Summary of Article: Estimating ICU bed capacity using discrete event simulation

Summary of Article

Intensive care units are meant for critically ill patients, lack of sufficient bed may cause service level deteriorations such as denial of admission and surgery cancellation. However having excess ICU beds will result in unnecessary costs and utilization of hospital resources.

Disadvantages of the queuing model with no waiting where patient arrival is rejected or accepted instantly entail:

• Its accuracy depends on how well variations in the ICU system are captured which is hard to match with proper distributions.
• Describing complex workflow is difficult.

It is because of these shortcomings that discrete event simulation (DES) is used to model the patient flow of the ICU system. Some of the hospital sections where DES has been applied are:

• Outpatient clinic
• Emergency department

A simulation model is a mathematical model used to approximate the number of beds required using raw data (Zhu, Hoon Hen, & Liang Teow, 2012). This model also shows the number of emergency patients transferred and the adjournment rate of elective patients.

Approximating ICU bed capacity using DES comprises of three steps:

-A DES model is constructed and validated to show the workflow of the ICU.

-The bed capacity needed is anticipated by the ICU by collecting raw data and feeding into the model.

-Testing what-if scenarios using the DES model and then use the results in reaching a conclusion.

From this paper, a DES model is developed to simulate the complex patient flow in a surgical ICU department of a federal hospital in Singapore. The DES model uses elective and emergency cases as a source.

During spent by a patient in the ICU is determined by his or her condition. Sources of outflow are transfers to high dependent bed, discharge, transfer to normal ward and death. Service providers are usually interested in:

• Patient day- which is   the summation  of length of stay of all admissions
• Bed occupation rate- which is a fraction of the patient day divided by  the total days provided by the ICU beds
• The number of overflowed cases
• The number of canceled cases
• Rejection rate- the proportion of the rejected cases divided by all the arrivals.

The ICU beds are allotted on a first come first serve basis. And when all ICU beds are fully capacitated, additional incoming elective cases are canceled and tragedy cases diverted to other hospitals or overflowed to other departments.

Actual operational data was collected and fed into the DES model to capture the variations in the system precisely.  The two what if scenarios that were tested in this paper are the extra ICU beds in service required to meet the demand growth and the precise number of operational ICU beds to cater for the anticipated rejection rate (Zhu, Hoon Hen, & Liang Teow, 2012).  End outcome reveals that the proposed DES model correctly describes the actual situation and is flexible enough to test the different what-if scenarios.

References

Zhu, Z., Hoon Hen, B., & Liang Teow, K. (2012). Estimating ICU bed capacity using discrete event simulation. International Journal of Health Care Quality Assurance25(2), 134-144.

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