OBJECTIVE: Advances in healthcare information technology have provided opportunities to present data in new, more effective ways. In this study, we designed a laboratory display that features small, data-dense graphics called sparklines, which have recently been promoted as effective representations of medical data but have not been well studied. The effect of this novel display on physicians' interpretation of data was investigated. DESIGN: Twelve physicians talked aloud as they assessed laboratory data from four patients in a pediatric intensive care unit with the graph display and with a conventional table display. MEASUREMENTS: Verbalizations were coded based on the abnormal values and trends identified for each lab variable. The correspondence of interpretations of variables in each display was evaluated, and patterns were investigated across participants. Assessment time was also analyzed. RESULTS: Physicians completed assessments significantly faster with the graphical display (3.6 min vs 4.4 min, p=0.042). When compared across displays, 37% of interpretations did not match. Graphs were more useful when the visual cues in tables did not provide trend information, while slightly abnormal values were easier to identify with tables. CONCLUSIONS: Data presentation format can affect how physicians interpret laboratory data. Graphic displays have several advantages over numeric displays but are not always optimal. User, task and data characteristics should be considered when designing information displays.

Many approaches to work analysis have been proposed so as to enhance the requirements elicitation for systems design. However, systems delivered at dynamic, complex and socio-technical workplaces, have still failed at satisfying the needs of users successfully, mainly because they are unable to support activities entirely, especially those related with cognition and collaboration. This paper describes the Observation Conceptual Model, as a complementary approach, to elicit high-quality requirements through systematic field studies about organizations. We also include some ideas for the elaboration of a collaborative system that supports the conceptual model.

Determining information requirements for tasks with distributed data in complex work environments is challenging. Well-defined requirements can help inform the design of decision support systems and identify training needs. Multiple methods should be employed to discover apparent and latent information requirements and available information that is not used in the observed environment. Managing acid-base balance in a pediatric intensive care unit (PICU) is presented as an example of the approach in the medical domain. Patient cases are often used to teach physicians how to interpret acid-base data. Current scenarios do not incorporate the extent of knowledge necessary in practice. Specifically, they do not represent complex disorders often seen in ICUs. This paper reviews acid-base balance and the tools available for tracking it, and defines a set of information requirements to inform the design of decision support systems and acid-base clinical training scenarios for ICUs.

MOTIVATION: Mathematically optimal alignments do not always properly align active site residues or well-recognized structural elements. Most near-optimal sequence alignment algorithms display alternative alignment paths, rather than the conventional residue-by-residue pairwise alignment. Typically, these methods do not provide mechanisms for finding effectively the most biologically meaningful alignment in the potentially large set of options. RESULTS: We have developed Web-based software that displays near optimal or alternative alignments of two protein or DNA sequences as a continuous moving picture. A WWW interface to a C++ program generates near optimal alignments, which are sent to a Java Applet, which displays them in a series of alignment frames. The Applet aligns residues so that consistently aligned regions remain at a fixed position on the display, while variable regions move. The display can be stopped to examine alignment details.

Previous field studies show that surgery residents and medical students have difficulty recognizing appropriate anatomic cues during laparoscopic surgery, causing delays in procedures and errors. Such observations led to the development of an anatomy recognition training intervention, specifically the use of an ordered set of video clips that show the main steps of a laparoscopic procedure. Each procedural step is shown several times in succession, with each repetition coming from a different surgery, thus exposing the learner to varied anatomy and crucial maneuvers. A factorial experiment with 30 medical students showed that the group using these ordered, perceptual learning modules had a significant increase in scores on questions assessing perceptual knowledge and procedural knowledge, with no corresponding increase for the control group who watched the videos from the same cases but in an unstructured format for the same amount of time (p< 0.05). Neither group showed improvement on strategic or declarative knowledge tests. The study suggests that ordered perceptual learning modules are a potential means for training perceptual and procedural knowledge in an effective, safe, and efficient manner, serving as a complement to other types of training methods that teach physical dexterity, strategic and declarative knowledge.

Designing decision support systems for revolutionary domains is problematic because approaches such as cognitive task and work analyses are difficult to implement due to the lack of established domains or users. An example of such a conundrum is the development of in-flight retargeting capabilities for the US Navy's Tomahawk missile. The current version is a "fire-and-forget" missile. However, a new upgrade will allow redirection of missiles in-flight. This new capability means that not only will battlefield commanders have more flexibility, but also that an entire system of human control will be needed where none previously existed. For decision support systems in revolutionary domains like that of the new Tomahawk, the lack of an existing domain makes it difficult to design an effective decision support system. Using the Tomahawk as a case study, this paper demonstrates how the cognitive work analysis can be modified for application to intentional revolutionary domains.

In this paper, we describe the design of a decision support system for operators of model-based predictive controllers (MPC). MPC is a form of advanced automatic control that is increasingly common in process operations due to its ability to control and optimize large sections of a process. A cognitive task analysis revealed that current operating displays, which rely on displaying tables of numeric information across several display pages, do not effectively support human operator monitoring, diagnosis, and control of MPC. This case study shows how we applied representation aiding and workspace management design principles to better support the human-automation interaction requirements of monitoring, understanding, and adjusting these complex, semi-autonomous; process controllers. We show how effective user interface design can significantly reduce the complexity of operating with advanced automation, and can lead to improved understanding of how the automation works.

We examine characteristics common to successful intelligent decision support systems. In doing this, we attempt to bridge the gap between disparate communities engaged in building various parts of these systems. Three systems were examined in detail from widely different applications and more than 20 additional systems were considered at a lower level of detail. By examining deployed decision support systems within the context of a broad framework we hope to capture the characteristics that can guide future development efforts. We see this as a first step in developing an in-depth compendium that will help bridge the gap between important yet typically isolated fields.

Antibody identification is a laboratory task where medical technologists must select tests to run and interpret the results in order to determine the antibodies in a patient's blood. It has the classical characteristics of an abduction task, including masking and problems with noisy data. Based on a cognitive analysis of both successful and error-producing performances, a tutoring system (the transfusion medicine tutor, TMT), was developed that uses expert systems technology to provide immediate, context-sensitive feedback as students solve actual patient cases. Development of this system required consideration of aspects of artificial intelligence, education, psychology, and human factors engineering, as well as the domain of study (i.e., allo-antibody identification). In a formal field evaluation, when used by an instructor as a tool to assist with tutoring in a class laboratory setting, use of TMT resulted in improvements in antibody identification performance of 87-93% (p<.001) as compared to a passive control version which improved performance by 20%.