Clinical work is characterised by frequent interjection of external prompts causing clinicians to switch from a primary task to deal with an incoming secondary task, a phenomenon associated with negative effects in experimental studies. This is an important yet underexplored aspect of work in safety critical settings in general, since an increase in task length due to task‐switching implies reduced efficiency, while decreased length suggests hastening to compensate for the increased workload brought by the unexpected secondary tasks, which is a potential safety issue. In such observational settings, longer tasks are naturally more likely to have one or more task‐switching events: a form of length bias. To assess the effect of task‐switching on task completion time, it is necessary to estimate counterfactual task lengths had they not experienced any task‐switching, while also accounting for length bias. This is a problem that appears simple at first, but has several counterintuitive considerations resulting in a uniquely constrained solution space. We review the only existing method based on an assumption that task‐switches occur according to a homogeneous Poisson process. We propose significant extensions to flexibly incorporate heterogeneity that is more representative of task‐switching in real‐world contexts. The techniques are applied to observations of emergency physicians’ workflow in two hospital settings.

中文翻译：

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在存在重复的长度偏向采样的情况下检测由于任务切换而导致的任务长度变化

临床工作的特点是频繁插入外部提示，导致临床医生从主要任务切换为处理即将到来的次要任务，这种现象与实验研究的负面影响有关。一般而言，这是安全关键环境下工作的一个重要但尚未充分探索的方面，因为由于任务切换而导致的任务长度增加意味着效率降低，而长度减少则意味着必须尽快弥补意外的次要任务带来的工作量增加，是潜在的安全问题。在这样的观察环境中，较长的任务自然更可能发生一个或多个任务切换事件：一种长度偏差形式。要评估任务切换对任务完成时间的影响，如果他们没有经历任何任务切换，则有必要估算反事实任务的长度，同时还要考虑长度偏差。这个问题一开始看起来很简单，但是有一些违反直觉的考虑，导致解决方案空间受到唯一约束。我们基于任务切换是根据同类Poisson过程发生的假设，回顾了现有的唯一方法。我们提出了重要的扩展，以灵活地融合异构性，从而更能代表现实环境中的任务切换。该技术适用于在两个医院环境中观察急诊医生的工作流程。但是有一些违反直觉的考虑，导致了唯一受约束的解决方案空间。我们基于任务切换是根据同类Poisson过程发生的假设，回顾了现有的唯一方法。我们提出了重要的扩展，以灵活地融合异构性，从而更能代表现实环境中的任务切换。该技术适用于在两个医院环境中观察急诊医生的工作流程。但是有一些违反直觉的考虑，导致了唯一受约束的解决方案空间。我们基于任务切换是根据同类Poisson过程发生的假设，回顾了现有的唯一方法。我们提出了重要的扩展，以灵活地融合异构性，从而更能代表现实环境中的任务切换。该技术适用于在两个医院环境中观察急诊医生的工作流程。