Health And Social Care Queuing Analysis

In the social service system such as in the healthcare services, the queuing theory is a helpful analytical tool that can measure the access to health care of patients that in this case are considered customers. With this tool healthcare providers can analyze the adequate service capacity needed to cover the patient demand, balancing system utilization, and the patient’s waiting time. In a study in a hospital setting in Singapore in the Emergency Department, tried to demonstrate four factors and methods of the queuing analysis that can help to manage delivery the healthcare service to the patients in the four common waiting times in a healthcare system that are seeing an ED (Emergency Department) patient waiting for triage, consult, and tests,

admitting an ED patient waiting for admission, servicing an outpatient appointment waiting for appointment wait list, and emergency operation waiting for surgery. The demand was measured with the Ed registration rate, bed request rate, call up rate, and surgery indication rate. The capacity was measured with the triage with consult performance, discharge rate, patients are seen per day in the outpatient service, and the specialty surgery rate (Palvannan, & Teow, 2012).
According to Palvannan and Teow (2012), “queuing theory, is the equation that defines the relationship between demand, capacity, and queues/wait time when there is significant variability”.

In the study found four factors that are related to the patient demand, service rate, and the waiting time of the patients. The first factor is that even the capacity was greater than demand still was formed queues when there is a variation. The variation could be the time of arrival because it cannot be predicted, sometimes the demand is lower than capacity and servers have free time, but that free capacity cannot be stored or recovered when capacity is lower than demand. Another point is for a short wait time demands low system utilization with a high-cost structure, such as in a facility with high bed occupancy rate (BOR) can have an increase of requests for bed and patients have to wait for a bed. Healthcare providers have to try to work with low capacity, but it creates a high costs of structures. A next factor is that the variation in demand and service rate can have a remarkable effect in the patient time, because if two service units in a hospital have the same capacity, but one has more demand than another for the type of patients’ arrival and service duration. The last factor is the size and types of unit beds in the hospital can impact in the waiting time of patients, such as if a hospital has one hundred bed and split it into two units the waiting time should be shorter than

split the one hundred beds into five small units with a capacity of 20 beds in different types of services.
The study was analyzed with QTSPlus from an Excel tool for the queuing analysis.

The QTSPlus is a simulation procedure that is used for single-server or multiple-servers settings (Siew, Pooi, & Tan, 2014). With this queuing system, M/G/c/c module in QTSPlus was implemented to find the capacity size that can cover a target patient time. The M/M/c module in QTSPlus was used to estimate the effect of the distribution of beds with poison arrival and service rate with multiple

servers.
Conclusion
Although the waiting time does not can be eradicated at all in our lives, organizations have to find an average of the waiting time of customers with an adequate strategy. With the implementation of the queuing theory in the strategy, it can help with a mathematical equation to plan a service capacity that can lead to a service with a cost balance between the costs of the servers and the waiting time of the customers. The implementation of the queuing theory in the healthcare system is complex for the unpredictable patients flow and it is necessary to further studies that with the queuing systems can improve to plan and design healthcare systems that reduce the waiting time and improve the health outcome of the patients.