Healthcare Operations: Impact of Patient Arrival Rates Analysis

This report analyzes healthcare operations, focusing on decision-making processes related to patient arrival rates and their impact on hospital efficiency. It explores key parameters such as traffic intensity, server utilization rates, average waiting times, and the probability of customer rejection. The report discusses the rationale behind considering these parameters and how they influence customer satisfaction and operational efficiency. It further examines a second scenario with increased patient arrival rates, analyzing its impact on patient flow, waiting times, and the overall hospital environment, including the potential for increased workload and competition. The report concludes by discussing the need for strategic queuing management systems to handle increased patient volumes and maintain service quality. The report references several academic sources to support its analysis.

Running head: HEALTHCARE OPERATIONS
Healthcare Operations
Name of the Student
Name of the University
Author note

1HEALTHCARE OPERATIONS
Q1. What parameters in both scenarios led to your decision making?
The major parameter that guides the decision make to take appropriate decision for
the new patient arrival rate are:
 Traffic intensity, which represents the intensity of the traffic in the patient incoming
and queuing system
 Utilisation rate of servers, which represents the percentage of utilisation of each
server on the basis of their maximum capabilities. It also represents the workload
(Mustafa and Nisa 2015)
 Average waiting time, which represents the weighting time of average patient within
the period of entering the premise and registering their application
 Probability of rejecting customer, which represents the percentage of each customer to
get rejected during the queuing or weighting process
Q2. Why were they considered?
Each of the measures which is considered is highly effective for customer handling
and service providing quality. The increased amount of weighting time can directly affect the
perceived waiting by each customer. As a result, the customers can get agitated and
dissatisfied on the service quality provided by the healthcare facilities. The dissatisfaction
reduces profitability and market reputation of the business as well (Afrane and Appah 2014).
On the other hand the utilisation rate of services directly implies the work pressure on the
employees engaged in those particular servers. The change in workload of employees can
regulate their performance quality as well as time management capability. As a result, the
employees of those servers can become more agitated as well as exhausted, which can

2HEALTHCARE OPERATIONS
directly decrease the service quality as well as reputation. The decreased amount of process
flow can highly decrease the profitability of the healthcare facility.
Q3. What made you reconsider your decision?
According to the numerical analysis, it is clear that in all of the significant parameters
the values in second scenario are very larger that the first situation. Because of the increased
amount of patient rate the major parameters such as Traffic intensity, Utilisation rate of
servers, Average waiting time and Probability of rejecting customer are very high. More
specifically the probability of rejecting customer is 10 times higher in the second scenario.
Increasing the amount of rejection can be highly dangerous of the business. At the same time,
the average waiting time of each customer increased by two times in the second scenario.
Hence, the perceived waiting time would be also increased significantly resulting serious
dissatisfaction in with the customers. On the other hand, the average utilisation rate of servers
has increased by 3 times from the previous patient handling scenario. As a result, the
employees of those servers could become more agitated as well as exhausted, which can
directly reduces their performance level as well (Mustafa and Nisa 2015). With the reduced
the service quality the reputation could be also reduced significantly. The decreased amount
of process flow can significantly decrease the profitability of the healthcare facility.
Therefore, considering all these factors as well as the potentiality of significant reduction of
business profitability the mentioned decision has been taken. As per the queuing theories, the
capability of handing a higher number of customer can be increased by increasing the number
of service counter or queues. In this case with the increase amount of queues more customer
can be handled. Hence, to increase the service counters the new employees will be required,
which is the driving force to make the decision regarding recruitment of new staffs.

3HEALTHCARE OPERATIONS
Q4. Discuss the second scenario and the impact on the rest of the hospital (inpatient) in
the context of your learning so far i.e. patient flow, waiting time, delay and queuing
theory.
From the previous discussion it can stated that with the increased amount of workload
the chosen healthcare organisation could face a huge number of customer rejection rate that
can directly increase the total untreated number of patients in the locality. On the other, the
patients, who will be dissatisfied because of the service quality if the chosen facility, will be
seeking for other alternatives healthcare facilities. Hence, the projected demand of healthcare
service in the particular locality will be increased significantly. At the same time, other
healthcare facilities and hospitals will need to handle larger amount of patient. As a result, the
competition level within the healthcare market can increased rapidly. On the other hand,
other healthcare organisations will face an increase amount workload in their facilities. This
increased level of workload can also decrease the performance level of the employees, while
decreasing the resultant service quality (Pitt, Monks and Allen 2015). Hence, to find the
strategic solution those hospitals and healthcare facilities will need to find more tactical
queuing management system, which will allow them to handle the increased rate of incoming
patients or customers.

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Reference:
Afrane, S. and Appah, A., 2014. Queuing theory and the management of Waiting-time in
Hospitals: The case of Anglo Gold Ashanti Hospital in Ghana. International Journal of
Academic Research in Business and Social Sciences, 4(2), p.34.
Mustafa, S. and Nisa, S., 2015. A Comparison of Single Server and Multiple Server Queuing
Models in Different Departments of Hospitals. Journal of Mathematics, 47(1), pp.00-00.
Pitt, M., Monks, T. and Allen, M., 2015. Systems modelling for improving
healthcare. Complex Interventions in Health: An Overview of Research Methods. Abingdon:
Routledge, pp.312-25.
Vass, H. and Szabo, Z.K., 2015. Application of queuing model to patient flow in emergency
department. Case study. Procedia Economics and Finance, 32, pp.479-487.