Department of Decision Analytics and Risk, University of Southampton, Centre for Risk Research, Southampton, UK.
South Central Ambulance Service, NHS Foundation Trust, Southern House, Otterbourne, Sparrowgrove, UK.
Risk Anal. 2022 Aug;42(8):1834-1851. doi: 10.1111/risa.13909. Epub 2022 Mar 14.
Medical emergency response staff are exposed to incidents which may involve high-acuity patients or some intractable or traumatic situations. Previous studies on emergency response staff stress-related absence have focused on perceived factors and their impacts on absence leave. To date, analytical models on absenteeism risk prediction use past absenteeism to predict risk of future absenteeism. We show that these approaches ignore environment data, such as stress factors. The increased use of digital systems in emergency services allows us to gather data that were not available in the past and to apply a data-driven approach to quantify the effect of environment variables on the risk of stress-related absenteeism. We propose a two-stage data-driven framework to identify the variables of importance and to quantify their impact on medical staff stress-related risk of absenteeism. First, machine learning techniques are applied to identify the importance of different stressors on staff stress-related risk of absenteeism. Second, the Cox proportional-hazards model is applied to estimate the relative risk of each stressor. Four significant stressors are identified, these are the average night shift, past stress leave, the squared term of death confirmed by the Emergency Services and completion of the safeguarding form. We discuss counterintuitive results and implications to policy.
医疗应急响应人员会面临可能涉及高风险患者或某些棘手或创伤性情况的事件。之前关于应急响应人员与压力相关缺勤的研究主要集中在感知因素及其对缺勤的影响上。迄今为止，关于旷工风险预测的分析模型使用过去的旷工来预测未来旷工的风险。我们表明，这些方法忽略了环境数据，例如压力因素。数字系统在应急服务中的广泛使用使我们能够收集过去无法获得的数据，并采用数据驱动的方法来量化环境变量对与压力相关的旷工风险的影响。我们提出了一个两阶段的数据驱动框架，以确定重要变量，并量化它们对医疗人员与压力相关的旷工风险的影响。首先，应用机器学习技术来确定不同压力源对员工与压力相关的旷工风险的重要性。其次，应用 Cox 比例风险模型来估计每个压力源的相对风险。确定了四个重要的压力源，分别是平均夜班、过去的压力休假、由紧急服务部门确认的死亡平方项和完成保障表格。我们讨论了违反直觉的结果和对政策的影响。
