Physician adoption of a practice guideline: Effect on the cost of medical care for adult patients with diabetes

In the demanding environment of healthcare reform, reduction of unwanted physician practice variation is promoted, often through evidence-based guidelines. Guidelines represent innovations that direct change(s) in physician practice; however, compliance has been disappointing. Numerous studies have analyzed guideline development and dissemination, while few have evaluated the consequences of guideline adoption. The primary purpose of this study was to explore and analyze the relationship between physician adoption of the glycated hemoglobin test guideline for management of adult patients with diabetes, and the cost of medical care. The study also examined six personal and organizational characteristics of physicians and their association with innovativeness, or adoption of the guideline. ^ Cost was represented by approved charges from a managed care claims database. Total cost, and diabetes and related complications cost, first were compared for all patients of adopter physicians with those of non-adopter physicians. Then, data were analyzed controlling for disease severity based on insulin dependency, and for high cost cases. There was no statistically significant difference in any of eight cost categories analyzed. This study represented a twelve-month period, and did not reflect cost associated with future complications known to result from inadequate management of glycemia. Guideline compliance did not increase annual cost, which, combined with the future benefit of glycemic control, lends support to the cost effectiveness of the guideline in the long term. Physician adoption of the guideline was recommended to reduce the future personal and economic burden of this chronic disease. ^ Only half of physicians studied had adopted the glycated hemoglobin test guideline for at least 75% of their diabetic patients. No statistically significant relationship was found between any physician characteristic and guideline adoption. Instead, it was likely that the innovation-decision process and guideline dissemination methods were most influential. ^ A multidisciplinary, multi-faceted approach, including interventions for each stage of the innovation-decision process, was proposed to diffuse practice guidelines more effectively. Further, it was recommended that Organized Delivery Systems expand existing administrative databases to include clinical information, decision support systems, and reminder mechanisms, to promote and support physician compliance with this and other evidence-based guidelines. ^

ERP Software Selection Processes: A Case Study in the Metal Transformation Sector

When a firm decides to implement ERP softwares, the resulting consequences can pervade all levels, includ- ing organization, process, control and available information. Therefore, the first decision to be made is which ERP solution must be adopted from a wide range of offers and vendors. To this end, this paper describes a methodology based on multi-criteria factors that directly affects the process to help managers make this de- cision. This methodology has been applied to a medium-size company in the Spanish metal transformation sector which is interested in updating its IT capabilities in order to obtain greater control of and better infor- mation about business, thus achieving a competitive advantage. The paper proposes a decision matrix which takes into account all critical factors in ERP selection.

A MAUT approach for reusing ontologies

Knowledge resource reuse has become a popular approach within the ontology engineering field, mainly because it can speed up the ontology development process, saving time and money and promoting the application of good practices. The NeOn Methodology provides guidelines for reuse. These guidelines include the selection of the most appropriate knowledge resources for reuse in ontology development. This is a complex decision-making problem where different conflicting objectives, like the reuse cost, understandability, integration workload and reliability, have to be taken into account simultaneously. GMAA is a PC-based decision support system based on an additive multi-attribute utility model that is intended to allay the operational difficulties involved in the Decision Analysis methodology. The paper illustrates how it can be applied to select multimedia ontologies for reuse to develop a new ontology in the multimedia domain. It also demonstrates that the sensitivity analyses provided by GMAA are useful tools for making a final recommendation.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

A Maut aprroach for reusing domain ontologies on the basis of the NeOn Methodlogy

Knowledge resource reuse has become a popular approach within the ontology engineering field, mainly because it can speed up the ontology development process, saving time and money and promoting the application of good practices. The NeOn Methodology provides guidelines for reuse. These guidelines include the selection of the most appropriate knowledge resources for reuse in ontology development. This is a complex decision-making problem where different conflicting objectives, like the reuse cost, understandability, integration workload and reliability, have to be taken into account simultaneously. GMAA is a PC-based decision support system based on an additive multi-attribute utility model that is intended to allay the operational difficulties involved in the Decision Analysis methodology. The paper illustrates how it can be applied to select multimedia ontologies for reuse to develop a new ontology in the multimedia domain. It also demonstrates that the sensitivity analyses provided by GMAA are useful tools for making a final recommendation.

An Integrated Transport Planning Framework Involving a Combined Utility-Regret Approach

Sustainable transport planning requires an integrated approach involving strategic planning, impact analysis and multi-criteria evaluation. This study aims at relaxing the utility-based decision-making assumption by newly embedding anticipated-regret and combined utility-regret decision mechanisms in an integrated transport planning framework. The framework consists of a two-round Delphi survey, an integrated land-use and transport model for Madrid, and multi-criteria analysis. Results show that (i) regret-based ranking has similar mean but larger variance than utility-based ranking; (ii) the least-regret scenario forms a compromise between the desired and the expected scenarios; (iii) the least-regret scenario can lead to higher user benefits in the short-term and lower user benefits in the long-term; (iv) utility-based, regret-based and combined utility-regret-based multi-criteria analysis result in different rankings of policy packages; and (v) the combined utility-regret ranking is more informative compared with utility-based or regret-based ranking.

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

Modelling spatial decisión support systems focused on the development of a data warehouse: applying software engineering

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning médium and long-term ?the modus operandi? of these organizations. Despite the growing importance of these systems, most proposals do not include its total evelopment, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

How can irrigated agriculture adapt to climate change? Insights from the Guadiana Basin in Spain

Climate change is already affecting many natural systems and human environments worldwide, like the semiarid Guadiana Basin in Spain. This paper illustrates a systematic analysis of climate change adaptation in the Guadiana irrigation farming region. The study applies a solution-oriented diagnostic framework structured along a series of sequential analytical steps. An initial stage integrates economic and hydrologic modeling to evaluate the effects of climate change on the agriculture and water sectors. Next, adaptation measures are identified and prioritized through a stakeholder-based multi-criteria analysis. Finally, a social network analysis identifies key actors and their relationships in climate change adaptation. The study shows that under a severe climate change scenario, water availability could be substantially decreased and drought occurrence will augment. In consequence, farmers will adapt their crops to a lesser amount of water and income gains will diminish, particularly for smallholder farms. Among the various adaptation measures considered, those related to private farming (new crop varieties and modern irrigation technologies) are ranked highest, whereas public-funded hard measures (reservoirs) are lowest and public soft measures (insurance) are ranked middle. In addition, stakeholders highlighted that the most relevant criteria for selecting adaptation plans are environmental protection, financial feasibility and employment creation. Nonetheless, the social network analysis evidenced the need to strengthen the links among the different stakeholder groups to facilitate the implementation of adaptation processes. In sum, the diagnostic framework applied in this research can be considered a valuable tool for guiding and supporting decision making in climate change adaptation and communicating scientific results.

Estudio de un modelo multicriterio para estudiar la viabilidad de un cultivo energético en la Península Ibérica. Aplicación a la comarca de Navalmoral de la Mata

En el presente estudio se propone una metodología para la evaluación de proyectos de implantación de cultivos energéticos, integrando una serie de factores de interés en un modelo de decisión, basado en un enfoque multicriterio. Mediante este modelo se pretende evaluar tanto los territorios más adecuados para la introducción un cultivo energético, como la especie más apropiada a los condicionantes que presenta el lugar elegido. Para este estudio se ha realizado una selección previa de cuatro especies forestales, cuyas características de crecimiento y producción las hace adecuadas para su aplicación en un proyecto de este tipo. Las cuatro especies escogidas han sido chopo, sauce, eucalipto y paulonia. La metodología propuesta ha consistido primero en un estudio ecológico en el ámbito de la Península Ibérica y Baleares, con el fin de identificar aquellas regiones óptimas para cada una de las cuatro especies estudiadas. En este proceso se han seleccionado una serie de factores climáticos, que vendrán definidos a partir de los condicionantes ecológicos de dichas especies. Posteriormente se ha propuesto un modelo multicriterio, basado en técnicas conocidas y de aplicación sencilla, donde se integran aspectos ambientales, económicos y sociales, que vendrán a completar la información ecológica trabajada previamente. Este modelo incluye la técnica de comparación por pares propuesta por el Dr. Saaty en el año 1980, para la ponderación de los factores o criterios seleccionados. Posteriormente, y tras su valoración, se utiliza la suma lineal ponderada como técnica de decisión final. Una vez definido el modelo, se ha aplicado a una comarca en particular, la comarca agraria de Navalmoral de la Mata. A partir de la información recopilada referente a todos los criterios seleccionados previamente en el modelo, se ha procedido a valorar cada uno de ellos. Con estos valores y tras la ponderación de criterios, se ha aplicado el modelo, para obtener finalmente los territorios dentro de la comarca, y las especies forestales con mayor aptitud para el desarrollo de un proyecto de implantación de cultivos energéticos. ABSTRACT A methodology has been proposed for the evaluation of projects to implement energy crops; this includes a number of factors of interest in a decision model based on a multi-criteria approach. This model is to evaluate both the most suitable territories for introducing an energy crop, as the most appropriate species to the conditions presented by the place chosen For this study has made a preliminary selection of four species, with characteristics of growth and production, what making them suitable for use in a project of this type. The four species selected were poplar, willow, eucalyptus and paulownia. The proposed methodology consists first in an ecological study in the context of the Iberian Peninsula and the Balearic Islands, in order to identify those best regions for each of the four species studied. In this process has selected a series of climatic factors, which will be defined from the ecological conditions of these species. Then we have proposed a multi-criteria model based on known techniques and simple application where are integrated environmental, economic and social aspects, which will complement the ecological information previous. This model includes the technique proposed by Dr. Saaty in 1980, the weighting by pairs of factors or criteria selected. Then, after valuation, the weighted linear sum as final decision technique is used. After defining the model has been applied to a particular region, the agrarian region of Navalmoral de la Mata. From the information collected concerning to the criteria previously selected in the model, we proceeded to value each. With these values and assigned weights, the model has been applied to finally get the territories and forest species with greater aptitude for the development of a project to implement energy crops.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.

Intelligent Decision-Making System Frameworks for a Digital Plan of Work: A Theoretical Investigation for the Construction Industry

The construction industry is characterised by fragmentation and suffers from lack of collaboration, often adopting adversarial working practices to achieve deliverables. For the UK Government and construction industry, BIM is a game changer aiming to rectify this fragmentation and promote collaboration. However it has become clear that there is an essential need to have better controls and definitions of both data deliverables and data classification. Traditional methods and techniques for collating and inputting data have shown to be time consuming and provide little to improve or add value to the overall task of improving deliverables. Hence arose the need in the industry to develop a Digital Plan of Work (DPoW) toolkit that would aid the decision making process, providing the required control over the project workflows and data deliverables, and enabling better collaboration through transparency of need and delivery. The specification for the existing Digital Plan of Work (DPoW) was to be, an industry standard method of describing geometric, requirements and data deliveries at key stages of the project cycle, with the addition of a structured and standardised information classification system. However surveys and interviews conducted within this research indicate that the current DPoW resembles a digitised version of the pre-existing plans of work and does not push towards the data enriched decision-making abilities that advancements in technology now offer. A Digital Framework is not simply the digitisation of current or historic standard methods and procedures, it is a new intelligent driven digital system that uses new tools, processes, procedures and work flows to eradicate waste and increase efficiency. In addition to reporting on conducted surveys above, this research paper will present a theoretical investigation into usage of Intelligent Decision Support Systems within a digital plan of work framework. Furthermore this paper will present findings on the suitability to utilise advancements in intelligent decision-making system frameworks and Artificial Intelligence for a UK BIM Framework. This should form the foundations of decision-making for projects implemented at BIM level 2. The gap identified in this paper is that the current digital toolkit does not incorporate the intelligent characteristics available in other industries through advancements in technology and collation of vast amounts of data that a digital plan of work framework could have access to and begin to develop, learn and adapt for decision-making through the live interaction of project stakeholders.

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A way forward for land-use planning to achieve policy goals by using spatial modelling scenarios

This paper investigates how demographic (socioeconomic) and land-use (physical and environmental) data can be integrated within a decision support framework to formulate and evaluate land-use planning scenarios. A case-study approach is undertaken with land-use planning scenarios for a rapidly growing coastal area in Australia, the Shire of Hervey Bay. The town and surrounding area require careful planning of the future urban growth between competing land uses. Three potential urban growth scenarios are put forth to address this issue. Scenario A ('continued growth') is based on existing socioeconomic trends. Scenario B ('maximising rates base') is derived using optimisation modelling of land-valuation data. Scenario C ('sustainable development') is derived using a number of social, economic, and environmental factors and assigning weightings of importance to each factor using a multiple criteria analysis approach. The land-use planning scenarios are presented through the use of maps and tables within a geographical information system, which delineate future possible land-use allocations up until 2021. The planning scenarios are evaluated by using a goal-achievement matrix approach. The matrix is constructed with a number of criteria derived from key policy objectives outlined in the regional growth management framework and town planning schemes. The authors of this paper examine the final efficiency scores calculated for each of the three planning scenarios and discuss the advantages and disadvantages of the three land-use modelling approaches used to formulate the final scenarios.