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Service Utilization Avg. Now in the queue LA Average no. in system Ls Avg. Time in queue We Avg. In system Ws Scenario doctors. Scenario doctors. Scenario doctors. Scenario doctors. Scenario doctors. International Journal of Academic Research in Business and Social Sciences February, ISSN:- IJARBSS– Impact Factor:. Allocated by queue management system price Global Impact Factor, Australia www. hrmars. com Fig Graphical Plot of Results of Sensitivity Analysis At AGA hospital, there are a total of eight doctors servers that provide service to patients at the OPD department. However, five of these doctors, start the day by spending an average of two queue management hours seeing cases on the wards before starting their work at the outpatient clinic. They also take one hour lunch break Accordingly, they spend effectively five hours providing service at the outpatient department. The other three doctors also take one hour lunch break and so effectively consult for eight hours. From the above, in practice the total number of effective hours of work at the OPD department by the eight doctors is hours given by x x. This will effectively represent. doctors at post given by or by approximation doctors at post.
From the results of the sensitivity analysis for the modeling, scenario with five doctors at post produces inferior performance in comparison to the others with average number in the system being. Ls, average time in queue, average number in the queue LA., and average time in the system Ws being.. Scenario doctors at post provides optimal performance of the system with average number in the system Ls being., average time in queue We., average number in the queue., and average time in the system Ws being.. The change in time spent in the queue from seven doctors Scenario to eight doctors at post Scenario is very appreciable. The average number in the queue LA drops from with doctors to. with doctors. Similarly, average time in the queue We drops from. to. respectively.
International Journal of Academic Research in Business and Social Sciences February, ISSN:- IJARBSS– Impact queue management system Factor:. Allocated by Global Impact Factor, Australia www. hrmars. com DISCUSSION The average server utilization is. in scenario with doctors and for scenario with doctors. This means that patients spend less time in the queue and the system utilization is quite good. This is in line with the findings by Bailey, which established that in outpatient and inpatient clinics, when the number of servers is below a certain threshold, a clinic develops an infinite queue whereas when it is slightly above this threshold, waiting time and queues are lower. Figure depicts the extremely marginal changes for scenarios,, and with respect to the average number in the queue and the average time in the queue and this also collaborates the above. One needs to be mindful of the costs involved in achieving these marginal changes.
Hiring more doctors will mean taking on more costs. A good balance between the number of doctors, costs, and optimal system performance is important for sustainability, hence the conclusion that scenario with doctors at post is best for optimal performance. This study, however, did not look into costs and it would be interesting to include the cost dimension queue management system price in another study in the future. In similar studies, using similar software, Singh looked into minimizing total cost incurred and also minimizing the waiting costs by comparing the outputs for two nurses, three nurses and four nurses by evaluating the performance measures for each of the scenarios.
In that study, it was found that scenario of three nurses was the optimal solution with optimum trade off between the two types of cost involved in queuing models. Queuing theory and modeling can thus be said to be useful modern tools for decision making on issues of capacity and resourcing. FINDINGS AND RECOMMENDATIONS The study has established see more that at the OPD department at AGA hospital, the current situation effectively is one of doctors at post considering their effective working time. There are several nodes in the patient flow at the OPD. The average daily arrival rate is approximately patients per hour and the service rate averages patients per hour. Primary data analysis determined average waiting time before seeing a doctor to be hours.
