Abstract 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.
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