Using Intelligent Tools to Support Clinical Decision Making

Intelligent tools and collaborative systems can be used in healthcare contexts to support clinical decision making. Such an approach is concerned with identifying the way in which information is gathered and decisions are made along specific care pathways. This study develops a real-time collaborative system using an intelligent risk detection model (IRD) to improve decision efficiency in the clinical case of patients undergoing hip or knee arthroplasty. The benefits of adopting this improved clinical decision-making solution include increasing awareness, supporting communication, improving the decision making process for patients and caregivers while also improving information sharing between surgeons as key collaborative parties in the research case. This in turn leads to higher levels of patient and clinical satisfaction and better clinical outcomes.

Pervasive Business Intelligence Platform to Support the Decision-Making Process in Waiting Lists

In recent years, the increase of average waiting times in waiting lists is an issue that has been felt in health institutions. Thus, the implementation of new administrative measures to improve the management of these organizations may be required. Hereupon, the aim of this present work is to support the decision-making process in appointments and surgeries waiting lists in a hospital located in the north of Portugal, through a pervasive Business Intelligence platform that can be accessed anywhere and anytime by any device connected within the hospital's private network. By representing information that facilitate the analysis of information and knowledge extraction, the Web tool allows the identification in real-time of average waiting times outside the outlined patterns. Thereby, the developed platform permits their identification, enabling their further understanding in order to take the necessary measures. Thus, the main purpose is to enable the reduction of average waiting times through the analysis of information in order to, subsequently, ensure the satisfaction of patients.

Decision Support for Smart Manufacturing

This work introduces a formation and variety of decision-making models based on operations research modeling and optimization techniques in smart manufacturing environments. Unlike traditional manufacturing, the goal of Smart manufacturing is to optimizing concept generation, production, and product transaction and enable flexibility in physical processes to address a dynamic, competitive and global supply chains by using intelligent computerized control, advanced information technology, smart manufacturing technologies and high levels of adaptability. While research in the broad area of smart manufacturing and its challenges in decision making encompasses a wide range of topics and methodologies, we believe this chapter provides a good snapshot of current quantitative modeling approaches, issues, and trends within the field. The chapter aims to provide insights into the system engineering design, emphasizing system requirements analysis and specification, the use of alternative analytical methods and how systems can be evaluated.

A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets

This article describes how, recently, data mining has been in great use for extracting meaningful patterns from medical domain data sets, and these patterns are then applied for clinical diagnosis. Truly, any accurate, precise and reliable classification models significantly assist the medical practitioners to improve diagnosis, prognosis and treatment processes of individual diseases. However, numerous intelligent models have been proposed in this respect but still they have several drawbacks like, disease specificity, class imbalance, conflicting and lack adequacy for dimensionality of patient's data. The present study has attempted to design a hybrid prediction model for medical domain data sets by combining the decision tree based classifier (mainly C4.5) and the decision table based classifier (DT). The experimental results validate in favour of the claims.

Institutionalization of Business Intelligence for the Decision-Making Iteration

Business intelligence (BI) institutionalization has become a growing research area within the information systems (IS) discipline because of the decision-making iteration in businesses. Studies on BI application in improving decision support are not new. However, research on BI institutionalization seems sparse. BI institutionalization may positively contribute to a managerial role in using BI application repetitively for the decision-making iteration in businesses. This article aims to carry out an integrative literature review and report consolidated views of the body of knowledge. The study adopted a qualitative content analysis to generate themes about BI routinization in the decision-making iteration. Eighty-eight research articles were selected for the study. However, 57 articles were finally included for review. The findings suggest information management capability as the key necessity for BI application and its alignment with the organizational standard for BI institutionalization.

Decision Models and Group Decision Support Systems for Emergency Management and City Resilience

This paper introduces the concept of Group Decision Support Systems (GDSS) as a tool to support emergency management and resilience in coastal cities. As an illustration of the potential value of GDSS, we discuss the use of the Pointe Claire teaching case. Participants in the exercise work in groups to approach the case using four different computer-supported decision models to explore and recommend policies for emergency mitigation and city resilience. The case, as well as the decision models, can be a valuable GDSS tool, particularly in the mitigation stages of the emergency management cycle. We present preliminary results from the use of the case, models and a simulation environment in a graduate course. We finish the paper by presenting our experience as a framework for building more efficient and secure emergency management systems through the use of GDSS.

Designing Valid Humanitarian Logistics Scenario Sets

Literature about humanitarian logistics (HL) has developed a lot of innovative decision support systems during the last decades to support decisions such as location, routing, supply, or inventory management. Most of those contributions are based on quantitative models but, generally, are not used by practitioners who are not confident with. This can be explained by the fact that scenarios and datasets used to design and validate those HL models are often too simple compared to the real situations. In this chapter, a scenario-based approach based on a five-step methodology has been developed to bridge this gap by designing a set of valid scenarios able to assess disaster needs in regions subject to recurrent disasters. The contribution, usable by both scholars and practitioners, demonstrates that defining such valid scenario sets is possible for recurrent disasters. Finally, the proposal is validated on a concrete application case based on Peruvian recurrent flood and earthquake disasters.

A Multicriteria Group Decision Support System

In the present study, the authors propose a group decision support system (Web-GDSS), which allows multi-agents systems and multicriteria analysis systems to help decision-makers in order to obtain a collective decision, using web services. The proposed system operates on two main stages. First, decision-makers are in a different location away from each other. They must store their location in databases and invoke the appropriate web service. Second, in the case of negotiation between decision-makers, monotonic concession protocol will lead to an agreement using CONDORCET and BORDA voting methods.

Identifying Organizational Factors for Successful Business Intelligence Implementation

Business intelligence (BI) has proliferated due to its growing application for business decision support. Research on organizational factors may offer significant use in BI implementation. However, a limited number of studies focus on organizational factors for revealing adverse impacts on effective decision support. The aim of this theoretical study is to conduct a literature analysis to identify organizational factors relevant to BI implementation. Through a systematic literature review, a qualitative content analysis on 49 relevant sample articles for generating themes inductively is adopted to reveal organizational factors. Findings suggest two contexts: information management that integrates factors such as technological capability and personnel capability and organizational context that integrates factors such as organizational capability, managerial decision, and organizational culture for facilitating embedding information management capability for BI implementation in businesses. It is hoped that these contextual understanding can be useful for further BI implementations.

Clinical Decision Support Systems Question and Answering

A question answering system to help clinical practitioners in a cardiovascular healthcare environment to interface clinical decision support systems can be built by using an extended discourse representation structure, CIDERS, and an ontology framework, Ontology for General Clinical Practice. CIDERS is an extension of the well-known DRT (discourse representation theory) structures, intending to go beyond single text representation to embrace the general clinical history of a given patient represented in an ontology. The Ontology for General Clinical Practice improves the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty. The chapter shows the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). To be able to use the current reasoning techniques and methodologies, the authors made a thorough inventory of biomedical ontologies currently available in OWL2 format.