Examinando por Autor "Urtaran Laresgoiti, Maider"

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    Community based participatory research for the development of a compassionate community: the case of Getxo Zurekin
    (Ubiquity Press, 2022-01-17) Hasson, Naomi Mairead; Urtaran Laresgoiti, Maider; Nuño Solinís, Roberto; Moreno, Itziar; Espiau Idoiaga, Gorka; Grajales Sáenz, Maider ; Fonseca Peso, Janire
    In the face of a growing ageing population and rising care needs, compassionate communities seek to visualize the community as an equal partner in the complex task of providing quality social and health care at the end of life. Getxo Zurekin is a social innovation example for the creation of a compassionate community in Getxo, one of the most populated cities in the province of Biscay, with 25.46% of its population aged over 65. Mixed methodologies have been applied, active listening and co-creation of actions and strategies towards improving care and quality of life for people and families facing advanced disease and end of life situations, with more than 80 people interviewed to conform the basis for a collective sense making. The initiative has reached more than 1,000 people in Getxo. Following a systemic approach, horizontal relationships and cross-sectoral collaborations have allowed engaging the active involvement of local agents in the collective sense making and co-creation process. Getxo Zurekin represents an example of a participatory action research model, which has shown to be effective to meet initial targets towards creation of a compassionate community
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    Factors influencing healthcare experience of patients with self-declared diabetes: a cross-sectional population-based study in the basque country
    (MDPI AG, 2021-04-28) Nuño Solinís, Roberto; Ponce Marquez, Sara ; Urtaran Laresgoiti, Maider; Lázaro Pérez, Esther ; Errea Rodríguez, María
    Background: Diabetes affects more than 400 million people around the world. Few published studies incorporate questionnaires that comprehensively cover every aspect of a patient’s experience of healthcare. This study analyzes potential differences in the healthcare experience for patients with diabetes based on their sociodemographic, economic, and health-related characteristics from a comprehensive viewpoint in an integrated delivery system. Methods: We used data from the 2018 Basque Health Survey, which includes a questionnaire for the measurement of the experiences of patients with chronic problems. We present descriptive and regression analyses to explore differences by sociodemographic, economic, and health-related characteristics of patients’ experiences with different healthcare services. Results: Having diabetes plus other comorbidities significantly decreases the quality of the experience with all healthcare services and decreases the global healthcare experience score. When comorbidities are present, the elderly seem to report better experiences than younger patients. Some differences in experience can be explained by sociodemographic and economic factors. No differences exist between conditions co-occurring with diabetes. Conclusion: Patients with diabetes who also suffer from other conditions report worse experiences than individuals who suffer from diabetes only. No specific conditions explain the differences in care experience.
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    Inequalities in health care experience of patients with chronic conditions: results from a population-based study
    (MDPI AG, 2021-08-05) Nuño Solinís, Roberto ; Urtaran Laresgoiti, Maider ; Lázaro Pérez, Esther ; Ponce Marquez, Sara; Orueta, Juan F.; Errea Rodríguez, María
    Patients’ experience is an acknowledged key factor for the improvement of healthcare delivery quality. This study aims to explore the differences in healthcare experience among patients with chronic conditions according to individual sociodemographic and health-related variables. A population-based and cross-sectional study was conducted. The sample consisted of 3981 respondents of the Basque Health Survey (out of 8036 total respondents to the individual questionnaire), living in the Basque Country, aged 15 or older, self-reporting at least one chronic condition. Patient experience was assessed with the Instrument for Evaluation of the Experience of Chronic Patients questionnaire, which encompasses three major factors: interactions between patients and professionals oriented to improve outcomes (productive interactions); new ways of patient interaction with the health care system (the new relational model); and the ability of individuals to manage their care and improve their wellbeing based on professional-mediated interventions (self-management). We conducted descriptive and regression analyses. We estimated linear regression models with robust variances that allow testing for differences in experience according to sociodemographic characteristics, the number of comorbidities and the condition (for all chronic or for chronic patients’ subgroups). Although no unique inequality patterns by these characteristics can be inferred, females reported worse global results than males and older age was related to poorer experience with the new relational model in health care. Individuals with lower education levels tend to report lower experiences. There is not a clear pattern observed for the type of occupation. Multimorbidity and several specific chronic conditions were associated (positive or negatively) with patients’ experience. Health care experience was better in patients with greater quality of life. Understanding the relations among the patients’ experience and their sociodemographic and health-related characteristics is an essential issue for health care systems to improve quality of assistance.
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    Scalable healthcare assessment for diabetic patients using deep learning on multiple GPUS
    (IEEE Computer Society, 2019-10) Sierra-Sosa, Daniel; García-Zapirain, Begoña; Castillo Olea, Cristian; Oleagordia Ruiz, Ibon; Nuño Solinís, Roberto; Urtaran Laresgoiti, Maider; Elmaghraby, Adel Said
    The large-scale parallel computation that became available on the new generation of graphics processing units (GPUs) and on cloud-based services can be exploited for use in healthcare data analysis. Furthermore, computation workstations suited for deep learning are usually equipped with multiple GPUs allowing for workload distribution among multiple GPUs for larger datasets while exploiting parallelism in each GPU. In this paper, we utilize distributed and parallel computation techniques to efficiently analyze healthcare data using deep learning techniques. We demonstrate the scalability and computational benefits of this approach with a case study of longitudinal assessment of approximately 150 000 type 2 diabetic patients. Type 2 diabetes mellitus (T2DM) is the fourth case of mortality worldwide with rising prevalence. T2DM leads to adverse events such as acute myocardial infarction, major amputations, and avoidable hospitalizations. This paper aims to establish a relation between laboratory and medical assessment variables with the occurrence of the aforementioned adverse events and its prediction using machine learning techniques. We use a raw database provided by Basque Health Service, Spain, to conduct this study. This database contains 150 156 patients diagnosed with T2DM, from whom 321 laboratory and medical assessment variables recorded over four years are available. Predictions of adverse events on T2DM patients using both classical machine learning and deep learning techniques were performed and evaluated using accuracy, precision, recall and F1-score as metrics. The best performance for the prediction of acute myocardial infarction is obtained by linear discriminant analysis (LDA) and support vector machines (SVM) both balanced and weight models with an accuracy of 97%; hospital admission for avoidable causes best performance is obtained by LDA balanced and SVMs balanced both with an accuracy of 92%. For the prediction of the incidence of at least one adverse event, the model with the best performance is the recurrent neural network trained with a balanced dataset with an accuracy of 94.6%. The ability to perform and compare these experiments was possible through the use of a workstation with multi-GPUs. This setup allows for scalability to larger datasets. Such models are also cloud ready and can be deployed on similar architectures hosted on AWS for even larger datasets.
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    Solidarity against healthcare access restrictions on undocumented immigrants in Spain: the REDER case study
    (BioMed Central Ltd., 2019-06-06) Urtaran Laresgoiti, Maider ; Fonseca Peso, Janire ; Nuño Solinís, Roberto
    Background: In the context of public expenditure reduction and cuts, in 2012, the Spanish government approved the RDL 16/2012, which significantly affected the core values of the national health system. The measure particularly affected undocumented immigrants over 18 years of age, excluding them from accessing the full range of healthcare services in Spain, except for emergency care. In 2014, Red de Denuncia y Resistencia al RDL 16/2012 (REDER) was created as a public awareness and resistance network to defend universal access to healthcare and to stop its infringement. This study aims to analyse the social impact of REDER as a solidarity movement in response to the exclusion of undocumented immigrants from their universal right to health. Methods: Qualitative research methodologies were used for the research. Data were collected between November 2017 and December 2017, using eight semi-structured interviews with key informants from the main REDER stakeholders. Additionally, key publications, documents, and presentations of researchers and experts in the field were analysed. For data analysis, a framework extracted from the literature on exclusionary and transformative dimensions of solidarity was used to identify barriers and drivers in REDER's intervention. Results: From its creation to the present, REDER has been able to achieve many of its objectives to defend the right to medical care of groups in irregular situations, contributing to the identification of 4,755 cases of discrimination in healthcare access and helping solve over 90% of these cases by delivering either healthcare assistance or administrative support. REDER has also played an important role in: stimulating social activation and empowering citizens to claim their fundamental rights, organising actions against restrictions on accessibility and creating synergies to restore universal healthcare coverage. Conclusions: REDER has been shown to be effective in leading the defence of universal healthcare rights, and some achievements in the years following 2012 could be directly attributed to the work done by the network, such as the elimination of legal requirements to obtain health cards or the reduction of the minimum time required to access healthcare. Despite context particularities, the initiatives and main actions of this network may be implemented in other settings that are facing similar limitations to healthcare access, in order to address injustices and promote solidarity.