Improving Emergency Services: A Queuing Model Implementation

This report explores the application of queuing models, specifically the M/M/s parameters and Poisson's queuing model, to enhance operational capacity and efficacy in busy emergency environments like referral hospitals. It suggests that discrete event simulation, including management of patient admission, procedure scheduling, and reconstruction of entry and exit models, can significantly improve patient flow. The report also highlights the importance of comparing queuing model effectiveness across different healthcare institutions to develop universally applicable policies. Furthermore, it suggests regular assessment of service providers, a platform for workers' concerns, and increased staffing to improve service delivery, concluding that Poisson distribution models are essential for efficiency-oriented institutions aiming to provide quality services.

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QUEING MODEL AND EMERGENCY SERVICES
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The Name of the University
The City and State Where it is Located
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In any organization maximization of operational capacity and efficacy is the priority of
any management system. More so in a busy emergency environment like a referral Hospital. The
M/M/s parameters are applicable in this case for decision making on how to increase efficacy.
Basing on discrete event simulation, steps that can be taken include, management of admission
of patients and procedure scheduling, the patient flow could also be increased through
reconstruction of entry and exit models. Poisson’s queuing model is thus a very important tool in
increasing efficacy in emergency departments (Sharif, Stanford, Taylor and Ziedins, 2014, p. 76)
Different health care institutions may not necessarily use the same queuing principles in
their operations, however, if so, there is a sense in comparing the effectiveness of queuing
models in different healthcare institutions in spite of their implications (Lin, Patrick and Labeau,
2014, p. 95 ). This is because it can help in development of more concrete universally applicable
policies and decisions of how best and when the models can be utilized to enhance effectiveness.
The intern in PATA would have suggested more regular assessment of all service
providers in the facility and provision of an always available platform where workers can air
their concerns and suggestions as far as timely service provision is concerned (Sharif, Stanford,
Taylor and Ziedins, 2014, p. 74). Furthermore, hiring of more personal so as to enable relieving
of shifts on time should be another priority. All these coupled with other existing ethical virtues
imparted and undertaken by the able and hardworking teams in PATA would render the
institution the center of quality Health care in spite of the challenges behind them.
In conclusion, Poisson distribution models are key tools that ought to be incorporated in
any efficiency-oriented institution especially those with the desire to provide quality services for
the good of subjects or service seekers.

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References
Lin, D., Patrick, J. and Labeau, F., 2014. Estimating the waiting time of multi-priority
emergency patients with downstream blocking. Health care management science, 17(1),
pp.88-99.
Sharif, A.B., Stanford, D.A., Taylor, P. and Ziedins, I., 2014. A multi-class multi-server
accumulating priority queue with application to health care. Operations Research for
Health Care, 3(2), pp.73-79.