Reallocation of Shafa Hospital Beds in Kerman Using Goal Programming Model

Mehrolhasani, Mohammad Hossein; Khosravi, Sajad; Tohidi, Mahya

doi:10.19082/2733

Cited by 4 publications

(13 citation statements)

References 8 publications

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“…It encompasses several levels of scope, ranging from the national level (Devapriya et al. , 2015) to the level of hospitals (Mehrolhasani et al. , 2016; Thomas et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…, 2013). In this context, hospital beds play a central role in the capacity of a hospital to offer several services, such as clinical treatments, surgeries and emergency services (Mehrolhasani et al. , 2016; Tortorella et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…, 2015). An important issue in the context of hospital management is the management of beds that comprise a central resource to attend to patients (Mehrolhasani et al. , 2016; Tortorella et al.…”

Section: Introductionmentioning

confidence: 99%

“…, 2011) and the use of formal mathematical approaches for optimization purposes (e.g. Mehrolhasani et al ., 2016; Belciug and Florin, 2015; Mallor et al. , 2015; Zhecheng, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Managing inpatient bed setup: an action-research approach using lean technical practices and lean social practices

Reis

Fernandes

Silva

et al. 2023

JHOM

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PurposeThis article aims to introduce a guide to improving hospital bed setup by combining lean technical practices (LTPs), such as kaizen and value stream mapping (VSM) and lean social practices (LSPs), such as employee empowerment.Design/methodology/approachAction research approach was employed to analyze the process of reconfiguration of bed setup management in a Brazilian public hospital.FindingsThe study introduces three contributions: (1) presents the use of VSM focused specifically on bed setup, while the current literature presents studies mainly focused on patient flow management, (2) combines the use of LSPs and LTPs in the context of bed management, expanding current studies that are focused either on mathematical models or on social and human aspects of work, (3) introduces a practical guide based on six steps that combine LSPs and LSPs to improve bed setup management.Research limitations/implicationsThe research focused on the analysis of patient beds. Surgical beds, delivery, emergency care and intensive care unit (ICU) were not considered in this study. In addition, the process indicators analyzed after the implementation of the improvements did not contemplate the moment of the COVID-19 pandemic. Finally, this research focused on the implementation of the improvement in the context of only one Brazilian public hospital.Practical implicationsThe combined use of LSPs and LTPs can generate considerable gains in bed setup efficiency and consequently increase the capacity of a hospital to admit new patients, without the ampliation of the physical space and workforce.Social implicationsThe improvement of bed setup has an important social character, whereas it can generate important social benefits such as the improvement of the admission service to patients, reducing the waiting time, reducing hospitalization costs and improving the hospital capacity without additional physical resources. All these results are crucial for populations, their countries and regions.Originality/valueWhile the current literature on bed management is more focused on formal models or pure human and social perspectives, this article brings these two perspectives together in a single, holistic framework. As a result, this article points out that the complex bed management problem can be efficiently solved by combining LSPs and LTPs to present theoretical and practical contributions to the important social problem of hospital bed management.

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“…It encompasses several levels of scope, ranging from the national level (Devapriya et al. , 2015) to the level of hospitals (Mehrolhasani et al. , 2016; Thomas et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…, 2011) and the use of formal mathematical approaches for optimization purposes (e.g. Mehrolhasani et al ., 2016; Belciug and Florin, 2015; Mallor et al. , 2015; Zhecheng, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Managing inpatient bed setup: an action-research approach using lean technical practices and lean social practices

Reis

Fernandes

Silva

et al. 2023

JHOM

View full text Add to dashboard Cite

show abstract

“…A range of operational research (OR) methods have been developed and applied to problems of healthcare resource allocation [15], especially bed resources. These methods include queuing theory [16, 17], simulation [18, 19], goal programming [20, 21], and mathematical programming [22–24]. Most of the current research uses a single method for each study.…”

Section: Introductionmentioning

confidence: 99%

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

Luo

et al. 2019

Computational and Mathematical Methods in Medicine

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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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A systematic review of research design and modeling techniques in inpatient bed management

Hé

Madathil

Oberoi

et al. 2019

Computers & Industrial Engineering

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Reallocation of Shafa Hospital Beds in Kerman Using Goal Programming Model

Cited by 4 publications

References 8 publications

Managing inpatient bed setup: an action-research approach using lean technical practices and lean social practices

Managing inpatient bed setup: an action-research approach using lean technical practices and lean social practices

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

A systematic review of research design and modeling techniques in inpatient bed management

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