Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Using Pervasive Computing to Deliver Elder Care
IEEE Pervasive Computing
Evaluation of an Infrared/Radiofrequency Equipment-Tracking System in a Tertiary Care Hospital
Journal of Medical Systems
Automated Analysis of Nursing Home Observations
IEEE Pervasive Computing
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Workflow modeling in critical care: Piecing together your own puzzle
Journal of Biomedical Informatics
Cognitive simulators for medical education and training
Journal of Biomedical Informatics
Deviations from protocol in a complex Trauma environment: Errors or innovations?
Journal of Biomedical Informatics
Guest Editorial: Biomedical Complexity and Error
Journal of Biomedical Informatics
LocTrackJINQS: An Extensible Location-aware Simulation Tool for Multiclass Queueing Networks
Electronic Notes in Theoretical Computer Science (ENTCS)
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Classification of surgical processes using dynamic time warping
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Hi-index | 0.00 |
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.