Respiratory Medicine, Meir Medical Center, Kfar Saba, Israel
Department of Internal Medicine A, Meir Medical Center, Kfar Saba, Israel.
BMJ Health Care Inform. 2024 Sep 17;31(1):e101080. doi: 10.1136/bmjhci-2024-101080.
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.
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.
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.
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.
医院人满为患与一系列不良事件有关。急诊科（ED）的不当决策导致了过度拥挤。作为入院团队的一部分，每位医生的表现都是决定总体入院率的关键因素。虽然之前有尝试基于一系列变量来预测入院人数，但都没有包括对个别员工绩效的衡量。我们构建了可靠的员工绩效客观衡量标准，并将这些标准与其他因素一起用于预测每日入院人数。这种建模将使劳动力规划得到增强，并能及时进行干预，以减少不适当的入院和过度拥挤。
创建了一个包含 232245 例在以色列中部 Meir 医疗中心就诊的急诊科就诊的数据库，涵盖了 2016 年至 2021 年的数据。我们使用了几种医生绩效衡量标准，以及历史病例数据和其他变量，以推导出用于预测 ED 到达和入院人数的统计模型。
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 MAPE 为 10.6%，并提供了一个当日预警，以 75%的灵敏度和 20%的假阳性率来预测大量的入院负担。包括医生绩效衡量标准可以极大地提高模型性能。
到达人数和入院人数可以预测得足够准确，从而能够进行干预以减少多余的入院人数并使患者流量更加平稳。个别员工的绩效对入院率有很大的影响，是有效预测入院人数的关键变量。
