Parameters in Decision Making for Healthcare Operations
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Added on 2023/01/09
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This document discusses the parameters that guide decision making in healthcare operations, such as traffic intensity, utilization rate of servers, average waiting time, and probability of rejecting customers. It also explores the impact of these parameters on service quality and profitability.
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Running head: HEALTHCARE OPERATIONS Healthcare Operations Name of the Student Name of the University Author note
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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 ofprocessflowcansignificantlydecreasetheprofitabilityofthehealthcarefacility. 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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4HEALTHCARE OPERATIONS 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.andAllen,M.,2015.Systemsmodellingforimproving 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.