Estratégias de comunicação utilizadas por profissionais de saúde na atenção à pacientes sob cuidados paliativos

O presente estudo objetivou verificar a relevância e a utilização de estratégias de comunicação em cuidados paliativos. Estudo quantitativo multicêntrico, realizado entre agosto/2008 e julho/2009, junto a 303 profissionais de saúde que trabalhavam com pacientes sob cuidados paliativos, por meio da aplicação de questionário. Os dados foram submetidos a tratamento estatístico descritivo. A maioria (57,7%) não foi capaz de citar ao menos uma estratégia de comunicação verbal e apenas 15,2% mencionaram cinco sinais ou estratégias não verbais. As estratégias verbais mais citadas foram as de cunho interrogativo sobre a doença/tratamento e, dentre as não verbais, destacaram-se o toque afetivo, olhar, sorriso, proximidade física e escuta ativa. Embora os profissionais tenham atribuído alto grau de relevância para a comunicação em cuidados paliativos, evidenciaram escasso conhecimento de estratégias de comunicação. Faz-se necessária a capacitação dos profissionais no que tange à comunicação em cuidados paliativos.

Effects of an educational patient safety campaign on patients' safety behaviours and adverse events

Rationale, aims and objectives  The study aims to investigate the effects of a patient safety advisory on patients' risk perceptions, perceived behavioural control, performance of safety behaviours and experience of adverse incidents. Method  Quasi-experimental intervention study with non-equivalent group comparison was used. Patients admitted to the surgical department of a Swiss large non-university hospital were included. Patients in the intervention group received a safety advisory at their first clinical encounter. Outcomes were assessed using a questionnaire at discharge. Odds ratios for control versus intervention group were calculated. Regression analysis was used to model the effects of the intervention and safety behaviours on the experience of safety incidents. Results  Two hundred eighteen patients in the control and 202 in the intervention group completed the survey (75 and 77% response rates, respectively). Patients in the intervention group were less likely to feel poorly informed about medical errors (OR = 0.55, P = 0.043). There were 73.1% in the intervention and 84.3% in the control group who underestimated the risk for infection (OR = 0.51, CI 0.31-0.84, P = 0.009). Perceived behavioural control was lower in the control group (meanCon  = 3.2, meanInt  = 3.5, P = 0.010). Performance of safety-related behaviours was unaffected by the intervention. Patients in the intervention group were less likely to experience any safety-related incident or unsafe situation (OR for intervention group = 0.57, CI 0.38-0.87, P = 0.009). There were no differences in concerns for errors during hospitalization. There were 96% of patients (intervention) who would recommend other patients to read the advisory. Conclusions  The results suggest that the safety advisory decreases experiences of adverse events and unsafe situations. It renders awareness and perceived behavioural control without increasing concerns for safety and can thus serve as a useful instrument for communication about safety between health care workers and patients.

Application of the fault-tolerant deadline mechanism to a satellite on-board computer system /

"June 1980."

Architettura cloud per lo sviluppo multi-piattaforma di Sistemi Operativi: Applicazioni specifiche

Il mondo dell’Internet of Things e del single board computing sono settori in forte espansione al giorno d’oggi e le architetture ARM sono, al momento, i dominatori in questo ambito. I sistemi operativi e i software si stanno evolvendo per far fronte a questo cambiamento e ai nuovi casi d’uso che queste tecnologie introducono. In questa tesi ci occuperemo del porting della distribuzione Linux Sabayon per queste architetture, la creazione di un infrastruttura per il rilascio delle immagini e la compilazione dei pacchetti software.

Kommunikációs és kapcsolati tényezők jelentősége az egészségügyi szolgáltatások minőségében

A tanulmány célja, hogy egy szekunder kutatásra alapozva áttekintetést adjon az orvos-beteg kommunikáció elméleti hátteréről, a kommunikáció és az egészségügyi (pácienshez kapcsolódó) kimenetek közvetlen, valamint az együttműködés és az észlelt minőség által összekötött közvetett kapcsolatáról. Ez azért fontos, mert a kapcsolat a kommunikáció és az objektíven mérhető kimenetek (például egészségügyi állapot javulása) között – amelyeknek komoly gazdasági következményei is vannak – alátámasztja azt a feltételezést, hogy az orvos-beteg kommunikáció kutatására szükség van az egészségügy berkein kívül is. A felvázolt elméleti háttér segítségével pedig olyan modell kialakítása a cél, amely alapul szolgálhat egy későbbi primer kutatásnak.

See and avoid using on-board computer vision

The integration of unmanned aircraft into civil airspace is a complex issue. One key question is whether unmanned aircraft can operate just as safely as their manned counterparts. The absence of a human pilot in unmanned aircraft automatically points to a deficiency that is the lack of an inherent see-and-avoid capability. To date, regulators have mandated that an “equivalent level of safety” be demonstrated before UAVs are permitted to routinely operate in civil airspace. This chapter proposes techniques, methods, and hardware integrations that describe a “sense-and-avoid” system designed to address the lack of a see-and-avoid capability in UAVs.

A hybrid power system for remote areas in Jordan

This paper presents a techno-economic assessment for a unique Isolated Hybrid Power System (IHPS) design for remote areas isolated from the grid which also has the capability of being operated as a smart μ-grid. The share of renewable energy sources in resource poor developing countries is low. In these countries an increase in the share of alternative energy (wind, water and sun) delivered with inexpensive operationally robust generation and delivery systems is seen to the way forward. In our design also incorporates a novel storage system to increase the effectiveness of the Isolated IHPSs previously reported in the literature. The configuration reported is a system consisting of, the wind and sun powered generation complemented with batteries, fuel cell unit and a diesel generator. The modelling design and simulations were based on Simulations conducted using MATLAB/SIMULINK, and HOMER Energy Planning and Design software tools. The design and simulation of a new storage approach incorporating Hydrogen Peroxide (H2O2) fuel cell (increasing the efficiency of the fuel cell from 35% to 65%) and a single board computer (Raspberry Pi) used for the energy management and control the system are the novel features of our design. The novel control strategy implemented also includes a synchronization capability that facilitates IHPS to IHPS or IHPS to Main-Grid connection. In the paper after briefly but comprehensively detailing the design and simulations we will present the results on which we conclude that smart independent systems that can utilize indigenous renewable energy with a capability of being able to synchronize with the grid or each other are the most optimal way of electrifying resource poor developing countries in a sustainable way with minimum impact on the environment and also achieve reductions in Green House Gases.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Gestión de la información en redes de sensores inalámbricos

La gestión del conocimiento (KM) es el proceso de recolectar datos en bruto para su análisis y filtrado, con la finalidad de obtener conocimiento útil a partir de dichos datos. En este proyecto se pretende hacer un estudio sobre la gestión de la información en las redes de sensores inalámbricos como inicio para sentar las bases para la gestión del conocimiento en las mismas. Las redes de sensores inalámbricos (WSN) son redes compuestas por sensores (también conocidos como motas) distribuidos sobre un área, cuya misión es monitorizar una o varias condiciones físicas del entorno. Las redes de sensores inalámbricos se caracterizan por tener restricciones de consumo para los sensores que utilizan baterías, por su capacidad para adaptarse a cambios y ser escalables, y también por su habilidad para hacer frente a fallos en los sensores. En este proyecto se hace un estudio sobre la gestión de la información en redes de sensores inalámbricos. Se comienza introduciendo algunos conceptos básicos: arquitectura, pila de protocolos, topologías de red, etc.… Después de esto, se ha enfocado el estudio hacia TinyDB, el cual puede ser considerado como parte de las tecnologías más avanzadas en el estado del arte de la gestión de la información en redes de sensores inalámbricos. TinyDB es un sistema de procesamiento de consultas para extraer información de una red de sensores. Proporciona una interfaz similar a SQL y permite trabajar con consultas contra la red de sensores inalámbricos como si se tratara de una base de datos tradicional. Además, TinyDB implementa varias optimizaciones para manejar los datos eficientemente. En este proyecto se describe también la implementación de una sencilla aplicación basada en redes de sensores inalámbricos. Las motas en la aplicación son capaces de medir la corriente a través de un cable. El objetivo de esta aplicación es monitorizar el consumo de energía en diferentes zonas de un área industrial o doméstico, utilizando redes de sensores inalámbricas. Además, se han implementado las optimizaciones más importantes que se han aprendido en el análisis de la plataforma TinyDB. Para desarrollar esta aplicación se ha utilizado como sensores la plataforma open-source de creación de prototipos electrónicos Arduino, y el ordenador de placa reducida Raspberry Pi como coordinador. ABSTRACT. Knowledge management (KM) is the process of collecting raw data for analysis and filtering, to get a useful knowledge from this data. In this project the information management in wireless sensor networks is studied as starting point before knowledge management. Wireless sensor networks (WSN) are networks which consists of sensors (also known as motes) distributed over an area, to monitor some physical conditions of the environment. Wireless sensor networks are characterized by power consumption constrains for sensors which are using batteries, by the ability to be adaptable to changes and to be scalable, and by the ability to cope sensor failures. In this project it is studied information management in wireless sensor networks. The document starts introducing basic concepts: architecture, stack of protocols, network topology… After this, the study has been focused on TinyDB, which can be considered as part of the most advanced technologies in the state of the art of information management in wireless sensor networks. TinyDB is a query processing system for extracting information from a network of sensors. It provides a SQL-like interface and it lets us to work with queries against the wireless sensor network like if it was a traditional database. In addition, TinyDB implements a lot of optimizations to manage data efficiently. In this project, it is implemented a simple wireless sensor network application too. Application’s motes are able to measure amperage through a cable. The target of the application is, by using a wireless sensor network and these sensors, to monitor energy consumption in different areas of a house. Additionally, it is implemented the most important optimizations that we have learned from the analysis of TinyDB platform. To develop this application it is used Arduino open-source electronics prototyping platform as motes, and Raspberry Pi single-board computer as coordinator.

Communication net of modular processors for a multiprocessor computer /

"This work was submitted in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering, June 1967, and was supported in part by the AEC under Contract No. USAEC AT(11-1)1018.

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

Improved rural health through a collaborative model of care – qualitative findings

INTRODUCTION Health disparity between urban and rural regions in Australia is well-documented. In the Wheatbelt catchments of Western Australia there is higher incidence and rate of avoidable hospitalisation for chronic diseases. Structured care approach to chronic illnesses is not new but the focus has been on single disease state. A recent ARC Discovery Project on general practice nurse-led chronic disease management of diabetes, hypertension and stable ischaemic heart disease reported improved communication and better medical administration.[1] In our study we investigated the sustainability of such a multi-morbidities general practice –led collaborative model of care in rural Australia. METHODS A QUAN(qual) design was utilised. Eight pairs of rural general practices were matched. Inclusion criteria used were >18 years and capable of giving informed consent, at least one identified risk factor or diagnosed with chronic conditions. Patients were excluded if deemed medically unsuitable. A comprehensive care plan was formulated by the respective general practice nurse in consultation with the treating General Practitioner (GP) and patient based on the individual’s readiness to change, and was informed by available local resource. A case management approach was utilised. Shediaz-Rizkallah and Lee’s conceptual framework on sustainability informed our evaluation.[2] Our primary outcome on measures of sustainability was reduction in avoidable hospitalisation. Secondary outcomes were patients and practitioners acceptance and satisfaction, and changes to pre-determined interim clinical and process outcomes. RESULTS The qualitative interviews highlighted the community preference for a ‘sustainable’ local hospital in addition to general practice. Costs, ease of access, low prioritisation of self chronic care, workforce turnover and perception of losing another local resource if underutilised influenced the respondents’ decision to present at local hospital for avoidable chronic diseases regardless. CONCLUSIONS Despite the pragmatic nature of rural general practice in Australia, the sustainability of chronic multi-morbidities management in general practice require efficient integration of primary-secondary health care and consideration of other social determinants of health. What this study adds: What is already known on this subject: Structured approach to chronic disease management is not new and has been shown to be effective for reducing hospitalisation. However, the focus has been on single disease state. What does this study add: Sustainability of collaborative model of multi-morbidities care require better primary-secondary integration and consideration of social determinants of health.