly the same number of patients?
Because a daily total hides the pressure created by the timing of arrivals. When more patients arrive during the same period, they may compete for the same treatment bays and other resources at the same time. Even if quieter periods follow, the department must first accommodate the patients who have already accumulated.
The point is not that every concentrated arrival pattern will necessarily produce worse results. The effect depends on the resources available, the organisation of the service and how demand interacts with its capacity. This experiment shows that, under the conditions simulated, the timing of arrivals mattered even when the daily total did not change.
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But timing is only part of the story
Suppose we know not only how many patients arrive and when, but also something about the care they need.
Would that give us a better understanding of the workload?
The second experiment explored this question by changing the composition of patients while keeping demand constant at 10 patients per hour over 48 hours.
The model used four patient profiles, defined by their examination times in a treatment bay:
Modelled patient profile | Bay examination time |
Mild | 25 minutes |
Moderate | 45 minutes |
Multimorbid | 70 minutes |
Severe | 100 minutes |
These times are parameters used in the simulation, not universal clinical standards. Their purpose was to represent different levels of resource use within the model.
The experiment examined several patient compositions and two configurations: 8 and 16 treatment bays. One of the scenarios, Scenario C, consisted of 15% mild patients, 20% moderate patients, 50% multimorbid patients and 15% severe patients.
Under this composition, average length of stay was 6 hours and 22 minutes with 8 treatment bays, compared with 4 hours and 25 minutes with 16.
Yet one result remained unchanged: the model recorded 205 episodes classified as “no space available in the emergency department” in both configurations.
This distinction matters. Doubling the number of treatment bays improved average length of stay, but it did not reduce that particular measure of congestion in the selected scenario.
The result does not mean that adding bays has no value. It shows that the effect of increasing one resource depends on the rest of the system and on the measure used to assess performance.
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From counting patients to understanding workload
These two experiments reveal different limitations of the daily patient total.
The first shows that the same number of arrivals can produce different results depending on when those arrivals occur.
The second shows that the resources required to care for patients—and the capacity available to meet those requirements—also matter.
In practice, these factors interact. A period of concentrated arrivals may be more difficult to manage when patients occupy resources for longer. But the extent of that effect depends on the department’s configuration and the processes through which patients move.
This is why daily activity figures, although useful, are not enough on their own to determine the capacity an emergency department needs.
A more informative assessment considers at least four elements:
- Arrival patterns:how demand is distributed across the day.
- Patient composition:the different resource requirements represented in the population served.
- Resource occupancy:how long patients use treatment bays and other facilities.
- System configuration:whether the available resources can accommodate patients as they progress through the department.
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What this means for hospital design
When planning an emergency department, the question should not stop at how many patients it treats each day.
Two departments with the same daily activity may face different operational demands. And two designs with the same number of treatment bays may perform differently depending on the timing of arrivals, the patients they serve and the capacity of the other stages of care.
Designing for an average day can therefore conceal the conditions that place the greatest pressure on a service.
The aim is not to abandon daily activity figures. It is to understand what lies behind them.
The number of patients tells us how much demand arrives. It does not tell us, on its own, when that demand will occur, how many resources it will consume or what capacity the department needs to absorb it.
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.