An Intelligent Risk Detection Framework Using Knowledge Discovery To Improve Decision Efficiency In Healthcare Contexts: The Case Of Paediatric Congenital Heart Disease

Healthcare professionals, especially surgeons must make complex decisions with far reaching consequences and associated risks. As has been shown in other industries, the ability to drill down into pertinent data to explore knowledge behind the data greatly facilitates superior, informed decisions to ensue. This proposal proffers an Intelligent Risk Detection (IRD) Model using data mining techniques followed by Knowledge Discovery in order to detect the dominant risk factors across a complex surgical decision making process and thereby to predict the surgery results and hence support superior decision making. To illustrate the benefits of this model, the case of the Congenital Heart Disease (CHD) is presented[1]

Key Information Technology and Management Issues: 2011-12 Americas Study

The importance of the impact of IT for organizations around the world, especially in light of a sluggish recovery from the global financial crisis, has amplified the need to provide a better understanding of the specific geographic similarities and differences of IT managerial and technical trends. Going beyond identifying these influential factors is also the need to understand the considerations for addressing them. This helps in recognizing the respective local characteristics, especially when operating in a globally-linked environment. By comparing and contrasting IT trends from different geographies in the Americas, this paper presents important local and international factors necessary to prepare IT leaders for the challenges that await them. It can also serve as an indicator as the respective geographies evolve from the economic conundrum. The same questionnaire (albeit translated for the respective respondents), based on the long-running Society for Information Management (SIM) survey, was applied across the geographies and the results are analyzed and presented in this paper

An Intelligent Risk Detection Framework Using Business Intelligence Tools to Improve Decision Efficiency in Healthcare Contexts

Leading healthcare organizations are recognizing the need to incorporate the power of a decision efficiency approach drivenby intelligent solutions. The primary drivers for this include the time pressures faced by healthcare professionals coupledwith the need to process voluminous and growing amounts of disparate data and information in shorter and shorter timeframes and yet make accurate and suitable treatment decisions which have a critical impact on successful healthcareoutcomes. This research contends that such a context is appropriate for the application of real time intelligent risk detectiondecision support systems using Business Intelligence (BI) technologies. The following thus proposes such a model in thecontext of the case of Congenital Heart Disease (CHD), an area which requires complex high risk decisions which need to bemade expeditiously and accurately in order to ensure successful healthcare outcomes

STRATEGIC RESILIENCE MANAGEMENT MODEL: COMPLEX ENTERPRISE SYSTEMS UPGRADE IMPLEMENTATION

Abstract Managing large and complex enterprise systems (ES

Towards a resilience management framework for complex enterprise systems upgrade implementation

The lack of knowledge of how resilience management supports enterprise system (ES) projects accounts for the failure of firms to leverage their investments in costly ES implementations. Using a structured-pragmatic- situational (SPS) case study research approach, this paper reports on an investigation into the resilience management of a large utility company as it implemented an ES upgrade. Drawing on the literature and on the case study findings, we developed a process-based resilience management framework that involves three strategies (developing situation awareness, demystifying threats, and executing restoration plans) and four organisational capabilities that transform resilience management concepts into practices. We identified the crucial phases of ES upgrade implementation and developed indicators for how different strategies and capabilities of resilience management can assist managers at different stages of an ES upgrade. This research advances the state of existing knowledge by providing specific and verifiable propositions for attaining a state of resilience, the knowledge being grounded in the empirical reality of a case study. Moreover, the framework offers ES practitioners a roadmap to better identify appropriate responses and levels of preparedness