Metodologia para a implementação de um sistema de apoio à decisão no sistema nacional de saúde baseado nos indicadores para a saúde

Actualmente, o SIS depara-se com problemas relativos à normalização e qualidade de dados, interoperabilidade entre instituições e inexistência de sistemas que suportem e agilizem o processo da decisão estratégica no sector. Numa primeira fase, este trabalho caracteriza e clarifica o papel das diversas instituições que colaboram com o MS, a forma como é gerida a informação e o conhecimento e os pressupostos do PNS enquanto documento agregador de indicadores que permitem avaliar o estado da saúde em Portugal. Com base na caracterização do sector e na importância orientadora do PNS, apresenta-se uma metodologia que organiza e desenvolve um modelo de metadados, baseados nos indicadores para a saúde, presentes no PNS. A sua importância para o sector é evidente uma vez que permite servir de suporte ao futuro desenvolvimento de aplicações estratégicas de apoio à decisão, salvaguardando a implementação e a divulgação do PNS e dos seus indicadores. ABSTRACT; Currently, the SIS comes across with problems related with normalization and quality of data, cooperation between institutions and the inexistence of systems that support and speed the process of strategical decisions in the sector. ln a first phase, this work characterizes and simplifies the role of each institution that collaborates with MS, the form as it is managed the information and the knowledge and the fundamentals of PNS, as a document witch aggregates pointers that allow the evaluation of the state of health in Portugal. On the basis of this characterization and the orienting importance of PNS, this work demonstrates a metadata methodology that organizes and develops a model, based on health pointers, indicated in PNS. Its importance for the sector is evident because it can support future developments of strategical applications, safeguarding the implementation and the analysis of PNS and its pointers.

Diseño e implementación de un sistema de soporte de decisiones para el Centro de Documentación Regional “Juan Bautista Vázquez”

El volumen de datos en bibliotecas ha aumentado enormemente en los últimos años, así como también la complejidad de sus fuentes y formatos de información, dificultando su gestión y acceso, especialmente como apoyo en la toma de decisiones. Sabiendo que una buena gestión de bibliotecas involucra la integración de indicadores estratégicos, la implementación de un Data Warehouse (DW), que gestione adecuadamente tal cantidad de información, así como su compleja mezcla de fuentes de datos, se convierte en una alternativa interesante a considerar. El artículo describe el diseño e implementación de un sistema de soporte de decisiones (DSS) basado en técnicas de DW para la biblioteca de la Universidad de Cuenca. Para esto, el estudio utiliza una metodología holística, propuesto por Siguenza-Guzman et al. (2014) para la evaluación integral de bibliotecas. Dicha metodología evalúa la colección y los servicios, incorporando importantes elementos para la gestión de bibliotecas, tales como: el desempeño de los servicios, el control de calidad, el uso de la colección y la interacción con el usuario. A partir de este análisis, se propone una arquitectura de DW que integra, procesa y almacena los datos. Finalmente, estos datos almacenados son analizados y visualizados a través de herramientas de procesamiento analítico en línea (OLAP). Las pruebas iniciales de implementación confirman la viabilidad y eficacia del enfoque propuesto, al integrar con éxito múltiples y heterogéneas fuentes y formatos de datos, facilitando que los directores de bibliotecas generen informes personalizados, e incluso permitiendo madurar los procesos transaccionales que diariamente se llevan a cabo.

MACVIA-ARIA Sentinel NetworK for allergic rhinitis (MASK-rhinitis): the new generation guideline implementation

Several unmet needs have been identified in allergic rhinitis: identification of the time of onset of the pollen season, optimal control of rhinitis and comorbidities, patient stratification, multidisciplinary team for integrated care pathways, innovation in clinical trials and, above all, patient empowerment. MASK-rhinitis (MACVIA-ARIA Sentinel NetworK for allergic rhinitis) is a simple system centred around the patient which was devised to fill many of these gaps using Information and Communications Technology (ICT) tools and a clinical decision support system (CDSS) based on the most widely used guideline in allergic rhinitis and its asthma comorbidity (ARIA 2015 revision). It is one of the implementation systems of Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA). Three tools are used for the electronic monitoring of allergic diseases: a cell phone-based daily visual analogue scale (VAS) assessment of disease control, CARAT (Control of Allergic Rhinitis and Asthma Test) and e-Allergy screening (premedical system of early diagnosis of allergy and asthma based on online tools). These tools are combined with a clinical decision support system (CDSS) and are available in many languages. An e-CRF and an e-learning tool complete MASK. MASK is flexible and other tools can be added. It appears to be an advanced, global and integrated ICT answer for many unmet needs in allergic diseases which will improve policies and standards.

Comparison of the traditional pharmaceutical validation method versus an assisted pharmaceutical validation in hospitalized patients

Objective: To analyze pharmaceutical interventions that have been carried out with the support of an automated system for validation of treatments vs. the traditional method without computer support. Method: The automated program, ALTOMEDICAMENTOS® version 0, has 925 052 data with information regarding approximately 20 000 medicines, analyzing doses, administration routes, number of days with such a treatment, dosing in renal and liver failure, interactions control, similar drugs, and enteral medicines. During eight days, in four different hospitals (high complexity with over 1 000 beds, 400-bed intermediate, geriatric and monographic), the same patients and treatments were analyzed using both systems. Results: 3,490 patients were analyzed, with 42 155 different treatments. 238 interventions were performed using the traditional system (interventions 0.56% / possible interventions) vs. 580 (1.38%) with the automated one. Very significant pharmaceutical interventions were 0.14% vs. 0.46%; significant was 0.38% vs. 0.90%; non-significant was 0.05% vs. 0.01%, respectively. If both systems are simultaneously used, interventions are performed in 1.85% vs. 0.56% with just the traditional system. Using only the traditional model, 30.5% of the possible interventions are detected, whereas without manual review and only the automated one, 84% of the possible interventions are detected. Conclusions: The automated system increases pharmaceutical interventions between 2.43 to 3.64 times. According to the results of this study the traditional validation system needs to be revised relying on automated systems. The automated program works correctly in different hospitals.

A Metamodel for Collaboration Formalization

Part 11: Reference and Conceptual Models

Development of a cargo screening process simulator: a first approach

The efficiency of current cargo screening processes at sea and air ports is largely unknown as few benchmarks exists against which they could be measured. Some manufacturers provide benchmarks for individual sensors but we found no benchmarks that take a holistic view of the overall screening procedures and no benchmarks that take operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role. Our aim is to develop a decision support tool (cargo-screening system simulator) that will map the right technology and manpower to the right commodity-threat combination in order to maximise detection rates. In this paper we present our ideas for developing such a system and highlight the research challenges we have identified. Then we introduce our first case study and report on the progress we have made so far.

Modelling and analysing cargo screening processes: a project outline

The efficiency of current cargo screening processes at sea and air ports is unknown as no benchmarks exists against which they could be measured. Some manufacturer benchmarks exist for individual sensors but we have not found any benchmarks that take a holistic view of the screening procedures assessing a combination of sensors and also taking operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role. For such systems more advanced assessment methods need to be used, taking into account that the cargo screening process is of a dynamic and stochastic nature. Our project aim is to develop a decision support tool (cargo-screening system simulator) that will map the right technology and manpower to the right commodity-threat combination in order to maximize detection rates. In this paper we present a project outline and highlight the research challenges we have identified so far. In addition we introduce our first case study, where we investigate the cargo screening process at the ferry port in Calais.

Investigating key decision problems to optimize the operation and maintenance strategy of offshore wind farms

This paper investigates three decision problems with potential to optimize operation and maintenance and logistics strategies for offshore wind farms: the timing of pre-determined jack-up vessel campaigns; selection of crew transfer vessel fleet; and timing of annual services. These problems are compared both in terms of potential cost reduction and the stochastic variability and associated uncertainty of the outcome. Pre-determined jack-up vessel campaigns appear to have a high cost reduction potential but also a higher stochastic variability than the other decision problems. The paper also demonstrates the benefits and difficulties of considering problems together rather than solving them in isolation.

Towards an assessment of on-farm niches for improved forages in Sud-Kivu, DR Congo

Inadequate quantity and quality of livestock feed is a persistent constraint to productivity for mixed crop-livestock farming in eastern Democratic Republic of Congo. To assess on-farm niches of improved forages, demonstration trials and participatory on-farm research were conducted in four different sites. Forage legumes included Canavalia brasiliensis (CIAT 17009), Stylosanthes guianensis (CIAT 11995) and Desmodium uncinatum (cv. Silverleaf), while grasses were Guatemala grass (Tripsacum andersonii), Napier grass (Pennisetum purpureum) French Cameroon, and a local Napier line. Within the first six months, forage legumes adapted differently to the four sites with little differences among varieties, while forage grasses displayed higher variability in biomass production among varieties than among sites. Farmers’ ranking largely corresponded to herbage yield from the first cut, preferring Canavalia, Silverleaf desmodium and Napier French Cameroon. Choice of forages and integration into farming systems depended on land availability, soil erosion prevalence and livestock husbandry system. In erosion prone sites, 55–60% of farmers planted grasses on field edges and 16–30% as hedgerows for erosion control. 43% of farmers grew forages as intercrop with food crops such as maize and cassava, pointing to land scarcity. Only in the site with lower land pressure, 71% of farmers grew legumes as pure stand. When land tenure was not secured and livestock freely roaming, 75% of farmers preferred to grow annual forage legumes instead of perennial grasses. Future research should develop robust decision support for spatial and temporal integration of forage technologies into diverse smallholder cropping systems and agro-ecologies.

Production Diseases in European Organic Dairy Farms – Current Status and Identification of Drivers for Improvement by Means of a Systemic Approach

The aims of this thesis were to determine the animal health status in organic dairy farms in Europe and to identify drivers for improving the current situation by means of a systemic approach. Prevalences of production diseases were determined in 192 herds in Germany, France, Spain, and Sweden (Paper I), and stakeholder consultations were performed to investigate potential drivers to improve animal health on the sector level (ibid.). Interactions between farm variables were assessed through impact analysis and evaluated to identify general system behaviour and classify components according to their outgoing and incoming impacts (Paper II-III). The mean values and variances of prevalences indicate that the common rules of organic dairy farming in Europe do not result in consistently low levels of production diseases. Stakeholders deemed it necessary to improve the current status and were generally in favour of establishing thresholds for the prevalence of production diseases in organic dairy herds as well as taking actions to improve farms below that threshold. In order to close the gap between the organic principle of health and the organic farming practice, there is the need to formulate a common objective of good animal health and to install instruments to ensure and prove that the aim is followed by all dairy farmers in Europe who sell their products under the organic label. Regular monitoring and evaluation of herd health performance based on reference values are considered preconditions for identifying farms not reaching the target and thus in need of improvement. Graph-based impact analysis was shown to be a suitable method for modeling and evaluating the manifold interactions between farm factors and for identifying the most influential components on the farm level taking into account direct and indirect impacts as well as impact strengths. Variables likely to affect the system as a whole, and the prevalence of production diseases in particular, varied largely between farms despite some general tendencies. This finding reflects the diversity of farm systems and underlines the importance of applying systemic approaches in health management. Reducing the complexity of farm systems and indicating farm-specific drivers, i.e. areas in a farm, where changes will have a large impact, the presented approach has the potential to complement and enrich current advisory practice and to support farmers’ decision-making in terms of animal health.

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

Screening a Case Base for Stroke Disease Detection

Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality fig-ure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge rep-resentation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases re-trieval one. On the other hand, and aiming at an improvement of the CBR theo-retical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were re-written, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of one`s confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

Prediction of Length of Hospital Stay in Preterm Infants - A Case-Based Reasoning View

The length of stay of preterm infants in a neonatology service has become an issue of a growing concern, namely considering, on the one hand, the mothers and infants health conditions and, on the other hand, the scarce healthcare facilities own resources. Thus, a pro-active strategy for problem solving has to be put in place, either to improve the quality-of-service provided or to reduce the inherent financial costs. Therefore, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a case-based problem solving methodology to computing, that caters for the handling of incomplete, unknown, or even contradictory in-formation. The proposed model has been quite accurate in predicting the length of stay (overall accuracy of 84.9%) and by reducing the computational time with values around 21.3%.

Waiting Time Screening in Diagnostic Medical Imaging - A Case-Based View

Due to the high standards expected from diagnostic medical imaging, the analysis of information regarding waiting lists via different information systems is of utmost importance. Such analysis, on the one hand, may improve the diagnostic quality and, on the other hand, may lead to the reduction of waiting times, with the concomitant increase of the quality of services and the reduction of the inherent financial costs. Hence, the purpose of this study is to assess the waiting time in the delivery of diagnostic medical imaging services, like computed tomography and magnetic resonance imaging. Thereby, this work is focused on the development of a decision support system to assess waiting times in diagnostic medical imaging with recourse to operational data of selected attributes extracted from distinct information systems. The computational framework is built on top of a Logic Programming Case-base Reasoning approach to Knowledge Representation and Reasoning that caters for the handling of in-complete, unknown, or even self-contradictory information.

A Case Based approach to assess Waiting Time Prediction at an Intensive Care Unity

Waiting time at an intensive care unity stands for a key feature in the assessment of healthcare quality. Nevertheless, its estimation is a difficult task, not only due to the different factors with intricate relations among them, but also with respect to the available data, which may be incomplete, self-contradictory or even unknown. However, its prediction not only improves the patients’ satisfaction but also enhance the quality of the healthcare being provided. To fulfill this goal, this work aims at the development of a decision support system that allows one to predict how long a patient should remain at an emergency unit, having into consideration all the remarks that were just stated above. It is built on top of a Logic Programming approach to knowledge representation and reasoning, complemented with a Case Base approach to computing.