Decision-Maker Modeling for Decision Support Application

Managing Attribute Complexity for User-centered Decision Support Systems

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

With the issues of climate change continuously taking center stage, there is thus a pressing need for the pharmaceutical companies to assess the sustainability of their supply chains. However, the highly regulated nature of the industry has prompted various supply chain decisions such as material sourcing, siting of plants, and distribution network configuration to be locked into the registration of the new drug. This could consequently lead to inefficient supply chain operation during the drug commercial stage, yielding high greenhouse gas emissions per unit of drug product. Previously, we have proposed a systematic framework for design of a sustainable commercial-stage pharmaceutical supply chain network that can be driven from the early drug development stages. In this paper, we extend the framework by incorporating more functionalities. We also introduce a novel decision support system, called PharmaSC, which implements the framework. PharmaSC system has been developed by integrating two computer tools—an Excel-based analytic hierarchy process and LLamasoft’s Supply Chain Guru—into one coherent framework for selecting the most suitable suppliers and manufacturers, mapping a supply chain superstructure network, and optimizing the network to identify and resolve trade-offs between economic and environmental factor. To illustrate the system, an industrially motivated case study involving a paclitaxel production is solved.

We are in the midst of a healthcare paradigm shift driven by the wide adoption of ubiquitous computing and various modes of information communications technologies. As a result, cities worldwide are undergoing a major process of urbanization with ever increasing wealth of sensing capabilities – hence the Internet of Things (IoT). These trends impose great pressure on how healthcare is done. This paper describes the design and implementation of a situated clinical decision support (SCDSS) system, most appropriate for smart cities. The SCDSS was prototyped and enhanced in a clinic. The SCDSS was then used in a clinic as well as in a university hospital centre. In this article, the system’s architecture, subcomponents and integrated workflow are described. The systems’ design was the result of a knowledge acquisition process involving interviews with five specialists and testing with 50 patients. The reports (specialist consultation report) generated by the SCDSS were shown to general practitioners who were not able to distinguish them from human specialist reports. We propose a context-aware CDSS and assess its effectiveness in managing a wide medical range of patients. Five different patient cases were identified for analysis. The SCDSS was used to produce draft electronic specialist consultations, which were then compared to the original specialists’ consultations. It was found that the SCDSS-generated consults were of better quality for a number of reasons discussed herein. SCDSSs have great promise for their use in the clinical environment of smart cities. Valuable insights into the integration and use of situated clinical decision support systems are highlighted and suggestions for future research are given.

Naturalistic decision making (NDM) describes how people make decisions with time pressure and other complexities in familiar and meaningful environments. The complexities of NDM, although essential for a thorough understanding of experts, have limited the use of NDM in decision support system (DSS) design and decision analysis. There have been attempts to computationally model NDM theories; however, to do so has required significant programming skill, resulting in opaque models while still leaving significant portions of the theories unrepresented. To provide a path toward simple, representative, mathematical models of NDM, we present a general mathematical form that can model most of the components of the judgment and decision-making strategies from the fast-and-frugal heuristics (FFH) program. As a case study, the NDM quick test process was transformed into representative FFH components and then into mathematical form. The results show that the quick test process is similar to the FFH strategy tallying, which itself is a type of fast-and-frugal tree. Although the mathematical form does not capture all the cognitive processes at work, it does provide a simple, representative form for use in DSS design and decision analysis. Moreover, the model’s basis in the extensive human-subjects, mathematical, and computational analyses completed by the FFH program provides another method for integrating FFH and NDM by providing (a) a framework for generating constructive questions about how NDM theories account for FFH components; (b) a basis for prescriptive NDM decision support tools that are easy to communicate, understand, and apply; and (c) a method for approximating experience, expertise, and time pressure.

Decision support systems enhanced by Artificial Intelligence (AI) are increasingly being used in high-stakes scenarios where errors or biased outcomes can have significant consequences. In this work, we explore the conditions under which AI-based decision support systems affect the decision accuracy of humans involved in face matching tasks. Previous work suggests that this largely depends on various factors, such as the specific nature of the task and how users perceive the quality of the decision support, among others. Hence, we conduct extensive experiments to examine how both task difficulty and the precision of the system influence human outcomes. Our results show a strong influence of task difficulty, which not only makes humans less precise but also less capable of determining whether the decision support system is yielding accurate suggestions or not. This has implications for the design of decision support systems, and calls for a careful examination of the context in which they are deployed and on how they are perceived by users.

This paper addresses the design of effective simulation-based decision support systems (DSS). An experiment was conducted using three different DSS tools developed around three types of simulation model—traditional, conventional visual interactive simulation (VIS), and ‘paired-systems’ VIS. Subjects were asked to perform a decision making task and their performance was evaluated. Subjects who used the DSS based on a ‘paired systems’ VIS model were found to be both the most effective and the most efficient at the problem-solving task. Subjects provided with the DSS based upon a conventional VIS model were found to be more effective at the task than the group provided with the traditional simulation-based DSS.

Task constraints and user-system interaction process under personnel decision support.

PT. Jayatama Selaras is a manufacturer of plastic packaging in Indonesia. The problem faced by the company is the delay in the arrival of raw materials and the quality of raw materials that are not in accordance with the specifications provided by the company, so the process of returning or re-ordering needs to be carried out to new suppliers, which increases the time needed to procure raw materials. In addition, there is no integrated system in data storage that is related to the process of procuring raw materials, such as supplier data, and raw material data. The purpose of this study is to design a decision support system in providing an objective selection of alternative suppliers for the Purchasing Department of PT. Jayatama Selaras. The design of a decision support system starts with analyzing the current system using the PIECES framework. Next is creating a database using MySQL based on data management components. The next system design is designing component management models. The model used in the design of decision support systems at PT. Jayatama Selaras is AHP and TOPSIS. The last stage is designing components of dialogue management or user interfaces using the Visual Basic programming language. The results of the decision support system design show that the system can determine the best alternative suppliers, for PVC and LDPE raw materials, for LDPE raw materials to produce alternative sequences of suppliers PT. Indo Thai Trading, PT. Itochu Indonesia, PT. Tempo Nagadi Trading, PT. Kharisma Karya Plast, PT. Akino Indonesia Trading, PT. Panca Budi Pratama and PT. Unipack Plasindo. In addition, a decision support system designed to produce a database of existing suppliers and facilitate the ordering of raw materials to suppliers. The design of this decision support system is addressed to the purchasing department, production department, quality control department, and accounting department.

A Decision Support System for market-driven product positioning and design

Clinical decision support software (CDSS) has been increasingly implemented to assist improved prescribing practice. Reviews and studies report generally positive results regarding prescribing changes and, to a lesser extent, patient outcomes. Little information is available, however, concerning the use of CDSS in community pharmacy practice. Given the apparent paucity of publications examining this topic, we conducted a review to determine whether CDSS in community pharmacy practice can improve medication use and patient outcomes. A literature search of articles on CDSS relevant to community pharmacy and published between 1 January 2005 and 21 October 2013 was undertaken. Articles were included if the healthcare setting was community pharmacy and the article indicated that pharmacy use of CDSS was part of the study intervention. Eight studies were found which assessed counselling, selected drug interactions, inappropriate prescribing and under-prescribing. One study was halted due to insufficient data collection. Six studies showed statistically significant improvements in the measured outcomes: increased patient counselling, 31% reduced frequency of drug-drug interactions (DDIs), reduced frequency of inappropriate medications in the elderly (2·2-1·8% patients) and in pregnant women (5·5-2·9% patients), and increased pharmacists' interventions for under-prescribed low-dose aspirin (1·74 vs. 0·91 per 100 patients with type 2 diabetes) and over-prescribed high-dose proton-pump inhibitors (PPIs) (1·67 vs. 0·17 interventions per 100 high-dose PPI prescriptions). Most studies showed improved prescribing practice, via direct communication between pharmacists and doctors or indirectly via patient education. Factors limiting the impact of improved prescribing included alert fatigue and clinical inertia. No study investigated patient outcomes and little investigation had been undertaken on how CDSS could be best implemented. Few studies have been undertaken in community pharmacy practice, and based on the positive findings reported, further research should be directed in this area, including investigation of patient outcomes.

Knowledge acquisition for the design of clinical decision support systems can be facilitated when clinical practice guidelines serve as a knowledge source. We describe application of the Guideline Elements Model (GEM) in the design of a decision support system to promote smoking cessation. Following selection of relevant recommendations and markup of knowledge components with the GEM Cutter editor, the Extractor stylesheet was used to create a list of decision variables and actions for further processing. Decision variables and actions that reflect similar concepts were consolidated. Action types were identified. Extracting the critical concepts from the narrative text facilitates clarification of necessary content. The guideline-centric approach promotes accurate translation of guideline knowledge.

On the design of group decision support systems

ObjectiveThe study investigates users’ tendency to access decision support (DS) systems as a function of the correlation between the DS information and the information users already have, the ongoing interaction with such systems, and the effect of correlated information on subjective trust.BackgroundPrevious research has shown inconclusive findings regarding whether people prefer information that correlates with information they already have. Some studies conclude that individuals recognize the value of noncorrelated information, given its unique content, while others suggest that users favor correlated information as it aligns with existing evidence. The impact of the level of correlation on performance, subjective trust, and the decision to use DS remains unclear.MethodIn an experiment (N = 481), participants made classification decisions based on available information. They could also purchase additional DS with different degrees of correlation with the available information.ResultsParticipants tended to purchase information more often when the DS was not correlated with the available information. Correlated information reduced performance, and the effect of correlation on subjective trust and performance depended on DS sensitivity.ConclusionAdditional information may not improve performance when it is correlated with available information (i.e., it is redundant). Hence, the benefits of additional information and DS depend on the information the system and the operator use.ApplicationIt is essential to analyze the correlations between information sources and design the available information to allow optimal task performance and possibly minimize redundancy (e.g., by locating sensors in different positions to capture independent data).

Decision support systems for corporate environmental management and ecological sustainability are proliferating, this paper explores their directions in a multifaceted literature via an integrative literature review. Besides delineating the interrelated areas of decision support systems for sustainability, we study the role of internal stakeholders in their implementation. In our review, we examine a relatively mature field (Environmental Decision Support System, EDSS) and an emerging field (Green Information System, Green IS), and their potential interplays. The importance of stakeholder inclusion in general, and the participation of end-users in the design and implementation of decision support systems, in particular, are analysed in the EDSS literature. The Green IS has a prospective future in decision support with its capacity to capture corporate environmental performance. Green IS literature, however, has just recently identified the significance of employee participation: barriers and potentials to engage employees have already been identified but still to be explored.