COVID-19 Pandemic Simulation Modelling in Anaesthesia Residency Training to Predict Delays and Workforce Deficiencies : A Case Study of the Singapore Residency Training Program
Copyright © 2024, Davies et al..
Background COVID-19 has been the worst pandemic of this century, resulting in economic, social, and educational disruptions. Residency training is no exception, with training restrictions delaying the progression and graduation of residents. We sought to utilize simulation modelling to predict the impact on future cohorts in the event of repeated and prolonged movement restrictions due to COVID-19 and future pandemics of a similar nature. Method A Delphi study was conducted to determine key Accreditation Council for Graduate Medical Education-International (ACGME-I) training variables affected by COVID-19. Quantitative resident datasets on these variables were collated and analysed from 2018 to 2021. Using the Vensim® software (Ventana Systems, Inc., Harvard, MA), historical resident data and pandemic progression delays were used to create a novel simulation model to predict future progression delay. Various durations of delay were also programmed into the software to simulate restrictions of varying severity that would impact resident progression. Results Using the model with scenarios simulating varying pandemic length, we found that the estimated average delay for residents in each accredited year ranged from an increase of one month for year 2 residents to more than three months for year 4 residents. Movement restrictions lasting a year would require up to six years before the program returned to a pre-pandemic equilibrium. Conclusion Systems dynamic modelling can be used to predict delays in residency training programs during a pandemic. The impact on the workforce can thus be projected, allowing residency programs to institute mitigating measures to avoid progression delay.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
Cureus - 16(2024), 1 vom: 01. Jan., Seite e51852 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Davies, Lucy J [VerfasserIn] |
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| Links: |
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| Themen: |
Anaesthesiology |
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| Anmerkungen: |
Date Revised 10.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.7759/cureus.51852 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368171523 |
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| 245 | 1 | 0 | |a COVID-19 Pandemic Simulation Modelling in Anaesthesia Residency Training to Predict Delays and Workforce Deficiencies |b A Case Study of the Singapore Residency Training Program |
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| 520 | |a Copyright © 2024, Davies et al. | ||
| 520 | |a Background COVID-19 has been the worst pandemic of this century, resulting in economic, social, and educational disruptions. Residency training is no exception, with training restrictions delaying the progression and graduation of residents. We sought to utilize simulation modelling to predict the impact on future cohorts in the event of repeated and prolonged movement restrictions due to COVID-19 and future pandemics of a similar nature. Method A Delphi study was conducted to determine key Accreditation Council for Graduate Medical Education-International (ACGME-I) training variables affected by COVID-19. Quantitative resident datasets on these variables were collated and analysed from 2018 to 2021. Using the Vensim® software (Ventana Systems, Inc., Harvard, MA), historical resident data and pandemic progression delays were used to create a novel simulation model to predict future progression delay. Various durations of delay were also programmed into the software to simulate restrictions of varying severity that would impact resident progression. Results Using the model with scenarios simulating varying pandemic length, we found that the estimated average delay for residents in each accredited year ranged from an increase of one month for year 2 residents to more than three months for year 4 residents. Movement restrictions lasting a year would require up to six years before the program returned to a pre-pandemic equilibrium. Conclusion Systems dynamic modelling can be used to predict delays in residency training programs during a pandemic. The impact on the workforce can thus be projected, allowing residency programs to institute mitigating measures to avoid progression delay | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a covid-19 | |
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| 650 | 4 | |a pandemic | |
| 650 | 4 | |a residency | |
| 650 | 4 | |a simulation | |
| 700 | 1 | |a Mathew, Christopher |e verfasserin |4 aut | |
| 700 | 1 | |a Pourghaderi, Ahmad R |e verfasserin |4 aut | |
| 700 | 1 | |a Leong, Adeline Xin Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Diana Xin Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Koh, Darren Liang Khai |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Addy Yong Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Ong, Caroline Yu Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Ong, John |e verfasserin |4 aut | |
| 700 | 1 | |a Lam, Sean Shao Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Ong, Sharon Gek Kim |e verfasserin |4 aut | |
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