Cognitive Systems Engineering in Health Care by Ann M. Bisantz, Catherine M. Burns, Rollin J. Fairbanks

By Ann M. Bisantz, Catherine M. Burns, Rollin J. Fairbanks

Cognitive platforms engineering has been extensively and effectively utilized within the layout of defense severe platforms akin to nuclear strength, aviation, and armed forces command-and-control. extra lately, those equipment are being utilized to the layout of wellbeing and fitness and clinical structures so as to increase health and wellbeing care caliber, lessen mistakes and antagonistic occasions, and increase efficiencies. Cognitive platforms Engineering in wellbeing and fitness Care presents an outline of cognitive platforms engineering rules within the context of overall healthiness care. It comprises cutting-edge examples of cognitive structures purposes that may be tailored by means of future health care practitioners attracted to systematic engineering methods to structures improvement.

The ebook highlights present cognitive engineering-oriented examine, analyses, and purposes in settings comparable to cardiac surgical procedure, obstetrics, and emergency drugs. It specializes in the effect cognitive engineering analyses may have in assisting communique and coordination with health and wellbeing care groups. The textual content then demonstrates using cognitive engineering tips on how to tell the layout of data know-how. It then info the systematic edition and alertness of particular cognitive engineering equipment within the scientific area. The ebook concludes with examples of ways in-depth cognitive engineering analyses may end up in confirmed advancements in healthiness care environments.

Through a chain of pattern experiences, the booklet delivers a deeper figuring out of the way cognitive engineering ways will be utilized within the well-being care area. You’ll see universal subject matters that underline the complexity of the well-being care area and this perception can construct a deep recognize for the services of the practitioners who paintings in it. via determining the abstractions that carry consistent during this area, you could construct options for that would evolve to address new purposes, demanding situations, and approaches.

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Mahwah, NJ: Lawrence Erlbaum Associates. A. et al. (2012). Qualitative evaluation of a diabetes electronic decision support tool: Views of users. BMC Medical Informatics and Decision Making, 12, 61. L. (2009). What makes diagnosis hard? Advances in Health Science Education, 14(1), 19–25. J. (2007). Status boards in accident and emergency departments: Support for shared cognition. Theoretical Issues in Ergonomics Science, 8(5), 371–380. , and Fone, J. (2007). Emergency department status boards: User-evolved artefacts for inter- and intra-group coordination.

By examining the information needs, information currently available, and information gaps, designers are able to provide support for more complex macrocognitive skills. This is particularly important in health care where clinicians must respond to novel events, make important assessments and judgments with incomplete and ambiguous information, and manage conflicting goals and priorities. Many of the complex tasks health care professionals must accomplish can be informed by base rates and probabilities, but that is only one piece of the puzzle.

Designers of algorithmbased decision support seek innovative ways to reduce information in the environment to computable elements. Cognitive engineering, in contrast, seeks to identify the information needs required to complete key tasks in challenging situations—regardless of the form of that information. By examining the information needs, information currently available, and information gaps, designers are able to provide support for more complex macrocognitive skills. This is particularly important in health care where clinicians must respond to novel events, make important assessments and judgments with incomplete and ambiguous information, and manage conflicting goals and priorities.

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