Heuristic evaluation, when modified for medical devices, is a useful, efficient, and low cost method for evaluating patient safety features of medical devices through the identification of usability problems and their severities.

Numerous health care systems are designed without consideration of user-centered design guidelines. Consequently, systems are created ad hoc, users are dissatisfied and often systems are abandoned. This is not only a waste of human resources, but economic resources as well. In order to salvage such systems, we have combined different methods from the area of computer science, cognitive science, psychology, and human-computer interaction to formulate a framework for guiding the redesign process. The paper provides a review of the different methods involved in this process and presents a life cycle of our redesign approach. Following the description of the methods, we present a case study, which shows a successfully applied example of the use of this framework. A comparison between the original and redesigned interfaces showed improvements in system usefulness, information quality, and interface quality.

An interruption was found to have no consistent definition in either healthcare or nonhealthcare literature. Walker and Avant's 8-step method of concept analysis was used to clarify, define, and develop a conceptual model of interruption. The analysis led to the identification of 5 defining attributes that include (1) a human experience; (2) an intrusion of a secondary, unplanned, and unexpected task; (3) discontinuity; (4) externally or internally initiated; and (5) situated within a context. Use of the defining attributes will be extended to form a category of interruption within a taxonomy of activity.

Background-The emergency department has been characterized as interrupt-driven. Government agencies and patient safety organization recognize that interruptions contribute to medical errors. The purpose of this study was to observe, record, and contextualize activities and interruptions experienced by physicians and Registered Nurses (RNs) working in a Level One Trauma Center.

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

Theoretically, the proposed cognitive taxonomy provides a method to systematically categorize medical errors at the individual level along cognitive dimensions, leads to a better understanding of the underlying cognitive mechanisms of medical errors, and provides a framework that can guide future studies on medical errors. Practically, it provides guidelines for the development of cognitive interventions to decrease medical errors and foundation for the development of medical error reporting system that not only categorizes errors but also identifies problems and helps to generate solutions. To validate this model empirically, we will next be performing systematic experimental studies.

n recent years there has been increasing interest in describing complicated information processing systems in terms of the knowledge they have, rather than by the details of their implementation. This requires a means of modeling the knowledge in a system. Several different approaches to knowledge modeling have been developed by researchers working in Artificial Intelligence (AI). Most of these approaches share the view that knowledge must be modeled with respect to a goal or task. In this article, we outline our modeling approach in terms of the notion of a task-structure, which recursively links a task to alternative methods and to their subtasks. Our emphasis is on the notion of modeling domain knowledge using tasks and methods as mediating concepts. We begin by tracing the development of a number of different knowledge-modeling approaches. These approaches share many features, but their differences make it difficult to compare systems that have been modeled using different approaches. We present these approaches and describe their similarities and differences. We then give a detailed description, based on the task structure, of our knowledge-modeling approach and illustrate it with task structures for diagnosis and design. Finally, we show how the task structure can be used to compare and unify the other approaches.

Providing timely and effective care in the emergency department (ED) requires the management of individual patients as well as the flow and demands of the entire department. Strategic changes to work processes, such as adding a flow coordination nurse or a physician in triage, have demonstrated improvements in throughput times. However, such global strategic changes do not address the real-time, often opportunistic workflow decisions of individual clinicians in the ED. We believe that real-time representation of the status of the entire emergency department and each patient within it through information visualizations will better support clinical decision-making in-the-moment and provide for rapid intervention to improve ED flow. This notion is based on previous work where we found that clinicians' workflow decisions were often based on an in-the-moment local perspective, rather than a global perspective. Here, we discuss the challenges of designing and implementing visualizations for ED through a discussion of the development of our prototype Throughput Dashboard and the potential it holds for supporting real-time decision-making.