Suitability analysis for siting MSW landfills and its multicriteria spatial decision support system: method, implementation and case study

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision analysis (MCDA) methods for incorporating conflicting objectives and decision makers' (DMs') preferences into spatial decision models. This article presents a raster-based MC-SDSS that combines the analytic hierarchy process (AHP) and compromise programming methods, such as TOPSIS (technique for order preference by similarity to the ideal solution) and Ideal Point Methods. To the best of our knowledge it is the first time that a synergy of AHP and compromise programming methods is implemented in raster-driven GIS-based landfill suitability analysis. This procedure is supported by a spatial decision support system (SDSS) that was developed within a widely used commercial GIS software package. A real case study in the Thrace region in northeast Greece serves as a guide on how to conduct a suitability analysis for a MSW landfill site with the proposed MC-SDSS. Moreover, the procedure for identifying MSW disposal sites is accomplished by performing four computational models for synthesizing the DMs per criterion preferential system. Based on the case study results, a comparison analysis is performed according to suitability index estimations. According to them Euclidean distance metric and TOPSIS present strong similarities. When compared with Euclidean distance metric, TOPSIS seems to generate results closer to that derived by Manhattan distance metric. The comparison of Chebychev distance metric with all the other approaches revealed the greatest deviations.

Creating a transparent and dynamic view of staffing as a foundation for improving quality and efficiency

As the United States moves into health care reform, there will be renewed debate on staffing models and ratios. The state of Massachusetts has developed a dashboard that will prepare them for nursing care models of the future while demonstrating that a variety of staffing templates can result in quality patient care today. Nursing leaders, legislators, and the public have access to information that demonstrates that staffing is complex and cannot be based on precise nursing ratios in all hospital settings.

Value-based resource management: a model for best value nursing care

With the health care environment shifting to a value-based payment system, Catholic Health Initiatives nursing leadership spearheaded an initiative with 14 hospitals to establish best nursing care at a lower cost. The implementation of technology-enabled business processes at point of care led to a new model for best value nursing care: Value-Based Resource Management. The new model integrates clinical patient data from the electronic medical record and embeds the new information in care team workflows for actionable real-time decision support and predictive forecasting. The participating hospitals reported increased patient satisfaction and cost savings in the reduction of overtime and improvement in length of stay management. New data generated by the initiative on nursing hours and cost by patient and by population (Medicare severity diagnosis-related groups), and patient health status outcomes across the acute care continuum expanded business intelligence for a value-based population health system.

Discrete event simulation for healthcare organizations: a tool for decision making

Healthcare organizations face challenges in efficiently accommodating increased patient demand with limited resources and capacity. The modern reimbursement environment prioritizes the maximization of operational efficiency and the reduction of unnecessary costs (i.e., waste) while maintaining or improving quality. As healthcare organizations adapt, significant pressures are placed on leaders to make difficult operational and budgetary decisions. In lieu of hard data, decision makers often base these decisions on subjective information. Discrete event simulation (DES), a computerized method of imitating the operation of a real-world system (e.g., healthcare delivery facility) over time, can provide decision makers with an evidence-based tool to develop and objectively vet operational solutions prior to implementation. DES in healthcare commonly focuses on (1) improving patient flow, (2) managing bed capacity, (3) scheduling staff, (4) managing patient admission and scheduling procedures, and (5) using ancillary resources (e.g., labs, pharmacies). This article describes applicable scenarios, outlines DES concepts, and describes the steps required for development. An original DES model developed to examine crowding and patient flow for staffing decision making at an urban academic emergency department serves as a practical example.

Quest for business intelligence in health care

In an era of reform, providers are examining more forward-thinking business intelligence strategies, according to a recent study. Enterprise business intelligence tool sets offer a breadth of design and functionality that often are capable of serving the enterprise. One limitation of broader tool sets is that they may lack needed application-specific functionality or prebuilt healthcare content for a specific department.

Healthcare security staffing for smaller facilities: where science meets art

Obtaining effective security resourcing and staffing for smaller healthcare facilities presents many difficulties, according to the author In this article, he provides guidance to security practitioners on taking existing data and translating it into a language that administration will understand and appreciate.

Delivering antibiotic resistance information specifically tailored to location and time

Antibiotic resistance poses a significant threat to humanity. Hundred years since the beginning of the era of antibacterial drugs, we are facing increasing numbers of infections with multi-resistant pathogens. The current approach of distributing information on antibiotic resistance in printed form in the clinics has disadvantages with respect to the actuality of the data and the regional heterogeneity of resistance patterns. We developed an application named qRe using representational state transfer as a communication standard to deliver antibiotic resistance percentage information to the end user. The data is selected specifically for his/her geographic location. The user can display the information using either the application for Android smart phones or the web application. With the presented software we show the technical feasibility of delivering antibiotic resistance information specifically tailored to location and time. A short evaluation of the software showed an overall positive response from physicians. Based on recommendations of previous investigations, we expect a measurable clinical impact.

How business intelligence can improve value

Case studies of three healthcare organizations reinforce the premise that business intelligence--the ability to convert data into actionable information for decision making--is critical to demonstrating improved value.

ARDEN2BYTECODE: a one-pass Arden Syntax compiler for service-oriented decision support systems based on the OSGi platform

Patient empowerment might be one key to reduce the pressure on health care systems challenged by the expected demographic changes. Knowledge based systems can, in combination with automated sensor measurements, improve the patients' ability to review their state of health and make informed decisions. The Arden Syntax as a standardized language to represent medical knowledge can be used to express the corresponding decision rules. In this paper we introduce ARDEN2BYTECODE, a newly developed open source compiler for the Arden Syntax. ARDEN2BYTECODE runs on Java Virtual Machines (JVM) and translates Arden Syntax directly to Java Bytecode (JBC) executable on JVMs. ARDEN2BYTECODE easily integrates into service oriented architectures, like the Open Services Gateway Initiative (OSGi) platform. Apart from an evaluation of compilation performance and execution times, ARDEN2BYTECODE was integrated into an existing knowledge supported exercise training system and recorded training sessions have been used to check the implementation.

Business process modeling in healthcare

The importance of the process point of view is not restricted to a specific enterprise sector. In the field of health, as a result of the nature of the service offered, health institutions' processes are also the basis for decision making which is focused on achieving their objective of providing quality medical assistance. In this chapter the application of business process modelling - using the Business Process Modelling Notation (BPMN) standard is described. Main challenges of business process modelling in healthcare are the definition of healthcare processes, the multi-disciplinary nature of healthcare, the flexibility and variability of the activities involved in health care processes, the need of interoperability between multiple information systems, and the continuous updating of scientific knowledge in healthcare.

Decision support system to divide a large network into suitable District Metered Areas

This paper presents a new approach to divide large Water Distribution Networks (WDN) into suitable District Metered Areas (DMAs). It uses a hydraulic simulator and two operational models to identify the optimal number of DMAs, their entry points and boundary valves, and the network reinforcement/replacement needs throughout the project plan. The first model divides the WDN into suitable DMAs based on graph theory concepts and some user-defined criteria. The second model uses a simulated annealing algorithm to identify the optimal number and location of entry points and boundary valves, and the pipes reinforcement/replacement, necessary to meet the velocity and pressure requirements. The objective function is the difference between the economic benefits in terms of water loss reduction (arising from the average pressure reduction) and the cost of implementing the DMAs. To illustrate the proposed methodology, the results from a hypothetical case study are presented and discussed.

State of the art in pathology business process analysis, modeling, design and optimization

For analyzing current workflows and processes, for improving them, for quality management and quality assurance, for integrating hardware and software components, but also for education, training and communication between different domains' experts, modeling business process in a pathology department is inevitable. The authors highlight three main processes in pathology: general diagnostic, cytology diagnostic, and autopsy. In this chapter, those processes are formally modeled and described in detail. Finally, specialized processes such as immunohistochemistry and frozen section have been considered.

Model-based knowledge acquisition in environmental decision support system for wastewater integrated management

The main goal of the Water Framework Directive is to achieve good chemical and ecological status of water bodies by 2015. The implementation of integrated river basin management, including sewer systems, wastewater treatment plants and receiving water bodies, is essential to accomplishing this objective. Integrated management is complex and therefore the implementation of control systems and the development of decision support systems are needed to facilitate the work of urban wastewater system (UWS) managers. Within this context, the objective of this paper is to apply integrated modelling of an UWS to simulate and analyse the behaviour of the 'Congost' UWS in Spain, and to optimize its performance against different types of perturbations. This analysis results in optimal operating set-points for each perturbation, improves river water quality, minimizes combined sewer overflows and optimizes flow lamination from storm water tanks. This is achieved by running Monte Carlo simulations and applying global sensitivity analysis. The set-points will become part of the knowledge base composed of a set of IF-THEN rules of the environmental decision support system being developed for this case study.

Use of decision analysis interviews to support the sustainable use of the forests in Finnish Upper Lapland

Controversy between alternative uses of forests in Finnish Upper Lapland has been going on for decades, and in recent years it has been escalated to a serious conflict. The core of the conflict is the adverse impacts of forestry on old forests which are important grazing areas for reindeer and which are regarded as intact nature and wilderness areas. This paper describes the experiences of applying multi-criteria decision analysis interview approach on this conflict. The approach provides tools for structuring the problem and preferences of the stakeholders as well as for analyzing the effects of different alternatives in a common framework. We focus on the practical experiences gained from the application of this approach in this context. Multi-criteria decision analysis was found to be a useful approach to evaluate the economic, ecological and cultural aspects of this intense conflict. The obtained experiences also support the view that the approach works best when tightly integrated into the planning process.

Effect of descriptive information and experience on automation reliance

OBJECTIVE

The present research addresses the issue of reliance on decision support systems for the long-term (DSSLT), which help users develop decision-making strategies and long-term planning. It is argued that providing information about a system's future performance in an experiential manner, as compared with a descriptive manner, encourages users to increase their reliance level.

BACKGROUND

Establishing appropriate reliance on DSSLT is contingent on the system developer's ability to provide users with information about the system's future performance.

METHOD

A sequence of three studies contrasts the effect on automation reliance of providing descriptive information versus experience for DSSLT with two different positive expected values of recommendations.

RESULTS

Study I demonstrated that when automation reliance was determined solely on the basis of description, it was relatively low, but it increased significantly when a decision was made after experience with 50 training simulations. Participants were able to learn to increase their automation reliance levels when they encountered the same type of recommendation again. Study 2 showed that the absence of preliminary descriptive information did not affect the automation reliance levels obtained after experience. Study 3 demonstrated that participants were able to generalize their learning about increasing reliance levels to new recommendations.

CONCLUSION

Using experience rather than description to give users information about future performance in DSSLT can help increase automation reliance levels.

APPLICATIONS

Implications for designing DSSLT and decision support systems in general are discussed.

GeoCREV: veterinary geographical information system and the development of a practical sub-national spatial data infrastructure

This paper illustrates and discusses the key issues of the geographical information system (GIS) developed by the Unit of Veterinary Epidemiology of the Veneto region (CREV), defined according to user needs, spatial data (availability, accessibility and applicability), development, technical aspects, inter-institutional relationships, constraints and policies. GeoCREV, the support system for decision-making, was designed to integrate geographic information and veterinary laboratory data with the main aim to develop a sub-national, spatial data infrastructure (SDI) for the veterinary services of the Veneto region in north-eastern Italy. Its implementation required (i) collection of data and information; (ii) building a geodatabase; and (iii) development of a WebGIS application. Tools for the management, collection, validation and dissemination of the results (public access and limited access) were developed. The modular concept facilitates the updating and development of the system according to user needs and data availability. The GIS management practices that were followed to develop the system are outlined, followed by a detailed discussion of the key elements of the GIS implementation process (data model, technical aspects, inter-institutional relationship, user dimension and institutional framework). Problems encountered in organising the non-spatial data and the future work directions are also described.

Devils lake emergency outlet diversion conflict

The Devils Lake Emergency Outlet Diversion conflict is systematically studied from a strategic viewpoint using the Graph Model for Conflict Resolution in order to obtain insights about the resolution of this nagging international dispute. By modelling the conflict for the situation existing as of July 2005, just before the project began operation, the dispute is put into proper perspective and, subsequently, a stability analysis is carried out to obtain potential resolutions or equilibria. The results of a sensitivity analysis accurately predict the deal which actually took place when Canada and the American state of North Dakota reached a negotiated settlement. Finally, suggestions are put forward for improving the Boundary Waters Treaty of 1909 between Canada and the United States.

Finding the sweet spot: how to get the right staffing for variable workloads

All too often, hospital department managers set their staff schedules too much in anticipation of high levels of demand for services, leading to higher-than-necessary staffing costs when demand is lower than expected. The opposite approach of scheduling too few staff to meet demand, then relying on on-call or callback staff to address the shortage, also results in higher-than-necessary costs due to the premium wages that such staff must be paid. A staffing and workload simulation tool allows hospital departments to find the right balance between these extremes.

Computerised Decision Support Systems for the management of freshwater radioecological emergencies: assessment of the state-of-the-art with respect to the experiences and needs of end-users

Assessment of the environmental and radiological consequences of a nuclear accident requires the management of a great deal of data and information as well as the use of predictive models. Computerised Decision Support Systems (CDSS) are essential tools for this kind of complex assessment and for assisting experts with a rational decision process. The present work focuses on the assessment of the main features of selected state-of-the-art CDSS for off-site management of freshwater ecosystems contaminated by radionuclides. This study involved both developers and end-users of the assessed CDSS and was based on practical customisation exercises, installation and application of the decision systems. Potential end-users can benefit from the availability of several ready-to-use CDSS that allow one to run different kinds of models aimed at predicting the behaviour of radionuclides in aquatic ecosystems, evaluating doses to humans, assessing the effectiveness of different kinds of environmental management interventions and ranking these interventions, accounting for their social, economic and environmental impacts. As a result of the present assessment, the importance of CDSS "integration" became apparent: in many circumstances, different CDSS can be used as complementary tools for the decision-making process. The results of this assessment can also be useful for the future development and improvement of the CDSS.

A knowledge-based control system for air-scour optimisation in membrane bioreactors

Although membrane bioreactors (MBRs) technology is still a growing sector, its progressive implementation all over the world, together with great technical achievements, has allowed it to reach a mature degree, just comparable to other more conventional wastewater treatment technologies. With current energy requirements around 0.6-1.1 kWh/m3 of treated wastewater and investment costs similar to conventional treatment plants, main market niche for MBRs can be areas with very high restrictive discharge limits, where treatment plants have to be compact or where water reuse is necessary. Operational costs are higher than for conventional treatments; consequently there is still a need and possibilities for energy saving and optimisation. This paper presents the development of a knowledge-based decision support system (DSS) for the integrated operation and remote control of the biological and physical (filtration and backwashing or relaxation) processes in MBRs. The core of the DSS is a knowledge-based control module for air-scour consumption automation and energy consumption minimisation.