Written by Mohammad Hosein Mehrolhasani, Ali Mouseli, Leila Vali, Zahra Mastaneh
Parent Category: Year 2017, Volume 9
Category: Volume 9, Issue 2, February 2017
Introduction: Nurses account for the majority of human resources in hospitals, as such that 62% of the workforce and 36% of hospital expenditures are related to nurses. Considering its vital role in offering round-the-clock emergency healthcare services, an Emergency Department (ED) requires adequate nurses. Therefore, this study was conducted to optimize the number of nurses in ED.
Methods: This was an applied study conducted using a Linear Programming (LP) model in 2015. The study population were selected by census who were all ED nurses (n=84) and patients referred to ED (n=3342). To obtain the statistics related to the number of patients and nurses, the hospital information system and human resources database were employed respectively. To determine the optimum number of nurses per shift, LP model was created via literature review and expert advice, and it was executed in WinQSB software.
Results: Before implementing the model, the number of nurses required for ED morning shift, evening shift, and night shift (2 shifts) was 26, 24 and 34 respectively. The optimum number of nurses who worked in ED after running the model was 62 nurses, 17 in the morning shift, 17 in the evening shift and 28 in the night shift (2 shifts). This reduced to 60 nurses after conducting sensitivity analysis.
Conclusion: The estimated number of nurses using LP was less than the number of nurses working in ED. This discrepancy can be reduced by scientific understanding of factors affecting allocation and distribution of nurses in ED and flexible organization, to reach the optimal point.
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Keywords: Emergency Department; Nurses; Quantitative Optimization; Linear Programming Model
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The worldwide spread of COVID-19 as an emerging, rapidly evolving situation, and the dramatic need of urgent medicine or vaccine, has rapidly brought new hypotheses for pathophysiology and potential medicinal agents to the fore. It is crucial that the research community provide a way to publish this research in a timely manner.
To contribute to this important public health discussion, the Electronic Physician Journal is excited to announce a fast-track procedure to help researchers publish their articles on COVID-19 related subjects that fall under the broad definition of public health, internal medicine, and pharmacology. We are especially welcome to all hypotheses about the pathological basis of the COVID-19 infection and the possible characteristics of potential medicine and vaccine. Submit your manuscript here
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Call for applications
Deadline for submission: 7 March 2019, 16:00 (GMT)
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About the Instructor
Dr. Michael Borenstein, one of the authors of Introduction to Meta-Analysis, is widely recognized for his ability to make statistical concepts accessible to researchers as well as to statisticians. He has lectured widely on meta-analysis, including at the NIH, CDC, and FDA. Read more: