Clinical guidelines are developed to assist healthcare practitioners to make decisions on a patient&#039 / s medical problems and as such they communicate with external applications to retrieve patient data, to initiate medical actions through clinical workflows and to transmit information to alert/reminder systems. The interoperability problems in the healthcare IT domain for interacting with heterogeneous clinical workflow systems and Electronic Healthcare Record (EHR) Systems prevent wider deployment of clinical guidelines because each deployment requires a tedious custom adaptation phase. In this thesis, we provide machine processable mechanisms that express the semantics of clinical guideline interfaces so that automated processes can be used to access the clinical resources for guideline deployment and execution. For this purpose, we propose a semantically enriched clinical guideline representation formalism by extending one of the computer interpretable guideline representation languages, GuideLine Interchange Format (GLIF). To be able to deploy the semantically extended guidelines to healthcare settings semi-automatically, the underlying application&#039 / s semantics must also be available. We describe how this can be achieved based on two prominent implementation technologies in use in the eHealth domain: Integrating Healthcare Enterprise (IHE) Cross Enterprise Document Sharing Integration Profile (XDS) for discovering and exchanging EHRs and Web service technology for interacting with the clinical workflows and wireless medical sensor devices. Since the deployment and execution architecture should be dynamic, and address the heterogeneity of underlying clinical environment, the deployment and execution is coordinated by a multi-agent system. The system described in this thesis is realized within the scope of the SAPHIRE Project.

This thesis work describes the formulation of a Bayesian approach along with new tools to systematically reduce uncertainty in Research&Development (R&D) alternatives. During the initial stages of R&D many alternatives are considered and high uncertainty exists for all the alternatives. The ideal approach in addressing the many R&D alternatives is to find the one alternative which is stochastically dominant i.e. the alternative which is better in all possible scenarios of uncertainty. Often a stochastically dominant alternative does not exist. This leaves the R&D manager with two alternatives, either to make a selection based on user defined utility function or to gather more information in order to reduce uncertainty in the various alternatives. From the decision makers perspective the second alternative has more intrinsic value, since reduction of uncertainty will improve the confidence in the selection and further reduce the high downside risk involved with the decisions made under high uncertainty. The motivation for this work is derived from our preliminary work on the evaluation of biorefiney alternatives, which brought into limelight the key challenges and opportunities in the evaluation of R&D alternatives. The primary challenge in the evaluation of many R&D alternatives was the presence of uncertainty in the many unit operations within each and every alternative. Additionally, limited or non-existent experimental data made it infeasible to quantify the uncertainty and lead to inability to develop an even simple systematic strategy to reduce it. Moreover, even if the uncertainty could be quantified, the traditional approaches (scenario analysis or stochastic analysis), lacked the ability to evaluate the key group of uncertainty contributors. Lastly, the traditional design of experiment approaches focus towards reduction in uncertainty in the parameter estimates of the model, whereas what is required is a design of experiment approach which focuses on the decision (selection of the key alternative). In order to tackle all the above mentioned challenges a Bayesian framework along with two new tools is proposed. The Bayesian framework consists of three main steps: a. Quantification of uncertainty b. Evaluation of key uncertainty contributors c. Design of experiment strategies, focussed on decision making rather than the traditional parameter uncertainty reduction To quantify technical uncertainty using expert knowledge, existing elicitation methods in the literature (outside chemical engineering domain) are used. To illustrate the importance of quantifying technical uncertainty, a bio-refinery case study is considered. The case study is an alternative for producing ethanol as a value added product in a Kraft mill producing pulp from softwood. To produce ethanol, a hot water pre-extraction of hemi-cellulose is considered, prior to the pulping stage. Using this case study, the methodology to quantify technical uncertainty using experts' knowledge is demonstrated. To limit the cost of R&D investment for selection or rejection of an R&D alternative, it is essential to evaluate the key uncertainty contributors. Global sensitivity analysis (GSA) is a tool which can be used to evaluate the key uncertainties. But quite often global sensitivity analysis fails to differentiate between the uncertainties and assigns them equal global sensitivity index. To counter this failing of GSA, a new method conditional global sensitivity (c-GSA) is presented, which is able to differentiate between the uncertainties even when GSA fails to do so. To demonstrate the value of c-GSA many small examples are presented. The third and the last key method in the Bayesian framework is the decision oriented design of experiment. Traditional 'Design of Experiment' (DOE) approaches focus on minimization of parameter error variance. In this work, a new "decision-oriented" DOE approach is proposed that takes into account how the generated data, and subsequently, the model developed based on them will be used in decision making. By doing so, the parameter variances get distributed in a manner such that its adverse impact on the targeted decision making is minimal. Results show that the new decision-oriented DOE approach significantly outperforms the standard D-optimal design approach. The new design method should be a valuable tool when experiments are conducted for the purpose of making R&D decisions. Finally, to demonstrate the importance of the overall Bayesian framework a bio-refinery case study is considered. The case study consists of the alternative to introduce a hemi-cellulose pre-extraction stage prior to pulping in a thermo-mechanical pulp mill. Application of the Bayesian framework to address this alternative, results in significant improvement in the prediction of the true potential value of the alternative.

<p>In this study, five real-life problem situations were used to explore the challenges of developing and implementing decision support tools for management of an urban water utility in Uganda. The study sought to explore how the degree of adoption of formal decision support tools in practice, generally perceived to be low, could be improved. In the study, an Action Research (AR) approach was used. AR is an inquiry process that involves partnership between researchers and practitioners for the purpose of addressing a real-life problem issue, while simultaneously gener-ating scientific knowledge. Unlike other research methods where the researcher seeks to study organizational phenomena but not to change them, the action researcher attempts to create or-ganizational change and simultaneously to study the process. It is recognized that AR methods provide a potential avenue to improve the practical relevance of Information Systems (IS) re-search.</p><p>The five cases that were considered in the study involved participatory problem structuring to address water distribution bottlenecks; identification of Non-Revenue Water (NRW) reduction strategies; facilitation of decentralized management of customer accounts; monitoring and con-trol of procurements and expenditure; and geospatial investigation of declining water sales. Dur-ing the study, participation in problem identification was achieved through discussions and brain-storming sessions bringing together top and middle managers within the organization. A number of prototype decision support tools were developed and implemented. Maps and other geovisu-alization tools were also used to inform and enhance the processes of collective problem identifi-cation and structuring.</p><p>Results of the study emphasized the need for proper problem structuring prior to the formula-tion of actions; the challenge of moving from planning to action; the importance of user in-volvement in the development of tools; and the need to manage IS implementation as part of a holistic, organization-wide change process. The challenges of embedding formal decision support within existing work systems in organizations were highlighted, and recommendations were made on how best to achieve this. The AR approach was found to be useful in bridging the gap be-tween academic research and technological practice, thus supporting the development of IS with immediate and practical benefits to organizations.</p>

Thesis (doctoral)--Universität St. Gallen, 2001.

Thesis (Ph. D.)--York University, 1999. Graduate Programme in Business. / Typescript. Includes bibliographical references (leaves 187-208). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ56268.

Thesis (Ph. D.)--University of Florida, 1993. / Typescript. Vita. Includes bibliographical references (leaves 150-158).

Thesis (M. Sc.(Computer Science))--University of Pretoria, 2003. / Includes bibliographical references.

Thesis (Ph. D.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains ix, 144 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references.

In September 2009 exploratory testing of an old geothermal power well caused a blowout at the El Tatio geothermal field of northern Chile. El Tatio is the largest geyser field in the southern hemisphere. The blowout was a paradigm-shifting event for the management of the El Tatio geothermal field and drew attention to the disparity and critical nature of scientific information sharing. This study uses the El Tatio incident as a case study for examining problems of common-pool resource management and geothermal energy development. It explores how differing valuations of geothermal resources resulted in a breakdown of coherent regulation and negative outcomes for all stakeholders. Contingent valuation methods were used to create an elicitive interview process in order to assess how differences in valuation drove these conflicts and negative outcomes. The sharing of scientific information through Decision Support Systems (DSS) is identified as an important element in resolving these conflicts and creating new policies for common-pool resource management. These methods are presented as tools that can be used by stakeholders to find common ground and seek mutually beneficial outcomes. In addition, these tools can help with the critical issue of social perception of scientific data and science driven solutions to these problems. This study posits that the path forward is to ensure not only that scientific data is communicated in modes appropriate to the community and problem at hand, but that the acquisition and interpretation of this data is informed by stakeholder needs. / text

M. Tech. Information Networks / This dissertation reports on a systematic evaluation of the quality of services that are provided by clothing industry in South Africa. Textile and apparel clothing industry is an important leading segment of the world economy. Clothing retail stores are continuously striving to improve their service quality towards delivering customer satisfaction, which has surfaced as one of the foundational blocks of modern competitive business. The methodological evaluation of clothing stores follows two essential steps. The first step applies Partial Least Squares algorithm to Taiwan-, Europe- and American customer satisfaction models to identify suitable quality criteria that influence customer satisfaction of clothing store service. The second step uses the identified quality criteria in a multiple criteria decision making algorithm to evaluate a set of 17 popular clothing stores in South Africa. The results of the evaluation of the clothing stores revealed the core competence of South African clothing industry in quality service delivery. The results of this study can be used to guide clothing stores on how to better improve the quality of their services. This supplies confirmation for additional enhancement on corporate competitiveness.