Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms.
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2021-12-14
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Abstract
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To develop a 2-stage discrete events simulation (DES) based framework for the evaluation of elective surgery cancellation strategies and resumption scenarios across multiple operational outcomes.Materials and methods
Study data was derived from the data warehouse and domain knowledge on the operational process of the largest tertiary hospital in Singapore. 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 were extracted for the study. A clustering approach was used in stage 1 of the modelling framework to develop the groups of surgeries that followed distinctive postponement patterns. These clusters were then used as inputs for stage 2 where the DES model was used to evaluate alternative phased resumption strategies considering the outcomes of OR utilization, waiting times to surgeries and the time to clear the backlogs.Results
The tool enabled us to understand the elective postponement patterns during the COVID-19 partial lockdown period, and evaluate the best phased resumption strategy. Differences in the performance measures were evaluated based on 95% confidence intervals. The results indicate that two of the gradual phased resumption strategies provided lower peak OR and bed utilizations but required a longer time to return to BAU levels. Minimum peak bed demands could also be reduced by approximately 14 beds daily with the gradual resumption strategy, whilst the maximum peak bed demands by approximately 8.2 beds. Peak OR utilization could be reduced to 92% for gradual resumption as compared to a minimum peak of 94.2% with the full resumption strategy.Conclusions
The 2-stage modelling framework coupled with a user-friendly visualization interface were key enablers for understanding the elective surgery postponement patterns during a partial lockdown phase. The DES model enabled the identification and evaluation of optimal phased resumption policies across multiple important operational outcome measures.Lay abstract
During the height of the COVID-19 pandemic, most healthcare systems suspended their non-urgent elective surgery services. This strategy was undertaken as a means to expand surge capacity, through the preservation of structural resources (such as operating theaters, ICU beds, and ventilators), consumables (such as personal protective equipment and medications), and critical healthcare manpower. As a result, some patients had less-essential surgeries postponed due to the pandemic. As the first wave of the pandemic waned, there was an urgent need to quickly develop optimal strategies for the resumption of these surgeries. We developed a 2-stage discrete events simulation (DES) framework based on 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 captured in the Singapore General Hospital (SGH) enterprise data warehouse. The outcomes evaluated were OR utilization, waiting times to surgeries and time to clear the backlogs. A user-friendly visualization interface was developed to enable decision makers to determine the most promising surgery resumption strategy across these outcomes. Hospitals globally can make use of the modelling framework to adapt to their own surgical systems to evaluate strategies for postponement and resumption of elective surgeries.Type
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Abdullah, Hairil Rizal, Sean Shao Wei Lam, Boon Yew Ang, Ahmadreza Pourghaderi, Francis Ngoc Hoang Long Nguyen, David Bruce Matchar, Hiang Khoon Tan, Marcus Eng Hock Ong, et al. (2021). Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms. International journal of medical informatics, 158. p. 104665. 10.1016/j.ijmedinf.2021.104665 Retrieved from https://hdl.handle.net/10161/24332.
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Scholars@Duke
David Bruce Matchar
My research relates to clinical practice improvement - from the development of clinical policies to their implementation in real world clinical settings. Most recently my major content focus has been cerebrovascular disease. Other major clinical areas in which I work include the range of disabling neurological conditions, cardiovascular disease, and cancer prevention.
Notable features of my work are: (1) reliance on analytic strategies such as meta-analysis, simulation, decision analysis and cost-effectiveness analysis; (2) a balancing of methodological rigor the needs of medical professionals; and (3) dependence on interdisciplinary groups of experts.
This approach is best illustrated by the Stroke Prevention Patient Outcome Research Team (PORT), for which I served as principal investigator. Funded by the AHCPR, the PORT involved 35 investigators at 13 institutions. The Stroke PORT has been highly productive and has led to a stroke prevention project funded as a public/private partnership by the AHCPR and DuPont Pharma, the Managing Anticoagulation Services Trial (MAST). MAST is a practice improvement trial in 6 managed care organizations, focussing on optimizing anticoagulation for individuals with atrial fibrillation.
I serve as consultant in the general area of analytic strategies for clinical policy development, as well as for specific projects related to stroke (e.g., acute stroke treatment, management of atrial fibrillation, and use of carotid endarterectomy.) I have worked with AHCPR (now AHRQ), ACP, AHA, AAN, Robert Wood Johnson Foundation, NSA, WHO, and several pharmaceutical companies.
Key Words: clinical policy, disease management, stroke, decision analysis, clinical guidelines
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