Is team training in briefings for surgical teams feasible in simulation?

We developed a module for surgical team training using briefings in simulated crisis scenarios and here we report preliminary findings. Nine surgical teams (34 trainees) participated in a pre-training simulation, followed by an interactive workshop on briefing and checklists, and then a post-training simulation. Both technical and non-technical skills were assessed via observation during simulations by expert trainers who provided feedback on performances at the end of simulation. Trainees also reported their attitudes to briefings and evaluated the training. Pre-training attitudes to briefing were positive, some of which improved post-training and trainees’ evaluation of the training was positive. Surgeons’ technical skill improved significantly post-training, but their decision-making skill was rated lower than other non-technical skills, compared to other trainees. The training did not appear to greatly improve non-technical skill performance. Training surgical teams in simulation is feasible but much more work is needed on measurement development and training strategy to confirm its efficacy and utility.     

Formal analysis of human operator behavioural patterns in interactive surveillance systems

An important area of Human Reliability Assessment in interactive systems is the ability to understand the causes of human error and to model their occurrence. This paper investigates a new approach to analysis of task failures based on patterns of operator behaviour, in contrast with more traditional event-based approaches. It considers, as a case study, a formal model of an Air Traffic Control system operator’s task which incorporates a simple model of the high-level cognitive processes involved. The cognitive model is formalised in the CSP process algebra. Various patterns of behaviour that could lead to task failure are described using temporal logic. Then a model-checking technique is used to verify whether the set of selected behavioural patterns is sound and complete with respect to the definition of task failure. The decomposition is shown to be incomplete and a new behavioural pattern is identified, which appears to have been overlooked in the informal analysis of the problem. This illustrates how formal analysis of operator models can yield fresh insights into how failures may arise in interactive systems.

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Design and evaluation of systems to support interaction capture and retrieval

Although many recent systems have been built to support Information Capture and Retrieval (ICR), these have not generally been successful. This paper presents studies that evaluate two different hypotheses for this failure, firstly that systems fail to address user needs and secondly that they provide only rudimentary support for ICR. Having first presented a taxonomy of different systems built to support ICR, we then describe a study that attempts to identify user needs for ICR. On the basis of that study we carried out two user-oriented evaluations. In the first, we carried out a task-based evaluation of a state-of-the-art ICR system, finding that it failed to provide users with abstract ways to view meetings data, and did not present users with information categories that they considered to be important. In a second study, we introduce a new method for comparative evaluation of different techniques for accessing meetings data. The second study showed that simple interface techniques that extracted key information from meetings were effective in allowing users to extract gist from meetings data. We conclude with a discussion of outstanding issues and future directions for ICR research.     

Transferring Case Knowledge To Adaptation Knowledge: An Approach for Case-Base Maintenance

In this article we propose a case-base maintenance methodology based on the idea of transferring knowledge between knowledge containers in a case-based reasoning (CBR) system. A machine-learning technique, fuzzy decision-tree induction, is used to transform the case knowledge to adaptation knowledge. By learning the more sophisticated fuzzy adaptation knowledge, many of the redundant cases can be removed. This approach is particularly useful when the case base consists of a large number of redundant cases and the retrieval efficiency becomes a real concern of the user. The method of maintaining a case base from scratch, as proposed in this article, consists of four steps. First, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case base. Second, clustering of cases is carried out to identify different concepts in the case base using the acquired feature-weights knowledge. Third, adaptation rules are mined for each concept using fuzzy decision trees. Fourth, a selection strategy based on the concepts of case coverage and reachability is used to select representative cases. In order to demonstrate the effectiveness of this approach as well as to examine the relationship between compactness and performance of a CBR system, experimental testing is carried out using the Traveling and the Rice Taste data sets. The results show that the testing case bases can be reduced by 36 and 39 percent, respectively, if we complement the remaining cases by the adaptation rules discovered using our approach. The overall accuracies of the two smaller case bases are 94 and 90 percent, respectively, of the originals.     

Attitudes to telecare among older people,professional care workers and informal carers: a preventative strategy or crisis management?

This paper reports findings from an attitudinal survey towards telecare that emerged from 22 focus groups comprising 92 older people, 55 professional stakeholders and 39 carers. These were convened in three different regions of England as a precursor to telecare service development. The results from this study suggest that informants’ views were shaped by prior knowledge of conventional health and social care delivery in their locality, and the implication is that expectations and requirements with respect to telecare services in general are likely to be informed by wider perceptions about the extent to which community care should operate as a preventative strategy or as a mechanism for crisis management.

Learning and retention of associations between auditory icons and denotative referents: implications for the design of auditory warnings

OBJECTIVE: This study examined the way in which the type and preexisting strength of association between an auditory icon and a warning event affects the ease with which the icon/event pairing can be learned and retained. BACKGROUND: To be effective, an auditory warning must be audible, identifiable, interpretable, and heeded. Warnings consisting of familiar environmental sounds, or auditory icons, have potential to facilitate identification and interpretation. The ease with which pairings between auditory icons and warning events can be learned and retained is likely to depend on the type and strength of the preexisting icon/event association. METHOD: Sixty-three participants each learned eight auditory-icon/denotative-referent pairings and attempted to recall them 4 weeks later. Three icon/denotative-referent association types (direct, related, and unrelated) were employed. Participants rated the strength of the association for each pairing on a 7-point scale. RESULTS: The number of errors made while learning pairings was greater for unrelated than for either related or direct associations, whereas the number of errors made while attempting to recall pairings 4 weeks later was greater for unrelated than for related associations and for related than for direct associations. Irrespective of association type, both learning and retention performance remained at very high levels, provided the strength of the association was rated greater than 5. CONCLUSION: This suggests that strong preexisting associations are used to facilitate learning and retention of icon/denotative-referent pairings. APPLICATION: The practical implication of this study is that auditory icons having either direct or strong, indirect associations with warning events should be preferred.     

Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning

'Grid cells' in the dorsocaudal medial entorhinal cortex (dMEC) are activated when a rat is located at any of the vertices of a grid of equilateral triangles covering the environment. dMEC grid cells have different frequencies and phase offsets. However, cells in the dentate gyrus (DG) and hippocampal area CA3 of the rodent typically display place fields, where individual cells are active over only a single portion of the space. In a model of the hippocampus, we have shown that the connectivity from the entorhinal cortex to the dentate granule cells could allow the dentate granule cells to operate as a competitive network to recode their inputs to produce sparse orthogonal representations, and this includes spatial pattern separation. In this paper we show that the same computational hypothesis can account for the mapping of EC grid cells to dentate place cells. We show that the learning in the competitive network is an important part of the way in which the mapping can be achieved. We further show that incorporation of a short term memory trace into the associative learning can help to produce the relatively broad place fields found in the hippocampus.     

Flexible Image-Based Photometric Reconstruction using Virtual Light Sources

Photometric reconstruction is the process of estimating the illumination and surface reflectance properties of an environment, given a geometric model of the scene and a set of photographs of its surfaces. For mixed-reality applications, such data is required if synthetic objects are to be correctly illuminated or if synthetic light sources are to be used to re-light the scene. Current methods of estimating such data are limited in the practical situations in which they can be applied, due to the fact that the geometric and radiometric models of the scene which are provided by the user must be complete, and that the position (and in some cases, intensity) of the light sources must also be specified a-priori. In this paper, a novel algorithm is presented which overcomes these constraints, and allows photometric data to be reconstructed in less restricted situations. This is achieved through the use of virtual light sources which mimic the effect of direct illumination from unknown luminaires, and indirect illumination reflected off unknown geometry. The intensity of these virtual light sources and the surface material properties are estimated using an iterative algorithm which attempts to match calculated radiance values to those observed in photographs. Results are presented for both synthetic and real scenes that show the quality of the reconstructed data and its use in off-line mixed-reality applications.