Are clinical documents optimised for patient safety? A critical analysis of patient safety outcomes using the EDA error model

Iatrogenic errors and patient safety in clinical processes are an increasing concern. The quality of process information in hardcopy or electronic form can heavily influence clinical behaviour and decision making errors. Little work has been undertaken to assess the safety impact of clinical process planning documents guiding the clinical actions and decisions. This paper investigates the clinical process documents used in elective surgery and their impact on latent and active clinical errors. Eight clinicians from a large health trust underwent extensive semi- structured interviews to understand their use of clinical documents, and their perceived impact on errors and patient safety. Samples of the key types of document used were analysed. Theories of latent organisational and active errors from the literature were combined with the EDA semiotics model of behaviour and decision making to propose the EDA Error Model. This model enabled us to identify perceptual, evaluation, knowledge and action error types and approaches to reducing their causes. The EDA error model was then used to analyse sample documents and identify error sources and controls. Types of knowledge artefact structures used in the documents were identified and assessed in terms of safety impact. This approach was combined with analysis of the questionnaire findings using existing error knowledge from the literature. The results identified a number of document and knowledge artefact issues that give rise to latent and active errors and also issues concerning medical culture and teamwork together with recommendations for further work.

Language learning in the public eye: an analysis of newspapers and official documents in England

This article considers the issue of low levels of motivation for foreign language learning in England by exploring how language learning is conceptualised by different key voices in that country through the examination of written data: policy documents and reports on the UK's language needs, curriculum documents, and press articles. The extent to which this conceptualisation has changed over time is explored, through the consideration of documents from two time points, before and after a change in government in the UK. The study uses corpus analysis methods in this exploration. The picture that emerges is a complex one regarding how the 'problems' and 'solutions' surrounding language learning in that context are presented in public discourse. This, we conclude, has implications for the likely success of measures adopted to increase language learning uptake in that context.

Typographic layout and first impressions: testing how changes in text layout influence reader's judgments of documents

This study explores how the typographic layout of information influences readers' impressions of magazine contents pages. Thirteen descriptors were used in a paired comparison procedure that assessed whether participants' rhetorical impressions of a set of six controlled documents change in relation to variations in layout. The combinations of layout attributes tested were derived from the structural attributes associated with three patterns of typographic differentiation (high, moderate, and low) described in a previous study (see Moys, 2014). The content and the range of stylistic attributes applied to the test material were controlled in order to focus on layout attributes. Triangulation of the quantitative and qualitative data indicates that, even within the experimental confines of limited stylistic differentiation, the layout attributes associated with patterns of high, moderate, and low typographic differentiation do influence readers' rhetorical judgments. In addition, the findings emphasize the importance of considering inter-relationships between clusters of typographic attributes rather than testing isolated variables.

Document engineering approaches toward scalable and structured multimedia, web and printable documents

Document engineering is the computer science discipline that investigates systems for documents in any form and in all media. As with the relationship between software engineering and software, document engineering is concerned with principles, tools and processes that improve our ability to create, manage, and maintain documents (http://www.documentengineering.org). The ACM Symposium on Document Engineering is an annual meeting of researchers active in document engineering: it is sponsored by ACM by means of the ACM SIGWEB Special Interest Group. In this editorial, we first point to work carried out in the context of document engineering, which are directly related to multimedia tools and applications. We conclude with a summary of the papers presented in this special issue.

Teachers as bricoleurs : producing plausible readings of curriculum documents

This paper describes teachers as bricoleurs who are professionals engaging in rigorous theoretical work as they construct meaningful assemblages of classroom practice. The author uses examples of two teachers taking up critical literacy discourses in their teaching and conversations to explain the construction of the teacher as bricoleur. Drawing on work by Deleuze and Guattari, a rhizotextual analysis of data is undertaken to explain the connections between the disparate discourses used by the two teachers and the documents of the Queensland English Syllabus. This understanding of the professional work of teachers negates assumptions about teachers as atheoretical and blind followers of departmental policies and curriculum directives. The author concludes with a description of one method for giving teachers the time and reflexive space to theoretically engage with issues surrounding their professional practice.

Reconsidering the scope of the equitable mortgage arising from deposit of title documents

This article examines the underlying fairness of applying equitable security presumptions to the deposit of title documents belonging to third parties. It argues that within such transactions, the focus of the equitable jurisdiction must be upon the intention of the owner of the title documents rather than presumptions arising from the fact of the deposit. It suggests that there is no logic in applying equitable presumptions, founded on the principles of part performance, to infer a security intention in transactions involving third party title documents. The fact that the parties to a loan advance may have intended to create a mortgage between themselves does not mean that the third party owner of the title documents also intended to create a mortgage. In third party transactions, the objectives of the equity jurisdiction are best achieved through a comprehensive assessment of the intention of all parties and the abolition of presumptions based upon the bare fact of title deposit.

Concept learning of text documents

Concept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used. These keywords should be fewer and comprehensible. A naïve method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords. Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods.

Finding short patterns to classify text documents

Many classification methods have been proposed to find patterns in text documents. However, according to Occam's razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm

Image featuring for retrieval of multimedia documents

The utilization of massive multimedia documents collections, such as multimedia documents in the global Internet, needs search engines which can rank using both text and image evidence. Massive size and (dynamic) nature of collection can make manual indexing prohibitively expensive in such situations. Traditional search engines utilize only text components of multimedia documents. But there are information needs, which require the utilization of image evidence. In this paper, we investigate image-feature for large and heterogeneous collections. Both the nature and complexities of information needs are key elements for an effective retrieval. Retrieval needs that depend on perceptual similarities (as found in art galleries, building architecture) require the utilization of visual cues. In such situations, the retrieval of multimedia document based on image ranking can provide higher effectiveness. Experimental results show that effectiveness of ranking based on image feature can be higher where perceptual similarities are key elements for retrieval than the retrieval effectiveness of algorithms based on text ranking algorithms

Rhizo-textual analyses of educational policy documents

In this paper, I describe methodological applications of Deleuze and Guattari's (1987) understanding of rhizomatic knowledge systems. I draw on rhizo-textual analyses of two different policy documents used in Australia to direct teachers in their teaching of English in primary (elementary) schools.

A rhizo-textual analysis is not concerned with following traditional, scientifically rigorous channels through a study; from data collection, through analysis, to findings that report some objectively discovered truth. Rather, a rhizo-textual analysis is a mapping of connections, of the fleshy tubers that are the rhizome. The mapping draws on various, and often contradictory work, ideas and concepts. What would seem to be "disparate phenomena" enables me to "connect diverse fragments of data in ways that produce new linkages and reveal discontinuities" (Alvermann, 2000, p. 118).

As Deleuze and Guattari point out, "A rhizome ceaselessly establishes connections between semiotic chains, organizations of power, and circumstances relative to the arts, social sciences, and social struggles" (1987, p. 7). This ceaselessness of the connections between rhizomes shifts attention away from the construction of a particular reading of any text towards a new careful attendance to the multiplicity of linkages that can be mapped between any text and other texts, other readings, other assemblages of meaning. Elizabeth Grosz describes rhizomatic texts as "a process of scattering thoughts, scrambling terms, concepts and practices, forging linkages, becoming a form of action" (1995, p. 126)

Within the policy texts I have analysed, this scrambling and scattered process establishes connections between disparate discursive systems, about literacy, about texts, about students and how students learn, and about teaching so that the version of literacy teaching that is produced seems to be normative, to be unquestionably rational, and therefore to be beyond critique.

Information retrieval in documents using semantic criteria

The central problem of automatic retrieval from unformatted text is that computational devices are not adequately trained to look for associated information. However for complete understanding and information retrieval, a complete artificial intelligence would have to be built. This paper describes a method for achieving significant information retrieval by using a semantic search engine. The underlying semantic information is stored in a network of clarified words, linked by logical connections. We employ simple scoring techniques on collections of paths in this network to establish a degree of relevance between a document and a clarified search criterion. This technique has been applied with success to test examples and can be easily scaled up to search large documents.