The knowledge-based perspective of risk management in healthcare organisations

The aim of this research is to investigate how risk management in a healthcare organisation can be supported by knowledge management. The subject of research is the development and management of existing logs called "risk registers", through specific risk management processes employed in a N.H.S. (Foundation) Trust in England, in the U.K. Existing literature on organisational risk management stresses the importance of knowledge for the effective implementation of risk management programmes, claiming that knowledge used to perceive risk is biased by the beliefs of individuals and groups involved in risk management and therefore is considered incomplete. Further, literature on organisational knowledge management presents several definitions and categorisations of knowledge and approaches for knowledge manipulation in the organisational context as a whole. However, there is no specific approach regarding "how to deal" with knowledge in the course of organisational risk management. The research is based on a single case study, on a N.H.S. (Foundation) Trust, is influenced by principles of interpretivism and the frame of mind of Soft Systems Methodology (S.S.M.) to investigate the management of risk registers, from the viewpoint of people involved in the situation. Data revealed that knowledge about risks and about the existing risk management policy and procedures is situated in several locations in the Trust and is neither consolidated nor present where and when required. This study proposes a framework that identifies required knowledge for each of the risk management processes and outlines methods for conversion of this knowledge, based on the SECI knowledge conversion model, and activities to facilitate knowledge conversion so that knowledge is effectively used for the development of risk registers and the monitoring of risks throughout the whole Trust under study. This study has theoretical impact in the management science literature as it addresses the issue of incomplete knowledge raised in the risk management literature using concepts of the knowledge management literature, such as the knowledge conversion model. In essence, the combination of required risk and risk management related knowledge with the required type of communication for risk management creates the proposed methods for the support of each risk management process for the risk registers. Further, the indication of the importance of knowledge in risk management and the presentation of a framework that consolidates knowledge required for the risk management processes and proposes way(s) for the communication of this knowledge within a healthcare organisation have practical impact in the management of healthcare organisations.

A Generic model for the design and analysis of production systems

This dissertation studies the process of operations systems design within the context of the manufacturing organization. Using the DRAMA (Design Routine for Adopting Modular Assembly) model as developed by a team from the IDOM Research Unit at Aston University as a starting point, the research employed empirically based fieldwork and a survey to investigate the process of production systems design and implementation within four UK manufacturing industries: electronics assembly, electrical engineering, mechanical engineering and carpet manufacturing. The intention was to validate the basic DRAMA model as a framework for research enquiry within the electronics industry, where the initial IDOM work was conducted, and then to test its generic applicability, further developing the model where appropriate, within the other industries selected. The thesis contains a review of production systems design theory and practice prior to presenting thirteen industrial case studies of production systems design from the four industry sectors. The results and analysis of the postal survey into production systems design are then presented. The strategic decisions of manufacturing and their relationship to production systems design, and the detailed process of production systems design and operation are then discussed. These analyses are used to develop the generic model of production systems design entitled DRAMA II (Decision Rules for Analysing Manufacturing Activities). The model contains three main constituent parts: the basic DRAMA model, the extended DRAMA II model showing the imperatives and relationships within the design process, and a benchmark generic approach for the design and analysis of each component in the design process. DRAMA II is primarily intended for use by researchers as an analytical framework of enquiry, but is also seen as having application for manufacturing practitioners.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

Form follows function:model-driven engineering for clinical trials

We argue that, for certain constrained domains, elaborate model transformation technologies-implemented from scratch in general-purpose programming languages-are unnecessary for model-driven engineering; instead, lightweight configuration of commercial off-the-shelf productivity tools suffices. In particular, in the CancerGrid project, we have been developing model-driven techniques for the generation of software tools to support clinical trials. A domain metamodel captures the community's best practice in trial design. A scientist authors a trial protocol, modelling their trial by instantiating the metamodel; customized software artifacts to support trial execution are generated automatically from the scientist's model. The metamodel is expressed as an XML Schema, in such a way that it can be instantiated by completing a form to generate a conformant XML document. The same process works at a second level for trial execution: among the artifacts generated from the protocol are models of the data to be collected, and the clinician conducting the trial instantiates such models in reporting observations-again by completing a form to create a conformant XML document, representing the data gathered during that observation. Simple standard form management tools are all that is needed. Our approach is applicable to a wide variety of information-modelling domains: not just clinical trials, but also electronic public sector computing, customer relationship management, document workflow, and so on. © 2012 Springer-Verlag.

Time for action? Elementary engineering education - Challenging teachers, policy makers and parents

Grounded in the findings of a three year exploratory student whereby teachers' and policy makers' perceptions of elementary level engineering education were analysed, this paper focuses upon three strands of engineering education activity: Pedagogy: Practice, and: Policy. Taking into account the challenges associated with introducing engineering education at an elementary level across the UK, the paper critiques the role played by the 'competition model' in promoting engineering to children and 4 to 11 years. In considering the 'added value' that appropriately developed engineering education activities can offer in the classroom the discussion argues that elementary level engineering has the potential to reach across the curriculum, offering context and depth in many different areas. The paper concludes by arguing that by introducing the discipline to children at a foundational level, switching on their 'Engineering Imaginations' and getting them to experience the value and excitement of engineering, maths and applied science a new "Educational Frontier" will be forged. © American Society for Engineering Education, 2014.

Using observation ageing to improve Markovian model learning in QoS engineering

Markovian models are widely used to analyse quality-of-service properties of both system designs and deployed systems. Thanks to the emergence of probabilistic model checkers, this analysis can be performed with high accuracy. However, its usefulness is heavily dependent on how well the model captures the actual behaviour of the analysed system. Our work addresses this problem for a class of Markovian models termed discrete-time Markov chains (DTMCs). We propose a new Bayesian technique for learning the state transition probabilities of DTMCs based on observations of the modelled system. Unlike existing approaches, our technique weighs observations based on their age, to account for the fact that older observations are less relevant than more recent ones. A case study from the area of bioinformatics workflows demonstrates the effectiveness of the technique in scenarios where the model parameters change over time.