Discussion: Queuing System Optimization for Emergency First Responder

Discussion: Queuing System Optimization for Emergency First Responder ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Discussion: Queuing System Optimization for Emergency First Responder C reate a PowerPoint Presentation using article reviews attached. Based on the topic: Queuing System Optimization – Emergency/First Responder Issues build a Presentation containing answers to the following: Discussion: Queuing System Optimization for Emergency First Responder Have you seen a similar problem in your current review of literature? If so, what solutions could you adapt to resolve a similar i ssue? Is your problem actually an opportunity to implement something new? If so, are there solutions (or other industry examples) you could adopt as a recommended solution? Is your problem specific enough to run your own short survey ? If so, build a short pilot study (survey) using surveymonkey.com and send the survey link to a target audience (like a LinkedIn or Facebook list), giving them 2 – 4 days to complete. Add the results to your presentation. Survey monkey populates results that you could incorporate into your presentation (Summation Required). attachment_1 Article Review: Queuing System Optimization – Emergency/First Responder Issues Article 1 Reference Information: Bettinelli, Andrea & Cordone, Roberto & Ficarelli, Federico & Righini, Giovanni. (2014). Simulation and optimization models for emergency medical systems planning. Journal of emergency management (Weston, Mass.). 12. 287-301. 10.5055/jem.2014.0180. Purpose of Study : To address the problems experienced in the strategic planning of emergency medical systems. The study focused on three crucial decisions that are usually considered in planning. The first was the number of ambulances to deploy at a given time in an identified territory, depending on demand forecasts. Second, when should the ambulances be deployed in addressing services that are non-urgent and when they should be kept idle awaiting urgent requests? Thirdly, the determination of an optimal combination of contracts for hiring ambulances from private organizations to address predicted demand at the lowest cost possible. Sampling Comments: the study relied on historical data about the ambulances as aggregated in three years, 2005, 2006, and 2007. This data was obtained from the emergency medical systems in Milan. The data obtained contained figures for the three key issues the study had directed its focus. Measures: the study used two queuing theory models and the discrete simulation models to analyze the system in each of the three years. The first model focused on determining the optimal fleet size, and the second model focused on queuing minor requests, and the third model focused on defining the optimal contract mix. Findings/Results/Main Points : it was found that the determination of the number of ambulances requested for minor services was time-dependent. This would require a time adjustment ranging between 30 and 60 minutes. As a result of this, it was challenging to have ambulances kept idle waiting for urgent requests. On the optimal contract mix, it was established that the ideal number of ambulances to keep was challenging to determine due to organizational and operational constraints. Conclusion: the problem in the strategic planning of EMS cannot be entirely by using the existing data and developing mathematical techniques and models. The study can be used as a foundation for the introduction of smart EMS, which can be extended to different levels of service. Article 2 Reference information Reuter-Oppermann, M., Berg, P. L. V. D., & Vile, J. L. (2017). Logistics for Emergency Medical Service systems. Health Systems , 6 (3), 187–208. doi: 10.1057/s41306-017-0023-x Purpose of Study: This paper seeks to show the logistical problems that arise for EMS providers showing how the problems are intertwined and related. Each planning problem is well described and possible solutions provided and given. Sampling Comments : the study uses a generic discrete event simulation-based model of analysis that decides the methods of scheduling patient transports from their homes to other medical institutions and how to solve requests arising from the emergency. Measures: Every emergency call once received is categorized as either life-threatening where the response should be within 8 minutes, and all other calls where the patient’s condition is not life-threatening and targets are set locally. The common norm is to send the ambulance that is closely available. Although ambulances are fixed to a particular station, they are readily available to relocate to a particular hotspot during the day. Findings/Results: quality improvement is described as the main objective when doing planning for any ems system. It’s considered the most difficult task to clearly define the quality of an EMS system, which is a harder task than any other area Due to the nature and extremity of the working conditions, difficult situations brought up with unexpected circumstances. Additionally, the ambulance team’s view may differ from the health insurance of the patient creating a significant difference. The provision of a clear definition of quality may not offer clarity of how affected by changes in the system quality would be. EMSs systems complexity require simulations to give an estimate of performance on newer situations Conclusion: An overview of the logistical problems arising in EMS systems and providers. Solving a problem at a time is not a good option hence advocating for simultaneous problem-solving. It’s essential to determine the location of ambulance bases on a fete that has been a complex. In future studies, it’s imperative to determine how call centers can be the center of operation together with incorporating border operations between countries and regions, which are neighbors. Article Review 3 Reference information Liu, M., Yang, D., & Hao, F. (2017, August 30). Optimization for the Locations of Ambulances under Two-Stage Life Rescue in the Emergency Medical Service: A Case Study in Shanghai, China. Retrieved March 21, 2020, from https://doi.org/10.1155/2017/1830480 Purpose of the Study : the study focused on assessing the existing problem of high demand for EMS in highly populated areas. The study used a case of Shanghai City in China. The authors further wanted to know whether there is a way the resources could be planned to meet healthcare demands. Sampling Comments : This article relied on historical data obtained from previous studies and the National Science Foundation of China. The authors then used this data in the next section of their study to determine ambulance location and hospital selection. Measures: the study used a modified double standard model in order to take advantage of the demand points that occur at least twice within the least criteria of coverage. The challenge in using this model was overcome by the use of integer linear programming techniques and computational software (CPLEX). This helped in comparing the locations and solutions that exist in the emergency medical system within the selected case study. Findings/Results: the study showed that the number of response vehicles in the district was capable of meeting the existing demand. However, it was found out that the majority of the facilities locations in the chosen district was the major challenge for the vehicles to respond to requests effectively. The research found that the response time and rate of demand coverage can be effectively enhanced by changing the location of the existing facilities without necessarily increasing the number of vehicles. Conclusion : After conducting the study and analysis of the ambulance location in the chosen district in China, it was concluded that emergency medical services are significantly affected by the locations of the hospitals and the location of the ambulances. Article 4 Reference information Alavi-Moghaddam, M., Forouzanfar, R., Alamdari, S., Shahrami, A., Kariman, H., Amini, A., Shirvani, A. (2016). Application of Queuing Analytic Theory to Decrease Waiting Times in Emergency Department: Does it Make Sense? Archives of Trauma Research, 1(3), 101–7. doi: 10.5812/atr.7177 Purpose of Study : Many patients in emergency departments are left unattended in the waiting queues. The purpose of this study is to determine whether queuing theory analysis is applicable in shortening the waiting period of patients admitted in emergency wards. Sampling Method : This study uses several methods in different phases. The queuing analysis theory in the first phase during a field study and modeling analysis in the second phase to create a simulation and improve the movement of patients and the third phase of the study used modeling for assessment of the impact of various operational approaches, on the queue waiting time of patients receiving care in Emergency Departments and the several strategies employed on patients on the queue Measures: Patient flow in the emergency departments requires passage to three different routes the input throughput and output where the complaints from different patients are recorded and solved. Findings/Results : The trauma sections of the emergency department took patients a maximum of three hours, while non-trauma sections recorded a time of four hours, respectively. Modeling tested used common scenarios while in the third phase showed that an addition of a senior resident to each unit decreased the overall time spent from four hours to 3.75 hours. The more the staff added, the lesser the time a patient takes and no effect on the staying length of a patient. If an emergency department added a bed to the intensive care unit or the critical care unit, a patient’s occupancy rate reduces significantly by close to 9 percent. Conclusion: Applying the introduction of queuing analysis theory reduces and improves the waiting time of patients in relation to the emergency department throughput. In other words, reducing the waiting time of patients in the emergency department and increasing the flow of services. Usage of real-time analysis software allows for finding of emergency backlogs and clogging and finding ways of reducing the overcrowding. Essentially, it could improve the management of patients without having to increase staff by only changing staff positions from wards to emergency departments or vice versa. Article 5 Reference Information: Bahadori, M., Teymourzadeh, E., Hosseini, S. H., & Ravangard, R. (2017). Optimizing the Performance of Magnetic Resonance Imaging Department Using Queuing Theory and Simulation. Shiraz E-Medical Journal , 18 (1). doi: 10.17795/semj43958 Purpose of the Study: The aim was to optimize the performance of an identified Magnetic Resonance Imaging (MRI) department by applying simulation and queuing theory. This was done in order to improve the productivity of the department as well as the satisfaction of patients. Sampling Method : the study used a quota sampling and sampled 264 patients in a selected hospital. The selection of the patients was made according to arrival time into the hospital, and the average time they waited to get the service, which could be linked to a queuing network. Measures: The obtained sample was subjected to several measures that allow comparisons before the determination of the optimum performance of the department. Descriptive data was processed and calculations based on the queuing theory parameters determined. The main factor that was measured was the waiting time the sampled patients had to take before getting the service from the MRI department. Findings/Results: The study established that, on average, the highest waiting time for the patients was attributable to taking turns that occurred until a patient was admitted. The productivity of the MRI department, on average, was calculated as fifty-two percent, which indicated a high capacity of the system that had not been utilized. Upon optimization, a dramatic reduction in the waiting time, and an increase in the required services were noticed. This optimization involved small changes in the number of personnel, the number of working hours, and the number of MRI machines. Conclusions: The optimization of the department using the queuing theory is a desirable situation. It helps in improving the waiting time of patients and the productivity of the personnel. After carrying out a cost-benefit analysis, it was proposed that this approach should be implemented. Article 6 Reference Information : Paula I, A. & Reinaldo M. and Cem, S. (2017). “Optimizing large-scale emergency medical system operations on highways using the hypercube queuing model,” Socio-Economic Planning Sciences, Elsevier, vol. 45(3), pages 105-117, September. Purpose of the Study : To present a well-known series of methods of optimization to address two decisions attributed to the large-scale design of ambulance operations, particularly on highways. Sampling Method: The study sampled 100 ambulances that operated on highways on small-scale, which had a history of facing a lot of problems. Measures: Among the tested measures included the question of location and the districting issue. Due to runtime constraints and computer storage, past approaches had been considering small-to-moderate scale scenarios that generally use hypercube queuing models incorporated into procedures for optimization. The study overcame the existing problems by employing an accurate and fast hypercube algorithm for approximation. This algorithm was adapted for policies to carry out backup dispatch in both multi-start and single greedy heuristics. Findings/Results : It was established that the new approaches were viable and could be used alternatively to analyze and configure emergency medical systems on highways on a large-scale. This is because they provided reasonable, affordable, and accurate run times. Conclusion : The small-scale emergency medical systems on highways have been working problematically due to a lack of exploring new alternatives. Optimization of the systems using the hypercube queuing model can help achieve better services even to the tune of allowing operations on a large-scale. Discussion: Queuing System Optimization for Emergency First Responder Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10