Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre

Eshghali, Masoud; Kannan, Devika; Salmanzadeh-Meydani, Navid; Sikaroudi, Amir Mohammad Esmaieeli

doi:10.1007/s10479-023-05168-x

Cited by 18 publications

(3 citation statements)

References 65 publications

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“…Coordination of this complex system for optimal performance could be enhanced with artificial intelligence, for example to more accurately predict case duration, 46 hospital length of stay, 47 and case scheduling. 48 There exist multiple ongoing efforts in private industry and in academic research in collaboration with hospitals to test new artificial intelligence-based systems to improve operating room scheduling to increase output and efficiency, which hold future promise for implementation in the community and academic setting.…”

Section: Resource Allocation and Optimizationmentioning

confidence: 99%

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Han,

Char,

Aghaeepour

et al. 2024

Anesthesiology

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Section: Resource Allocation and Optimizationmentioning

confidence: 99%

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Han,

Char,

Aghaeepour

et al. 2024

Anesthesiology

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“…Equations (18)(19)(20)(21)(22)(23) echo the constraints from the first stage. Equations (24)(25)(26)(27) indicate scenarios where patients in the second stage extend their LOS, with the resultant extended LOS being rounded up to the nearest integer. Lastly, Eqs.…”

Section: Allocation Model Of the Second Stagementioning

confidence: 99%

Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty

Wan,

Fondrevelle,

Wang

et al. 2023

Sci Rep

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Due to the impact of COVID-19, a significant influx of emergency patients inundated the intensive care unit (ICU), and as a result, the treatment of elective patients was postponed or even cancelled. This paper studies ICU bed allocation for three categories of patients (emergency, elective, and current ICU patients). A two-stage model and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to obtain ICU bed allocation. In the first stage, bed allocation is examined under uncertainties regarding the number of emergency patients and their length of stay (LOS). In the second stage, in addition to including the emergency patients with uncertainties in the first stage, it also considers uncertainty in the LOS of elective and current ICU patients. The two-stage model aims to minimize the number of required ICU beds and maximize resource utilization while ensuring the admission of the maximum number of patients. To evaluate the effectiveness of the model and algorithm, the improved NSGA-II was compared with two other methods: multi-objective simulated annealing (MOSA) and multi-objective Tabu search (MOTS). Drawing on data from real cases at a hospital in Lyon, France, the NSGA-II, while catering to patient requirements, saves 9.8% and 5.1% of ICU beds compared to MOSA and MOTS. In five different scenarios, comparing these two algorithms, NSGA-II achieved average improvements of 0%, 49%, 11.4%, 9.5%, and 17.1% across the five objectives.

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“…Several metaheuristics algorithms, such as genetic algorithms and simulated annealing, have also been applied to the surgical scheduling problem (Lin et al, 2023). The scheduling of surgical procedures continues to be a topic of interest among academic researchers (Eshghali et al,2023;Wang et al, 2022).…”

Section: State Of the Art And Contributionmentioning

confidence: 99%

A Bi-Objective Paediatric Operating Theater Scheduling

Dekhici

Belkadi

2023

International Journal of Healthcare Information Systems and Informatics

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In this paper, a bi- objective Operating Theater scheduling is proposed. The problem is subject to order and assignment constraints. The first objective is the minimization of the operating theater opening total time also called makespan in manufacturing systems while the second is to maximize constraints satisfaction. The scheduling problem is considered as a two-stage hybrid flow shop with blocking. Several metaheuristics are compared: the firefly algorithm, bats algorithm, particles swarm optimization and local search. In addition to the care specific qualitative and quantitative parameters, the average deviation from the lower bound is used in order to confirm the effectiveness of the methods. The implementation is done on the operating theater of the paediatric hospital of Oran when it is properly and improperly sized.

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Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre

Cited by 18 publications

References 65 publications

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty

A Bi-Objective Paediatric Operating Theater Scheduling

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