scholarly journals Resuming Elective Surgery After COVID-19: A Simulation Modelling Framework for Guiding the Phased Opening of Operating Rooms

2021 ◽  
pp. 104665
Author(s):  
Hairil Rizal Abdullah ◽  
Sean Shao Wei Lam ◽  
Ang Boon Yew ◽  
Ahmadreza Pourghaderi ◽  
Francis Ngoc Hoang Long Nguyen ◽  
...  
Keyword(s):  
Elective Surgery ◽  
Simulation Modelling ◽  
Operating Rooms ◽  
Modelling Framework

scholarly journals Operating Rooms efficiency: leveraging data to improve management of elective surgery

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
C Quercioli ◽  
G A Carta ◽  
G Cevenini ◽  
G Messina ◽  
N Nante ◽  
...  
Keyword(s):  
Historical Data ◽  
Elective Surgery ◽  
District General Hospital ◽  
Operating Rooms ◽  
Accurate Estimation ◽  
Surgical Time ◽  
Accurate Analysis ◽  
Management Efficiency ◽  
Scheduling Model ◽  
The Mean

Abstract Background Careful scheduling of elective surgery Operating Rooms (ORs) is crucial for their efficient use, to avoid low/over utilization and staff overtime. Accurate estimation of procedures duration is essential to improve ORs scheduling. Therefore analysis of historical data about surgical times is fundamental to ORs management. We analyzed the effect, in a real setting, of an ORs scheduling model based on estimated optimum surgical time in improving ORs efficiency and decreasing the risk of overtime. Methods We studied all the 2014-2019 elective surgery sessions (3,758 sessions, 12,449 interventions) of a district general hospital in Siena's Province, Italy. The hospital had3 ORs open 5 days/week 08:00-14:00. Surgery specialties were general surgery, orthopedics, gynecology and urology. Based on a pilot study conducted in 2016, which estimated a 5 times greater risk of having an OR overtime for sessions with a surgical time (incision-suture)>200 minutes, from 2017 all the ORs were scheduled using a maximum surgical time of 200 minutes calculated summing the mean surgical times for intervention and surgeon (obtained from 2014-2016 data). We carried out multivariate logistic regression to calculate the probability of ORs overtime (of 15 and 30 minutes) for the periods 2014-2016 and 2017-2019adjusting for raw ORs utilization. Results The 2017-2019 risk of an OR overtime of 15 minutes decreased by 25% compared to the 2014-2016 period (OR = 0.75, 95%CI=0.618-0.902, p = 0.003); the risk of a OR overtime of 30 minutes decreased by 33% (OR = 0.67, 95%CI= 0.543-0.831, p < 0.001). Mean raw OR utilization increase from 62% to 66% (p < 0.001). Mean number of interventions per surgery sessions increased from 3.1 to 3.5 (p < 0.001). Conclusions This study has shown that an analysis of historical data and an estimate of the optimal surgical time per surgical session could be helpful to avoid both a low and excessive use of the ORs and therefore to increase the efficiency of the ORs. Key messages An accurate analysis of surgical procedures duration is crucial to optimize operating room utilization. A data-based approach can improve OR management efficiency without extra resources.

scholarly journals Scheduling Delayed Treatment and Surgeries Post-Pandemic: A Stakeholder Analysis

2020 ◽  
Vol 9 (1) ◽  
pp. 10-14
Author(s):  
Emily S. Patterson ◽  
Elizabeth Lerner Papautsky ◽  
Jessica L. Krok-Schoen ◽  
Clara Lee ◽  
Ko Un Park ◽  
...  
Keyword(s):  
Local Community ◽  
Elective Surgery ◽  
Stakeholder Analysis ◽  
Healthcare Providers ◽  
Hospital Staff ◽  
Operating Rooms ◽  
Inpatient Setting ◽  
Care Needs ◽  
Delayed Treatment ◽  
Increased Risk

Many are interested in how to safely ramp up elective surgeries after national, state, and voluntary shutdowns of operating rooms to minimize the spread of COVID-19 infections to patients and providers. We conducted an analysis of diverse perspectives from stakeholders regarding how to trade off risks and benefits to patients, healthcare providers, and the local community. Our findings indicate that there are a large number of different categories of stakeholders impacted by the post-pandemic decisions to reschedule delayed treatments and surgeries. For a delayed surgery, the primary stakeholders are the surgeon with expertise about the clinical benefits of undergoing an operation and the patient’s willingness to tolerate uncertainty and the increased risk of infection. For decisions about how much capacity in the operating rooms and in the inpatient setting after the surgery, the primary considerations are minimizing staff infections, preventing patients from getting COVID-19 during operations and during post-surgical recovery at the hospital, conserving critical resources such as PPE, and meeting the needs of hospital staff for quality of life, such as child care needs and avoiding infecting members of their household. The timing and selection of elective surgery cases has an impact on the ability of hospitals to steward finances, which in turns affects decisions about maintaining employment of staff when operating rooms and inpatient rooms are not being used.

scholarly journals A Hybrid Simulation Modelling Framework for Combining System Dynamics and Agent-Based Models

2021 ◽  
Author(s):  
Le Khanh Ngan Nguyen ◽  
◽  
Susan Howick ◽  
Itamar Megiddo ◽  
◽  
...  
Keyword(s):  
System Dynamics ◽  
Hybrid Simulation ◽  
Simulation Modelling ◽  
Agent Based Models ◽  
Agent Based ◽  
Modelling Framework

scholarly journals The application of a hybrid simulation modelling framework as a decision-making tool for TPM improvement

2019 ◽  
Vol 25 (3) ◽  
pp. 476-498 ◽  
Author(s):  
Omogbai Oleghe ◽  
Konstantinos Salonitis
Keyword(s):  
Discrete Event Simulation ◽  
Hybrid Model ◽  
Discrete Event ◽  
Simulation Modelling ◽  
Hybrid Modelling ◽  
Content Type ◽  
Event Simulation ◽  
Modelling Framework ◽  
Field Notes

Purpose The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM. Design/methodology/approach The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance. Findings Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation. Research limitations/implications The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems. Practical implications The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts. Originality/value The investigation of TPM using SD-DES hybrid modelling is a novelty.

scholarly journals Quota allocation in mixed fisheries: a bioeconomic modelling approach applied to the Channel flatfish fisheries

2011 ◽  
Vol 68 (7) ◽  
pp. 1580-1591 ◽  
Author(s):  
Paul Marchal ◽  
L. Richard Little ◽  
Olivier Thébaud
Keyword(s):  
Simulation Modelling ◽  
Marine Science ◽  
Economic Return ◽  
Effort Allocation ◽  
Modelling Framework ◽  
Bioeconomic Modelling ◽  
Quota Allocation ◽  
Eastern Channel ◽  
Modelling Approach

Abstract Marchal, P., Little, L. R., and Thébaud, O. 2011. Quota allocation in mixed fisheries: a bioeconomic modelling approach applied to the Channel flatfish fisheries. – ICES Journal of Marine Science, 68: 1580–1591. A simulation modelling approach is used to assess the respective performances of different regimes of quota allocation (fixed or transferable), quota ownership (owned or not by fishers), and taxation for catching fish above quota. The simulations account for a variety of fleet behaviours (ranging from fixed by tradition to dynamic economics-driven). The modelling framework is applied to the Channel flatfish mixed fisheries. Transferable quota allocation regimes would particularly benefit small netters and beam trawlers, which would achieve a profit of €50–150 million without compromising the conservation of eastern Channel sole, but it could impair the sustainability of other stocks. If quota is owned by fishers, the least fishing-efficient fleet stops fishing, but makes substantial profit from leasing quotas to beam trawlers and to small and large netters, which remain actively fishing. The highest economic return for quota owners (€200–300 million) is achieved when effort allocation is fixed by tradition. The profit achieved by small netters is greatest when fleets are almost entirely economics-driven. Increasing overquota landing taxes generally leads to conservation benefits for all stocks, but at the expense of lower profitability for the fishery overall.

scholarly journals Predicting operating room case load: An aid to resource allocation

2013 ◽  
Vol 2 (4) ◽  
pp. 151 ◽  
Author(s):  
Elizabeth M. Geary ◽  
Martin Goldberg ◽  
A. G. Greenburg ◽  
Thomas E. Johnson
Keyword(s):  
Block Scheduling ◽  
Elective Surgery ◽  
Resource Planning ◽  
Operating Rooms ◽  
Black Box ◽  
Hospital Patient ◽  
Hospital Administrators ◽  
Open Time ◽  
Operating Suite ◽  
Surgical Operations

Hospital patient bed utilization can reach 100% with an impact on elective surgery schedules. Analysis of the demand for beds created by elective surgical operations is desirable to manage overall resources under these conditions. For planning and allocating operating rooms, staff, beds and equipment on any given day, hospital administrators would benefit from an accurate prediction of the number of surgical cases that will be completed. Current scheduling techniques do not predict, for a given day in the future, the number of cases that will actually be performed. A study was performed at a 247 bed hospital with 10 operating rooms. The operating rooms were available for reservation more than two weeks in advance. Both block scheduling and open time were available. Using reservation data with a simple Black Box model allows the prediction of the total number of cases to be performed up to two weeks in advance with 90% accuracy. The resultant predictive demand should allow for better resource planning for the Operating Suite as well as required post-op hospital patient beds. 

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