Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 111
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium Cognitive Engineering Applications in Health Care ANN M. BISANTZ University at Buffalo, State University of New York The goal of cognitive engineering is to support the cognitive activities associated with behavior, particularly in complex working environments, through the design of system components, such as user interfaces, automation, decision aids, and training. Health care is an environment with classic complexities—time pressure, risk, uncertainties, and many interacting components. The health care environment is further complicated by multiple levels or domains of concern. For instance, even an individual patient consists of numerous, interacting systems that may not all be well understood and for which only limited or indirect information may be available. The complexity of the patient domain is compounded by the complex sociotechnical working environment that addresses the patient’s needs—the health care system—which is comprised of many people working both individually and in teams, who must coordinate their actions and who have different, sometimes competing goals (e.g., health care providers vs. government regulators vs. insurance companies vs. hospital administrators). In the health care environment, individuals interact with a variety of information sources and technologies, ranging from handwritten charts to pagers and phones to electronic medical records and digital imaging systems. Resources in the health care environment, such as caregiver time
OCR for page 112
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
OCR for page 113
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium communication has been cited, for example, as a frequent cause of errors in the administration of medications (c.f. Rogers et al., 2004). Numerous cognitive engineering-oriented studies in medical environments have been conducted on communication functions, patterns, and sometimes breakdowns. For example, Fairbanks et al. (2007) described aspects of communication, such as the type of partner, communication mode (e.g., face-to-face, phone), duration, and location of communication in a hospital emergency department (ED). They shadowed 20 caregivers (including attending physicians, residents, ED nurses, and charge nurses) to construct networks showing the communication pathways radiating from, and connecting, caregivers. Results provided insights into typical patterns of communication and the individuals or positions that were key communication nodes in the ED. For instance, nurses played a central role in communication; most communication was face-to face; and overall, there were frequent communications of short duration. Potential gaps in information flow were also identified. For instance, triage nurses and ambulance personnel (emergency medical services [EMS]), who have initial contact with patients, were observed to communicate primarily with charge nurses (responsible for workflow and patient assignment) but not with the physicians who would care for the patients. This gap may indicate an opportunity for intervention, such as a change in training or procedures or the development of new technologies, such as real-time patient information systems that can be accessed by both EMS and ED staff. Similarly, Moss et al. (2002) characterized the mode, recipient, and topic of communications by an operating room charge nurse responsible for coordinating patient, surgical team, equipment, and room preparation; the goal of the study was to suggest how electronic scheduling systems could be shared and used effectively. Guerlain et al. (2007) found that training surgeons in specific types of communication and teamwork skills, such as methods of conducting pre-operative briefings, improved communication. Several studies have investigated communication strategies during shift changes and other transitions, when one group of caregivers must transfer information about patient status to another (Nemeth et al., 2006; Patterson et al., 2005; Sharit et al., 2005; Wears et al., 2003). Patterson et al. (2005) observed nurses during shift changes in acute-care units to identify the strategies and technologies they used to obtain necessary information. Audiotaped and face-to-face communications led to different strategies. For instance, if the information was audiotaped, incoming staff could not directly question outgoing staff; however, incoming nurses tended to listen to audiotaped information as a group and talk about the status of patients, which could result in a shared awareness of patient states and team coordination to meet patients’ needs. Wears et al. (2003) contrasted two transitions between ED physicians. In one, the transition was the source of error recovery because incoming physicians suggested an alternative, ultimately correct, diagnosis. In the second, poor com-
OCR for page 114
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium munication was the source of a breakdown because critical information about the state of a medication order was misunderstood, and an essential treatment was delayed. NEW TECHNOLOGIES AND UNINTENDED CONSEQUENCES Advanced technology has often been advocated as a way to reduce errors and adverse events in health care (Aspden et al., 2004; IOM, 1999, 2001). In many cases, however, new technologies are designed without an in-depth understanding of the work they need to support, or they are designed to address functions other than patient care (e.g., record keeping, billing). Unless the designers understand how new technologies will be used in practice and are aware of potential barriers to their use, these technologies can lead to unanticipated, undesirable consequences (Ash et al., 2004, 2007; Bisantz and Wears, 2008; Webster and Cao, 2006), such as increased workload (because of the need for new processes or workarounds to integrate them into the workflow), or serious safety compromises (if new systems are bypassed or abandoned or if critical tasks are interrupted). For instance, in a study of new operating room technology that integrated multiple monitoring systems into a single electronic display, Cook and Woods (1996) found that the change forced practitioners to adapt their activities, as well as some aspects of the new system, to ensure that the critical information was displayed at appropriate times. In another case, Patterson et al. (2002, 2006) studied unanticipated effects and workarounds developed after the implementation of a system intended to reduce errors by using bar codes on medications and patient wristbands to confirm the type, dosage, and timing of medication administration. Unanticipated effects included fewer physician reviews of current medications, because it was more difficult for them to access information in the computerized system than in the old paper record; and nurses feeling pressured to administer medication “on time,” even when other higher priority tasks were necessary (both of which increased the chances of adverse events). A key workaround was that nurses would type a patient’s bar code number into the system or scan a secondary wristband kept separate from the patient to save time and avoid several problems. First, the cart with the scanner was difficult to maneuver, and in some cases a computer had to be plugged in to maintain battery life. Second, they no longer had to disturb sleeping patients. Finally, scanning the second wristband was often more reliable than scanning the wristband on the patient, especially for long-term patients whose wristbands had become worn or smudged. In addition, nurses could “pre-pour” medications (place medications in cups for many patients at once, rather than scanning a wristband, scanning and administering medication(s), and moving to the next patient), to increase efficiency. Scanning medications in batches also made it more likely that medications
OCR for page 115
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium were recorded as administered “on time” (which eliminated the work associated with documenting late medications). In the end, although the bar code system could reduce the chances that the wrong type or dosage of medication would be chosen, the workarounds could increase the chances of a medication being given to the wrong patient. The researchers suggested both changes in the system design (e.g., simplifying the system interface; using wireless or easily maneuverable scanners; and using longer lasting computer batteries) and changes in procedures (e.g., using more realistic times for medication administration) that could reduce the likelihood of unanticipated effects or workarounds that would increase the chances of errors. LEARNING FROM EXISTING TOOLS AND TECHNOLOGIES Understanding how extant tools and artifacts work in a system is a critical step in designing new systems to support the functional purposes of an artifact, rather than merely duplicating its surface features (Nemeth, 2004; Pennathur et al., 2007; Xiao, 2005). Bauer et al. (2006) conducted a detailed analysis of an artifact used in intensive care to inform the design of an electronic system. The artifact, a patient flow sheet, is a paper form that accommodates both structured and unstructured data capture (e.g., grids for sequential vital signs and free-form notes). By observing the flow sheet in use, they were able to identify the characteristics that had to be included in an electronic system. Some features may not have been included if the new system had simply duplicated the surface features of the form. For instance, the paper form allowed information to be entered flexibly, rather than sequentially, allowed unstructured annotations (e.g., information did not have to be entered in a particular place or with keyboard characters), and allowed users to leave information out (for a discussion of the functionality of paper artifacts, see Sellen and Harper, 2003). The paper form also supported work because it was portable, grouped information in ways that allowed comparisons to be made easily, allowed flexible annotations to accommodate unique circumstances, and allowed data to be represented in familiar notation. An electronic system could provide additional functionality, such as automated data analysis and calculations, and could give multiple caregivers access to the information at the same time. However, the new technology still had to support flexibility in annotation and commonly used notations and comparisons. Some of our own work has focused on the implementation of new technologies in hospital emergency rooms (Pennathur et al., 2007, 2008a,b; Wears et al., 2005), where electronic patient-tracking systems are replacing manual status boards (“whiteboards”). Manual status boards (see Figure 1) provide medical and logistical information about patients and information about patient status (e.g., designated providers, treatment status, test and laboratory results, location), as well as higher level information about hospital states (e.g., number of patients
OCR for page 116
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium FIGURE 1 Manual whiteboard with the names of patients and providers obscured. Reprinted from Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2008. Reprinted with permission. in the ED, admitted patients still in the ED, available ED beds, rooms that need cleaning) and team coordination information (e.g., assignments of providers to patients or bed zones; status of on-call providers). Information on whiteboards is encoded in locally developed (e.g., by providers in the hospital or department) and locally meaningful ways. Whiteboards are used to track the process of patient care through annotations that indicate potential diagnoses, progress through treatment plans, the need for consultations or tests, and admission or discharge processes. Typically, they are located in central areas of the ED so that information is available to all care providers and can be used to coordinate activities across individuals and time (Figure 2). Electronic status boards may mimic the look and layout of manual boards (see Figure 3), support automated recording keeping and reporting, and allow information to be accessed at different locations in the hospital, but they also impose new constraints. The ability to add or change information is limited by available computer terminals, which typically require sign-on sequences; the form of information is limited to the characters or icons available on a keyboard or through the interface, and local methods of encoding are often lost; and the length and placement of entries is prescribed (e.g., free-form annotations cannot be added). We studied the transition from manual to electronic status boards in two university-affiliated, urban hospital EDs (Pennathur et al., 2007, 2008b; Wears
OCR for page 117
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium FIGURE 2 A whiteboard being viewed by multiple providers in an ED. FIGURE 3 Electronic patient-tracking system screen. Source: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2008. Reprinted with permission.
OCR for page 118
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium and Perry, 2007; Wears et al., 2005). One hospital had made the transition 10 months prior to our study but had continued to use manual boards along with the new system. We studied the second hospital before and after the transition. In this hospital, the manual boards were removed and replaced with the electronic system. We conducted a combination of semistructured interviews, focus groups, and observations with care providers, secretaries, information technology specialists, and administrators. We also took photographs or screen shots of the status boards at one hospital, so we could make detailed comparisons of the content and form of information in both systems. The results of our studies indicated a number of problems related to the transition to a new technology. Shortly after the electronic system was implemented at the second hospital, providers felt that the change had a negative impact on communication and their ability to “make sense” of the overall state of the ED, in part because the system could only be viewed on desktop screens, which had limited room for displaying information and limited flexibility for documenting information about treatment plans and diagnoses. For instance, a limited number of entries were visible in the column showing treatment plans, and providers could no longer use hand-drawn checkboxes to indicate progress. Because it was more difficult for providers to document and track patient progress, some providers resorted to carrying notes; this supported the work of individual providers, but the information was no longer publicly available, thus decreasing support for coordination among caregivers. The staff also found an unanticipated use for the system—tracking patients’ dietary needs and providing a printed list of diets to the meal-delivery staff. Although this function provided a benefit to some caregivers/staff, the constraints on space in the area where dietary information was entered meant that others could not use that space to display critical clinical information (e.g., lab values). In fact, at the first hospital, where both electronic and manual boards were used, clinicians tended to rely on the manual boards, while nonclinical staff used the electronic system for administrative functions, such as finding patients or assessing room status. Some of these difficulties could be traced to the particular implementation and interface for the system, but others were more fundamental (e.g., the removal of a public, easily modified information source that supported relatively simple coordination for each individual and among individuals). We subsequently decided to investigate the impact of electronic patient-tracking systems on caregivers’ understanding of the overall ED state, as well as specific patient information. We developed a simulation-based tracking system that allows system parameters to be varied and tested by ED staff in a laboratory setting (Pennathur et al., 2008a). Immersive, simulated environments like this are used by cognitive engineers in many domains, such as aviation and driving, to test the impact of technology designs, situations, and tasks on human operators’ activities and performance (Lee et al., 2002; Sarter and Woods, 2000).
OCR for page 119
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium The tracking simulation we developed is based on a discrete-event simulation model of a real hospital ED and incorporates both clinical information and operational information that can be used by study participants. Historic data on patient volume and the severity of their medical conditions were used to develop the model. This model was used to generate sets of patients with medical conditions of different levels of severity, process events (e.g., waiting, registration, triage, caregiver visits, and laboratory tests), and the duration of those events. The simulated information was augmented with demographic information, medical complaints, and time-indexed medical information (e.g., tests, results, admission decisions, and the resulting information that would be shown on a whiteboard) to create “scripts” for each simulated patient. Different scenarios were created based on different levels of demand for ED services. The scenarios were used as input to a patient-tracking display that was created for use by participants during experiments. The scenarios were augmented with secondary tasks (e.g., phone calls or pages that had to be answered) and simulation-freeze techniques for measuring participants’ awareness of information represented in the system (Endsley, 1995). This integrated experimental system can be used to test the impact of different display-related variables (e.g., display size, mode, and format of information); operational parameters (e.g., type of caregiver, number of patients); operational tasks (use of overall monitoring and monitoring during care transitions, such as a shift change); or how ED personnel interact with and interpret information on the electronic system. CONCLUSION The health care system has critical needs for improvements in efficiency, effectiveness, and safety. To meet those needs, we must first understand the complexities faced by health care workers and the knowledge, strategies, and tools they use. Cognitive engineering provides methods and tools for developing and implementing new technologies for this environment. ACKNOWLEDGMENTS Funding for the studies of ED patient-tracking systems has been provided by the Emergency Medicine Foundation and the Agency for Research on Health Care Quality (grant number 1 U18 HS016672).
OCR for page 120
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium REFERENCES Ash, J.S., M. Berg, and E.W. Coiera. 2004. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. Journal of the American Medical Informatics Association 11(2): 103–112. Ash, J.S., D.F. Sittig, R.H. Dykstra, K. Guappone, J.D. Carpenter, and V. Sehsadri. 2007. Categorizing the unintended sociotechnical consequences of computerized provider order entry. International Journal of Medical Informatics 76(Suppl. 1): S21–S27. Aspden, P., J.M. Corrigan, J. Wolcott, and S.M. Erickson, eds. 2004. Patient Safety: Achieving a New Standard for Care. Washington, D.C.: The National Academies Press. Bauer, D., S.A. Guerlain, and P.J. Brown. 2006. Evaluating the Use of Flowsheets in Pediatric Intensive Care to Inform Design. Pp. 1054–1058 in Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. Bisantz, A.M., and C.M. Burns, eds. 2008. Applications of Cognitive Work Analysis. Boca Raton, Fla.: CRC Press. Bisantz, A.M., and E.M. Roth. 2008. Analysis of Cognitive Work. Pp. 1–32 in Reviews of Human Factors and Ergonomics, Vol. 3, edited by D.A. Boehm-Davis. Santa Monica, Calif.: Human Factors and Ergonomics Society. Bisantz, A.M., and W.L. Wears. 2008. Forcing functions: the need for restraint. Annals of Emergency Medicine, forthcoming. Burns, C.M., Y. Enomoto, and K. Momtahan. 2008. A Cognitive Work Analysis of Cardiac Care Nurses Performing Teletriage. In Advances in Cognitive Work Analysis, edited by A.M. Bisantz and C.M. Burns. Boca Raton, Fla.: Taylor and Francis. Cook, R.I., and D.D. Woods. 1996. Adapting to new technology in the operating room. Human Factors 38(4): 593–613. Crandall, B., G.A. Klein, and R.R. Hoffman. 2006. Working Minds: A Practitioner’s Guide to Cognitive Task Analysis. Cambridge, Mass.: The MIT Press. Endsley, M. 1995. Measurement of situation awareness in dynamic systems. Human Factors 37(1): 65–84. Enomoto, Y., C.M. Burns, K. Momtahan, and W. Caves. 2006. Effects of Visualization Tools on Cardiac Telephone Consultation Process. Pp. 1044–1048 in Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. Fairbanks, R.J., A.M. Bisantz, and M. Sunm. 2007. Emergency department communication links and patterns. Annals of Emergency Medicine 50(4): 396–406. Guerlain, S.A., B.E. Turrentine, D.T. Bauer, J.F. Calland, and R. Adams. 2007. Crew resource management training for surgeons: feasibility and impact. Cognition, Technology & Work. Available online at <http://www.springerlink.com/content/a831373358120000/fulltext.pdf>. Hajdukiewicz, J., D.J. Doyle, P. Milgram, K.J. Vicente, and C.M. Burns. 1998. A Work Domain Analysis of Patient Monitoring in the Operating Room. Pp. 1034–1042 in Proceedings of the Human Factors and Ergonomics Society 44th Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. Hall, T.J., J.W. Rudolph, and C.G.L. Cao. 2006. Fixation and Attention Allocation and Anesthesiology Crisis Management: An Abstraction Hierarchy Perspective. Pp. 1064–1067 in Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. IOM (Institute of Medicine). 1999. To Err Is Human: Building a Safer Health System, edited by L.T. Kohn, J.M. Corrigan, and M.S. Donaldson. Washington, D.C.: National Academy Press. IOM. 2001. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C.: National Academy Press.
OCR for page 121
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium Lee, J.D., D.V. McGehee, T. Brown, and M. Reyes. 2002. Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high fidelity driving simulator. Human Factors 44(2): 314–334. Miller, A. 2004. A work domain analysis framework for modelling intensive care unit patients. Cognition Technology and Work 6(4): 207–222. Moss, J., Y. Xiao, and S. Zubaidah. 2002. The operating room charge nurse: coordinator and communicator. Journal of the American Medical Informatics Association 9(6 Suppl. 1): S70–S74. Nemeth, C.P. 2004. Using cognitive artifacts to understand distributed cognition. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 34(6): 726–735. Nemeth, C.P., J. Kowalsky, M. Brandwijk, M. Kahana, P.A. Klock, and R.I. Cook. 2006. Before I Forget; How Clinicians Cope with Uncertainty through ICU Sign-Outs. Pp. 939–943 in Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. Patterson, E.S., R.I. Cook, and M.L. Render. 2002. Improving patient safety by identifying side effects from introducing bar coding in medication administration. Journal of the American Medical Informatics Association 9(5): 540–553. Patterson, E.S., E.M. Roth, and M.L. Render. 2005. Handoffs During Nursing Shift Changes in Acute Care. Pp. 1057–1061 in Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. Patterson, E.S., M. Rogers, R.J. Chapman, and M.L. Render. 2006. Compliance with intended use of bar code medication administration in acute and long-term care: an observational study. Human Factors 48(1): 15–22. Pennathur, P., A.M. Bisantz, R.J. Fairbanks, S. Perry, F. Zwemer, and R.L. Wears. 2007. Assessing the Impact of Computerization on Work Practice: Information Technology in Emergency Departments. Pp. 377–381 in Proceedings of the Human Factors and Ergonomics Society 51st Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. Pennathur, P., D. Cao, A.M. Bisantz, L. Lin, J.L. Brown, R.J. Fairbanks, T.K. Guarrera, S. Perry, and R.L. Wears. 2008a. A Simulation Study of Patient Tracking Systems. Presentation at the International Conference on Healthcare Systems, Ergonomics, and Patient Safety (HEPS). Strasbourg, France, June 25–27, 2008. Pennathur, P., T.K. Guarrera, A.M. Bisantz, R.J. Fairbanks, S. Perry, and R.L. Wears. 2008b. Cognitive Artifacts in Transition: An Analysis of Information Content Changes between Manual and Electronic Patient Tracking Systems. In Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting (accepted). Santa Monica, Calif.: Human Factors and Ergonomics Society. Rasmussen, J., A.M. Pejtersen, and L.P. Goodstein. 1994. Cognitive Systems Engineering. New York: Wiley and Sons. Rogers, M., R.L. Cook, R. Bower, M. Molloy, and M.L. Render. 2004. Barriers to implementing wrong site surgery guidelines: a cognitive work analysis. IEEE Transactions on Systems, Man, and Cybernetics: Part A: Systems and Humans 34(6): 757–763. Sarter, N.B., and D.D. Woods. 2000. Team play with a powerful and independent agent: a full mission simulation study. Human Factors 42(3): 390–402. Sellen, A.J., and R.H.R. Harper. 2003. The Myth of the Paperless Office. Cambridge, Mass.: The MIT Press. Sharit, J., L. McCane, D.M. Thevenin, and P. Barach. 2005. Examining Issues in Communicating Patient Care Information across Shifts in a Critical Care Setting. Pp. 1062–1066 in Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society.
OCR for page 122
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2008 Symposium Sharp, T.D., and A.J. Helmicki. 1998. The Application of the Ecological Interface Design Approach to Neonatal Intensive Care Medicine. Pp. 350–354 in Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. Vicente, K.J. 1999. Cognitive Work Analysis. Mahwah, N.J.: Erlbaum. Watson, M.O., and P. Sanderson. 2007. Designing for attention with sound: challenges and extensions to ecological interface design. Human Factors 49(2): 331–346. Wears, R.L., and S. Perry. 2007. Status boards in accident and emergency departments: support for shared cognition. Theoretical Issues in Ergonomics Science 8(5): 371–380. Wears, R.L., S. Perry, M. Shapiro, C. Beach, P. Croskerry, and R. Behara. 2003. Shift Changes among Emergency Physicians: Best of Times, Worst of Times. Pp. 1420–1423 in Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting. Santa Monica, Calif.: Human Factors and Ergonomics Society. Wears, R.L., A.M. Bisantz, S. Perry, and R.J. Fairbanks. 2005. Consequences of Technical Change in Cognitive Artefacts for Managing Complex Work. Pp. 317–322 in Human Factors in Organizational Design and Management-VIII, edited by P. Carayon, M. Robertson, B.M. Kleiner, and P.L.T. Hoonakker. Santa Monica, Calif.: IEA Press. Webster, J.L., and C.G.L. Cao. 2006. Lowering communication barriers in operating room technology. Human Factors 48(4): 747–759. Xiao, Y. 2005. Artifacts and collaborative work in healthcare: methodological, theoretical, and technological implications of the tangible. Journal of Biomedical Informatics 38(1): 26–33.