Physician performance scores used to predict emergency department admission numbers and excessive admissions burden

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Andy Eyre, Gideon Y Stein, Jacob Chen, Danny Alon
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Abstract

Background Overcrowding in hospitals is associated with a panoply of adverse events. Inappropriate decisions in the emergency department (ED) contribute to overcrowding. The performance of individual physicians as part of the admitting team is a critical factor in determining the overall rate of admissions. While previous attempts to model admission numbers have been based on a range of variables, none have included measures of individual staff performance. We construct reliable objective measures of staff performance and use these, among other factors, to predict the number of daily admissions. Such modelling will enable enhanced workforce planning and timely intervention to reduce inappropriate admissions and overcrowding.Methods A database was created of 232 245 ED attendances at Meir Medical Center in central Israel, spanning the years 2016–2021. We use several measures of physician performance together with historic caseload data and other variables to derive statistical models for the prediction of ED arrival and admission numbers.Results Our models predict arrival numbers with a mean absolute percentage error (MAPE) of 6.85%, and admission numbers with a MAPE of 10.6%, and provide a same-day alert for heavy admissions burden with 75% sensitivity for a false-positive rate of 20%. The inclusion of physician performance measures provides an essential boost to model performance.Conclusions Arrival number and admission numbers can be predicted with sufficient fidelity to enable interventions to reduce excess admissions and smooth patient flow. Individual staff performance has a strong effect on admission rates and is a critical variable for the effective modelling of admission numbers.
用于预测急诊科入院人数和过度入院负担的医生绩效评分
背景 医院人满为患与一系列不良事件有关。急诊科(ED)不恰当的决定是造成过度拥挤的原因之一。作为入院团队的一部分,医生个人的表现是决定总体入院率的关键因素。虽然以前的入院人数模型是基于一系列变量建立的,但没有一个模型包括对员工个人绩效的衡量。我们构建了可靠的员工绩效客观指标,并利用这些指标和其他因素来预测每日入院人数。这种建模将有助于加强劳动力规划和及时干预,以减少不适当的入院人数和过度拥挤现象。方法 在以色列中部梅厄医疗中心建立了一个包含 232 245 名急诊室就诊者的数据库,时间跨度为 2016-2021 年。结果 我们的模型预测到达人数的平均绝对百分比误差 (MAPE) 为 6.85%,预测入院人数的平均绝对百分比误差 (MAPE) 为 10.6%,并可在同一天发出入院负担沉重的警报,灵敏度为 75%,假阳性率为 20%。结论 到达人数和入院人数的预测具有足够的可信度,可以采取干预措施,减少过多入院人数,使病人流动更加顺畅。工作人员的个人绩效对入院率有很大影响,是有效建立入院人数模型的关键变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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