Solving a tactical operating room planning problem by a column-generation-based heuristic procedure with four criteria

2008 ◽  
Vol 166 (1) ◽  
pp. 91-108 ◽  
Author(s):  
H. Fei ◽  
C. Chu ◽  
N. Meskens
Keyword(s):  
Operating Room ◽  
Column Generation ◽  
Planning Problem ◽  
Heuristic Procedure ◽  
Operating Room Planning

A Markov decision model with dead ends for operating room planning considering dynamic patient priority

2019 ◽  
Vol 53 (5) ◽  
pp. 1819-1841
Author(s):  
Jian Zhang ◽  
Mahjoub Dridi ◽  
Abdellah El Moudni
Keyword(s):  
Dynamic Programming ◽  
Operating Room ◽  
Waiting Times ◽  
Waiting List ◽  
Planning Problem ◽  
Operating Room Planning ◽  
Markov Decision ◽  
Markov Decision Model ◽  
Programming Algorithms ◽  
Patient Waiting

This paper addresses an operating room planning problem with surgical demands from both the elective patients and the non-elective ones. A dynamic waiting list is established to prioritize and manage the patients according to their urgency levels and waiting times. In every decision period, sequential decisions are taken by selecting high-priority patients from the waiting list to be scheduled. With consideration of random arrivals of new patients and uncertain surgery durations, the studied problem is formulated as a novel Markov decision process model with dead ends. The objective is to optimize a combinatorial cost function involving patient waiting times and operating room over-utilizations. Considering that the conventional dynamic programming algorithms have difficulties in coping with large-scale problems, we apply several adapted real-time dynamic programming algorithms to solve the proposed model. In numerical experiments, we firstly apply different algorithms to solve the same instance and compare the computational efficiencies. Then, to evaluate the effects of dead ends on the policy and the computation, we conduct simulations for multiple instances with the same problem scale but different dead ends. Experimental results indicate that incorporating dead ends into the model helps to significantly shorten the patient waiting times and improve the computational efficiency.

A stochastic model for operating room planning with elective and emergency demand for surgery

2008 ◽  
Vol 185 (3) ◽  
pp. 1026-1037 ◽  
Author(s):  
Mehdi Lamiri ◽  
Xiaolan Xie ◽  
Alexandre Dolgui ◽  
Frédéric Grimaud
Keyword(s):  
Stochastic Model ◽  
Operating Room ◽  
Operating Room Planning

New Members of Cardıac Surgery Operatıng Room “Plannıng of Operatıng Room Technıcıan's Educatıon Currıculum”

2018 ◽  
Vol 121 (8) ◽  
pp. e159-e160
Author(s):  
Gül Özlem Yıldırım ◽  
Gülseren Keskin ◽  
Halil Yıldırım ◽  
Elif Aktekin ◽  
Nurcan Güzelay ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Operating Room ◽  
Education Curriculum ◽  
New Members ◽  
Operating Room Planning

scholarly journals Assessment of operative times of multiple surgical specialties in a public university hospital

2017 ◽  
Vol 15 (2) ◽  
pp. 200-205 ◽  
Author(s):  
Altair da Silva Costa Jr
Keyword(s):  
Operating Room ◽  
Operative Time ◽  
Cross Sectional Study ◽  
Public University ◽  
University Hospital ◽  
Cross Sectional ◽  
Mean Values ◽  
Operating Room Planning ◽  
Duration Of Surgery ◽  
The Mean

ABSTRACT Objective To evaluate the indicators duration of anesthesia, operative time and time patients stay in the operating rooms of different surgical specialties at a public university hospital. Methods It was done by a descriptive cross-sectional study based on the operating room database. The following stages were measured: duration of anesthesia, procedure time and patient length of stay in the room of the various specialties. We included surgeries carried out in sequence in the same room, between 7:00 a.m. and 5 p.m., either elective or emergency. We calculated the 80th percentile of the stages, where 80% of procedures were below this value. Results The study measured 8,337 operations of 12 surgical specialties performed within one year. The overall mean duration of anesthesia of all specialties was 178.12±110.46 minutes, and the 80th percentile was 252 minutes. The mean operative time was 130.45±97.23 minutes, and the 80th percentile was 195 minutes. The mean total time of the patient in the operating room was 197.30±113.71 minutes, and the 80th percentile was 285 minutes. Thus, the variation of the overall mean compared to the 80th percentile was 41% for anesthesia, 49% for surgeries and 44% for operating room time. In average, anesthesia took up 88% of the operating room period, and surgery, 61%. Conclusion This study identified patterns in the duration of surgery stages. The mean values of the specialties can assist with operating room planning and reduce delays.

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