Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms.
Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms.
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DOI：
10.1016/j.ijmedinf.2021.104665
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发表时间：
2021-12-14
影响因子：
4.9
通讯作者：
Ong MEH
中科院分区：
文献类型：
--
作者：
Abdullah HR;Lam SSW;Ang BY;Pourghaderi A;Nguyen FNHL;Matchar DB;Tan HK;Ong MEH
开发一个基于2阶段离散事件模拟（DES）的框架，用于评价多个手术结局中的择期手术取消策略和恢复方案。研究数据来自新加坡最大的三级医院的数据仓库和业务流程领域知识。本研究提取了2019年1月1日至2020年5月31日期间在43个手术室（OR）和18个外科学科进行的34，025例独特病例。在建模框架的第1阶段中使用聚类方法来开发遵循不同延迟模式的手术组。然后将这些聚类用作第2阶段的输入，其中DES模型用于评估替代的分阶段恢复策略，考虑OR利用率的结果、手术等待时间和清除积压的时间。该工具使我们能够了解COVID-19局部封锁期间的选择性延期模式，并评估最佳的分阶段复课策略。基于95%置信区间评价性能指标的差异。结果表明，两个逐步分阶段恢复策略提供了较低的峰值OR和床位利用率，但需要更长的时间才能恢复到BAU水平。在逐步恢复服务的策略下，每日最低高峰期病床需求亦可减少约14张病床，而最高高峰期病床需求则可减少约8. 2张病床。与完全恢复策略的最低峰值94.2%相比，逐渐恢复的峰值OR利用率可降至92%。2阶段建模框架加上用户友好的可视化界面是理解部分封锁阶段期间择期手术推迟模式的关键因素。DES模型使得能够在多个重要的运营成果措施中确定和评估最佳的分阶段恢复政策。在COVID-19疫情高峰期，大部分医疗系统暂停非紧急择期手术服务。该策略旨在通过保留结构性资源（如手术室、ICU病床和呼吸机）、消耗品（如个人防护设备和药物）和关键医疗人力，扩大应急能力。因此，一些患者因疫情推迟了不太重要的手术。随着第一波大流行的消退，迫切需要迅速制定恢复这些手术的最佳策略。我们基于新加坡综合医院（SGH）企业数据仓库中采集的2019年1月1日至2020年5月31日期间在43个手术室（OR）和18个外科学科进行的34，025例独特病例，开发了一个2阶段离散事件模拟（DES）框架。评价的结果是手术室利用率、手术等待时间和清除积压的时间。开发了一个用户友好的可视化界面，使决策者能够在这些结果中确定最有希望的手术恢复策略。全球的医院可以利用建模框架来适应自己的手术系统，以评估推迟和恢复择期手术的策略。
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. 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. 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. 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. 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.
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影响因子：
2.1
作者：
Joshi D;Jalali A;Whipple T;Rehman M;Ahumada LM
通讯作者：
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影响因子：
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通讯作者：
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DOI：
10.1016/j.jamcollsurg.2020.03.019
发表时间：
2020-06-01
影响因子：
5.2
作者：
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通讯作者：
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