Automated Workflow Analysis and Tracking Using Radio Frequency Identification Technology

The health care industry faces a number of challenges and arguably one of the most important ones lies in maintaining high levels of patient safety. A much-cited report released by the Institute of Medicine [1] estimates that as many as 98,000 people die

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Automated Workflow Analysis and Tracking Using Radio Frequency Identification Technology Mithra Vankipuram, Thomas G. Kannampallil, Zhe (Eric) Li, and Kanav Kahol

Introduction The health care industry faces a number of challenges and arguably one of the most important ones lies in maintaining high levels of patient safety. A much-cited report released by the Institute of Medicine [1] estimates that as many as 98,000 people die each year due to medical errors [1]. The causal determinants of these errors can be traced to a variety of medical, cognitive and social challenges in the clinical workplace. These challenges are exacerbated in critical care environments that are characterized by distributed, interdependent, episodic and non-linear work activities. The dynamic nature of the care process in critical care environment affects the nature and timing of work activities of clinicians, and often increases the possibility

Portions of this chapter has appeared in (a) Vankipuram et al., Toward automated workflow analysis and visualization in clinical environments. Journal of Biomedical Informatics. 44(3): 432–440, with permissions from Elsevier; (b) Kannampallil et al., Making sense: sensor-based investigation of clinician activities in complex critical care environments, Journal of Biomedical Informatics. 44(3), 441–454, with permissions from Elsevier and (c) an article in the Proceedings of the 2009 Annual Symposium American Medical Informatics Association, Vankipuram et al., Visualization and analysis of activities in critical care environments. M. Vankipuram, MS, PhD (*) Analytics Lab, Hewlett-Packard (HP) Labs, Palo Alto, CA 94304, USA e-mail: [email protected] T.G. Kannampallil Center for Cognitive Studies in Medicine and Public Health, New York Academy of Medicine, New York, NY 10029, USA Z. (Eric) Li, MS Drchrono Inc., Mountain View, CA 94043, USA K. Kahol, PhD Affordable Health Technologies Division, Public Health Foundation of India, New Delhi, India V.L. Patel et al. (eds.), Cognitive Informatics in Health and Biomedicine, Health Informatics, DOI 10.1007/978-1-4471-5490-7_17, © Springer-Verlag London 2014

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of errors. Studying the work activities of clinicians in such environments can help in understanding the care delivery process, workflow, and interruptions that affect clinical work. Exploratory investigations of clinician activities are often performed using observational methods. While these methods provide a descriptive depth that cannot be matched by automated methods, use of participant observation methods [2, 3] in a critical care setting is often challenging, as capturing the work activities of multiple clinicians requires several observers who must be closely synchronized during their data capture sessions. The tools currently used for workflow analysis in clinical environments include methods such as ethnographic observation, shadowing of individual clinicians, surveys and questionnaires [4]. The data collected by these methods can be used to model work