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1.
Cad. Ibero Am. Direito Sanit. (Impr.) ; 10(1): 93-112, jan.-mar.2021.
Article in Portuguese | LILACS-Express | LILACS | ID: biblio-1151016

ABSTRACT

Objetivo: O Sistema Único de Saúde (SUS) vem investido nas tecnologias da internet das coisas ­ Internet of Things (IoT), em inglês ­ para coletar dados dos pacientes. Esse artigo aponta as fragilidades quanto à privacidade de usuários do SUS e propor uma solução teórica, ainda a ser testada a partir de uma infraestrutura pautada em armazenamento pessoal de dados ­ personal data stores (PDS), em inglês ­ ou, a partir da segurança da blockchain. Metodologia: realizou-se revisão narrativa da literatura nacional e internacional relacionados a instrumentos, políticas e casos voltados a tecnologias de informação e comunicação na saúde a fim de apontar as fragilidades quanto à privacidade de usuários desse sistema. Resultados: percebeu-se que ainda existe uma falta de transparência no tratamento dos dados pessoais e pouco accountability por parte dos cidadãos, se fazendo necessária uma mudança de estratégia tecnológica e de governança. Conclusão: o PDS, de fato, empodera o usuário na medida que dá maior controle e transparência sobre o tratamento de seus dados. No entanto, essa solução, em um sistema como o utilizado pelo Departamento de Informática do SUS, pode comprometer a precisão dos dados usados nas políticas públicas, ao mesmo tempo que pode comprometer alguns direitos dos cidadãos, pois são dados salvos em registros e os metadados estão disponíveis publicamente. A implementação do PDS ainda não possui perspectiva de resultado ótimo. Ainda existem algumas restrições metodológicas quanto aos direitos dos cidadãos ou à eficiência do Estado, mas é um passo no empoderamento civil e uma melhoria exigida por lei quanto à privacidade e à proteção de dados pessoais.


Objective: Brazilian Unified Health System (SUS, in Portuguese) has invested in Internet of Things (IoT) technologies to collect data from patients. This article aims to point out the weaknesses regarding the privacy of users of the SUS and to propose a theoretical solution, yet to be evaluated, and based on a Personal Data Storages (PDS) infrastructure or on blockchain security. Methods: aA narrative review of national and international literature related to instruments, policies, and cases related to information and communication technologies in health was conducted to point out the weaknesses regarding the privacy of users of this system. Results: there is still a lack of transparency in the treatment of personal data and little accountability on the part of citizens, making it necessary to change the technological and governance strategy. Conclusion: PDS empowers users as it gives greater control and transparency over the treatment of data. However, this solution, in a system like the one used by their Computer Department, can compromise the accuracy of the data used in public policies, while it can compromise some citizens' rights, as this data is saved in records and the metadata is publicly available. The implementation of a solution like this does not yet have the prospect of an optimal result, without any methodological restriction on citizens' rights or the efficiency of the State, but it is a step in civil empowerment and an improvement required by law concerning privacy and protection of personal data. The implementation of the PDS does not yet have the prospect of an optimal result. There are still methodological restrictions regarding the rights of citizens or the efficiency of the State. But it is a step in civil empowerment and an improvement required by law in terms of privacy and the protection of personal data.


Objetivo: el Sistema Único de Salud de Brasil (SUS) ha invertido en tecnologías de Internet de las cosas (IoT) para recopilar datos de los pacientes. Este artículo tiene como objetivo señalar las debilidades con respecto a la privacidad de los usuarios del SUS y proponer una solución teórica, aún por probar basada en una infraestructura basada en Personal Data Storages (PDS) o almacenamiento personal de datos, basado en la seguridad de blockchain. Metodología: se realizó una revisión narrativa de la literatura nacional e internacional relacionada con instrumentos, políticas y casos relacionados con las tecnologías de la información y la comunicación en salud con el fin de señalar las debilidades en cuanto a la privacidad de los usuarios de este sistema. Resultados: se notó, entonces, que aún existe falta de transparencia en el tratamiento de estos datos personales y poca rendición de cuentas por parte de la ciudadanía, por lo que es necesario cambiar la estrategia tecnológica y de gobernanza. Conclusión: se concluye que PDS, de hecho, empodera a los usuarios ya que brinda un mayor control y transparencia sobre el tratamiento de sus datos. Sin embargo, esta solución, en un sistema como el utilizado por el Departamento de Computación del SUS, puede comprometer la precisión de los datos utilizados en las políticas públicas, al mismo tiempo que puede comprometer algunos derechos civiles, ya que estos datos se guardan en registros y metadatos están disponibles públicamente. La implementación de una solución como esta todavía no tiene la perspectiva de un resultado óptimo, sin ninguna restricción metodológica sobre los derechos de los ciudadanos o la eficiencia del Estado, pero es un paso en el empoderamiento civil y una mejora requerida por la ley con respecto a la privacidad y protección de datos personales. La implementación del PDS aún no tiene la perspectiva de un resultado óptimo. Aún existen algunas restricciones metodológicas en cuanto a los derechos de los ciudadanos o la eficiencia del Estado. Pero es un paso en el empoderamiento civil y una mejora requerida por la ley en términos de privacidad y protección de datos personales.

2.
Article in Chinese | WPRIM | ID: wpr-876477

ABSTRACT

Objective To investigate the epidemiological characteristics of acute myocardial infarction (AMI) in Yichang City in the last 5 years, and to provide a basis for targeted prevention and treatment. Methods The annual estimated percentage was used to evaluate the trend of morbidity and mortality of AMI by using the monitoring data from 2015 to 2019 from the health big data platform of Yichang. Results There were 1 976 new cases of AMI in Yichang from 2015 to 2019, with a crude morbidity of 41.96/100 000, and standardized morbidity of 87.52/100 000. Among them the crude incidence rate in males was 57.69/100 000, and 29.84/100 000 in females. The difference was statistically significant (χ2=15.76, P2=45.65, P<0.001). The morbidity and mortality of males and females were increased with age. Conclusion From 2015 to 2019, the morbidity of AMI in Yichang was at a moderately low level in China, but the mortality was higher than the national average. The morbidity showed an upward trend, with men and elderly people aged ≥60 being more serious. Appropriate intervention measures should be taken for different groups of people to reduce the incidence of AMI.

3.
Article in Chinese | WPRIM | ID: wpr-880470

ABSTRACT

Based on 18 hospitals including the Chinese People's Liberation Army General Hospital and Peking University People's Hospital, and based on the "Specifications for Perioperative Data", explore the construction and application of perioperative multi-center data centers in the era of medical big data. The use of data ferry technology avoids hidden safety hazards in hospitals, realizes the integration and sharing of perioperative medical data of various medical institutions, and forms a complete data chain combining patient medical data and follow-up data.


Subject(s)
Hospitals, Military , Humans , Military Personnel , United States
4.
Journal of Medical Biomechanics ; (6): E984-E989, 2021.
Article in Chinese | WPRIM | ID: wpr-920714

ABSTRACT

Objective To study the research progress of big data analysis in gait biomechanics. Methods Based on the scientific and technological literature related to big data analysis in gait biomechanics during the year 2011-2020 as the research object, content analysis method was used to analyze and discuss from four aspects, including topic structure, hierarchy level, model type and analysis technology. On this basis, the future research of gait biomechanics big data analysis was prospected. Results The application of big data analysis in gait biomechanics mainly involves five research directions, namely, intervention and rehabilitation, exercise training, prosthesis design and evaluation, understanding of etiology and diagnosis, understanding of human movement characteristics. Big data analysis in gait biomechanics is divided into three levels, of which descriptive analysis is the most used type, accounting for about 41%. The models and specific techniques of big data analysis in gait biomechanics field were reviewed. Topological data analysis is a promising big data exploration tool for future research. Conclusions Big data technology has great potential in gait biomechanics and clinical medicine research.

5.
Acta Pharmaceutica Sinica B ; (6): 1379-1399, 2021.
Article in English | WPRIM | ID: wpr-888810

ABSTRACT

Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics (PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes

6.
Article in Chinese | WPRIM | ID: wpr-888633

ABSTRACT

Based on ASP.NET framework, The Intelligent Estimated System for Rational Deployment of Medical Equipment (MERDIS) is designed and developed with SQL Server 2012 database and C# language. The system is used to realize the rational deployment suggestions and evaluation of medical equipment in hospitals. The system input the data of hospital medical equipment and clinical pathway into the database, and then feedback the deployment information to users which are calculated by big data information, so as to achieve the purpose of giving rational deployment of hospital medical equipment.


Subject(s)
Databases, Factual , Equipment Design , Hospitals
7.
Article in Chinese | WPRIM | ID: wpr-886828

ABSTRACT

Objective To clarify the basic situation of enterprises with occupational hazard factors and current situation of occupational health management in enterprises in Yichang City. Methods Using big data and spatial geography technology, combined with enterprise on-site review, conduct a thorough investigation of the basic situation of the existing occupational disease hazard enterprises in the city. Results A total of 2 219 enterprises with occupational hazard factors were investigated in the city. The largestindustry distribution were in non-metallic mineral products, electric power, heat production and supply, and metal products. The most common types of economy were other foreign-invested enterprises, cooperative enterprises (Hong Kong or Macao, Taiwan-funded), and collective enterprises. The scale of the enterprises was mainly small and micro-quantity. Noise, dust and chemical poisons are the main hazards of the people exposed to the victims, accounting for 65.23%, 53.58%, and 29.76% of the total number of people exposed. The city's overall regular on-site inspection rate in the past three years was 20.73%, and the occupational health examination rate was 23.56%. Conclusion The occupational health management of industrial enterprises in our city needs to be further strengthened.

8.
Article in Chinese | WPRIM | ID: wpr-912722

ABSTRACT

One of the challenges to diagnosis-intervention packet is how to detect and avoid the institutional behavior of pursuing a higher score group. Based on the analysis method of big data, the authors analyzed the objective distribution characteristics of the treatment methods corresponding to a diagnosis, and compared the distribution of diseases with high and low scores in the region to find out the selection trend of treatment methods for the same diagnosis in various hospitals. Combined with hospital positioning, the authors found out whether there was a tendency of pursuing a higher score group. Scientific support will be provided for the reasonable payment of medical insurance expenses and the development planning of hospitals.

9.
Article in Chinese | WPRIM | ID: wpr-912721

ABSTRACT

Objective:To explore the price formation method and price standard in the big data diagnosis-intervention packet.Methods:The expenditure data and income data of 95 medical institutions in Shanghai in 2018 were used for analysis, including 33 municipal hospitals and 62 district hospitals. After using the standardized data of disease score, the medical institutions in the region were divided into four quadrants with the regional average of unit price per index and cost per index as the coordinate axis. The best quadrant of income and cost was found out, namely the high quality range. The geometric center was calculated in the high quality range, and the unit price per index of the geometric center was taken as the cost standard.Results:For the district hospitals, there were 20 hospitals in the first quadrant, 8 in the second quadrant, 24 in the third quadrant and 10 in the fourth quadrant; For the municipal hospitals, there were 7 hospitals in the first quadrant, 5 in the second quadrant, 12 in the third quadrant and 9 in the fourth quadrant. In the third quadrant, the average income and cost of medical institutions were lower than the average of the city, and the income could cover the cost. The third quadrant was the high quality range. The unit price per index of the third quadrant geometric center of district hospitals was 14 115.4 yuan, and that in municipal hospitals was 15 559.1 yuan, which could be used as the corresponding cost standard.Conclusions:The price discovery mechanism based on objective data and high-quality interval geometric center method can remove the impact of unreasonable charges or unreasonable behavior on medical income, and reflect the guidance of the standard price of medical insurance payment.

10.
Article in Chinese | WPRIM | ID: wpr-912720

ABSTRACT

Medical insurance payment model is transforming from project-based purchases to service bundle-based strategic purchases. The new form of bundled purchases should found on a scientifically-led design process of such bundles. The core to bundled purchase would be the payment standard, and the key to its success would be process control. Establishment of such a foundation, a core, and a key, would promote the current price standards, and lead service providers to a standardized medical service standard, so as to ensure a precise rewarding system of payment and service. The big data diagnosis-intervention packet(DIP)is able to fulfill mentioned ambitions by integrating insurance payment and supervision into one management. DIP is a full-process payment mode that encompasses pre-service estimation, in-service process control, post-service grading, and resource allocation. It is an innovative practice in line with China′s national conditions for the modern governance of medical security and medical services.

11.
Article in Chinese | WPRIM | ID: wpr-912496

ABSTRACT

Objective:We aimed to explore a colorectal cancer risk prediction model through machine learning algorithm based on the big data in laboratory medicine.Methods:According to the labeling of colonoscopy combined with pathology or referring to the ICD-10 code, the colonoscopy patients in Shanghai Changhai Hospital from 2013.1.1 to 2019.6.30 and the outpatients and inpatients from 2010.1.1 to 2019.6.30 were divided into colorectal cancer groups and non-colorectal cancer group. Four machine learning algorithms, Extreme gradient boosting(Xgboost),Artificial Neural Network(ANN),Support Vector Machine(SVM),Random Forest(RF), are used to mine all routine laboratory test item data of the enrolled patients, select model features and establish a classification model for colorectal cancer. And the effectiveness of the model was prospectively verified in patients in the whole hospital of Changhai Hospital from 2019.7.1 to 2020.8.31.Result:A colorectal cancer risk prediction model (CRC-Lab7) including 7 characteristics of fecal occult blood, carcinoembryonic antigen, red blood cell distribution width, lymphocyte count, albumin/globulin, high-density lipoprotein cholesterol and hepatitis B virus core antibody was constructed by the XgBoost algorithm. The AUC of the model in the validation set and prospective validation set were 0.799 and 0.816, respectively, which was significantly higher than that of fecal occult blood (AUC was 0.68 and 0.706, respectively). It also has high diagnostic accuracy for colorectal cancer with negative fecal occult blood or under 50 years old.Conclusion:In this study, a colorectal cancer risk prediction model was established by mining routine laboratory big data. The model′s performance is better than fecal occult blood, and it has high diagnostic accuracy for colorectal cancer in patients with negative fecal occult blood and younger than 50 years old.

12.
Article in Chinese | WPRIM | ID: wpr-912452

ABSTRACT

Objective:To establish the sex-, age-and season-specific (month) reference intervals (RI) for thyroid stimulating hormone (TSH) measurement by big data and indirect method in adults.Methods:TSH data of anonymous patients were collected from Beijing Chaoyang Hospital Affiliated to Capital Medical University in 2016, the data were selected and outliers were removed. Indirect methods (Hoffmann method and Bhattacharya method) were used to calculate TSH reference intervals of whole population, different genders, ages and seasons (months). TSH RI from two indirect methods of total population, selected population, physical examination population was compared with RI from reagent instruction according to reference change value ( RCV) based on biological variability. Results:A total of 61 599 records were obtained from 90 699 records including 18 776 males and 42 823 females. The TSH RI were obtained by Hoffmann method: the whole population, 0.59-5.59 μIU/ml (1 μIU/ml=1 mIU/L), male, 0.53-5.16 μIU/ml, female, 0.59-6.11 μIU/ml. The upper limits of TSH RI were higher with age and in winter (January): 18-30 years old, 0.62-5.57 μIU/ml, 71-80 years old, 0.49-6.45 μIU/ml; January, 0.59-6.40 μIU/ml, August, 0.60-5.56 μIU/ml; The RI of TSH by Bhattacharya method: the whole population, 0.58-5.80 μIU/ml, male, 0.55-5.02 μIU/ml, female, 0.62-6.21 μIU/ml. The upper limits of TSH RI were also higher with age and in winter (January): 18-30 years old, 0.65-5.67 μIU/ml, 71-80 years old, 0.46-5.99 μIU/ml, January: 0.61-6.52 μIU/ml, August: 0.61-5.69 μIU/ml. Compared to RI from reagent instruction, the differences of TSH RI from two indirect methods of total population, selected population, physical examination population were acceptable.Conclusions:TSH RI was established by indirect method. With the increase of age and winter, the upper limit of TSH reference interval tends to increase.

13.
Article in Chinese | WPRIM | ID: wpr-912437

ABSTRACT

Objective:To establish an interpretive reporting system for urinalysis based on artificial intelligence (AI).Methods:Urine tests were collected from the First Affiliated Hospital, College of Medicine, Zhejiang University from 2008 to 2018, including 2 899 917 patient tests and 710 971 physical check-up tests. Then we set up a large population distribution with the frequency of different results of each item and established a health index of each sample and an abnormal level of each item according to data distribution, importance and degree of abnormality. We collected data of seven diseases, such as diabetes mellitus, urinary tract infection, glomerulonephritis and nephrotic syndrome, and matched them with a same number of healthy control group by gender and age. An integrated learner based on the AdaBoost algorithm was used to establish a diagnostic model and assess its algorithm performance. JAVA was used to develop data presentation software. The accuracy of the AI model for disease judgment was assessed by manual verification using 199 abnormal urine tests.Results:Each report could be graded as four levels: normal, abnormal, ill and critical. Each item could be judged as normal, mild, moderate, severe or extreme and the population distribution was provided with big data. The training accuracy, true positive rate and area under the curve were ≥88.3%, ≥80.0%, and ≥0.954 respectively using the machine learning model based on AdaBoost. The developed JAVA software presented the above results and displayed medical records and results, historical results, personalized advice, patient education and position in large population data. By manual verification, the accuracy rate of the AI model for disease judgment was 82.41% (166/199).Conclusion:This study established an intelligent interpretive reporting system for urine test results. It can distinguish the abnormality of each report, predict the disease of patients, and make personalized clinical decisions.

14.
Article in Chinese | WPRIM | ID: wpr-908862

ABSTRACT

Combining the psychological "big data" of college students with the data of the psychological expert database, using data mining algorithm to extract characteristic attributes, deriving the warning results from a stable algorithm model, the psychological early warning platform constructed by this method can not only monitor students' psychological dynamics in real time, but also predict their psychological behavior trend, which makes up for the lag and low accuracy of traditional early warning methods. The psychological early warning platform provides data support and the psychological early warning system provides operational guarantee. The psychological early warning mechanism formed by the combination of the two can provide scientific basis for educators and finally achieve the goal of using big data technology to improve the mental health of college students.

15.
Journal of Preventive Medicine ; (12): 1189-1198, 2021.
Article in Chinese | WPRIM | ID: wpr-906789

ABSTRACT

@#A large cohort study of high-risk population of stroke based on the real world is of great significance for stroke prevention and control. However, the data element structures, variable definitions and scopes of regional big data platforms are inconsistent, which will be an obstacle for data sharing, summary, and analysis among different regions. In this study, we formed an expert consensus on a unified minimum dataset standard for the cohort study of high-risk population of stroke, considering the categories and definitions of risk factors of stroke, and the existing database of the regional big data platforms. The consensus shall provide a reference for the comparison, integration, and sharing of real world data within and between regions, and play an important role in the cohort study on risk factors of stroke, as well as the implementation and evaluation of prevention and control measures.

16.
Cad. saúde colet., (Rio J.) ; 29(spe): 51-58, 2021. graf
Article in Portuguese | LILACS-Express | LILACS | ID: biblio-1364657

ABSTRACT

Resumo Introdução O termo "big data" no ambiente acadêmico tem deixado de ser uma novidade, tornando-se mais comum em publicações científicas e em editais de fomento à pesquisa, levando a uma revisão profunda da ciência que se faz e se ensina. Objetivo Refletir sobre as possíveis mudanças que as ciências de dados podem provocar nas áreas de estudos populacionais e de saúde. Método Para fomentar esta reflexão, artigos científicos selecionados da área de big data em saúde e demografia foram contrastados com livros e outras produções científicas. Resultados Argumenta-se que o volume dos dados não é a característica mais promissora de big data para estudos populacionais e de saúde, mas a complexidade dos dados e a possibilidade de integração com estudos convencionais por meio de equipes interdisciplinares são promissoras. Conclusão No âmbito do setor de saúde e de estudos populacionais, as possibilidades da integração dos novos métodos de ciência de dados aos métodos tradicionais de pesquisa são amplas, incluindo um novo ferramental para a análise, monitoramento, predição de eventos (casos) e situações de saúde-doença na população e para o estudo dos determinantes socioambientais e demográficos.


Abstract Background The term big data is no longer new in the academic environment and has become more common in scientific publications and research grants, leading to a profound revision of the way science is being made and taught. Objective To reflect on the possible changes that data science can induce in population and health related studies. Method To foster this debate, scientific articles selected from the big data field in health and demography were contrasted with books and other scientific productions. Results It is argued that volume is not the most promising characteristic of big data for population and health related studies, but rather the complexity of data and the possibilities of integration with traditional studies by means of interdisciplinary teams. Conclusion In population and health related studies, the possibilities of integration between new and traditional methods are broad, and include new toolboxes for analysis, monitoring, prediction of events (cases) and health-disease processes in the population, and for the study of sociodemographic and environmental determinants.

17.
Braz. J. Psychiatry (São Paulo, 1999, Impr.) ; 42(6): 591-598, Nov.-Dec. 2020. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1132147

ABSTRACT

Objective: To assess a large set of metadata made public by the Brazilian Ministry of Health on older subjects who visited outpatient mental health services in Brazil from 2008 to 2012. Methods: We extracted data from the Brazilian Unified Health System Information Technology Department (Departamento de Informática do Sistema Único de Saúde, DATASUS), then calculated rates of visits per population in each of the five regions of Brazil, using census data for each year. Finally, logistic regressions were performed with depressive disorders or dementias as dependent variables, controlled by age and year of visit, stratified by region. Results: Mood disorders were the leading reason for visits to outpatient mental health services by older adults, followed by delusional disorders. The calculated rates were lower than the known prevalence of depressive disorders and dementias, but the regressions revealed typical patterns. Males were less likely to present with a depressive disorder, while older subjects were more likely to present with depression and dementia. Conclusions: Publicly available data from DATASUS may not enable inferences about the prevalence of mental disorders in elders, but inferential analyses match what is known about these conditions. This approach is supplemental to other more common ones and is of special importance for policymakers and health system managers.

18.
Cad. Ibero Am. Direito Sanit. (Impr.) ; 9(3): 153-165, jul.-set.2020.
Article in Portuguese | LILACS-Express | LILACS | ID: biblio-1121822

ABSTRACT

Objetivo: discutir as implicações bioéticas a partir do anúncio da Saúde Digital por parte da Organização Mundial da Saúde e do uso do big data na produção de sistemas preditivos de vigilância em saúde no Brasil. Metodologia: realizou-se revisão narrativa a partir da busca de artigos nas plataformas Scielo, Bireme, Jstor e na página da Organização Mundial da Saúde e do Ministério da Saúde do Brasil, com os descritores big data, bioética e ética, de maio a julho de 2020. Resultados:foram evidenciados limites no uso do big data como ferramenta de vigilância epidemiológica preditiva, notadamente com o seu uso durante a pandemia de Covid-19, apesar de justificável a partir da teoria da bioética da proteção e da ética da saúde pública. Os maiores limites observados foram ausência de legislação de proteção de dados adequada e viés dos dados obtidos. Conclusão: para análise dos impactos bioéticos do uso do big data na medicina do futuro é imprescindível aprofundar a discussão sobre os possíveis impactos que o uso dessas tecnologias pode gerar na vida em sociedade, com ênfase no desenvolvimento do capitalismo de vigilância, na interferência na vida social e no acirramento das desigualdades regionais.


Objective: to discuss the bioethical implications from the announcement of Digital Health by the World Health Organization and the use of big data in the production of predictive health surveillance systems in Brazil. Methods:conducted a narrative review based on the search for articles on Scielo, Bireme, Jstor platforms and at the World Health Organization and the Brazilian Ministry of Health webpages, with the keywords big data, bioethics and ethics, from May to July 2020. Results: limits were evidenced in the useof big data as an epidemiological forecasting surveillance tool, notably with its use during the Covid-19 pandemic, although justified by the Bioethics of Protection Theory and Public Health Ethics. The greatest limits observed were the absence of adequate data protection legislation and bias in the data obtained. Conclusion:in order to analyze the bioethical impacts of the use of big data in the medicine of the future, it is essential to deepen the discussion on the possible impacts that the use of thesetechnologies can have on life in society, with an emphasis on the development of surveillance capitalism, on interference in life and the intensification of regional inequalities.


Objetivo: discutir las implicaciones bioéticas del anuncio de la Salud Digital por laOrganización Mundial de la Salud y el uso de macrodatos en la producción de sistemas devigilancia predictiva de salud en Brasil. Metodología:se realizó una revisión narrativa a partirde la búsqueda de artículos en las plataformas Scielo, Bireme, Jstor y en la página de laOrganización Mundial de la Salud y el Ministerio de Salud de Brasil, con las palabras clavemacrodatos, bioética y ética, de mayo a julio, 2020. Resultados:se evidenciaron límites enel uso de macrodatos como herramienta de vigilancia epidemiológica predictiva, destacandosu uso durante la pandemia Covid-19, aunque justificado a partir de la teoría de la Bioéticade Protección y la Ética en Salud Pública. Los mayores límites observados fueron laausencia de una legislación de protección de datos adecuada y el sesgo en los datosobtenidos. Conclusión:para analizar los impactos bioéticos del uso de macrodatos en lamedicina del futuro, es fundamental profundizar la discusión sobre los posibles impactos queel uso de estas tecnologías puede tener en la vida en sociedad, con énfasis en el desarrollodel capitalismo de vigilancia, en la interferencia en la vida y la intensificación de lasdesigualdades regionales.

19.
RECIIS (Online) ; 14(3): 597-618, jul.-set. 2020. graf, ilus
Article in Portuguese | LILACS | ID: biblio-1121781

ABSTRACT

Este artigo busca responder a alguns dos desafios de sistematização, indexação e divulgação de variados documentos acadêmicos da área de pensamento social no Brasil pela Biblioteca Virtual do Pensamento Social (BVPS). Argumentamos que a importância da discussão sobre preservação digital para a BVPS cumpre dois objetivos: o de disponibilizar documentos digitalizados a um público mais amplo e o de mapear a produção contemporânea da área, com intuito de criar uma memória intelectual. Neste artigo, nos deteremos sobretudo no segundo objetivo, tendo em vista definir bem como disponibilizar ao público da biblioteca os critérios de seleção e organização do acervo. Dentro dos limites do recorte proposto, por meio de redes de acoplamento bibliográfico, cocitação e mapas semânticos, apresentaremos aqui uma análise preliminar da produção de artigos na área de pensamento social no Brasil. A atual pesquisa é fundamental para a definição das próximas etapas de ampliação do conteúdo da biblioteca, notadamente a definição de novos seletores de busca, a integração de novos autores e autoras à seção Intérpretes e a indexação de trabalhos com temáticas e abordagens caras à área de pensamento social no Brasil.


This article seeks to respond to some of the challenges of systematization, indexing and dissemination of various academic documents in the field of social thought in Brazil by the BVPS ­ Biblioteca Virtual do Pensamento Social (Virtual Library of Social Thought). We argue that the importance of the discussion on digital preservation for the BVPS fulfills two objectives: that of making digitized documents available to a wider audience and that of mapping contemporary production in that field in order to create an intellectual memory. In this article, we will focus mainly on the second objective, in order to define as well as make available to the library public the selection and organization criteria of the collection. Within the limits of the proposed clipping, we will present here a preliminary analysis of the production of articles in the field of social thought in Brazil through networks of bibliographic coupling, co-quotation and semantic maps. The current research is fundamental for the definition of the next steps to expand the content of the library, notably the definition of new search options the integration of new authors in the section Interpreters and the indexing of works containing important themes and approaches for the area of social thought in Brazil.


Este artículo busca responder a algunos de los desafíos de la sistematización, indexación y difusión de diferentes tipos de documentos académicos en el campo del pensamiento social en Brasil por la BVPS ­ Biblioteca Virtual do Pensamento Social (Biblioteca Virtual del Pensamiento Social). Argumentamos que la importancia de la discusión sobre la preservación digital para la BVPS cumple dos objetivos: el de hacer que los documentos digitalizados estén disponibles para una audiencia más amplia y el de mapear la producción contemporánea en el área para crear una memoria intelectual. En este artículo, nos centraremos principalmente en el segundo objetivo, para definir como también para poner a la disposición del público de la biblioteca los criterios de selección y organización de la colección. Dentro de los límites del recorte propuesto, presentaremos aquí un análisis preliminar de la producción de artículos en el campo del pensamiento social en Brasil a través de redes de acoplamiento bibliográfico, cocitación y mapas semánticos. La investigación actual es fundamental para la definición de los próximos pasos para expandir el contenido de la biblioteca, en particular la definición de nuevos selectores de búsqueda, la integración de nuevos autores y autoras en la sección Intérpretes y la indexación de trabajos conteniendo temas y enfoques relevantes para el área de pensamiento social en Brasil.


Subject(s)
Humans , Brazil , Information Storage and Retrieval , Libraries, Digital , Big Data , Anthropology, Cultural , Sociology , Records , Information Management
20.
RECIIS (Online) ; 14(3): 724-733, jul.-set. 2020. ilus
Article in Spanish | LILACS | ID: biblio-1121946

ABSTRACT

En esta entrevista a Reciis, Miquel Térmens discute la importancia de la preservación digital para crear un sistema de salud que sea bueno no solo para el futuro, pero para el presente. Estamos en una fase de recopilación y almacenamiento de una gran cantidad de datos sobre el nuevo coronavirus para asegurar su rápida utilización, y su preservación a largo plazo es de interés tanto de los gobiernos como de los grupos de investigación que están trabajando a favor de las soluciones. El gran reto de nuestro presente es investigar cómo hacer preservación digital a una nueva escala, incorporando datos de las redes sociales, datos de investigación y Big Data, pero eso solo va a ser posible con normalización y planificación. Miquel Térmens Graells es doctor en Documentación por la Universidad de Barcelona, es profesor titular y decano de la Facultad de Información y Medios Audiovisuales de la misma universidad.


Subject(s)
Humans , Organization and Administration , Health Systems , Data Curation , Big Data , Data Analysis , Data Collection , Information Storage and Retrieval , Access to Information
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