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Добірка наукової літератури з теми "Patient admission scheduling"

Автор: Grafiati
Опубліковано: 21 грудня 2024

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Статті в журналах з теми "Patient admission scheduling"

1

Zhang, Li Ming, Hui You Chang, and Rui Tian Xu. "The Patient Admission Scheduling of an Ophthalmic Hospital Using Genetic Algorithm." Advanced Materials Research 756-759 (September 2013): 1423–32. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1423.

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Currently, FCFS scheduling method is widely used in hospitals for patient admission scheduling, which ignores the impacts of patient length of stay and surgery arrangement on the usage of hospital resources. This paper proposes a more comprehensive mathematical model and evaluation mechanism for the patient admission scheduling of an ophthalmic hospital. A genetic algorithm (GA) is proposed to optimize the model, which can provide detailed scheduling of patient admission in the hospital for everyday. The result is compared with that of the traditional FCFS method, which indicates that the GA helps to reduce the preoperative waiting time for patients. Besides, GA can provide different kinds of scheduling for the hospital to select by adjusting the relative weights of different objectives in the algorithm.
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2

Abu Doush, Iyad, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Abdelaziz I. Hammouri, Ra’ed M. Al-Khatib, Saba ElMustafa, and Habes ALkhraisat. "Harmony Search Algorithm for Patient Admission Scheduling Problem." Journal of Intelligent Systems 29, no. 1 (April 28, 2018): 540–53. http://dx.doi.org/10.1515/jisys-2018-0094.

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Abstract The patient admission scheduling (PAS) problem is an optimization problem in which we assign patients automatically to beds for a specific period of time while preserving their medical requirements and their preferences. In this paper, we present a novel solution to the PAS problem using the harmony search (HS) algorithm. We tailor the HS to solve the PAS problem by distributing patients to beds randomly in the harmony memory (HM) while respecting all hard constraints. The proposed algorithm uses five neighborhood strategies in the pitch adjustment stage. This technique helps in increasing the variations of the generated solutions by exploring more solutions in the search space. The PAS standard benchmark datasets are used in the evaluation. Initially, a sensitivity analysis of the HS algorithm is studied to show the effect of its control parameters on the HS performance. The proposed method is also compared with nine methods: non-linear great deluge (NLGD), simulated annealing with hyper-heuristic (HH-SA), improved with equal hyper-heuristic (HH-IE), simulated annealing (SA), tabu search (TS), simple random simulated annealing with dynamic heuristic (DHS-SA), simple random improvement with dynamic heuristic (DHS-OI), simple random great deluge with dynamic heuristic (DHS-GD), and biogeography-based optimization (BBO). The proposed HS algorithm is able to produce comparably competitive results when compared with these methods. This proves that the proposed HS is a very efficient alternative to the PAS problem, which can be efficiently used to solve many scheduling problems of a large-scale data.
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3

Abdalkareem, Zahraa A., Mohammed Azmi Al-Betar, Amiza Amir, Phaklen Ehkan, Abdelaziz I. Hammouri, and Omar H. Salman. "Discrete flower pollination algorithm for patient admission scheduling problem." Computers in Biology and Medicine 141 (February 2022): 105007. http://dx.doi.org/10.1016/j.compbiomed.2021.105007.

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4

Barz, Christiane, and Kumar Rajaram. "Elective Patient Admission and Scheduling under Multiple Resource Constraints." Production and Operations Management 24, no. 12 (July 14, 2015): 1907–30. http://dx.doi.org/10.1111/poms.12395.

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5

Ashraf, Shahzaib, Noor Rehman, Saleem Abdullah, Bushra Batool, Mingwei Lin, and Muhammad Aslam. "Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology." Mathematical Biosciences and Engineering 19, no. 3 (2022): 3147–76. http://dx.doi.org/10.3934/mbe.2022146.

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<abstract><p>Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.</p></abstract>
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6

Chaouch, Rihab, Jihene Tounsi, Issam Nouaouri, and Sabeur Elkosantini. "Mixed Integer Programming For Patient Admission Scheduling in Hospital Network." IFAC-PapersOnLine 58, no. 19 (2024): 259–64. http://dx.doi.org/10.1016/j.ifacol.2024.09.185.

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7

Ceschia, Sara, and Andrea Schaerf. "Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays." Journal of Scheduling 19, no. 4 (November 27, 2014): 377–89. http://dx.doi.org/10.1007/s10951-014-0407-8.

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8

Bolaji, Asaju La’aro, Akeem Femi Bamigbola, and Peter Bamidele Shola. "Late acceptance hill climbing algorithm for solving patient admission scheduling problem." Knowledge-Based Systems 145 (April 2018): 197–206. http://dx.doi.org/10.1016/j.knosys.2018.01.017.

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9

Bastos, Leonardo S. L., Janaina F. Marchesi, Silvio Hamacher, and Julia L. Fleck. "A mixed integer programming approach to the patient admission scheduling problem." European Journal of Operational Research 273, no. 3 (March 2019): 831–40. http://dx.doi.org/10.1016/j.ejor.2018.09.003.

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10

Range, Troels Martin, Richard Martin Lusby, and Jesper Larsen. "A column generation approach for solving the patient admission scheduling problem." European Journal of Operational Research 235, no. 1 (May 2014): 252–64. http://dx.doi.org/10.1016/j.ejor.2013.10.050.

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Більше джерел

Дисертації з теми "Patient admission scheduling"

1

Liu, Haichao. "Model-driven solution approaches for patient scheduling in admission and surgery management." Electronic Thesis or Diss., Angers, 2024. http://www.theses.fr/2024ANGE0022.

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Анотація:
La planification efficace des patients est essentielle pour améliorer l’efficacité des ressources médicales et la satisfaction des patients dans le système de santé. Cette thèse présente des modèles mathématiques et des approches de solution pour le problème de planification des admissions de patients statiques/stochastiques et le problème de planification des cas chirurgicaux dans des salles d’opération flexibles sous incertitude. En raison de la complexité de ces problèmes, qui impliquent de multiples contraintes et incertitudes, des méthodes de résolution innovantes telles que l’optimisation en deux phases, l’approximation hybride de la moyenne des échantillons avec variable d’état (SAA-SV), la planification intégrée proactive et réactive, et l’optimisation de simulation en trois phases sont proposées. De plus, des approches de modélisation avancées comme l’agrégation de contraintes, la modélisation aléatoire basée sur des scénarios, et la modélisation à variable d’état sont développées pour créer des modèles de taille réduite afin d’améliorer l’efficacité de leur résolution. Des études computationnelles réalisées sur un ensemble d’instances de référence démontrent l’efficacité des méthodes proposées en comparaison avec les méthodes de l’état de l’art. Des expériences supplémentaires sont menées pour évaluer le rôle des composants clés des méthodes proposées
Efficient patient scheduling plays a crucial role in improving the efficiency of medical resources and patient satisfaction in the healthcare system. This thesis presents mathematical modeling and solution approaches for the static/stochastic patient admission scheduling problem and the surgical case scheduling problem in flexible operating rooms under uncertainty. Given the complexity of these problems with multi-constraint and uncertainty, innovative solution approaches — two-stage optimization, hybrid sample average approximation and state-variable (SAA-SV), integrated proactive and reactive scheduling, and three-phase simulation optimization — are proposed to solve the problems efficiently. Especially, advanced modeling approaches — constraint aggregation, scenario based modeling, and state-variable modeling — are used to build reduced models to improve the solution efficiency. Computational studies performed on a set of benchmark instances show the effectiveness of the proposed methods in comparison with the state-of-the-art methods. Additional experiments are conducted to evaluate the roles of the key ingredients of the proposed methods
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Частини книг з теми "Patient admission scheduling"

1

Guido, Rosita, Vittorio Solina, and Domenico Conforti. "Offline Patient Admission Scheduling Problems." In Springer Proceedings in Mathematics & Statistics, 129–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67308-0_14.

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2

Guido, Rosita, Vittorio Solina, Giovanni Mirabelli, and Domenico Conforti. "Offline Patient Admission, Room and Surgery Scheduling Problems." In AIRO Springer Series, 275–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00473-6_30.

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3

Oueslati, Imen, Moez Hammami, Issam Nouaouri, Ameni Azzouz, Lamjed Ben Said, and Hamid Allaoui. "A Honey Bee Mating Optimization HyperHeuristic for Patient Admission Scheduling Problem." In Communications in Computer and Information Science, 89–104. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69257-4_7.

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4

Borchani, Rahma, Malek Masmoudi, Bassem Jarboui, and Patrick Siarry. "Heuristics-based on the Hungarian Method for the Patient Admission Scheduling Problem." In Operations Research and Simulation in Healthcare, 33–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45223-0_2.

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5

Nikfarjam, Adel, Amirhossein Moosavi, Aneta Neumann, and Frank Neumann. "Computing High-Quality Solutions for the Patient Admission Scheduling Problem Using Evolutionary Diversity Optimisation." In Lecture Notes in Computer Science, 250–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_18.

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Тези доповідей конференцій з теми "Patient admission scheduling"

1

MENDES, LÚCIO GALVÃO, PEDRO OLIVEIRA ONORIO, DJONATHAN LUIZ DE OLIVEIRA QUADRAS, DANILO RIBAMAR SÁ RIBEIRO, and ENZO MOROSINI FRAZZON. "A Novel Resilient Patient Admission Scheduling Conceptual Model." In International Joint Conference on Industrial Engineering and Operations Management. International Joint Conference on Industrial Engineering and Operations Management, 2024. http://dx.doi.org/10.14488/ijcieom2024_full_0057_37982.

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2

Zhang, Liming, Huiyou Chang, and Ruitian Xu. "The Patient Admission Scheduling of an Ophthalmic Hospital Using Genetic Algorithm." In 2nd International Conference on Computer and Information Applications (ICCIA 2012). Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/iccia.2012.1.

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3

Mazier, Alexandre, Xiaolan Xie, and Marianne Sarazin. "Scheduling inpatient admission under high demand of emergency patients." In 2010 IEEE International Conference on Automation Science and Engineering (CASE 2010). IEEE, 2010. http://dx.doi.org/10.1109/coase.2010.5584679.

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4

Bruballa, Eva, Alvaro Wong, Dolores Rexachs, Emilio Luque, and Francisco Epelde. "Scheduling model for non-critical patients admission into a hospital emergency department." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248014.

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5

Kaixi, Wang, and Yang Fangchun. "A Dynamically Scheduling Policy with A Greedy Admission Control Algorithm Based on the Customer (Im)Patience." In 2007 Second International Conference on Communications and Networking in China. IEEE, 2007. http://dx.doi.org/10.1109/chinacom.2007.4469335.

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