Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic

Abstract

ObjectivesWe describe a hospital’s implementation of predictive models to optimise emergency response to the COVID-19 pandemic.MethodsWe were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system dynamics to derive a series of chain susceptible, infected and recovered (SIR) models. We then built a discrete event simulation using the system dynamics output and bootstrapped electronic medical record data to approximate the weekly effect of tuning surgical volume on hospital census. We evaluated performance via a model fit assessment and cross-model comparison.ResultsWe outlined the design and implementation of predictive models to support management decision making around areas impacted by COVID-19. The fit assessments indicated the models were most useful after 30 days from onset of local cases. We found our subreports were most accurate up to 7 days after model run.DiscusssionOur model allowed us to shape our health system’s executive policy response to implement a ‘hospital within a hospital’—one for patients with COVID-19 within a hospital able to care for the regular non-COVID-19 population. The surgical scheduleis modified according to models that predict the number of new patients withCovid-19 who require admission. This enabled our hospital to coordinateresources to continue to support the community at large. Challenges includedthe need to frequently adjust or create new models to meet rapidly evolvingrequirements, communication, and adoption, and to coordinate the needs ofmultiple stakeholders. The model we created can be adapted to other health systems,provide a mechanism to predict local peaks in cases and inform hospitalleadership regarding bed allocation, surgical volumes, staffing, and suppliesone for COVID-19 patients within a hospital able to care for the regularnon-COVID-19 population.ConclusionPredictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.

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

“…There are only 11 (3%) papers concerning hybrid simulation: Six papers were a combination of ABM and DES (Asgary et al, 2020 ; Cimini et al, 2021 ; Possik et al, 2021 ; Qiu et al, 2021 ; Stapelberg et al, 2021 ; Tofighi et al, 2021 ), three papers were a combination of DES and SDM (Kang et al, 2021 ; Lu, Guan, et al, 2021 ; Warde et al, 2021 ) and two papers were an integration of SDM and ABM (Guo, Tong, et al, 2021 ; Mokhtari et al, 2021 ).…”

“…Twenty‐nine papers focused on COVID‐19 epidemic prediction, of which seven tried to estimate the R 0 in different regions (Müller et al, 2021 ; Rypdal et al, 2021 ; Yang et al, 2020 ) and countries (Guo & Xiao, 2020 ; Hoertel, Blachier, Blanco, Olfson, Massetti, Rico, et al, 2020 ; Kolokolnikov & Iron, 2021 ; Krivorotko et al, 2022 ). Most studies made prediction regarding cumulative infections (Hunter & Kelleher, 2021 ; Latkowski & Dunin‐Kȩplicz, 2021 ) and deaths (Ghaffarzadegan & Rahmandad, 2020 ), mortality (Benneyan et al, 2021 ; Lu, Guan, et al, 2021 ), daily testing capacity required (Fiore et al, 2021 ), hospital admissions (Warde et al, 2021 ) and demand for intensive care unit (ICU) beds (Bartz‐Beielstein et al, 2021 ; Garcia‐Vicuña et al, 2021 ; Irvine et al, 2021 ) and so forth as different interventions, such as physical distancing (Aghaei & Lohrasebi, 2021 ), various lockdown (Hoertel, Blachier, Blanco, Olfson, Massetti, Rico, et al, 2020 ; Uansri et al, 2021 ) and vaccination strategy (Suphanchaimat, Nittayasoot, et al, 2021 ; Suphanchaimat, Tuangratananon, et al, 2021 ). The rest predicted the future spread under school reopening (España et al, 2021 ; Rypdal et al, 2021 ; Son & RISEWIDs Team, 2020 ), city reopening (Yin et al, 2021 ), society activities reopening (Cremonini & Maghool, 2020 ) and international borders reopening (Pham et al, 2021 ).…”

Effect of COVID-19 on the delivery of care for thoracic surgical patients

“…39 Phased reopening of operative services began in late May 2020, guided by the implementation of predictive models to estimate COVID-19 cases in upcoming weeks. 20 Using these models, the following practices were implemented: adaptive alignment of resources commensurate with real-time COVID-19 and non-COVID-19 clinical needs, segregation of COVID-19 cases from the rest of the general patient population (hospital-in-a-hospital model), stringent preventive measures (PPE utilization, social distancing, and preadmission RT-PCR nasopharyngeal swabs for all patients requiring admission), coordination of care delivery by observing directives from state and county public health departments, and regular updates of hospital staff regarding hospital resources and COVID-19 volumes. This adaptive realignment of resources via the predictive model projections of COVID-19 cases allowed supply chain teams to adjust PPE allocation/procurement in real time and anticipate the number of beds, particularly negative-pressure rooms to accommodate COVID-19 admissions.…”

“…19 Patients scheduled to undergo surgical procedures were required to have a negative SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) nasopharyngeal swab within 48 hours of the procedure. We started a deliberate phased reopening starting in June 2020, as described, 20,21 allowing properly triaged elective surgical cases to proceed and remain fully operational while weathering the second and third surges of SARS-CoV-2, which began in July and October 2020, respectively (Figure 1). 22 We sought to determine the effect of the COVID-19 pandemic, including the closure of surgical services to ration resources, on the delivery of thoracic surgical care.…”