A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department

Saadouli, Hadhemi; Jerbi, Badreddine; Dammak, Abdelaziz; Masmoudi, Lotfi; Bouaziz, Abir

doi:10.1016/j.cie.2014.11.021

Cited by 111 publications

(34 citation statements)

References 43 publications

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“…Recovery beds and operating rooms scheduling was formulated as a stochastic optimization problem [25]. The randomness was from capacity of resources and undetermined in the duration of surgery and recovery.…”

Section: Stochastic Optimizationmentioning

confidence: 99%

“…Three kinds of optimization algorithms, evolutionary [8][9][10][11][12][13][14][15][16][17], stochastic [18][19][20][21][22][23][24][25][26][27][28][29] and combinatorial optimization [30][31][32][33][34][35][36][37][38] will be addressed. For machine learning algorithms, the discussion is based on un-supervised learning [39][40][41][42][43][44][45][46][47][48][49], supervised learning and semi-supervised learning [71][72][73][74][75][76][77][78]…”

Section: Introductionmentioning

confidence: 99%

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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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Section: Stochastic Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…Por ejemplo, Xiang et al (14) midieron la efectividad de un algoritmo de optimización en la programación de diez salas de cirugías, a partir de una muestra de diez días de trabajo y alrededor de 450 cirugías. En este estudio, al igual que lo propuesto en Saadouli et al (15), se tomó una muestra de 31 días de trabajo del servicio de cirugía de un hospital privado, compuesto por diez salas para procedimientos electivos y una sala dedicada a la atención de urgencias.…”

Section: Discusión Y Conclusionesunclassified

Control de la variabilidad en la programación de pacientes electivos en salas de cirugía

Díaz-López

Fuquen-Fraile

Barrera

et al. 2015

RGYPS

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El Departamento de Cirugía genera una buena parte de los ingresos de un hospital. Este estudio evalúa la variabilidad en la programación de las cirugías y cómo esta atenta contra la eficiencia del servicio. Este estudio implementó un algoritmo para la generación de dos programaciones que luego fueron comparadas con la realizada manualmente en un hospital en Colombia. Las dos programaciones generadas se diferencian entre sí al incluir o no las máximas desviaciones en los tiempos de duración de las cirugías, asumiendo que estas son causadas por factores externas al proceso y pueden ser eliminadas. Los resultados muestran que al emplear un algoritmo automático para programar las cirugías, podrían mejorarse el tiempo de respuesta y la utilización de las salas. Aún más, se pudo concluir que la variabilidad asociada a la programación manual tiene un mayor impacto que la asociada a otros factores en los indicadores de desempeño del servicio.

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“…In a hospital, perioperative services is an area that can carry the highest expenditures while simultaneously accounting for the largest portion of net income . Peters et al estimated that more than 65% of the net revenue in a high‐functioning hospital can originate in perioperative services.…”

mentioning

confidence: 99%

Synergism in Perioperative Services

Elliott-Dawe¹

2018

AORN Journal

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This article presents an analysis of the concept of synergism in perioperative services. Perioperative services can account for a hospital's highest expenditures and net income or loss, and leaders must understand how to maximize outcomes by creating value above expectations. Using Walker and Avant's methodology, I examined the use of synergism in the literature across multiple databases. The concept has been used in business, information technology, psychology, engineering, and theology. In health care, it is used to explain the effects of combined medications, teamwork, biological systems, patient care, cleaning effectiveness, and health promotion partnerships. I identified two attributes: an emergent property and increased positivity. Optimized outcomes, increased patient and employee satisfaction, and high net revenue are evidence of perioperative synergism. Synergism in this context is dependent on team cohesion, communication, workplace adaptability, and the tactical allocation of resources to ensure unimpeded flow of the patient through the system.

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A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department

Cited by 111 publications

References 43 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Control de la variabilidad en la programación de pacientes electivos en salas de cirugía

Synergism in Perioperative Services

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