Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Distilling Mobile Privacy Requirements from Qualitative Data

As mobile computing applications have become commonplace, it is increasingly important for them to address end-users' privacy requirements. Mobile privacy requirements depend on a number of contextual socio-cultural factors to which mobility adds another level of contextual variation. However, traditional requirements elicitation methods do not sufficiently account for contextual factors and therefore cannot be used effectively to represent and analyse the privacy requirements of mobile end users. On the other hand, methods that investigate contextual factors tend to produce data which can be difficult to use for requirements modelling. To address this problem, we have developed a Distillation approach that employs a problem analysis model to extract and refine privacy requirements for mobile applications from raw data gathered through empirical studies involving real users. Our aim was to enable the extraction of mobile privacy requirements that account for relevant contextual factors while contributing to the software design and implementation process. A key feature of the distillation approach is a problem structuring framework called privacy facets (PriF). The facets in the PriF framework support the identification of privacy requirements from different contextual perspectives namely - actors, information, information-flows and places. The PriF framework also aids in uncovering privacy determinants and threats that a system must take into account in order to support the end-user's privacy. In this work, we first show the working of distillation using qualitative data taken from an empirical study which involved social-networking practices of mobile users. As a means of validating distillation, another distinctly separate qualitative dataset from a location-tracking study is used, in both cases, the empirical studies relate to privacy issues faced by real users observed in their mobile environment

Generating Sex Trafficking Networks From Text Documents

Qualitative coding is a long and strenuous process that requires a well-skilled investigator. Natural language processing techniques have made leaps and bounds as far as usability and application domain, although it does not work for every task. In this work, we have created a natural language processing framework to help qualitative coders automatically obtain the nodes and node arcs from federal case files, dockets, and indictments within a sex trafficking network. The produced nodes and arcs allows us to perform network modeling by providing us with the information needed to create network structures that can then be used for interdiction simulation. The network models can also be analyzed for patterns, trends, and contrasts. Another goal for these networks is to apply Operations Research (OR) methods to better understand the operations of sex trafficking networks. Results fared better for the node extraction task, begging the question, does automation belong in the process of coding sex trafficking networks? If yes, then future implementations should avoid rule-based matching, despite the high structure of court documents. Additionally, more data would help improve accuracy of a model; however, obtaining ground truth data requires human coders. This thesis helps to address the question of how automated techniques, such as natural language processing and machine learning, can play a role in qualitative coding and thematic analysis. Further, by focusing on obtaining networks from text documents, it provides a basis for inputs into operations research models

Global Forest Decimal Classification (GFDC)

The English and German sections are provides as two separate files

Knowledge discovery from post project reviews

Many construction companies conduct reviews on project completion to enhance learning and to fulfil quality management procedures. Often these reports are filed away never to be seen again. This means that potentially important knowledge that may assist other project teams is not exploited. In order to ascertain whether useful knowledge can be gleaned from such reports, Knowledge Discovery from Text (KDT) and text mining (TM) are applied. Text mining avoids the need for a manual search through a vast number of reports, potentially of different formats and foci, to seek trends that may be useful for current and future projects. Pilot tests were used to analyse 48 post-project review reports. The reports were first reviewed manually to identify key themes. They were then analysed using text mining software to investigate whether text mining could identify trends and uncover useful knowledge from the reports. Pilot tests succeeded in finding common occurrences across different projects that were previously unknown. Text mining could provide a potential solution and would aid project teams to learn from previous projects. However, a lot of work is currently required before the text mining tests are conducted and the results need to be examined carefully by those with domain knowledge to validate the results obtained

Development and application of methodologies to get Sustainable industrial systems

This thesis aims to develop and apply methodologies to get sustainable industrial systems. Three tools, included in the scope of Life Cycle Thinking, are considered for this purpose: Best Available Technique (BAT) Analysis, Material and Energy Flow Analysis (MEFA), and process simulation. All these tools are individually analysed and validated in different real case studies, so that their advantages and limitations can be identified and overcome in the proposal of three methodologies that differently combine them. The nexus between the combined tools are the Improvable Flows (IF), defined during this work as those material or energy flows whose management in the considered process is not optimised, and can be improved from a sustainable point of view. The results are integrated methodologies that thoroughly analyse the considered process, identify the potential IF, propose alternatives to enhance such flows and, in some cases, evaluate the suitability and the potential benefits of the proposed alternatives

An overview of systematic literature reviews in social media marketing

Systematic literature reviews (SLRs) adopt a specified and transparent approach, in order to scope the literature in a field or sub-field. However, there has been little critical comment on their purpose and processes in practice. By undertaking an overview of SLRs in the field of social media (SM) marketing, this article undertakes a critical evaluation of the SLR purposes and processes in a set of recent SLRs and presents a future research agenda for social media marketing. The overview shows that the purposes of SLRs include: making sense (of research in a field), developing a concept matrix/taxonomy, and supporting research and practice. On SLR processes, whilst there is some consensus on the stages of the process, there is considerable variation in how these processes are executed. This article offers a resource to inform practice and acts as a platform for further critical debate regarding the nature and value of SLR

EU Country Profiles in the Raw Materials Information System (RMIS): Belgium: Country-level key data and information related to non-food, non-energy raw materials

The module European Country Profiles of the European Commission’s Raw Materials Information System (RMIS) provides country-specific data and indicators related to non-food, non-energy raw materials. These data and indicators are derived from data from official sources and well-established data providers, or by their elaboration. Each profile is structured into nine thematic sections: i) Key indicators; ii) Investment and regulatory framework; iii) research, development and innovation; iv) Resources and reserves; v) Supply; vi) Raw materials use; vii) Trade; viii) Environment; and ix) Social & Policy. The current country report presents the data and indicators for Belgium, mirroring the EU Country Profile for Belgium included in the RMIS in May 2019, which is the reference month of the data used.JRC.D.3-Land Resource