Satisfaction of users of primary health care with nursing care : sample of the central region of Portugal

Introdução: A satisfação dos doentes constitui um indicador indispensável para a avaliação da qualidade dos cuidados e há evidência da sua correlação com os resultados em saúde. A satisfação com os cuidados de saúde é um conceito multidimensional que considera aspetos como acesso, organização e interação doente - profissional. Consideramos que os cuidados de enfermagem, em particular, são fundamentais no processo saúde/doença. Objetivos: Validar uma escala para avaliar a satisfação dos utentes face aos cuidados de enfermagem, adaptado do instrumento EUROPEP e avaliar a satisfação dos utentes dos cuidados de saúde primários da região centro de Portugal. Material e métodos: Estudo transversal, com uma amostra de 827 utentes adultos (maioria do sexo feminino 64,4%) com uma média de idade de 50,08±18,58 anos. Os dados foram recolhidos através de um questionário, constituído por variáveis sociodemográficas, o instrumento EUROPEP (Ferreira, 1995) para avaliar a satisfação com os cuidados de saúde primários e para avaliar a satisfação especificamente com a equipa de enfermagem elaboramos questões adaptadas do instrumento EUROPEP e agrupadas nas dimensões relação de ajuda, dimensão interpessoal e instrumental. A consistência interna, reprodutibilidade e análise de conteúdo foram avaliados com recurso ao SPSS 23.0; considerando a consistência aceitável para um de Cronbach > 0,70. O coeficiente para cada item é apresentado com um intervalo de confiança de 95%. Resultados: Em todas as dimensões do questionário EUROPEP, a maior percentagem de satisfação com os cuidados situou-se entre “boa” e “muito boa”. As dimensões criadas para avaliar especificamente os cuidados de enfermagem apresentaram um coeficiente de α de Cronbach total de 0,972. Conclusões: Estes resultados sugerem que as dimensões criadas para avaliar os cuidados de enfermagem serão úteis para a investigação na população Portuguesa. A satisfação do utente é decisiva para a qualidade e eficiência dos cuidados prestados, sendo necessário o compromisso de todos os prestadores na implementação de práticas sistemáticas de gestão que conduzam à satisfação, dando particular atenção à melhoria contínua dos processos organizacionais. Palavras-chave: Satisfação dos Utentes; cuidados de saúde primários, cuidados de enfermagem, adulto, Portugal

Structured professional judgement approach to risk assessment: generalisability across patient groups for the prediction of adverse outcomes in secure mental health care

This is a redacted version of the the final thesis. Copyright material has been removed to comply with UK Copyright Law.

Do health care providers use online patient ratings to improve the quality of care?

Background: Physician-rating websites have become a popular tool to create more transparency about the quality of health care providers. So far, it remains unknown whether online-based rating websites have the potential to contribute to a better standard of care. Objective: Our goal was to examine which health care providers use online rating websites and for what purposes, and whether health care providers use online patient ratings to improve patient care. Methods: We conducted an online-based cross-sectional study by surveying 2360 physicians and other health care providers (September 2015). In addition to descriptive statistics, we performed multilevel logistic regression models to ascertain the effects of providers' demographics as well as report card-related variables on the likelihood that providers implement measures to improve patient care. Results: Overall, more than half of the responding providers surveyed (54.66%, 1290/2360) used online ratings to derive measures to improve patient care (implemented measures: mean 3.06, SD 2.29). Ophthalmologists (68%, 40/59) and gynecologists (65.4%, 123/188) were most likely to implement any measures. The most widely implemented quality measures were related to communication with patients (28.77%, 679/2360), the appointment scheduling process (23.60%, 557/2360), and office workflow (21.23%, 501/2360). Scaled-survey results had a greater impact on deriving measures than narrative comments. Multilevel logistic regression models revealed medical specialty, the frequency of report card use, and the appraisal of the trustworthiness of scaled-survey ratings to be significantly associated predictors for implementing measures to improve patient care because of online ratings. Conclusions: Our results suggest that online ratings displayed on physician-rating websites have an impact on patient care. Despite the limitations of our study and unintended consequences of physician-rating websites, they still may have the potential to improve patient care.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.