A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic

Abstract

This study presents two simulation modelling tools to support the organisation of networks of dialysis services during the COVID-19 pandemic. These tools were developed to support renal services in the South of England (the Wessex region caring for 650 dialysis patients), but are applicable elsewhere. A discrete-event simulation was used to model a worst case spread of COVID-19, to stress-test plans for dialysis provision throughout the COVID-19 outbreak. We investigated the ability of the system to manage the mix of COVID-19 positive and negative patients, the likely effects on patients, outpatient workloads across all units, and inpatient workload at the centralised COVID-positive inpatient unit. A second Monte-Carlo vehicle routing model estimated the feasibility of patient transport plans. If current outpatient capacity is maintained there is sufficient capacity in the South of England to keep COVID-19 negative/recovered and positive patients in separate sessions, but rapid reallocation of patients may be needed. Outpatient COVID-19 cases will spillover to a secondary site while other sites will experience a reduction in workload. The primary site chosen to manage infected patients will experience a significant increase in outpatients and inpatients. At the peak of infection, it is predicted there will be up to 140 COVID-19 positive patients with 40 to 90 of these as inpatients, likely breaching current inpatient capacity. Patient transport services will also come under considerable pressure. If patient transport operates on a policy of one positive patient at a time, and two-way transport is needed, a likely scenario estimates 80 ambulance drive time hours per day (not including fixed drop-off and ambulance cleaning times). Relaxing policies on individual patient transport to 2-4 patients per trip can save 40-60% of drive time. In mixed urban/rural geographies steps may need to be taken to temporarily accommodate renal COVID-19 positive patients closer to treatment facilities.

Modeling the impact of COVID-19 on Retina Clinic Performance

“…Our study is also among the first to use EMR-timestamped reports to study the clinical transformation, efficiency, and patient flow changes to outpatient systems during the COVID-era. While other fields of medicine such as gastroenterology, cardiology and nephrology utilized discrete-event simulation to study changes in operations during the COVID-19 period [ 12 – 16 ], the present study is the first to do so in ophthalmology. Using EMR-derived timestamps from actual patient encounters, we present a discrete-event simulation model capable of extrapolating from clinic-level data the impact of COVID-era changes on patient flow with the aim of planning for future patient volume increases.…”

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

“…There are 32 (8.5%) simulation papers using DES. The simulation models were mainly used to assess the impact of COVID‐19 on an organization's workflow and emphasized its optimization (Allen, Bhanji, et al, 2020 ; Das, 2020 ; de Brito Jr et al, 2021 ; Kim et al, 2021 ; VanDeusen et al, 2021 ; Zeinalnezhad et al, 2020 ). Meanwhile, DES was also applied to process analysis and optimization of service facilities that had effects on COVID‐19 spread, including testing facility (Çaglayan et al, 2022 ; El Hage et al, 2021 ; Gowda et al, 2021 ; Saidani et al, 2021 ; Saidani & Kim, 2021 ), vaccination centres (Pilati et al, 2021 ) and COVID‐19‐related hospitals (Frichi et al, 2021 ; Melman et al, 2021 ).…”

“…Twenty papers explored the other sectors at the national and regional levels, including industrial network (Song et al, 2020 ), tourism (Gu et al, 2021 ; Luo et al, 2021 ), national security (Prikazchikov et al, 2021 ), food–energy–water (Calder et al, 2021 ), economy (Chen et al, 2021 ; Fosco & Zurita, 2021 ; Inoue et al, 2021 ; Inoue & Todo, 2020 ; Sharma et al, 2021 ), financial (Spelta et al, 2021 ), social activity (de Brito Jr et al, 2021 ; Schmidt & Albert, 2021 ; Weibrecht et al, 2021 ), healthcare (Schlüter et al, 2021 ), employment (Marreros et al, 2021 ) and transport and land‐use (Habib & Anik, 2021 ). As the pandemic led to great collateral damage or process disruption to a variety of organizations, including banks (Shahabi et al, 2021 ), airlines (Delcea et al, 2020 ; Milne et al, 2020 , 2021 ), ambulatory endoscopy centres (Das, 2020 ), heart clinics (Zeinalnezhad et al, 2020 ), laboratories (Lim et al, 2020 ) and outpatient dialysis services (Allen, Bhanji, et al, 2020 ), necessary countermeasures were adopted to lower the risk of transmission and to improve effectiveness of these measures. Simulation models can help organizations across diverse sectors develop and evaluate scenarios, ask counterfactual ‘what‐if’ questions and identify and implement cost‐effective organization‐level infection prevention and control mechanisms.…”

Proceedings of SW21 The OR Society Simulation Workshop

“…De-sim is an object orientated (OO) framework for developing complex interacting DES models. The de-sim authors argue that their OO framework is an advancement over Simpy's process-based worldview; although we note that Simpy itself is highly flexible and can easily be used within an OO framework; for example see (Allen, Bhanji, Willemsen, Dudfield, Logan, and Monks 2020).…”

“…Simpy has a process based simulation worldview and has continued to be maintained (last update Apr 2020). It has been used in several Operations Research relevant publications (Bovim, Gullhav, Andersson, Dale, and Karlsen 2021, Allen, Bhanji, Willemsen, Dudfield, Logan, and Monks 2020, Monks, Harper, Anagnostou, and Taylor 2022).…”