Where is the theory? Evaluating the theoretical frameworks described in decision support technologies

In the first decade of the 21st century, citizenship is increasingly channeled into and organized through digital communications and experiences (Center for Technology in Government, 1999; Fountain, 2001; Larsen & Rainie, 2002). This article presents an overview of a set of new information technologies—online modeling, simulation, and decision-support technologies that have the potential to transform the way citizens understand complex issues and communicate that understanding to fellow citizens and governmental decision makers. Those who have thought deeply about democracy and technology tend to understand that new technologies often possess both beneficial and detrimental traits. These traits are also likely to occur with respect to simulation technologies for citizen participation (Shapiro, 1999; Sunstein, 2000). Whether simulation technologies will ultimately be more beneficial than detrimental will ultimately be determined by how the next generation of simulation designers do their jobs. One purpose of this article is to identify factors that these designers should consider. What defines and makes simulation technologies unique is their ability to act as a new mode of communication. Models, simulations, and decision-support technologies communicate as much through experience as they do through any direct effort to convey a specific message. As Marshall McLuhan (1967) suggested, things are different when the medium becomes a major part of the message. In the case of models, simulations, and decision-support technologies, the medium can be much more than the video presentations that McLuhan identified as having such an impact on our culture. Specifically, these technologies can exist as entire miniature worlds of experience and can possess features that address multiple senses at one time. Moreover, simulations can be designed to specifically stimulate higher cognitive functions as well as our sense of discovery and history. Although the terms model, simulation, and decision support are similar (see Key Terms) in that all of them involve some use of representations to help human beings understand processes, each term possesses some connotations that can be useful in different circumstances. For example, simulations seems to imply a greater level of experience on the part of the user when compared to models and decision-support technologies. Similarly, the term decision-support technologies (or expert systems) tend to imply a more goal-oriented representation of a problem. Although it is important to understand the subtle differences among these terms, for the purposes of this article I will use the term simulation to refer to the entire spectrum of these technologies. Scientists and engineers have long used computer modeling, simulation, and decision support, but only recently have these technologies been employed in support of citizen participation in policy development. This article describes the uses of, the rationales for, and the trends behind the employment of these simulation technologies in this manner. Specifically, I examine three major factors that are driving the trend toward the use of simulations to promote citizen participation as well as to identify factors that will enable citizens and public managers to make better use these technologies.

Patient decision aids (PDAs) help to support patients in making an informed and value-based decision. Despite advancement in decision support technologies over the past 30 years, most PDAs are still inaccessible and few address individual needs. Health innovation may provide a solution to bridge these gaps. Information and computer technology provide a platform to incorporate individual profiles and needs into PDAs, making the decision support more personalised. Health innovation may enhance accessibility by using mobile, tablet and Internet technologies; make risk communication more interactive; and identify patient values more effectively. In addition, using databases to capture patient data and the usage of PDAs can help: developers to improve PDAs' design; clinicians to facilitate the decisionmaking process more effectively; and policy makers to make shared decision making more feasible and cost-effective. Health innovation may hold the key to advancing PDAs by creating a more personalised and effective decision support tool for patients making healthcare decisions.

Large language models present new questions for decision support

: Future Joint Force Commanders (JFCs) will use the Global Command and Control System (GCCS) for their command and control needs. This study examines this automated decision support system for command and control: the Global Command and Control System. It briefly describes the functional areas of this system and some of the specific applications currently fielded. This overview analyzes the functional areas to determine the various types of information that the system is able to track. The needs of a strategic military leader are investigated to determine if the GCCS system can provide the required decision support. For needs not currently met, decision support technologies in artificial intelligence are examined to see what is currently available external to GCCS that can be used as a decision aid to assist a strategic level commander. The paper addresses and presents designs for incorporating these decision aids into the current and proposed future system.

Intelligent Modelling Environment: An Approach to Decision Support Systems Design

The Naval Undersea Warfare Center, Division Newport (NUWCDIVNPT) and its partners have developed a prototype CTI (Command Technology Initiatives) Test Bed to demonstrate the utility of a facility where warfighters, government, academia and industry can evaluate the application of collaborate decision support and advanced computer graphics technologies to submarine command and control. The CTI Test bed is currently comprised of three components: Collaborative Visualization Environment (CVE) for Submarine Command and Control, which provides a coherent 3-D display of the perceived undersea battlespace. Individual windows can display multi-dimensional data/information to support a common picture of undersea battlespace management and tactical control; Submarine Fleet Mission Programming Library (SFMPL) which provides environmental data, such as transmission loss, to CVE; Command and Control Data Server which provides contact reports, areas of uncertainty, and ownship/contact motion to CVE Facilitated by a CORBA<SUP>4</SUP> (Common Object Request Broker Architecture) compliant architecture, remotely connected collaborators interact via a computer network to generate and share information. Additionally, collaborators communicate orally via network telephony. Currently, the CTI Test bed is configured to provide volumetric displays of: undersea battlespace w/ bathymetry; Detection/Counter-detection regions for a given probability of detection; Contact(s) Volume of Uncertainty The CTI Test Bed provides a CORBA compliant framework, which can be readily expanded to evaluate candidate applications of collaborative command and tactical decision support and advanced computer graphics technologies.

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 2.913 (2021 JCR, received in June 2022)The IJIC 20th Anniversary Issue was published in 2021.

In recent years, logistics service companies have been seeking numerous technologies and logistics information systems to support daily logistics operations. However, the planning and decision making of logistics activities is still done manually, where subjective judgment is made by Logistics Service Providers (LSP) through analysing multiple knowledge/information sources and various quantitative and qualitative criteria. In this paper, the use of an intelligent Decision Support System (DSS), namely, Knowledge and Analytical-based Intelligent System (KAIS), to embed the information communication and decision support technology in aiding such decision-making process is explained. KAIS is developed by integrating Analytic Hierarchy Process (AHP), Case-Based Reasoning (CBR) and portal technologies seamlessly, which is suitable for application in various dynamic logistics operations environment. By using KAIS in Eastern Logistics Limited (ELL), the overall logistics service quality and customer satisfaction level is significantly enhanced through making accurate decision and systematic planning of logistics operations.

To evaluate the efficacy and safety of basal-bolus insulin therapy in managing glycaemia during fasting periods in hospitalized patients with type 2 diabetes. We performed a post hoc analysis of two prospective, uncontrolled interventional trials that applied electronic decision support system-guided basal-bolus (meal-related and correction) insulin therapy. We searched for fasting periods (invasive or diagnostic procedures, medical condition) during inpatient stays. In a mixed model analysis, patients' glucose levels and insulin doses on days with regular food intake were compared with days with fasting periods. Out of 249 patients, 115 patients (33.9% female, age 68.3± 10.3 years, diabetes duration 15.1± 10.9 years, body mass index 30.1± 5.4kg/m2 , HbA1c 69 ± 20 mmol/mol) had 194 days with fasting periods. Mean daily blood glucose (BG) was lower (modelled difference [ModDiff]: -0.5± 0.2 mmol/L, P= .006), and the proportion of glucose values within the target range (3.9-10.0 mmol/L) increased on days with fasting periods compared with days with regular food intake (ModDiff: +0.06 ± 0.02, P= .005). Glycaemic control on fasting days was driven by a reduction in daily bolus insulin doses (ModDiff: -11.0± 0.9IU, P< .001), while basal insulin was similar (ModDiff: -1.1± 0.6IU, P= .082) compared with non-fasting days. Regarding hypoglycaemic events (BG < 3.9 mmol/L), there was no difference between fasting and non-fasting days (χ2 0.9% vs. 1.7%, P= .174). When using well-titrated basal-bolus insulin therapy in hospitalized patients with type 2 diabetes, the basal insulin dose does not require adjustment during fasting periods to achieve safe glycaemic control, provided meal-related bolus insulin is omitted and correction bolus insulin is tailored to glucose levels.

The article studied and realized basic structure and the function frame of based on the GIS relevant technology's decision support system, because limitation and imperfection are characteristic of grass forest disruptive disaster system which the present manual work way deal with. Take grass forest fire of Inner Mongolia region for example, according to assistant decision-making demand of emergency handling in the aspect of grass forest disruptive disaster, using JS as development language and SuperMap iServer Java of SuperMap company as development kit, a set of scientific decision-making support plan can be formed promptly, accurately, effectively. The system, s GIS data provides reasonable basis for decision-maker and decision-making organ making scientific rescue response decision-making and rescue resource allocation decision-making on spot of emergency commanding work. Thus the design aims to make command staff to carry out emergency rescue work orderly, it not only voids randomness of emergency commanding but also improves efficiency of emergency commanding decision-making and cuts down rescue organization time.

Flooding has become one of the most rapidly growing types of natural disaster that has spread around the globe. It is is one of the major natural hazards in many countries and mostly affected in the low-lying or flood prone areas. In order to minimize loss of life and economic losses, a detailed and comprehensive decision making tool is necessary for both flood control planning and emergency service operations. In this paper, we demonstrate our research design for mobile based decision support of Flood Early Warning System (FEWS). We outlined four research objectives. Firstly, critical criteria for flood risk assessment will be identified and the second step will involve develop measurement model for relative flood risk using Geographic Information System (GIS), Multi Attribute Decision Making (MADM) and data mining technique. In the third objectives, the holistic architectural design is develop by incorporating the communication technology and other related ICT requirements for the mobile decision support. The fourth objective is to validate the mathematical model and architectural design. Case study approach is chosen in order to understand the flood event and validate the decision support model. Following well-defined procedures, flood maps were drawn based on the data collected from expert responses to a questionnaire, the field survey, satellite images, and documents from flood management agencies. It is anticipates that by integrating of mathematical model, GIS and mobile application in flood risk assessment could provide useful detailed information for flood risk management, evacuation, communication. The decision support design from this study is perhaps to improve the warning system and contribute to reduction of casualties.

Creating an ideal system of care to address the care forpatients with ST-elevation myocardial infarction (STEMI) iscomplex from both the system’s and patient/family’s perspec-tives. In general, this care is unlike most other hospital care.It typically involves very fast and complex decision makingand, often, sudden transportation to another facility forpercutaneous coronary intervention (PCI). All of this occurswith a potentially critically ill patient and at a time when thefamily is often not immediately available. In this report, weaddress key perspectives from the patient and public point ofview of the current system of care for STEMI patients andhighlight the barriers and gaps that must be addressed by anideal system of care (Table 1).

The analysis of production systems using discrete, event-based simulation is wide spread and generally accepted as a decision support technology. It aims either at the comparison of competitive system designs or the identification of a best possible parameter configuration of a simulation model. Here, combinatorial techniques of simulation and optimization methods support the user in finding optimal solutions, but typically result in long computation times, which often prohibits a practical application in industry. To close this gap, this paper presents a fast converging procedure combining a Genetic Algorithm with a material flow simulation including an interactive analysis of simulation runs. An early termination of simulation runs is used for unpromising parameter configurations. The integrated implementation allows automated, distributed simulation runs for practical, complex production systems. A use-case shows the proof of concept with a reference model and demonstrates the resulting speed-up of this approach.

Focusing on the important points during the implementation of World Expo 2010 Shanghai, the general framework of integrated information system for construction and operation management is established. Key technologies of system implementation are also presented. These techonologies include service-oriented comprehensive information integrated architecture, virtual reality technology based on GIS platform, integrated information technology for large-scale & complex construction programme, intelligent video monitoring technology, decision support technology, information portal technology etc.

Artificial intelligence has become a large part of everyday life. The world is heading towards new heights of adaption of various decision support technologies. In the present era, the rate at which we are consuming natural finite resources and depleting them, through producing chemicals, soil pollution, water pollution, air pollution, etc., is destroying our ecosystem. We have tried several recycling methods to minimize wastage, but it is insignificant. Now there is a need to think about state-of-the-art technological support like artificial intelligence (AI). This chapter explores the prospects of artificial intelligence in the circular economy.