Healthcare Information Technology: A Systematic Mapping Study / 대한의료정보학회지

Objectives@#This paper presents a systematic mapping of studies related to information systems and technology in the field of healthcare, enabling a visual mapping of the different lines of knowledge that can provide an overview of the scientific literature in this field. This map can help to clarify critical aspects of healthcare informatics, such as the main types of information systems, the ways in which they integrate with each other, and the technological trends in this field. @*Methods@#Systematic mapping refers to a process of classifying information in a given area of knowledge. It provides an overview of the state of the art in a particular discipline or area of knowledge, establishing a map that describes how knowledge is structured in that particular area. In this study, we proposed and carried out a specific implementation of the methodology for mapping. In total, 1,619 studies that combine knowledge related to information systems, computer science, and healthcare were selected and compiled from prestigious publications. @*Results@#The results established a distribution of the available literature and identified papers related to certain research questions, thereby providing a map of knowledge that structures the different trends and main areas of research, making it possible to address the research questions and serving as a guide to deepen specific aspects of the field of study. @*Conclusions@#We project and propose future research for the trends that stand out because of their interest and the possibility of exploring these topics in greater depth.

Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images.

The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of available samples. This disease mainly affects the respiratory system of the patients and can lead to pneumonia and to severe cases of acute respiratory syndrome that result in the formation of several pathological structures in the lungs. These pathological structures can be explored taking advantage of chest X-ray imaging. As a recommendation for the health services, portable chest X-ray devices should be used instead of conventional fixed machinery, in order to prevent the spread of the pathogen. However, portable devices present several problems (specially those related with capture quality). Moreover, the subjectivity and the fatigue of the clinicians lead to a very difficult diagnostic process. To overcome that, computer-aided methodologies can be very useful even taking into account the lack of available samples that the COVID-19 affectation shows. In this work, we propose an improvement in the performance of COVID-19 screening, taking advantage of several cycle generative adversarial networks to generate useful and relevant synthetic images to solve the lack of COVID-19 samples, in the context of poor quality and low detail datasets obtained from portable devices. For validating this proposal for improved COVID-19 screening, several experiments were conducted. The results demonstrate that this data augmentation strategy improves the performance of a previous COVID-19 screening proposal, achieving an accuracy of 98.61% when distinguishing among NON-COVID-19 (i.e. normal control samples and samples with pathologies others than COVID-19) and genuine COVID-19 samples. It is remarkable that this methodology can be extrapolated to other pulmonary pathologies and even other medical imaging domains to overcome the data scarcity.

Viral shedding dynamics reveals sputum as a reliable and cost-saving specimen for SARS-CoV-2 diagnosis within the first 10 days since symptom onset: A prospective cohort study

BackgroundCoronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome virus (SARS-CoV-2) is challenging global public health, due to an increasing demand for testing and the shortage of diagnostic supplies. Nasopharyngeal swab (NPS) is considered the optimal sample for SARS-CoV2 diagnosis and sputum (SPT) has been proposed as an economic alternative. However, the temporal concordance of diagnosis in NPS and SPT has not been addressed. MethodsThrough a longitudinal study we compared the shedding dynamics of SARS-CoV-2 RNA evaluated by RT-qPCR in serially collected SPT and NPS obtained from 82 ambulatory and hospitalized patients during acute infection and convalescence. The concordance during the follow-up and cost analysis between both collected specimens was evaluated. FindingsWe analyzed 379 samples, 177 NPS and 202 SPT. The highest proportion of positive samples was detected within the first 15 days after the symptoms onset. The median time of positivity was higher for NPS (median= 25 days) than SPT (median= 21 days). There was no significant difference in the median RT-qPCR CT values between both sample types. The temporal categorization of matched-paired samples indicated substantial correlation (r=0{middle dot}6023) and substantial agreement (87{middle dot}23%) during the first ten days since symptoms onset (kappa = 0{middle dot}697). A cost analysis demonstrated a significant saving when the SPT specimen was used. InterpretationSputum is a feasible and cost-saving alternative to NPS, providing an equivalent value for the detection and follow-up of SARS-CoV-2 RNA. FundingAgencia Nacional de Investigacion y Desarrollo (ANID) of Chile, NIH-NIAID USA.

Deep convolutional approaches for the analysis of Covid-19 using chest X-Ray images from portable devices

The recent human coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared as a global pandemic on 11 March 2020 by the World Health Organization. Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role for the screening, early detection and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-Ray images due to its accessibility, widespread availability and benefits regarding to infection control issues, minimizing the risk of cross contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-Ray images acquired by portable equipment into 3 different clinical categories: normal, pathological and COVID-19. For this purpose, two complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of both approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset provided by the Radiology Service of the Complexo Hospitalario Universitario A Coruna (CHUAC) specifically retrieved for this research. Despite the poor quality of chest X-Ray images that is inherent to the nature of the portable equipment, the proposed approaches provided satisfactory results, allowing a reliable analysis of portable radiographs, to support the clinical decision-making process.

Fully automatic deep convolutional approaches for the analysis of Covid-19 using chest X-ray images

Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose complementary fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To face these classification tasks, we exploited and adapted to this topic a densely convolutional network architecture, which connects each layer to every other layer in a feed-forward fashion. To validate the designed approaches, several representative experiments were performed using images retrieved from different public chest X-ray images datasets. overall, satisfactory results were obtained from the designed experiments, facilitating the doctors work and allowing better an early diagnostic/screening and treatment of this relevant pandemic pathology.

Using m-learning on nursing courses to improve learning.

Modern handheld devices and wireless communications foster new kinds of communication and interaction that can define new approaches to teaching and learning. Mobile learning (m-learning) seeks to use them extensively, exactly in the same way in which e-learning uses personal computers and wired communication technologies. In this new mobile environment, new applications and educational models need to be created and tested to confirm (or reject) their validity and usefulness. In this article, we present a mobile tool aimed at self-assessment, which allows students to test their knowledge at any place and at any time. The degree to which the students' achievement improved is also evaluated, and a survey on the students' opinion of the new tool was also conducted. An experimental group of 20- to 21-year-old nursing students was chosen to test the tool. Results show that this kind of tool improves students' achievement and does not make necessary to introduce substantial changes in current teaching activities and methodology.

[Brain activity associated with a nouns-reading task]. / Activación cerebral asociada a una tarea de lectura de sustantivos.

BACKGROUND: Word reading involves several steps, from the visual perception of each of its constitutent elements to its recognition as an entity with a specific meaning. Various brain structures participate in these processes, depending of the linguistic and cognitive characteristics of the stimulus. Our objective was to characterize brain activity through the use of functional magnetic resonance imaging (FMRI) associated with the process of noun reading. METHODS: Eleven healthy right-handed volunteers participated in a lexical decision task involving 58 written nouns. An equal number of letter sequences were used as control stimuli. Reaction times were also recorded. RESULTS: There was a difference (p < 0.05) in reaction time between nouns and letter sequences in the lexical decision task. FMRI contrasted between conditions revealed significant activations in several areas involved in reading. CONCLUSIONS: The brain activation may reflect the different perceptual demands associated with the initial processing of nouns, as compared to meaningless letter sequences. We attribute the difference between our results and those previously reported to the particular characteristics of the pronunciation rules of written Spanish.

¿Quién debe facilitar la difusión de las guías de práctica clínica? / Who is responsible for providing us with the clinical practice guidelines?

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Política de calidad en la sanidad de Castilla y León / Healthcare quality policy in the Health Care Service of Castilla y León

Aunque la gestión de la calidad de los servicios relacionados con la salud ha venido inspirando el desarrollo de la política sanitaria en la Comunidad de Castilla y León desde hace más de una década, ha sido en los últimos años, sobre todo a partir de la descentralización de la gestión de la asistencia sanitaria desde el Estado a la Comunidad en el año 2002, cuando se ha producido su mayor despliegue normativo y operacional, orientado a la consecución de la mejora continua de la calidad de los servicios sanitarios desde la perspectiva del modelo europeo. La adopción de nuevas formas de planificación integradora tanto en el ámbito asistencial como en el sociosanitario; la promoción de las mejores prácticas a partir de la evidencia cientificotécnica disponible; la consideración de la calidad de vida laboral de los profesionales; el desarrollo de modelos de evaluación de la asistencia y de acreditación de centros y servicios; el diseño y la implementación de herramientas metodológicas basadas en la gestión de procesos, y el desarrollo de los derechos de los ciudadanos en relación con la salud y la orientación del sistema sanitario hacia la satisfacción de sus necesidades y expectativas, conforman los aspectos más relevantes del despliegue actual y futuro de la política sanitaria en Castilla y León. (AU)

[The perception of health, social support and family function in HIV seropositivity]. / Percepción de salud, apoyo social y función familiar en VIH positivo.

OBJECTIVE: To discover the perception of health, social support and family function for asymptomatic HIV carriers. DESIGN: This was an observation crossover study, using validated questionnaires. SETTING: Almanjayar and Cartuja Health Centres in Granada. PATIENTS AND OTHER PARTICIPANTS: 58 HIV+ patients belonging to the two basic areas were included. They were selected from the archives of the health centres and from the infectious diseases clinic of the referral hospital. There were an equal number of controls with similar socio-demographic characteristics. MEASUREMENTS AND MAIN RESULTS: The questionnaires used were as follows: GHQ, DUKE-UNC, the family APGAR, which measure health perception, social support and family function, respectively. Of seropositive patients, 58% presented a negative perception of their health, mainly expressed through symptoms of anguish and anxiety; as against 25% of the control group. As to social support, 29% of the HIV+ carriers perceived low levels of support, as against 6.9% of the control group. We found family dysfunction among 46% of seropositive patients, as against 12% of the control group. The Chi squared test was used for the analysis: all the differences were significant. CONCLUSIONS: Patients who are HIV carriers, even when they are asymptomatic, have a poor health perception, which can be attributed to the nature of the illness. The low level of social support detected, linked to the stress involved in being seropositive, may be the origin of the family dysfunction observed in 46% of our sample. We recommend action at individual, family and community levels in order to improve these patients' quality of life, strength their support structures and restore balance to family function.