Stress, cognitive appraisal, and psychological health: testing instruments for health professionals

The job of health professionals, including nurses, is considered inherently stressful (Lee & Wang, 2002; Rutledge et al., 2009), and thus it is important to improve and develop specific measures that are sensitive to the demands that health professionals face. This study analysed the psychometric properties of three instruments that focus on the professional experiences of nurses in aspects related to occupational stress, cognitive appraisal, and mental health issues. The evaluation protocol included the Stress Questionnaire for Health Professionals (SQHP; Gomes, 2014), the Cognitive Appraisal Scale (CAS; Gomes, Faria, & Gonçalves, 2013), and the General Health Questionnaire-12 (GHQ-12; Goldberg, 1972). Validity and reliability issues were considered with statistical analysis (i.e. confirmatory factor analysis, convergent validity, and composite reliability) that revealed adequate values for all of the instruments, namely, a six-factor structure for the SQHP, a five-factor structure for the CAS, and a two-factor structure for the GHQ-12. In conclusion, this study proposes three consistent instruments that may be useful for analysing nurses’ adaptation to work contexts.

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.

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.

Predicting plateau pressure in intensive medicine for ventilated patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm

Otimização do internamento no Hospital de Braga usando metodologia Kaizen

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Engenharia Clínica)

Pervasive patient timeline

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Estudo das necessidades de médicos especialistas na Região Norte de Portugal

Dissertação de mestrado em Engenharia Industrial

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

Deteção e classificação de regiões de interesse em vídeos de endoscopia

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)

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)

Psychosocial adjustment and physical health in children of divorce

Objective: To review the literature on the effects of parental divorce over the psychological maladjustment and physical health problems in children of divorced parents, thus contributing to the integration of existing scientific knowledge based on the biopsychosocial model of the impact of divorce on children’s physical health as proposed by Troxel and Matthews (2004). Sources: Review of the literature using MEDLINE and PsycInfo (1980-2007) databases, selecting the most representative articles on the subject. Special attention was paid to contributions by internationally renowned investigators on the subject. Summary of the findings: Divorce may be responsible for a decline of physical and psychological health in children. The developmental maladjustment of children is not triggered by divorce itself, but rather by other risk factors associated with it, such as interparental conflict, parental psychopathology, decline in socio-economic level, inconsistency in parenting styles, a parallel and conflicting co-parenting relationship between parents and low levels of social support. Such risk factors trigger maladjusted developmental pathways, marked by psychopathological symptoms, poor academic performance, worst levels of physical health, risk behavior, exacerbated psychophysiological responses to stress and weakening of the immune system. Conclusions: Clear links were observed between experiencing parental divorce and facing problems of physical and psychological maladjustment in children. Divorce is a stressor that should be considered by health professionals as potentially responsible for maladjusted neuropsychobiological responses and for decline in children’s physical health.

Pervasive adaptive data acquisition gateway for critical healthcare

The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.