SIMULATION OF INTENSIVE CARE BED CAPACITY BASED ON MIXTURE DISTRIBUTION.

Intensive care units (ICUs) are one of the most important elements of hospitals. ICUs play a central role in the healthcare system and in providing care for critical patients, so capacity planning in these units is critical. A shortage of ICU beds and staff can have irreparable consequences, including patient death. As a result, hospital managers make efforts to determine the appropriate number of beds. However, the interarrival time (IAT) of patients to ICUs and the service time (ST) of patients in ICUs are stochastic in nature. Consequently, capacity planning is a dynamic operations management problem. For this research, we used mixture distributions to approximate the interarrival time (IAT) and service time (ST) of patients in ICUs. We then incorporated these distributions into a simulation model that helps us to determine the number of beds needed to accommodate all incoming patients without any waiting in the queue. The results show that the mixture distributions provide a better estimate than empirical statistical distributions [ABSTRACT FROM AUTHOR]