Toward automated workflow analysis and visualization in clinical environments

Abstract

Lapses in patient safety have been linked to unexpected perturbations in clinical workflow. The effectiveness of workflow analysis becomes critical to understanding the impact of these perturbations on patient outcome. The typical methods used for workflow analysis, such as ethnographic observations and interviewing, are limited in their ability to capture activities from different perspectives simultaneously. This limitation, coupled with the complexity and dynamic nature of clinical environments makes understanding the nuances of clinical workflow difficult. The methods proposed in this research aim to provide a quantitative means of capturing and analyzing workflow. The approach taken utilizes recordings of motion and location of clinical teams that are gathered using radio identification tags and observations. This data is used to model activities in critical care environments. The detected activities can then be replayed in 3D virtual reality environments for further analysis and training. Using this approach, the proposed system augments existing methods of workflow analysis, allowing for capture of workflow in complex and dynamic environments. The system was tested with a set of 15 simulated clinical activities that when combined represent workflow in trauma units. A mean recognition rate of 87.5% was obtained in automatically recognizing the activities.