Queuing Theory In Nursing

looking at mortality rates in patients seeking emergency care conclude that the rate of death is substantially higher during times of crowding (Richardson, 2006, p. 213).

On Saturday 12/10/2016 at approximately 2028 hours, East Security staff was dispatched to the Special Care Unit room #40 in reference to a (51S) Patient Standby in ED. Security Officers Omar Alonso and I, Steven Evans responded to the scene. Upon arrival, we made contact with E.D. Nurse Jacquelyn Vaninguen who stated, she needed Security to stand by while she performed an EKG on the patient, Debra Lynn Bolger (DOB: 06/28/58 – Fin #86564069). Nurse Vaninguen entered the room and awoke the Baker Act Patient while Security stood by outside the room. Once EKG completed, upon leaving the patient became irate and attempted to leave her room at which time, I had to physically redirect the patient back to her bed. Nurse Vaninguen stated, she needed

Lengthy time wait can result in an individual becoming more sick, due to the lack of attention they are receiving. In the province of Alberta, a women says “her life is wasting away after a series of miss communication.” Its been almost 10 years, and she still hasn’t received her treatment. As a country viewed as one of the best countries in terms of healthcare, the government fails to provide the citizens with proper care. However, analyzing Canadian wait time from a decade ago to the present day, Canada is ranked number 11 on the lowest wait time according to First minister accord. Canada advance to the top of the ranking by improving on some steps, such as diagnosing problems fast. By diagnosing the problem first, health care worker are able to aid patients to get the best and most efficient health services. Also, the main foundation to a a shorter wait time is the a strong and cooperative staff. With a great staff that are communicating, its easier to get through many patients in a day. (https://secure.cihi.ca/free_products/HCIC2012-FullReport-ENweb.pdf) If majority of our taxes is contributing into healthcare, we should be provided with fast and efficient service. Another alteration with having a lengthy wait is it affects individual who are in need of a

A point prevalence study conducted by … fount that the mean reported waiting time was 3.7 hours per patient. The times for the longest boarded patient ranged from 15 minutes to 33 hours, with a mean maximum waiting time of 8.3 hours. The prolonged ED waiting time resulted from ED overcrowding has negative adverse outcomes upon different stakeholders, including not only patients, but also staff members and hospitals.

This research is trying to answer the question of how to reduce overcrowding in emergency rooms? Would people would want to access published wait times provided on the internet, and would this guide a patient make decisions on where they receive their care. The hypothesis being one, crowding maybe reduced with having upfront information, and therefore also provide increased patient satisfaction due to waiting less. Patients would have more choices available to them such as

Waiting in the emergency line of the hospital is even worse. In some cases patients have to wait for more then 10 hours. This is ridiculous! When a patient goes to emergency, it is urgent and cannot wait. One of the reasons for this is that hospitals are packed, and this is a disadvantage of free health care. For example, what if a patients sickness get worse or God forbid the patient dies while waiting for treatment. Who is going to be responsible for this? The answer is simple, it will be the government. For example, Statistics Canada found that about one fifth of patients who visited a specialist, and about 11 per cent of those waiting for non-emergency surgery, were adversely affected by their wait. Many reported experiencing worry, stress, anxiety, pain, and difficulties with daily activities. (Barua, 2014).

“Long wait times could potentially result in worse patient outcomes, greater patient suffering, patient dissatisfaction, more difficulty retaining and recruiting staff, a higher risk of infectious disease outbreaks and an increased risk of medical errors” (Ducharme, Alder, Pelletier, Murray, & Tepper, p. 456). This article explored how the addition of nurse practitioners and physician assistants in 6 Ontario emergency departments could help to reduce wait time, patient flow, and the number of patients who left without being seen (Ducharme, Alder, Pelletier, Murray, & Tepper, p. 455). This study was seen to be the first of its kind in analyzing effects in an emergency department on patient flow by adding nurse practitioners and physician assistants to the healthcare team (Ducharme, Alder, Pelletier, Murray, & Tepper, p. 459). The quantitative study design used for this study was the experimental design since it was based on implementation and evaluation of the effects (Keele, p. 41). The article does not address how the sample size was determined and actually

In response to this the government introduced the fast track systems which was aimed at reducing wait times within ED. But “Have fast-track systems in Emergency departments been effective in reducing wait

Other data such as patient satisfaction should be collected through questionnaires distributed to patients in a variety of forms. Using telephone prompts might collect more honest answers, but questionnaires also might produce the most results if handed to patients following their visit to the facility. These satisfaction results should be taken into consideration and weighed against the total number of wait time a patient was subject to during their visit.

Soleimanpour et. al (2011) explained that patient satisfaction is an essential component in the Emergency Department (ED) because it is the entrance for patients to receive their initial treatments. The most important and single possible cause for ED patients’ flow problem is the availability of inpatients beds (Peck et. al, 2012). The American College of Emergency Physicians (ACEP) (2011) explained that the primary cause of ED overcrowding is boarding; which was defined as holding patients in the ED after the admission had been completed due to unavailability of inpatient beds. Lutheran Medical Center faced this problem and in his paper will discuss analysis of the problem, determination of possible quality lapses, identification of performance measures, designing and evaluation of the interventions and reporting of the results.

This medical surgical schedule created based on 24 patients (approximately 8760 patients per year). Depends on patient diagnosis manager determine staffing needs. For example, 24 patients with the following acuities the required 138 nursing care hours, that need an average 5.8 nursing hours per patient per day (NHPPD).

As a nurse manager, and from my experience, I do not feel that the satisfaction of the team would be jeopardized based on whether there is an Associate degree level nurse verses a BSN level nurse. In my area, we have highly trained nurses with both skill levels. We are not compensated based on our degree levels, however, we are compensated by years of service and nursing experience. We all took the same exam to become licensed Registered Nurses. I do feel that it is important to provide the necessary orientation to the unit in order for the nurse to be equipped with the necessary skills to perform the job efficiently. The staffing model that I would use is the functional model of nursing. According to the text, "functional model nursing

Several researchers documented Dissatisfaction amongst the patients in Outpatient clinics, over many years. (Uehira and Kay, 2009; Bielen and Demoulin, 2007; Kujala et al., 2006; Barlow, 2002; Hart, 1996; Gupta et al., 1993; McKinnon et al., 1998). Hart (1996) claimed that patient dissatisfaction is one of the most consistent features that have been expressed for outpatient service. The overall efficacy of healthcare delivery with quality service in an outpatient setting is affected by the physical environment of a Hospital (A.I.A., 2004). Extra waiting time becomes the non-value adding time as the hospital resources are not used to improve patients’ medical condition (Kujala et al., 2006). Barlow (2002) argues that excessive waiting time is

The following table shows the data collected for 57 customers from 9:00 am to 10:00 am. From the table, the total queueing time is 460 minutes and the average queueing time is 8.07 minutes. The total service time is 214