Intensive care is becoming increasingly complex. If mistakes are to be avoided, there is a need for the large amount of clinical data to be presented effectively to the medical staff. Although the most common approach is to present the data graphically, it has been shown that textual summarisation can lead to improved decision making. As the first step in the BabyTalk project, a prototype is being developed which will generate a textual summary of 45 minutes of continuous physiological signals and discrete events (e.g.: equipment settings and drug administration). Its architecture brings together techniques from the different areas of signal analysis, medical reasoning, and natural language generation. Although the current system is still being improved, it is powerful enough to generate meaningful texts containing the most relevant information. This prototype will be extended to summarize several hours of data and to include clinical interpretation. 1

Information workers often have to balance many tasks and interruptions. In this work, we explore peripheral display techniques that improve multitasking efficiency by helping users maintain task flow, know when to resume tasks, and more easily reacquire tasks. Specifically, we compare two types of abstraction that provide different task information: semantic content extraction, which displays only the most relevant content in a window, and change detection, which signals when a change has occurred in a window (all designed as modifications to Scalable Fabric [17]). Results from our user study suggest that semantic content extraction improves multitasking performance more so than either change detection or our base case of scaling. Results also show that semantic content extraction provides significant benefits to task flow, resumption timing, and reacquisition. We discuss the implication of these findings on the design of peripheral interfaces that support multitasking. Author Keywords: Information visualization, peripheral displays, abstraction, multitasking

During exploratory data analysis, visualizations are often useful for making sense of complex data sets. However, as data sets increase in size and complexity, static information visualizations decrease in comprehensibility. Interactive techniques can yield valuable discoveries, but current data analysis tools typically support only opportunistic exploration that may be inefficient and incomplete. We present a refined architecture that uses systematic yet flexible (SYF) design goals to guide domain expert users through complex exploration of data over days, weeks and months. The SYF system aims to support exploratory data analysis with some of the simplicity of an e-commerce check-out while providing added flexibility to pursue insights. The SYF system provides an overview of the analysis process, suggests unexplored states, allows users to annotate useful states, supports collaboration, and enables reuse of successful strategies. The affordances of the SYF system are demonstrated by integrating it into a social network analysis tool employed by social scientists and intelligence analysts. The SYF system is a tool-independent component and can be incorporated into other data analysis tools.

amounts of patient data in various formats, making efficient processing of information by medical professionals difficult. Moreover, different stakeholders in the neonatal scenario, which include parents as well as staff occupying different roles, have different information requirements. This paper describes recent and ongoing work on building systems that automatically generate textual summaries of neonatal data. Our evaluation results show that the technology is viable and comparable in its effectiveness for decision support to existing presentation modalities. We discuss the lessons learned so far, as well as the major challenges involved in extending current technology to deal with a broader range of data types, and to improve the textual output in the form of more coherent summaries.

Neonatal Intensive

Inspired by systems based on user generated content, we have developed a prototype named OurWay, a collaborative route planning system utilizing user feedback (rating of route segments) to provide quality routes adapted to the users ’ abilities and needs. We report from an indoor experiment where users in wheelchairs solved navigational tasks with our prototype. Log data, observations, and interviews serve as a basis for discussing the feasibility of the Our-Way concept. We find that OurWay yields better routes for all users with aggregated route segment ratings produced throughout the experiment. However, ratings were largely produced by each individual to accomplish a selfish goal, namely that of solving a navigational task. In this respect, rating can be seen as a by-product of use, rather than as an intentional action on behalf of a community.