Development and psychometric properties of ECPICID-AVC to measure informal caregivers’ skills when caring for older stroke survivors at home

Introduction: Informal caregivers provide a significant part of the total care needed by dependent older people poststroke. Although informal care is often the preferred option of those who provide and those who receive informal care, informal caregivers often report lack of preparation to take care of older dependent people. This article outlines the development and psychometric testing of informal caregivers’ skills when providing care to older people after a stroke – ECPICID-AVC. Design: Prospective psychometric instrument validation study. Methods: Eleven experts participated in a focus group in order to delineate, develop and validate the instrument. Data were gathered among adult informal caregivers (n = 186) living in the community in Northern Portugal from August 2013 to January 2014. Results: The 32-item scale describes several aspects of informal caregiver’s skills. The scale has eight factors: skill to feed/hydrate by nasogastric feeding, skill to assist the person in personal hygiene, skill to assist the person for transferring, skill to assist the person for positioning, skill to provide technical aids, skill to assist the person to use the toilet, skill to feed/hydrate and skill to provide technical aids for dressing/undressing. Analysis demonstrated adequate internal consistency (Cronbach’s alpha = 0.83) and good temporal stability 0.988 (0.984–0.991). Conclusion: The psychometric properties of the measurement tool showed acceptable results allowing its implementation in clinical practice by the nursing community staff for evaluating practical skills in informal caregivers when providing care to older stroke survivors living at home.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

The role of temporary accommodation buildings for post-disaster housing reconstruction

The number of houses damaged or destroyed after disasters is frequently large, and re-housing of homeless people is one of the most important tasks of reconstruction programmes. Reconstruction works often last long and during that time, it is essential to provide victims with the minimum conditions to live with dignity, privacy, and protection. This research intends to demonstrate the crucial role of temporary accommodation buildings to provide spaces where people can live and gradually resume their life until they have a permanent house. The study also aims to identify the main problems of temporary accommodation strategies and to discuss some principles and guidelines in order to reach better design solutions. It is found that temporary accommodation is an issue that goes beyond the simple provision of buildings, since the whole space for temporary settlement is important. Likewise, temporary accommodation is a process that should start before a disaster occurs, as a preventive pre-planning. In spite of being temporary constructions, these housing buildings are one of the most important elements to provide in emergency scenarios, contributing for better recovery and reconstruction actions.

Segurança contra incêndios na reabilitação sustentável de edifícios antigos

Dissertação de mestrado em Construção e Reabilitação Sustentáveis

Entre Tapas e Beijos: estudo sobre a violência doméstica contra a mulher no Estado da Paraíba (Brasil), após a entrada em vigor da Lei Maria da Penha (Lei nº 11.340/2006)

Dissertação de mestrado em Direitos Humanos

Aplicação da metodologia ROC na avaliação de desempenho de índices de gravidade clínica em unidades de neonatologia de Portugal

Tese de Doutoramento em Engenharia Industrial e de Sistemas

Controlo e prevenção da infeção associada aos cuidados de saúde numa unidade de cuidados intensivos

Relatório de estágio de mestrado em Enfermagem da Pessoa em Situação Crítica

A morte em cuidados intensivos: uma reflexão fenomenológica sobre a experiência dos enfermeiros

Dissertação de mestrado em Enfermagem da Pessoa em Situação Crítica

A intervenção precoce: uma aliada dos cuidados de saúde prestados nas Unidades de Cuidados Intensivos Neonatais?

Dissertação de mestrado em Educação Especial (área de especialização em Intervenção Precoce)

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

A clinical recommendation system to maternity care

Nowadays in healthcare, the Clinical Decision Support Systems are used in order to help health professionals to take an evidence-based decision. An example is the Clinical Recommendation Systems. In this sense, it was developed and implemented in Centro Hospitalar do Porto a pre-triage system in order to group the patients on two levels (urgent or outpatient). However, although this system is calibrated and specific to the urgency of obstetrics and gynaecology, it does not meet all clinical requirements by the general department of the Portuguese HealthCare (Direção Geral de Saúde). The main requirement is the need of having priority triage system characterized by five levels. Thus some studies have been conducted with the aim of presenting a methodology able to evolve the pre-triage system on a Clinical Recommendation System with five levels. After some tests (using data mining and simulation techniques), it has been validated the possibility of transformation the pre-triage system in a Clinical Recommendation System in the obstetric context. This paper presents an overview of the Clinical Recommendation System for obstetric triage, the model developed and the main results achieved.