Ecological interface design (EID) is a theoretical framework for designing human-computer interfaces for complex sociotechnical systems. Its primary aim is to support knowledge workers in adapting to change and novelty. This literature review shows that in situations requiring problem solving, EID improves performance when compared with current design approaches in industry. EID has been applied to industry-scale problems in a broad variety of application domains (e.g., process control, aviation, computer network management, software engineering, medicine, command and control, and information retrieval) and has consistently led to the identification of new information requirements. An experimental evaluation of EID using a full-fidelity simulator with professional workers has yet to be conducted, although some are planned. Several significant challenges remain as obstacles to the confident use of EID in industry. Promising paths for addressing these outstanding issues are identified. Actual or potential applications of this research include improving the safety and productivity of complex sociotechnical systems.

Ecological Interface Design: Progress and Challenges

Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or not-for-profit purposes without prior permission or charge, provided: • The authors, title and full bibliographic details is credited in any copy; • A hyperlink and/or URL is included for the original metadata page; and • The content is not changed in any way. Cognitive engineering is the application of cognitive psychology and related disciplines to the design and operation of human-machine systems. Cognitive engineering combines both detailed and close study of the human worker in the actual work context and the study of the worker in more controlled environments. Cognitive engineering combines multiple methods and perspectives to achieve the goal of improved system performance. Given the origins of experimental psychology itself in issues regarding the design of human-machine systems, cognitive engineering is a core, or fundamental, discipline within academic psychology. Cognitive Engineering is an applied cognitive science, or as Woods and Roth state Cognitive Egieeig, is aa applied cognitive science that draws on the knowledge and techniques of cognitive psychology and related disciplines to provide the foundation for principle-driven design of person-aahie sstes 'ef , p.. The fous is o the appliatio of ogitive psychology to the design and construction of machines or human-machine systems. 2 While often described as the application of cognitive psychology in particular, most researchers in the field of Cognitive Engineering take an eclectic or ecumenical approach, borrowing from other disciplines as necessary, for example, anthropology and sociology. This interdisciplinary orientation enriches the methods available to cognitive engineers. Cognitive psychology, or applied experimental psychology, has strengths in the quantification of behaviour and experimental methods, but these can be supplemented with the qualitative and field methods developed by anthropologists and sociologists. 3 Cognitive Engineering is also best regarded as a goal-directed activity. As Norman relates, the goal of Cogitie Egieeig is to oe to udestaad the issues, to sho ho to ake ette choices when they exist, and to show what the tradeoffs are when, as is the usual case, an improvement in one domain leads to deficits in anothe 'ef , p.. To aaoplish this goal, cognitive engineers must first understand the human-machine system. This entails the need to become familiar with the specific work setting being examined, whether …

The genome and developmental transcriptome of the strongylid nematode Haemonchus contortus

: This report contributes to the development of a coherent theoretical and methodological approach for work domain analysis (WDA), the first phase of cognitive work analysis The report: (1) addresses a number of conceptual issues relating to WDA, including differences in the approaches of Rasmussen, Pejtersen, and Goodstein (1994) and Vicente (1999); (2) proposes a methodology for performing WDA; and (3) illustrates the theoretical concepts and methodology for WDA with a work domain of a home - a `system' that will be highly familiar to everyone This research will help to: make WDA more accessible to researchers and practitioners who were not involved in the development of WDA or who cannot be apprenticed to experts in WDA; reduce the amount of time and effort it takes to perform WDA even for experts in WDA; and facilitate the application of WDA to large-scale industry projects In addition, by making the methodology for WDA more explicit, this research will allow the methodology, or at least parts of the methodology, to be tested empirically

/pdf/work-domain-analysis-theoretical-concepts-and-methodology-43d3zhx39j.pdf

Work Domain Analysis: Theoretical Concepts and Methodology

Modeling a medical environment: an ontology for integrated medical informatics design.

Analysis of 145 reports of adverse occurrences involving patients in a medical-surgical intensive care unite (ICU), during the yearts 1974 through 1978, disclosed 92 instances of human error and 53 cases of equipment malfunction. A peak occurrence of reported incidents was found between midnight and 1 AM. Harm occurred more frequently if the patient was unattended (72%) than attended (49%) during the incident. Mortality for patients with an incident report filed during their ICU admission (41%) was higher than for all ICU patients (21%). The importance of a well-structured incident-reporting program to minimize problems of human error and device malfunction is stressed.

Adverse Occurrences in Intensive Care Units

A framework for a design methodology for ecological decision support systems is proposed. Specifically, a methodology is proposed to study and model unfamiliar man-made environments in a manner that supports efforts to create decision support systems. This methodology is demonstrated by the development of a clinical decision support system for neonatal intensive care. The resulting decision support system is shown to significantly improve the diagnostic accuracy of physicians in a simulated intensive care environment. This methodology is applicable to other complex man-made work environments.
Through evolution humans have developed a great ability to work in natural environments that are filled with probabilistic patterns of events. These patterns are what allow us to have meaningful interactions with our environment. However, things are changing people are now finding themselves in artificial man-made environments. These artificial environments are still governed by probabilistic patterns, however, the character of such patterns are not always natural to, and are sometimes hidden from, the people in the environments. Should people evolve new abilities to deal with these new environments? No, what must happen is that these man-made work environments must evolve to take advantage of people's natural ability.
One approach to address this problem is to design decision support systems that match the work environment to the cognitive resources of the people in the environment. The design of these systems is not easy, particularly in the cases were the developer is not an expert in the field. In this case the developer does not understand the probabilistic patterns that are important in the environment. To address these issues a control system theoretic framework is proposed. Appropriate model structures and data collection techniques are discussed. This combination of appropriate model structures and data collection techniques allow for work environments to be modeled in a manner that is useful for the design of decision support systems.
To demonstrate this methodology a clinical decision support system was developed for neonatal intensive care. This system was then empirically evaluated by comparing it to the existing environment. The support system was shown to significantly improve the diagnostic accuracy of physicians. This effect was most consistent with less experienced physicians, every physician in this group performed better with the new support system. Furthermore, the vast majority of the physicians preferred using the new support system.
This dissertation proposes a framework for this modeling methodology and explores several of its components. The ecological decision support systems developed by this framework have the potential to provide significant cost and time benefits in many different fields, for example, clinical decision support systems, executive decision support system, industrial process control. The potential field of applicability is large.

Progress towards a development methodology for decision support systems for use in time-critical, highly uncertain, and complex environments

Ecological interface design techniques were used to first model and then develop an interface to help clinicians assess tissue oxygenation in a neonatal intensive care unit. Several challenges were encountered during this work. The abstraction hierarchy model structure that was used has limitations in this environment. The first limitation concerned the depth of knowledge that was available. Tissue oxygenation is a complex, and not totally understood process. Furthermore, it is truly distributed in nature. Both these limit the level of detail that could be included in the system model. Second, in this environment the sensor suite is very limited and is defined a-priori. Therefore, many of the variables that were identified as important in the abstraction hierarchy model are impossible to measure. Despite these limitations, the interface that resulted from the abstraction hierarchy model compared favorably to the existing interface in a simulated clinical environment.

/pdf/the-application-of-the-ecological-interface-design-approach-3s4vs4zzrh.pdf

The Application of the Ecological Interface Design Approach to Neonatal Intensive Care Medicine

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Progress towards a development methodology for decision support systems for use in time-critical, highly uncertain, and complex environments

The Application of the Ecological Interface Design Approach to Neonatal Intensive Care Medicine