دانلود رایگان مقاله انگلیسی مدیریت ظرفیت بیمارستان بر اساس نظریه صف بندی - امرالد 2017

Abstract

Purpose This paper focuses on the contributions of Queueing Theory to hospital capacity management to improve organizational performance and deal with increased demand in the healthcare sector. Design/methodology/approach Models were applied to six months of inpatient records from a University hospital to determine operation measures such as utilization rate, waiting probability, estimated bed capacity, capacity simulations and demand behavior assessment. Findings Irrespective of the findings of the queueing model, the results showed that there is room for improvement in capacity management. Balancing admissions and the type of patient over the week represent a possible solution to optimize bed and nurse utilization. Patient mixing results in a highly sensitive delay rate due to length of stay (LOS) variability, with variations in both the utilization rate and the number of beds. Practical implications The outcomes suggest that operational managers should improve patient admission management, as well as reducing variability in length of stay and in admissions during the week. Originality/value The Queueing Theory revealed a quantitative portrait of the day-by-day reality in a fast and flexible manner which is very convenient to the task of management.

5. Conclusions

In this paper, we discussed how Queueing Theory can contribute to hospital operations management. Implementing solutions for healthcare systems requires the integration and optimization of resources, and queueing models can help achieve this goal by demonstrating the effect of variability in patient mix for delays, as well as the optimal number of beds and the target service level.

The application of the M/M/s model found the system to be busier than it really was, as the number of patients computed in the system (L) was 22.82 and the hospitalized patients ranged from 12 to 23, an average of 18.5. Even without a high occupation rate (72.8%), the unit had a utilization rate of 84.4%, and 31.89% probability of delay for arriving customers.

Applying the M/G/s/s model for the optimal number of servers in the system, it was found that the number of beds would be 17 instead of 25, with 31.89% probability of delay.

The M/G/∞ model revealed that the required capacity was 21 beds. Regardless of which queueing model was applied, the findings make it clear that there is room for improvement in capacity management. Such decisions might involve finding the right mix of permanent versus temporary workers to balance supply and demand (Roth and Menor, 2003).