Repository of Research and Investigative Information

Repository of Research and Investigative Information

Hormozgan University of Medical Sciences

Quantitative optimization of emergency department's nurses of an educational hospital: a case study

(2017) Quantitative optimization of emergency department's nurses of an educational hospital: a case study. Electronic Physician.

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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.

Item Type: Article
Keywords: Emergency Department; Nurses; Quantitative Optimization; Linear Programming Model
Subjects: general
public health
Divisions: Education Vice-Chancellor Department > Faculty of Health > Department of Occupational Health
Depositing User: مرکز تحقیقات غدد و متابولیسم

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