Connecting Landscapes: A decision support system to facilitate conservation led development

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.

Product development decisions represent challenging processes with various stakeholders from different disciplines and with different perspectives involved. Decisions can be highly complex due to the high amount of influencing factors that have to be considered simultaneously. Among other methods and tools, decision support systems (DSS) were developed to support decision-makers in handling complex decisions. One main disadvantage of DSS is the little adaptation to and integration of decision-maker needs and characteristics. Thus, current trends focus on the development of human- and decision-maker-centered DSS. This paper aims in providing a multilevel stepwise procedure for the modeling of decision-makers in order to enable a more effective decision support. The decision-maker modeling procedure is based on findings from literature research and results from research projects performed in cooperation with industry partners. The procedure serves as a guideline for generating diverse decision-maker models for different applications and for integrating human cognitive thinking procedures within the design and functionality of decision support methodologies and systems. The procedure represents an important design element of the human-centered decision support methodology currently being developed at the Institute of Product Development of the Technische Universität München (Germany).

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).

A statistical method for improving the accuracy of predictive mathematical models used in drilling operations is proposed. The scheme can be applied in drilling automation, for example for improving accuracy in automated control systems such as managed pressure drilling (MPD), and for more reliable real-time monitoring and decision support systems. The proposed method is applied to a simulated case of MPD. Hydraulic model predictions of BHP are continuously compared to PWD readings. A short sequence with varying pump rate is run to train the statistics, which are collected at each pump rate, and refined considering also the pump rate derivative. This is visualized as a 3D surface giving a clear illustration of the error signature of the simulated case. The estimated error is added to the set point of the choke control system, demonstrating a clear improvement of BHP control. We proposed a new statistical method that uses real-time sensor data to perform automatic fingerprinting of the predictive performance of a given model. The principle is simple but powerful and can be applied to many cases. It can be combined with smart visualization tools in decision support systems, helping to build confidence in the users, or even utilized to adjust the initial prediction for improved accuracy in control systems. The tool is independent of the internal calculations in the mathematical model, and it can therefore be utilized in combination with any model.

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund(TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a "systems"approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out post-project activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and maybe applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security.

Recently, very large-scale decision support systems (DSSs) have been developed, which tackle very complex problems, associated with very extensive and polymorphic information, which probably is geographically highly dispersed. The management, updating, modification and upgrading of the data and program core of such an information system is, as a rule, a very difficult task, which encompasses many hazards and risks. The purpose of the present work was (a) to list the more significant of these hazards and risks and (b) to introduce a new general methodology for designing decision support (DS) systems that are robust and circumvent these risks. The core of this new approach was the introduction of a meta-database, called teleological, on the base of which management, updating, modification, reduction, growth and upgrading of the system may be safely and efficiently achieved. The very same teleological meta-database can be used for the construction of a sound decision support system, incorporating elements of a previous one at a future stage.

We sought to develop a computerized clinical decision support for clinicians treating patients with type 2 diabetes mellitus (T2DM). We designed, developed, and tested a computer-assisted decision support (CADS) system using statistical analyses of self-monitoring of blood glucose data, laboratory data, medical and medication history, and individualized hemoglobin A1c goals. A rule-based expert system generated recommendations for changes in therapy and accompanying explanations. A clinical decision support system (CADS) was developed that considers 9 classes of medications and 69 regimens with combinations of up to 4 therapeutic agents. The preferred sequences of regimens can be customized. The program is integrated with a "comprehensive diabetes management system," electronic medical record systems, and a method for uploading data from memory glucose meters via telephone without use of a computer or the Internet. The software provides a report to the clinician regarding the overall quality of glycemic control and identifies problems, e.g., hypoglycemia, hyperglycemia, glycemic variability, and insufficient data. The program can recommend continuation of current therapy, adjustment of dosages of current medications, or change of regimen and can provide explanations for its recommendations. If the user rejects the recommendations, the program will recommend alternative approaches. The CADS also provides access to Food and Drug Administration-approved prescribing information, guidelines from professional organizations, and selections from the general medical literature. The system has been extensively tested with real and synthetic data and is ready for evaluation in multicenter clinical trials. A clinical decision support system to assist with the management of patients with T2DM was designed, developed, tested, and found to perform well.

Renal dosing clinical decision support (CDS) systems have demonstrated clinical effectiveness and potential benefits for patient outcomes. However, the high override rates consistently reported are problematic and undesirable. To understand providers' use patterns of renal dosing CDS, we investigated the override reasons obtained from primary care practices affiliated with two teaching hospitals. We selected a stratified random sample of 300 alerts and reviewed electronic medical records. Appropriateness criteria and an inter-rater reliability process were used. We found that two thirds of alerts were overridden inappropriately, and this proportion was similar for frequent over-riders as compared to the remainder of physicians. These findings imply that strategies are needed to convince providers to accept more clinically appropriate suggestions, though they need to be broadly targeted.

This commentary examines the historical importance of decision support to the information systems (IS) field from the viewpoint of four researchers whose work spans the several decades of decision support systems (DSS) research. Given this unique “generational” vantage point, we present the changes in and impact of DSS research as well as future considerations for decision support in the IS field. We argue that the DSS area has remained vital as technology has evolved and our understanding of decision-making processes has deepened. DSS work over the last several years has contributed both breadth and depth to decision-making research; the challenge now is to make sense of it all by placing it in an understandable context and by applying our analysis to the relevant issues looming in the future. One major outcome of this commentary is the identification of future trends in DSS research and what the users of these new DSS outlets can learn from the past. Trends include the increasing impact of social and mobile computing on DSS research, as well as knowledge management DSS and negotiation support systems that shift the focus to delivering more customer-centric and marketplace support.

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.

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.

An entity-relationship approach to decision support and expert systems

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.

Many decision support methods and systems in pharmacovigilance are built without explicitly addressing specific challenges that jeopardize their eventual success. We describe two sets of challenges and appropriate strategies to address them. The first are data-related challenges, which include using extensive multi-source data of poor quality, incomplete information integration, and inefficient data visualization. The second are user-related challenges, which encompass users' overall expectations and their engagement in developing automated solutions. Pharmacovigilance decision support systems will need to rely on advanced methods, such as natural language processing and validated mathematical models, to resolve data-related issues and provide properly contextualized data. However, sophisticated approaches will not provide a complete solution if end-users do not actively participate in their development, which will ensure tools that efficiently complement existing processes without creating unnecessary resistance. Our group has already tackled these issues and applied the proposed strategies in multiple projects.

There is a substantial difference between the development methodologies of the following two Information Systems: Transaction Support System (TSS) and Decision Support System (DSS). The central concern of the Transaction Support System that supports the operational world in organizations is transaction processing. The retrieval of information for analytical purposes is in the very focus of the Decision Support System. The DSS provoked a series of changes in RDBMS technology. In 1995 the data warehousing was recognized as a new concept. Multi-dimensional architecture of a data model emerged beside the existing relational one. The following year brought an explosion of the OLAP tools. Now the time has come for the full recognition of the DSS development methodology as a separate discipline. This methodology is presented on the following pages. There are obstacles that make it difficult to accept or have a clear view of it. These obstacles are highlighted through the comparison to the TSS developmentmethodology that represents the traditional point of view. Tasks, deliverables, techniques, approaches, differences, similarities and analogies are discussed for each development activity. It is irrelevant for the discussion whether the DSS comprises data warehouse or/and some other store of data of the similar type, e.g. data mart or/and operational data store (ODS). This paper tries to reveal why it is so hard to accept the Decision Support System development methodology.