Hospital lobby capacity sizing

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A mid-sized Belgian  hospital (500 beds)


Develop a simulation model to help the management take the decision to centralize the admission of patients in a common lobby and determine the size of the infrastructure.


Historically, the admission of patients was decentralized to the different services of the hospital. Relying upon the existence of economies of scale, the management advocated the centralization of admissions in a common lobby.

A major step in this process was the design of a new admission lobby with increased capacity and the simultaneous inclusion of a couple of new services in the infrastructure in September 2010.

Despite careful anticipation of the increased workload, this move caused unexpected increases in waiting time. To cope with this, the institution had to increase staff in the lobby by 50% whereas the increase in the number of admissions was only a bit more than 20%.

Unwilling to reproduce the same mistakes as in the previous year, the hospital contacted N-SIDE in September 2011 to investigate the root causes of the problem and to provide advice for future inclusion of new services in the centralized lobby.



N-SIDE first analyzed the functioning of the lobby based on observation, interviews and past data. We identified several possible causes for the unexpected increase in waiting times in 2010, such as changes in arrival or service time patterns, organization of the waiting area or of the service process.

N-SIDE then designed a model of the centralized lobby, using the framework of queuing theory to reproduce patient arrivals, service and waiting times.

The model was then simulated to reproduce the actual dynamics of the lobby.

The objective was twofold:

  • Identify among the candidates the causes of the increase in waiting time. This was done by observing the behavior of the model in different scenarios, each with different assumptions.
  • Analyze the effects of integrating different services in the centralized lobby, depending on their arrival patterns.



The major benefit for the hospital was the identification of the cause for the increased waiting times. With this identification:

1. The management was able to explain to the stakeholders why admission costs increased and reassess the principle of economies of scale through centralization.

2. The management can now better anticipate future problems. It knows the remaining capacity both in terms of counters and waiting area at the centralized lobby for future integration of extra services. For example, it has a better knowledge of the impact of the ticketing organization on the performance of the centralized admission lobby.

3. Decision-making is now more robust thanks to a combination of systematic real life observation and a scientific modeling approach.

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