Department of Industrial & Systems Engineering, Wayne State University, Detroit, Michigan, United States of America.
Department of Computer Science, Wayne State University, Detroit, Michigan, United States of America.
PLoS One. 2022 Apr 21;17(4):e0265101. doi: 10.1371/journal.pone.0265101. eCollection 2022.
During a medical surge, resource scarcity and other factors influence the performance of the healthcare systems. To enhance their performance, hospitals need to identify the critical indicators that affect their operations for better decision-making. This study aims to model a pertinent set of indicators for improving emergency departments' (ED) performance during a medical surge. The framework comprises a three-stage process to survey, evaluate, and rank such indicators in a systematic approach. The first stage consists of a survey based on the literature and interviews to extract quality indicators that impact the EDs' performance. The second stage consists of forming a panel of medical professionals to complete the survey questionnaire and applying our proposed consensus-based modified fuzzy Delphi method, which integrates text mining to address the fuzziness and obtain the sentiment scores in expert responses. The final stage ranks the indicators based on their stability and convergence. Here, twenty-nine potential indicators are extracted in the first stage, categorized into five healthcare performance factors, are reduced to twenty consentaneous indicators monitoring ED's efficacy. The Mann-Whitney test confirmed the stability of the group opinions (p < 0.05). The agreement percentage indicates that ED beds (77.8%), nurse staffing per patient seen (77.3%), and length of stay (75.0%) are among the most significant indicators affecting the ED's performance when responding to a surge. This research proposes a framework that helps hospital administrators determine essential indicators to monitor, manage, and improve the performance of EDs systematically during a surge event.
在医疗高峰期间，资源短缺和其他因素会影响医疗系统的性能。为了提高性能，医院需要确定影响运营的关键指标，以便做出更好的决策。本研究旨在为改善医疗高峰期间急诊部的性能建立一套相关的指标模型。该框架包括一个三阶段的过程，以系统的方法调查、评估和对这些指标进行排名。第一阶段是基于文献和访谈进行的调查，以提取影响急诊部性能的质量指标。第二阶段是由一组医疗专业人员组成的小组完成调查问卷，并应用我们提出的基于共识的改进模糊德尔菲方法，该方法结合文本挖掘来解决模糊性并获得专家回复中的情感分数。最后一个阶段是根据指标的稳定性和收敛性进行排名。在这里，在第一阶段提取了 29 个潜在指标，分为五个医疗绩效因素，然后简化为 20 个一致的指标来监测急诊部的疗效。曼-惠特尼检验证实了小组意见的稳定性（p<0.05）。协议百分比表明，在应对高峰时，急诊部病床（77.8%）、每位就诊患者的护士配置（77.3%）和住院时间（75.0%）是影响急诊部性能的最重要指标之一。本研究提出了一个框架，帮助医院管理人员确定在高峰事件期间系统地监测、管理和改善急诊部性能的关键指标。
