Interruption Management in the Intensive Care Unit: Predicting Resumption Times and Assessing Distributed Support

Interruptions are frequent in many work domains. Researchers in health care have started to study interruptions extensively, but their studies usually do not use a theoretically guided approach. Conversely, researchers conducting theoretically rich laboratory studies on interruptions have not usually investigated how effectively their findings account for humans working in complex systems such as intensive care units. In the current study, we use the memory for goals theory and prospective memory theory to investigate which properties of an interruption influence how long it takes nurses to resume interrupted critical care tasks. We collected data with a mobile eye tracker in an intensive care unit and developed multiple regression models to predict resumption times. In 55.8% of all interruptions there was a finite and therefore analyzable-resumption lag. For these cases, the main regression model explained 30.9% (adjusted R-2) of the variance. Longer interruptions (beta = .36, p < .001) and changes in physical location due to interruptions (beta = .40, p < .001) lengthened the resumption lag. We also calculated regression models on subsets of the data to investigate the generality of the above findings across different situations. In a further 37.6% of all interruptions, nurses used behavioral strategies that greatly diminished or eliminated individual prospective memory demands caused by interruptions, resulting in no analyzable resumption lag. We introduce a descriptive model that accounts for how nurses' behaviors affect the cognitive demand of resuming an interrupted task. Finally, we discuss how the disruptive effects of interruptions in the intensive care unit could be diminished or prevented.