Triage in emergency departments is often complex and subject to conditions of uncertainty. The need for timely and accurate clinical assessment based on restricted and ambiguous information; the need to be consistent with standard triage scale and the stressful environment contribute to complexity and uncertainty of triage decision-making. This paper proposes a model for mobile decision support that aims at assisting the nurse when determining treatment category of a triage patient. Our model integrates soft computing and mobile computing technologies to provide intelligent decision support. The paper describes the model and prototype implementation of the model.Presented at: IFIP International Conference on Decision Support Systems; 2004 Jul 1-3; Prato, Italy. p. 714-723.Rights: San Pedro, J., Burstein, F., Cao, P., Churilov, L., Zaslavsky, A., and Wassertheil, J. © 2004. The authors grant a non-exclusive licence to publish this document in full in the DSS2004 Conference Proceedings. This document may be published on the World Wide Web, CD-ROM, in printed form, and on mirror sites on the World Wide Web. The authors assign to educational institutions a non-exclusive license to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. Any other usage is prohibited without the express permission of the authors.

Mobile users making real-time decisions based on current information need confidence that their context has been taken into consideration in producing the system’s recommendations. This chapter reviews current use of mobile technologies for context-aware real-time decision support. Specifically, it describes a framework for assessing the impact of mobility in decision making. The framework uses dynamic context model of data quality to represent uncertainties in the mobile decision-making environment. This framework can be used for developing visual interactive displays for communicating to the user relevant changes in data quality when working in mobile environments. As an illustration, this chapter proposes a real-time decision support procedure for on-the-spot assistance to the mobile consumer when choosing the best payment option to efficiently manage their budget. The proposed procedure is based on multi-attribute decision analysis, scenario reasoning, and a quality of data framework. The feasibility of the approach is demonstrated with a mobile decision-support system prototype implementation.

In this paper we propose a model for intelligent multiattribute decision support for triage. Triage is a preliminary clinical assessment of a patient aimed at categorising the treatment category according to priority level or urgency. Our model uses a combination of rule-based reasoning and multiattribute decision-making to assist a nurse in selecting the best treatment category for a patient. Our proposed model potentially can address the issues of accuracy, consistency and timeliness in triage decisions.

Ambiguous triage scenarios in hospital emergency departments are often difficult to assess without decision support. Subjective assessments of such scenarios can either lead to under-triaging or over-triaging for which true conditions of patients are often not addressed within the required time. In this paper, we propose a decision support model that can guide a clinician when identifying the urgency of medical intervention when patient presents with ambiguous triage case. Our model is a heuristic approach that selects the best triage category, identifies corresponding discriminating attribute of the patient, and allows clinician to attach a level of confidence in the decision. We implemented this model as a mobile decision support system, called iTriage. Results of an initial evaluation of iTriage using fourteen paper-based adult triage scenarios showed that our model produced robust decisions for urgent scenarios. For non-urgent scenarios, the proposed model provided guidance especially when the scenarios were ambiguously stated.

This paper proposes multi-agent e-negotiation support architecture for boundary conflict resolution. We use intelligent agents to act as human assistants during the negotiation task and provide means and resources for making final decisions in case there is a discrepancy in opinions about the decision matter. As an illustration we describe a scenario in marine weather forecasting and discuss how the proposed framework may support forecasters when negotiating agreements toward a consistent forecast policy at the boundary of regional forecasting centres.Presented at: IFIP International Conference on Decision Support Systems; 2004 Jul 1-3; Prato, Italy. p. 724-733.Rights: San Pedro J., Burstein, F. and Linger, H. © 2004. The authors grant a non-exclusive licence to publish this document in full in the DSS2004 Conference Proceedings. This document may be published on the World Wide Web, CD-ROM, in printed form, and on mirror sites on the World Wide Web. The authors assign to educational institutions a non-exclusive licence to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. Any other usage is prohibited without the express permission of the authors.

Distributed group decision-making becomes prevalent in networked and informationbased organizations today. In addition to providing GDSS with modelling tools such as MCDM techniques to effectively support distributed group decision-making tasks, coordination is deemed to be critical to the success of such complicated processes. Current coordination theory in the field of distributed group decision-making does not explicitly deal with process using MCDM techniques, although a few studies have adapted organizational coordination theory in the field of general GDSS research. This paper aims to provide theoretical view of coordination in the field of group MCDM processes based on existing organizational theory and its extensions in GDSS. We propose such a theoretical extension by formulating parallel and sequential coordination methods in the context of distributed group MCDM, which represent the loosest and tightest task interdependence. We then present an implementation of these coordination methods in a GDSS environment with respect to system design and group decisionmaking procedures. The proposed coordination methods may provide guidelines for GDSS designers and practitioners in coordinating and structuring distributed group MCDM processes.

Case-based reasoning and multicriteria decision making have common grounds: they are both problem solving methodologies; both involve the selection, ranking and aggregation of best alternatives and provide tools for evaluating the alternatives in respect to multiple attributes or criteria. Each of the two methodologies has its own strengths and weaknesses. By integrating the two methodologies, we can take advantage of their strengths and complement each other's weaknesses. This paper proposes a multi-stage framework for intelligent decision support that integrates case-based reasoning and fuzzy multicriteria decision making techniques. We illustrated the proposed approach in the context of tropical cyclone prediction. We describe a prototype intelligent decision support system, which helps the forecaster in retrieving best-fitted solutions in terms of both usefulness and similarity to the current observed case.

In this paper, we propose a framework for mobile decision support that uses a dynamic context representation of quality of data to represent uncertainties in the mobile computing environment. We explore the notion of sensitivity analysis in these settings. Such analysis would be aimed at assessing the impact of con-text changes to decision outcomes. Our aim is to develop a decision support tool that intelligently adapts to changes in environment and sends alerts and advice on impact of these changes in decision outcomes. The paper describes how such system can be developed.

In this paper, we propose a model for intelligent decision support model that draws upon the integration of case-based reasoning and fuzzy multicriteria decision-making. The model provides intelligent assistance, retrieval, reminder, and advice to a decision-maker. The model is illustrated in the context of tropical cyclone track prediction, where it provides intelligent tools to assist a forecaster in understanding the current cyclone situation and make use of large volume of historical operational meteorological data to determine future location of a current cyclone.

This paper proposes a multi-stage framework for intelligent decision support. The proposed framework integrates case-based reasoning and fuzzy multicriteria decision making techniques. It potentially leads to more accurate, flexible and efficient retrieval of alternatives that are most similar and most useful to the current decision situation. Additionally, the framework provides intelligent assistance in articulating domain expert's preferences through outranking relations. We illustrated the proposed approach in the context of tropical cyclone prediction. Ten years of historical observation data about tropical cyclones was represented within fuzzy multicriteria decision-making problem. We describe a prototype intelligent decision support system, which helps the forecaster in retrieving best-fitted solutions in terms of both usefulness and similarity to the current observed case.

In this paper, we propose a framework for mobile decision support that uses a dynamic context representation of quality of data to represent uncertainties in the mobile computing environment. We explore the notion of sensitivity analysis in these settings. Such analysis would be aimed at assessing the impact of con-text changes to decision outcomes. Our aim is to develop a decision support tool that intelligently adapts to changes in environment and sends alerts and advice on impact of these changes in decision outcomes. The paper describes how such system can be developed.Presented at: 7th International Conference of the International Society for Decision Support Systems: DSS in the Uncertainty of the Internet Age; 2003 Jul 13-16; Ustron, Poland. 10 leaves.