A distributed representation approach to group problem solving
Journal of the American Society for Information Science - Special issue on user-centered cooperative systems
Putting ethnography to work: the case for a cognitive ethnography of design
International Journal of Human-Computer Studies - Understanding work and designing artefacts
A cognitive taxonomy of medical errors
Journal of Biomedical Informatics
Human-centered design of a distributed knowledge management system
Journal of Biomedical Informatics - Special issue: Human-centered computing in health information systems. Part 1: Analysis and design
Journal of Biomedical Informatics - Special issue: Human-centered computing in health information systems. Part 1: Analysis and design
Artificial Intelligence in Medicine
The adolescence of AI in Medicine: Will the field come of age in the '90s?
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
Toward formal models of biologically inspired, highly parallel machine cognition
International Journal of Parallel, Emergent and Distributed Systems
Manual Collaboration Systems: Decision Support or Support for Situated Choices
Proceedings of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges
Intra-operative decision making: More than meets the eye
Journal of Biomedical Informatics
Recovery at the edge of error: Debunking the myth of the infallible expert
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Towards personalized decision support in the dementia domain based on clinical practice guidelines
User Modeling and User-Adapted Interaction
Considering complexity in healthcare systems
Journal of Biomedical Informatics
Understanding infusion administration in the ICU through Distributed Cognition
Journal of Biomedical Informatics
Small worlds and Red Queens in the Global Workspace: An information-theoretic approach
Cognitive Systems Research
Distributed cognition for evaluating healthcare technology
BCS-HCI '11 Proceedings of the 25th BCS Conference on Human-Computer Interaction
Artificial Intelligence in Medicine
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Objective: The complex cognitive processes that underlie human performance in 'messy' contexts such as critical care medicine suggest a need for a cognitive model with broad scope to support the understanding of error in such domains. The objective of this research is to characterize the cognition that underlies patient care in the domain of emergency psychiatry in order to enhance the understanding of error in this context. Methods and materials: The theoretical framework of distributed cognition has been used to study collaborative decision-making in a number of similarly complex environments such as airline cockpits and air traffic control towers. These environments share certain characteristics with the critical care domain: the work is collaborative in nature, it is supported by artifacts that can be studied directly, and the consequences of error are dire. However, the nature of the work in this domain and the artifacts used to support it are unique. The application of the theoretical constructs of distributed cognition to this context is necessary in order to characterize the collective thinking that underlies critical care. Our research uses a combination of ethnographic and interview data to derive a distributed cognitive model of the psychiatric emergency department (PED), a high volume clinical unit dealing exclusively with the acute phases of psychiatric crises. The dynamics of workflow within the department are complex: several types of clinician collaborate by forming temporary multidisciplinary teams that attach to and manage particular patients. The component members of these teams change over time. Results: Using the theoretical framework of distributed cognition, we interpreted the collected data to derive a cognitive model of the distribution of work and information flow in the PED. This modeling process has revealed several latent flaws in the system related to the underlying distribution of cognition across teams, time, space and artifacts. Conclusions: The characterization of this distribution has enhanced our understanding of the cognitive dynamics underlying error in this environment, and will serve to guide future research on error management in the ED and inform the development of context-appropriate error-management systems.