Impact of primary care nursing workforce characteristics on the control of high-blood pressure: A multilevel analysis

Objective: To determine the impact of Primary Health Care (PHC) nursing workforce characteristics and of the clinical practice environment (CPE) perceived by nurses on the control of high-blood pressure (HBP). Design: Cross-sectional analytical study. Setting: Administrative and clinical registries of hypertensive patients from PHC information systems and questionnaire from PHC nurses. Participants: 76 797 hypertensive patients in two health zones within the Community of Madrid, North- West Zone (NWZ) with a higher socioeconomic situation and South-West Zone (SWZ) with a lower socioeconomic situation, and 442 reference nurses. Segmented analyses by area were made due to their different socioeconomic characteristics. Primary outcome measure: Poor HBP control (adequate figures below the value 140/90 mm Hg) associated with the characteristics of the nursing workforce and selfperceived CPE. Results: The prevalence of poor HBP control, estimated by an empty multilevel model, was 33.5% (95% CI 31.5% to 35.6%). In the multilevel multivariate regression models, the perception of a more favourable CPE was associated with a reduction in poor control in NWZ men and SWZ women (OR=0.99 (95% CI 0.98 to 0.99)); the economic immigration conditions increased poor control in NWZ women (OR=1.53 (95% CI 1.24 to 1.89)) and in SWZ, both men (OR=1.89 (95% CI 1.43 to 2.51)) and women (OR=1.39 (95% CI 1.09 to 1.76)). In all four models, increasing the annual number of patient consultations was associated with a reduction in poor control (NWZ women: OR=0.98 (95% CI0.98 to 0.99); NWZ men: OR=0.98 (95% CI 0.97 to 0.99); SWZ women: OR=0.98 (95% CI 0.97 to 0.99); SWZ men: OR=0.99 (95% CI 0.97 to 0.99). Conclusions: A CPE, perceived by PHC nurses as more favourable, and more patient–nurse consultations, contribute to better HBP control. Economic immigration condition is a risk factor for poor HBP control. Health policies oriented towards promoting positive environments for nursing practice are neededThe results presented here form part of a study that has been funded partially with the First Prize for National Research in Nursing (12th edition) from Hospital Universitario Marqués de Valdecilla (Santander) in 2010

Application of Artificial Intelligence Algorithms Within the Medical Context for Non-Specialized Users: the CARTIER-IA Platform

The use of advanced algorithms and models such as Machine Learning, Deep Learning and other related approaches of Artificial Intelligence have grown in their use given their benefits in different contexts. One of these contexts is the medical domain, as these algorithms can support disease detection, image segmentation and other multiple tasks. However, it is necessary to organize and arrange the different data resources involved in these scenarios and tackle the heterogeneity of data sources. This work presents the CARTIER-IA platform: a platform for the management of medical data and imaging. The goal of this project focuses on providing a friendly and usable interface to organize structured data, to visualize and edit medical images, and to apply Artificial Intelligence algorithms on the stored resources. One of the challenges of the platform design is to ease these complex tasks in a way that non-AI-specialized users could benefit from the application of AI algorithms without further training. Two use cases of AI application within the platform are provided, as well as a heuristic evaluation to assess the usability of the first version of CARTIER-IA. Year of Publication 2021 Journal International Journal of Interactive Multimedia and Artificial Intelligence Volume 6 Issue Regular Issue Number 6 Number of Pages 46-53 Date Published 06/2021 ISSN Number 1989-1660 URL https://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_5.pdf DOI 10.9781/ijimai.2021.05.005 DOI Google Scholar BibTeX EndNote X3 XML EndNote 7 XML Endnote tagged Marc RIS Attachment ijimai_6_6_5.pdf 932.11 K

Nursing workforce characteristics and control of diabetes mellitus in primary care: A multilevel analysis. Spain

Fundamentos: La actividad de enfermería está condicionada por las características de la plantilla. El objetivo fue determinar cómo afectan las características de la plantilla de enfermería de atención primaria (AP) al control de la diabetes mellitus (DM) en personas adultas. Método: Estudio analítico transversal. Instrumentos para la recogida de datos: sistemas de información de AP y cuestionario PES-Nursing Work Index. Participantes: 44.214 pacientes diabéticos en dos zonas de salud de la Comunidad de Madrid: Zona Noroeste (ZNO) con mejor situación socioeconómica y Zona Suroeste (ZSO) con peor situación socioeconómica y los 507 profesionales de enfermería de referencia. Se realizaron análisis multivariantes multinivel de regresión logística. La variable dependiente fue la DM estaba mal controlada (cuando los valores de Hb1Ac eran ≥ a 7%) Resultados: La prevalencia DM mal controlada fue de 40,1% (IC95%:38,2-42,1). Existía un riesgo de un 25% más de peor control si el paciente cambiaba de centro de salud y de un 27% si cambiaba de pareja médico de cabaecera y enfermera. En los modelos de regresión logística multivariante multinivel: para la ZSO a mayor ratio de pacientes mayores de 65 años aumentaba el riesgo de mal control (OR=1,00008 [IC95%:1,00006-1,001]); a mayor proporción de pacientes sin seguimiento por centro de salud peor control (OR=5,1 [IC95%:1,6-15,6]). En los dos modelos por zona de salud, la condición de ser inmigrante económico aumentó el riesgo de mal control, ZSO (OR=1,3 [IC95%:1,03-1,7]); y ZNO (OR=1,29 [IC95%:1,03-1,6]). Conclusiones: Son factores de riesgo de tener mal controlada la diabetes mellitus la mayor proporción de pacientes mayores de 65 años por enfermera, ser inmigrante y la proporción de pacientes sin seguimientoBackground: Nurse activity is determined by the characteristics of nursing staff. The objective was to determine the impact of Primary Health Care (PHC) nursing workforce characteristics on the control of Diabetes Mellitus (DM) in adults. Method: Cross-sectional analytical study. Administrative and clinical registries and questionnaire PES-Nursing Work Index from PHC nurses. Participants 44.214 diabetic patients in two health zones within the Community of Madrid, North-West Zone (NWZ) with higher socioeconomic situation and South-West Zone (SWZ) with lower socioeconomic situation, and their 507 reference nurses. Analyses were performed to multivariate multilevel logistic regression models. Primary outcome measure: Poor DM control (figures ≥ 7% HbA1c) Results: The prevalence of poor DM control was 40.1% [CI95%: 38.2-42.1]. There was a risk of 25% more of poor control if the patient changed centre and of 27% if changed of doctor-nurse pair. In the multilevel multivariate regression models: in SWZ increasing the ratio of patients over 65 years per nurse increased the poor control (OR=1.00008 [CI95%:1.00006-1.001]); and higher proportion of patients whose Hb1Ac was not measured at the centre contributed to poor DM control (OR=5.1 [CI95%:1.6-15.6]). In two models for health zone, the economic immigration condition increased poor control, in SWZ (OR=1.3 [CI95%:1.03-1.7]); and in NWZ (OR=1.29 [CI95%:1.03-1.6]). Conclusions: Higher 65 years old patients ratio per nurse, economic immigration condition and a higher proportion of patients whose Hb1Ac was not measured contribute to worse DM controlLos resultados presentados en este manuscrito forman parte de un proyecto financiado parcialmente por el Premio Nacional Marqués de Valdecilla (Santander) 2010 (12ª edición

Are Textual Recommendations Enough? Guiding Physicians Toward the Design of Machine Learning Pipelines Through a Visual Platform

The prevalence of artificial intelligence (AI) in our daily lives is often exaggerated by the media, leading to a positive public perception while overlooking potential problems. In the field of medicine, it is crucial to educate future healthcare professionals on the advantages and disadvantages of AI and to emphasize the importance of creating fair, ethical, and reproducible models. The KoopaML platform was developed to provide an educational and user-friendly interface for inexperienced users to create AI pipelines. This study analyzes the quantitative and interaction data gathered from a usability test involving physicians from the University Hospital of Salamanca, with the aim of identifying new interaction paradigms to improve the platform’s usability. The results shown that the platform is difficult to learn for inexperienced users due to its contents related to AI. Following these results, a set of improvements are proposed for the next version of KoopaML, focusing on reducing the interactions needed to create the pipelines

KoopaML, a Machine Learning platform for medical data analysis

Machine Learning allows facing complex tasks related to data analysis with big datasets. This Artificial Intelligence branch allows not technical contexts to get benefits related to data processing and analysis. In particular, in medicine, medical professionals are increasingly interested in Machine Learning to identify patterns in clinical cases and make predictions regarding health issues. However, many do not have the necessary programming or technological skills to perform these tasks. Many different tools focus on developing Machine Learning pipelines, from libraries for developers and data scientists to visual tools for experts or platforms to learn. However, we have identified some requirements in the medical context that raise the need to create a customized platform adapted to end-user found in this context. This work describes the design process and the first version of KoopaML, an ML platform to bridge the data science gaps of physicians while automatizing Machine Learning pipelines. The platform is focused on enhanced interactivity to improve the engagement of physicians while still providing all the benefits derived from the introduction of Machine Learning pipelines in medical departments, as well as integrated ongoing training during the use of the tool’s features

Flexible Heuristics for Supporting Recommendations Within an AI Platform Aimed at Non-expert Users

The use of Machine Learning (ML) to resolve complex tasks has become popular in several contexts. While these approaches are very effective and have many related benefits, they are still very tricky for the general audience. In this sense, expert knowledge is crucial to apply ML algorithms properly and to avoid potential issues. However, in some situations, it is not possible to rely on experts to guide the development of ML pipelines. To tackle this issue, we present an approach to provide customized heuristics and recommendations through a graphical platform to build ML pipelines, namely KoopaML, focused on the medical domain.With this approach, we aim not only at providing an easy way to apply ML for non-expert users, but also at providing a learning experience for them to understand how these methods work

Definición de un proceso de gestión de la innovación docente en la Universidad de Salamanca sobre la base de un sistema integral de gestión del conocimiento

Memoria ID-0045. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2015-2016

Implantación de un sistema integral de gestión del conocimiento para los procesos de innovación docente de la Universidad de Salamanca

Memoria ID-0312. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015

Implantación de un sistema integral de gestión del conocimiento para los procesos de innovación docente de la Universidad de Salamanca

Memoria ID-0312. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015