Supporting people with cognitive disabilities in decision making – processes and dilemmas

The exploratory study found that participants, including those with cognitive disability, mostly supported the broad concept of supported decision making. However supporters saw this as a complex, dynamic and frequently chaotic process. Fundamental to the process were relationships and tailoring support to the individual. The skills and knowledge required included communication skills, self-awareness, the capacity for reflective discussion, conflict resolution skills, and knowledge of strategies for tailoring the decision making process to the individual. The study revealed multiple dilemmas and tensions associated with supporting someone with cognitive disability to make a decision but most commonly mentioned were remaining neutral, managing conflicting perspectives amongst differing supporters, balancing rights with risk and best interests, and resource constraints. The study provides some key insights into the practice of supporting people with cognitive disability to make decisions and knowledge that can be incorporated into training programs for people in this role. The findings also highlight the need for further research in this area, particularly in relation to ‘what works’ in support for decision making for people with cognitive disabilit

Developing a knowledge management approach to support managing credit risk in Jordanian banks

It is becoming increasing clear that; in banks; the sharing of knowledge amongst the senior executives has not been as effective as it should have been. The lack of knowledge amongst senior executives about the level of risks taken in sub-prime lending, the resulting "toxic assets‟ and the global nature of the instruments used to spread risks is said to be the main contributing reason for the current worldwide crisis in banks. Banks in Jordan, the focus of this study, are not immune from the exposure to the risks. The banking sector in Jordan is the most important in the Jordanian national economy and has effectively contributed to improving economic development through its important role in mobilising savings and channelling them into different fields of investment. Therefore, the overall aim of this research is to propose that developing a knowledge management (KM) approach to support managing credit risk will help banks in general, and Jordanian banks in particular, in improving the process of managing credit risk. To reach the aim, several objectives have been constructed: 1. Reviewing current status of KM and its relationship with CRM 2. Developing a scale to measure KM behaviour and practices 3. Determining current KM status in Jordanian banks 4. Building a CR decision support system using internal implicit knowledge to reduce the rate of defaults. As a result of this research, a KM approach has been developed to support managing credit risk. The approach contains the following steps: identify, measure, analyse, improve, and evaluate. Using the new KM approach, the main conclusion of this research suggest that considering credit risk management and KM together gives a much stronger basis for banks to manage credit risk

Instruments to support decision competencies of an investment project manager

From among many competencies of a manager, the abilities of team, project and organization management become especially important. However, to make right decisions, one needs to have appropriate tools supporting effective company management. In case of companies carrying out investment, modernization or innovative projects, it is especially important. Implementation of those projects takes place in various conditions resulting from changing and turbulent environment. Thus, if the manager does not have sufficient information support, provided in time and allowing for effective decision making, which mitigates negative effects of previous actions, he is basically doomed to failure. In such a case, what decides about the situation in the project execution process is a coincidence, not intentional actions of the staff, based on their knowledge about potential risks. Such a knowledge, gained early enough, allows for taking more effective corrective actions. This paper is an attempt to define an operational model of a company along with principles of monitoring actions of an enterprise that carries out projects and functions in the current economic situation, illustrated by an example of a construction company. Its implementation is supposed to provide the managing staff with stores of information that efficiently support the company management process

Towards the integration of enterprise software: The business manufacturing intelligence

Nowadays, the Information Communication Technology has pervaded literally the companies. In the company circulates an huge amount of information but too much information doesn’t provide any added value. The overload of information exceeds individual processing capacity and slowdowns decision making operations. We must transform the enormous quantity of information in useful knowledge taking in consideration that information becomes obsolete quickly in condition of dynamic market. Companies process this information by specific software for managing, efficiently and effectively, the business processes. In this paper we analyse the myriad of acronyms of software that is used in enterprises with the changes that occurred over the time, from production to decision making until to convergence in an intelligent modular enterprise software, that we named Business Manufacturing Intelligence (BMI), that will manage and support the enterprise in the futurebusiness manufacturing intelligence, enterprise resource planning; business intelligence; management software; automation software; decision making software

Data mining techniques application for prediction in OLAP cube

Data warehouses represent collections of data organized to support a process of decision support, and provide an appropriate solution for managing large volumes of data. OLAP online analytics is a technology that complements data warehouses to make data usable and understandable by users, by providing tools for visualization, exploration, and navigation of data-cubes. On the other hand, data mining allows the extraction of knowledge from data with different methods of description, classification, explanation and prediction. As part of this work, we propose new ways to improve existing approaches in the process of decision support. In the continuity of the work treating the coupling between the online analysis and data mining to integrate prediction into OLAP, an approach based on automatic learning with Clustering is proposed in order to partition an initial data cube into dense sub-cubes that could serve as a learning set to build a prediction model. The technique of data mining by regression trees is then applied for each sub-cube to predict the value of a cell

Oral Health: Work Process and Interdisciplinarity

Objective: To contribute to the debate about tools that favor the organization of the health work process and its interface with interdisciplinary practices. Material and Methods: We opted for a textual construction based on more specific publications on the field of oral health care. Results: The matrix support is configured as a method of inter-professional activity in co-management that aims to favor the qualification of the health care network. From this perspective, it is an interdisciplinary practice capable of integrating two or more areas of knowledge for a better performance of the actors involved in a given organizational and decision-making process, whether in the clinical or health management scope. Conclusion: Matrix support is an essential tool for the practice of managing health services and amplifying interdisciplinary actions

Knowledge engineering with semantic web technologies for decision support systems based on psychological models of expertise

Machines that provide decision support have traditionally used either a representation of human expertise or used mathematical algorithms. Each approach has its own limitations. This study helps to combine both types of decision support system for a single system. However, the focus is on how the machines can formalise and manipulate the human representation of expertise rather than on data processing or machine learning algorithms. It will be based on a system that represents human expertise in a psychological format. The particular decision support system for testing the approach is based on a psychological model of classification that is called the Galatean model of classification. The simple classification problems only require one XML structure to represent each class and the objects to be assigned to it. However, when the classification system is implemented as a decision support system within more complex realworld domains, there may be many variations of the class specification for different types of object to be assigned to the class in different circumstances and by different types of user making the classification decision. All these XML structures will be related to each other in formal ways, based on the original class specification, but managing their relationships and evolution becomes very difficult when the specifications for the XML variants are text-based documents. For dealing with these complexities a knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. The aim is to explore how semantic web technologies can be employed to help the knowledge engineering process for decision support systems based on human expertise, but deployed in complex domains with variable circumstances. The research evaluated OWL as a suitable vehicle for representing psychological expertise. The task was to see how well it can provide a machine formalism for the knowledge without losing its psychological validity or transparency: that is, the ability of end users to understand the knowledge representation intuitively despite its OWL format. The OWL Galatea model is designed in this study to help in automatic knowledge maintenance, reducing the replication of knowledge with variant uncertainties and support in knowledge engineering processes. The OWL-based approaches used in this model also aid in the adaptive knowledge management. An adaptive assessment questionnaire is an example of it, which is dynamically derived using the users age as the seed for creating the alternative questionnaires. The credibility of the OWL Galatea model is tested by applying it on two extremely different assessment domains (i.e. GRiST and ADVANCE). The conclusions are that OWLbased specifications provide the complementary structures for managing complex knowledge based on human expertise without impeding the end users’ understanding of the knowledgebase. The generic classification model is applicable to many domains and the accompanying OWL specification facilitates its implementations