A mixed integer programming approach to the patient admission scheduling problem

Bastos, Leonardo S. L.; Marchesi, Janaina Figueira; Hamacher, Śılvio; Fleck, Julia L.

doi:10.1016/j.ejor.2018.09.003

Cited by 45 publications

(20 citation statements)

References 22 publications

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“…The patient scheduling problem addressed by Bastos et al (2018) and Burdett et al (2017) maximizes the number of patients of each type that a hospital can accept which is also different from our problem because our model maximizes the contribution margin associated with the acceptance of patients and overtime costs. Another relevant paper is Burdett and Kozan (2018) who develop constructive algorithms and hybrid meta-heuristics to schedule clinical pathways.…”

Section: Related Workmentioning

confidence: 98%

Flexible hospital-wide elective patient scheduling

Gärtner¹,

Padman

2019

Journal of the Operational Research Society

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In this paper, we build on and extend Gartner and Kolisch (2014)'s hospital-wide patient scheduling problem. Their contribution margin maximizing model decides on the patients' discharge date and therefore the length of stay. Decisions such as the allocation of scarce hospital resources along the clinical pathways are taken. Our extensions which are modeled as a mathematical program include admission decisions and flexible patient-to-specialty assignments to account for multi-morbid patients. Another flexibility extension is that one out of multiple surgical teams can be assigned to each patient. Furthermore, we consider overtime availability of human resources such as residents and nurses. Finally, we include these extensions in the rolling-horizon approach and account for lognormal distributed recovery times and remaining resource capacity for elective patients. Our computational study on real-world instances reveals that, if overtime flexibility is allowed, up to 5% increase in contribution margin can be achieved by reducing length of stay by up to 30%. At the same time, allowing for overtime can reduce waiting times by up to 33%. Our model can be applied in and generalized towards other patient scheduling problems, for example in cancer care where patients may follow defined cancer pathways.

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Section: Related Workmentioning

confidence: 98%

Flexible hospital-wide elective patient scheduling

Gärtner¹,

Padman

2019

Journal of the Operational Research Society

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“…In the work done by [ 32 ] the author proposed an exact method to address PASP. A new mathematical formulation bulit up, and mixed integer programming has been utilized with parameter free, and without pre- processing phase.…”

Section: Patient Admission Scheduling Problem (Pasp)mentioning

confidence: 99%

Healthcare scheduling in optimization context: a review

et al. 2021

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This paper offers a summary of the latest studies on healthcare scheduling problems including patients’ admission scheduling problem, nurse scheduling problem, operation room scheduling problem, surgery scheduling problem and other healthcare scheduling problems. The paper provides a comprehensive survey on healthcare scheduling focuses on the recent literature. The development of healthcare scheduling research plays a critical role in optimizing costs and improving the patient flow, providing prompt administration of treatment, and the optimal use of the resources provided and accessible in the hospitals. In the last decades, the healthcare scheduling methods that aim to automate the search for optimal resource management in hospitals by using metaheuristics methods have proliferated. However, the reported results are disintegrated since they solved every specific problem independently, given that there are many versions of problem definition and various data sets available for each of these problems. Therefore, this paper integrates the existing results by performing a comprehensive review and analyzing 190 articles based on four essential components in solving optimization problems: problem definition, formulations, data sets, and methods. This paper summarizes the latest healthcare scheduling problems focusing on patients’ admission scheduling problems, nurse scheduling problems, and operation room scheduling problems considering these are the most common issues found in the literature. Furthermore, this review aims to help researchers to highlight some development from the most recent papers and grasp the new trends for future directions.

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“…An example of a more complex healthcare scheduling problem is patient admission scheduling, as discussed by Bastos et al (2019), whereby patients are scheduled to scarce resources ("beds") over a period of 28 to 91 days. While this issue is more complex than our problem regarding the resource scheduling aspect, it does not include any aspects of forecasting (such as disease prediction).…”

Section: Related Workmentioning

confidence: 99%

Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling

Patan

Kallam²,

Gandomi

et al. 2021

Journal of the Operational Research Society

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Various big-data analytics tools and techniques have been developed for handling massive amounts of data in the healthcare sector. However, scheduling is a significant problem to be solved in smart healthcare applications to provide better quality healthcare services and improve the efficiency of related processes when considering large medical files. For this purpose, a new hybrid model called Gaussian Relevance Vector MapReduce-based Annealed Glowworm Optimization Scheduling (GRVM-AGS) was designed to improve the balancing of large medical data files between different physicians with higher scheduling efficiency and minimal time. First, a GRVM model was developed for the predictive analysis of input medical data. This model reduces the storage complexity of large medical data analysis by means of eliminating unwanted patient information and predicts the disease class with help of a Gaussian kernel function. Afterwards, GRVM performs AGS to schedule the efficient workloads among multiple datacenters based on the luciferin value in the smart healthcare environment with reduced scheduling time. Through computational experiments, we demonstrate that GRVM-AGS increases the scheduling efficiency and reduces the scheduling time of large medical data analysis compared to state-of-the-art approaches.

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A mixed integer programming approach to the patient admission scheduling problem

Cited by 45 publications

References 22 publications

Flexible hospital-wide elective patient scheduling

Flexible hospital-wide elective patient scheduling

Healthcare scheduling in optimization context: a review

Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling

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