Comparison of emergency department crowding scores: a discrete-event simulation approach
Comparison of emergency department crowding scores: a discrete-event simulation approach
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DOI：
10.1007/s10729-016-9385-z
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发表时间：
2018-03-01
影响因子：
3.6
通讯作者：
Mehrotra, Abhi
中科院分区：
文献类型：
--
作者：
Ahalt, Virginia;Argon, Nilay Tanik;Mehrotra, Abhi
根据美国急诊医师学会的定义，当所确定的急诊服务需求超过急诊室、医院或两者的可用患者护理资源时，就会出现急诊室（ED）拥挤的情况。急诊室拥挤是一个被广泛报道的问题，并且有人提出了几种拥挤评分方法，利用医院和患者数据作为输入来量化拥挤程度，以帮助医疗专业人员预测即将出现的拥挤问题。我们利用北卡罗来纳州一家大型学术医院的数据，通过评估三种拥挤评分（即EDWIN、NEDOCS和READI）各自的优缺点，特别是它们的预测能力，对这三种评分进行评估。我们首先建立一个急诊室离散事件模拟模型，根据所考虑的急诊室的观察结果对模拟模型的结果进行验证，然后利用模型结果在正常运行条件下以及在急诊室的两种模拟疫情爆发情景下对三种急诊室拥挤评分中的每一种进行研究。我们得出的结论是，对于这家医院来说，EDWIN和NEDOCS都被证明是衡量当前急诊室拥挤程度的有用指标，并且这两种评分都显示出预测即将出现的拥挤情况的能力。将EDWIN和NEDOCS评分与本研究中提出的阈值相结合使用，可以为临床医生提供实时警报，以预测即将出现的拥挤情况，这可能会促使更好的准备工作，并最终带来更好的患者护理结果。
According to American College of Emergency Physicians, emergency department (ED) crowding occurs when the identified need for emergency services exceeds available resources for patient care in the ED, hospital, or both. ED crowding is a widely reported problem and several crowding scores are proposed to quantify crowding using hospital and patient data as inputs for assisting healthcare professionals in anticipating imminent crowding problems. Using data from a large academic hospital in North Carolina, we evaluate three crowding scores, namely, EDWIN, NEDOCS, and READI by assessing strengths and weaknesses of each score, particularly their predictive power. We perform these evaluations by first building a discrete-event simulation model of the ED, validating the results of the simulation model against observations at the ED under consideration, and utilizing the model results to investigate each of the three ED crowding scores under normal operating conditions and under two simulated outbreak scenarios in the ED. We conclude that, for this hospital, both EDWIN and NEDOCS prove to be helpful measures of current ED crowdedness, and both scores demonstrate the ability to anticipate impending crowdedness. Utilizing both EDWIN and NEDOCS scores in combination with the threshold values proposed in this work could provide a real-time alert for clinicians to anticipate impending crowding, which could lead to better preparation and eventually better patient care outcomes.