ABSTRACT
Of all the duties performed by the critical care team in a hospital intensive care unit (ICU), a primary duty is the morning attending rounds. During and following the rounds, the ICU team devises a 24-hour plan of action comprised of patient-centered tasks. The aim of this doctoral research is to: (1) design and evaluate a novel task management tool that addresses breakdowns in critical care workflow and (2) introduce a new task management notification tool that mitigates workflow breakdowns by identifying the nature and type of notification/alert sent to the clinical team.
- Joint Commission Measure Reserve Library, (2010). Specifications Manual for National Hospital Quality Measures - ICU. Retrieved from http://www.jointcommission.org/PerformanceMeasurement/MeasureReserveLibrary/Spec+Manual+-+ICU.htmGoogle Scholar
- Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group, National Health Care Expenditures Data, January 2012.Google Scholar
- Winters, Bradford, et al. "Diagnostic errors in the intensive care unit: a systematic review of autopsy studies." BMJ quality & safety (2012).Google Scholar
- Khairat, S., and Yang Gong. "Enhancing patient safety through clinical communication knowledge representation." e-Health Networking Applications and Services (Healthcom), 2011 13th IEEE International Conference on. IEEE, 2011.Google Scholar
- Pryss, Rüdiger, et al. "Mobile Task Management for Medical Ward Rounds-The MEDo Approach." Business Process Management Workshops. Springer Berlin Heidelberg, 2013.Google Scholar
- Reader, Tom W., et al. "Developing a team performance framework for the intensive care unit*." Critical care medicine 37.5 (2009): 1787--1793.Google ScholarCross Ref
- Lucchiari, Claudio, and Gabriella Pravettoni. "The role of patient involvement in the diagnostic process in internal medicine: a cognitive approach." European journal of internal medicine 24.5 (2013): 411--415.Google Scholar
- O'Connor, Chris, et al. "The use of wireless e-mail to improve healthcare team communication." Journal of the American Medical Informatics Association 16.5 (2009), 705--713.Google ScholarCross Ref
Index Terms
- Modeling Clinical Workflow in Daily ICU Rounds to Support Task-based Patient Monitoring and Care
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