. "Cognitive Engineering Applications in Health Care--Ann M. Bisantz." Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium. Washington, DC: The National Academies Press, 2009.
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Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium
and hospital beds, are limited, and demands on the system (i.e., incoming patients and their conditions) are unpredictable.
Methods in cognitive engineering have been developed to uncover and represent both complexities in high-consequence fields such as health care and the knowledge and strategies experienced practitioners use to perform successfully (Bisantz and Burns, 2008; Bisantz and Roth, 2008; Crandall et al., 2006; Vicente, 1999). The results of cognitive engineering analyses can have a critical impact on the design of information, tasks, and training that will enhance, rather than disrupt, successful work practices and allow practitioners to respond appropriately to diverse, unpredictable events.
Cognitive engineering research in health care environments, which has a general goal of supporting safe and effective performance, has followed different research paths, including (1) characterizing complexities in the environment and demands on practitioners, sometimes with a focus on preventing medical errors; and (2) focusing on the design and/or impacts of new technologies. Understanding demands on practitioners, the strategies they use to meet those demands, and the role of information from different sources and technologies in work practice is essential to designing new information systems that can improve patient care.
CHARACTERIZING COMPLEXITY: SYSTEM STRUCTURE,STRATEGIES, AND COMMUNICATION
A common method of representing the complexities of the work domain (i.e., the abstraction hierarchy, see Rasmussen et al., 1994; Vicente, 1999) is to represent high-level goals, balances and priorities, processes, and physical structures. In the individual patient system, for instance, researchers have modeled physiological functions and anatomical structures, as well as methods of controlling them, to support diagnostic decision making, understand information needs among clinicians, and design monitoring displays (Hajdukiewicz et al., 1998; Miller, 2004; Sharp and Helmicki, 1998; Watson and Sanderson, 2007).
Enomoto et al. (2006) and Burns et al. (2008) conducted a study of the tasks of cardiac-care telehealth nurses, as well as the underlying patient structure and processes, to identify the challenges they faced and the strategies they used in diagnosing cardiac patients based on phone interviews. Various innovative visualizations were designed and tested, alternately emphasizing mapping symptoms to diagnoses, clusters of co-occurring symptoms, and symptom severity. Hall et al. (2006) used similar techniques to simultaneously represent aspects of a surgical team, the patient, and the equipment used to compare problem-solving strategies used by anesthesiologists.
A particular complexity of interest in medicine is the need for multiple individuals (e.g., physicians, nurses, technicians, support staff) to communicate with each other to coordinate patient care, particularly in hospital settings. Poor