Patient-Bed Allocation in Large Hospitals

There will be a 46 per cent shortage of intensivists by 2030. Currently, only 3 per cent of U.S. critical care is provided by surgeon-intensivists. Measurement of the current workload is needed to understand the ramifications of the expected shortage of surgeon-intensivists. The purpose of this study is to evaluate the self-reported workload of U.S. surgeon-intensivists. Over a 2-month period, a voluntary and anonymous survey of the surgery section of the Society of Critical Care Medicine was performed using Survey Monkey. Only surgeons were invited to participate. We assessed personnel resources and surgeon-intensivists workload in the intensive care unit (ICU) and on their postcall day. Two hundred sixty-two persons responded. Sixty-nine per cent had administrative responsibilities and 42 per cent covered bed allocation/transfer center duties while in the ICU. Seventy-six per cent covered trauma and general surgery call and 72 per cent covered the outpatient clinic or had elective surgery cases while responsible for the ICU. Only 14 per cent had no other responsibilities. Twenty-one per cent did not round with residents and 50 per cent did not round with a fellow. Thirty-six per cent did not work with advanced practitioners. The majority of surgeon-intensivists have significant responsibilities in addition to providing ICU care. This workload should be factored into the expected shortage of surgical intensivists relative to the expected increase in critical care demand.

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Adjusting Daily Inpatient Bed Allocation to Smooth Emergency Department Occupancy Variation

Healthcare ◽

10.3390/healthcare8020078 ◽

2020 ◽

Vol 8 (2) ◽

pp. 78

Author(s):

Jeffrey Che-Hung Tsai ◽

Shao-Jen Weng ◽

Shih-Chia Liu ◽

Yao-Te Tsai ◽

Donald F. Gotcher ◽

...

Keyword(s):

Emergency Department ◽

Discrete Event ◽

Allocation Strategy ◽

Quality Indices ◽

Study Objective ◽

Feasible Allocation ◽

Event Simulation ◽

Problem Method ◽

Resource Allocation Strategy ◽

Bed Allocation

Study Objective: Overcrowding in emergency departments (ED) is an increasingly common problem in Taiwanese hospitals, and strategies to improve efficiency are in demand. We propose a bed resource allocation strategy to overcome the overcrowding problem. Method: We investigated ED occupancy using discrete-event simulation and evaluated the effects of suppressing day-to-day variations in ED occupancy by adjusting the number of empty beds per day. Administrative data recorded at the ED of Taichung Veterans General Hospital (TCVGH) in Taiwan with 1500 beds and an annual ED volume of 66,000 visits were analyzed. Key indices of ED quality in the analysis were the length of stay and the time in waiting for outward transfers to in-patient beds. The model is able to analyze and compare several scenarios for finding a feasible allocation strategy. Results: We compared several scenarios, and the results showed that by reducing the allocated beds for the ED by 20% on weekdays, the variance of daily ED occupancy was reduced by 36.25% (i.e., the percentage of reduction in standard deviation). Conclusions: This new allocation strategy was able to both reduce the average ED occupancy and maintain the ED quality indices.

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A Data-Driven Hybrid Three-Stage Framework for Hospital Bed Allocation: A Case Study in a Large Tertiary Hospital in China

Computational and Mathematical Methods in Medicine ◽

10.1155/2019/7370231 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Li Luo ◽

Jialing Li ◽

Xueru Xu ◽

Wenwu Shen ◽

Lin Xiao

Keyword(s):

Integer Programming ◽

Mixed Integer Programming ◽

Programming Model ◽

Tertiary Hospital ◽

Data Driven ◽

Mixed Integer ◽

Hospital Bed ◽

Tertiary Hospitals ◽

Bed Allocation

Beds are key, scarce medical resources in hospitals. The bed occupancy rate (BOR) amongst different departments within large tertiary hospitals is very imbalanced, a situation which has led to problems between the supply of and the demand for bed resources. This study aims to balance the utilization of existing beds in a large tertiary hospital in China. We developed a data-driven hybrid three-stage framework incorporating data analysis, simulation, and mixed integer programming to minimize the gaps in BOR among different departments. The first stage is to calculate the length of stay (LOS) and BOR of each department and identify the departments that need to be allocated beds. In the second stage, we used a fitted arrival distribution and median LOS as the input to a generic simulation model. In the third stage, we built a mixed integer programming model using the results obtained in the first two stages to generate the optimal bed allocation strategy for different departments. The value of the objective function, Z, represents the severity of the imbalance in BOR. Our case study demonstrated the effectiveness of the proposed data-driven hybrid three-stage framework. The results show that Z decreases from 0.7344 to 0.0409 after re-allocation, which means that the internal imbalance has eased. Our framework provides hospital bed policy makers with a feasible solution for bed allocation.

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