Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.

The function of competitive intelligence in South African insurance post-COVID-19 outbreak

Background: Competitive intelligence (CI) involves monitoring competitors and providing organizations with actionable and meaningful intelligence. Some studies have focused on the role of CI in other industries post-COVID-19 pandemic. Objectives: This article aims to examine the impact of COVID-19 on the South African insurance sector and how the integration of CI and related technologies can sustain the South African insurance sector post-COVID-19 epidemic. Method: Qualitative research with an exploratory-driven approach was used to examine the impact of the COVID-19 pandemic on the South African insurance sector. Qualitative secondary data analyses were conducted to measure insurance claims and death benefits paid during the COVID-19 pandemic. Results: The research findings showed that the COVID-19 pandemic significantly impacted the South African insurance industry, leading to a reassessment of pricing, products, and risk management. COVID-19 caused disparities in death benefits and claims between provinces; not everyone was insured. Despite challenges, South African insurers remained well-capitalised and attentive to policyholders. Integrating CI and analytical technologies could enhance the flexibility of prevention, risk management, and product design. Conclusion: COVID-19 requires digital transformation and CI for South African insurers' competitiveness. Integrating artificial intelligence (AI), big data (BD), and CI enhances value, efficiency, and risk assessments. Contribution: This study highlights the importance of integrating CI strategies and related technologies into South African insurance firms' operations to aid in their recovery from the COVID-19 crisis. It addresses a research gap and adds to academic knowledge in this area.

Optimization of on-site supervision strategy based on big data of quality risk in drug enterprises / 中国药房

On-site supervision is a risk-based regulatory system that requires the scientific development of supervision plans for quality risks and hidden dangers in pharmaceutical enterprises， the rational allocation of supervision resources based on their risk levels， and the implementation of classified supervision measures. In this study， the quality risk monitoring business support system is set up for pharmaceutical enterprises by establishing the quality risk expert database and quality risk monitoring index system for pharmaceutical enterprises based on the difficulty analysis of on-site drug supervision. Based on this support system， the quality risk classification method， the differentiated spot check strategy and business auxiliary visualization system are established. This support system is used to learn the risk level of pharmaceutical enterprises， so as to innovate supervision methods and optimize monitoring strategies. Taking Jiangxi Province as an example， it is verified that the support system can guide the risk assessment of sample enterprises， can improve the targeting of on-site drug supervision in the process of technical review， scheme editing， on-site implementation and comprehensive evaluation， and can effectively improve the quality and efficiency of supervision.

Clinical profile and demographic distribution of pseudoexfoliation syndrome: An electronic medical record-driven big data analytics from an eye care network in India

Purpose: To describe the demographics and clinical profile of pseudoexfoliation syndrome (PXF or PES) in patients presenting to a multi?tier ophthalmology hospital network in India. Methods: This cross?sectional hospital?based study included 3,082,727 new patients presenting between August 2010 and December 2021. Patients with a clinical diagnosis of PXF in at least one eye were included as cases. The data were collected using an electronic medical record system. Results: Overall, 23,223 (0.75%) patients were diagnosed with PXF. The majority of the patients were male (67.08%) and had unilateral (60.96%) affliction. The most common age group at presentation was during the seventh decade of life with 9,495 (40.89%) patients. The overall prevalence was higher in patients from a lower socio?economic status (1.48%) presenting from the urban geography (0.84%) and in retired individuals (3.61%). The most common location of the PXF material was the pupillary margin (81.01%) followed by the iris (19.15%). The majority of the eyes had mild or no visual impairment (<20/70) in 12,962 (40.14%) eyes. PXF glaucoma was documented in 7,954 (24.63%) eyes. Krukenberg’s spindle was found in 64 (0.20%) eyes, phacodonesis in 328 (1.02%) eyes, and lens subluxation in 299 (0.93%) eyes. Among the surgical interventions, cataract surgery was performed in 8,363 (25.9%) eyes, trabeculectomy was performed in 966 (2.99%) eyes, and a combined procedure in 822 (2.55%) eyes. Conclusion: PXF more commonly affects males presenting during the seventh decade of life from lower socio?economic status and is predominantly unilateral. A quarter of the affected eyes are associated with glaucoma and the majority of the eyes have mild or no visual impairment.

Solar retinopathy in India: Clinical presentation and demographic distribution in 253 patients (349 eyes)

Purpose: To describe the demographics, clinical characteristics, and presentation of solar retinopathy in patients who presented to a multi?tier ophthalmology hospital network in India. Methods: This cross?sectional, hospital?based study included 3,082,727 new patients presenting to the hospital between August 2010 and December 2021. Patients with a clinical diagnosis of solar retinopathy in at least one eye were included in the study. All the data was collected using an electronic medical record system. Results: Three hundred and forty?nine eyes of 253 (0.01%) patients were diagnosed with solar retinopathy and included in the study, and 157 patients (62.06%) had a unilateral affliction. Solar retinopathy was noted to be significantly more common in males (73.12%) and adults (98.81%). The most common age group at presentation was during the sixth decade of life with 56 (22.13%) patients. They were more commonly from the rural geography (41.9%). Among the 349 eyes, 275 (78.8%) eyes had mild or no visual impairment (<20/70), which was followed by moderate visual impairment (>20/70–20/200) found in 45 (12.89%) eyes. The most commonly associated ocular comorbidity was cataract in 48 (13.75%) eyes, followed by epiretinal membrane in 38 (10.89%) eyes. The most common retinal damage seen was interdigitation zone (IZ) disruption (38.68%), followed by inner segment–outer segment (IS–OS) disruption (33.52%). Foveal atrophy was seen in 105 (30.09%) eyes. Conclusion: Solar retinopathy is predominantly unilateral and is more common in males. It usually presents during the sixth decade of life and rarely causes significant visual impairment. The most common retinal damage seen was disruption of the outer retinal layers

Clinical profile, demographic distribution, and outcomes of ocular siderosis: Electronic medical record–driven big data analytics from an eye care network in India

Purpose: To describe the demographics, clinical profile, and outcomes of ocular siderosis in patients presenting to a multi?tier ophthalmology hospital network in India. Methods: This cross?sectional and hospital?based study included 3,082,727 new patients who presented between August 2010 and December 2021. Patients with a clinical diagnosis of ocular siderosis in at least one eye were included. Results: Overall, 58 eyes of 57 patients (0.002%) were diagnosed with ocular siderosis. The majority were men (96.49%) and had unilateral (98.25%) affliction. The most common age group at presentation was during the third decade of life with 24 patients (42.11%). A clear history of ocular trauma was documented in 47 patients (81.03%). Major clinical signs included corneal pigment deposition in nearly half of the eyes (27/58 eyes, 46.55%), corneal scar (20/58 eyes, 34.48%), cataract (22/58 eyes, 37.93%) and retinal detachment (11/58 eyes, 18.96%). The intraocular foreign body (IOFB) was anatomically localized in a majority of the eyes (i.e., 45/58 eyes, 77.59%). The most common location of the IOFB was in the posterior segment (22/58 eyes, 37.93%). The eyes that underwent a vitreoretinal surgery with removal of IOFB had a slightly better BCVA (1.0 ± 1.01) when compared to eyes with non?removal of IOFB (1.58 ± 1.00). Conclusion: Ocular siderosis is a rare sight?threatening entity, with half of the affected eyes exhibiting severe visual impairment. Majority of the eyes in ocular siderosis will have a detectable IOFB. Surgical removal of IOFB may lead to a better visual gain when compared to non?removal.

"Brain-inspired intelligence" in dental implant decision-making / 口腔疾病防治

@#At present, implant surgery robots have basically achieved "surgical intelligence", but "brain-inspired intelligence" of robots is still in the stage of theory and exploration. The formulation of a clinical implantation plan depends on the timing of implantation, implantation area, bone condition, surgical procedure, patient factors, etc., which need to evaluate the corresponding clinical decision indicators and clinical pathways. Inspired by evidence-based medicine and the potential of big data and deep learning, combined with the data characteristics of clinical decision indicators and clinical pathways that can be quantitatively or qualitatively analyzed, this review simulates the cognitive behavior and neural mechanisms of the human brain and proposes a feasible brain-inspired intelligence scheme by predicting the decision indices and executing clinical pathways intelligently, that is, "select clinical indicators and clarify clinical pathways -- construct database -- use deep learning to intelligently predict decision indicators -- intelligent execution of clinical pathways -- brain-inspired intelligence of implant decision-making". Combined with the previous research results of our team, this review also describes the process of realization of brain-inspired intelligence for immediate implant timing decisions, providing an example of the comprehensive realization of brain-inspired intelligence of implant surgery robots in the future. In the future, how to excavate and summarize other clinical decision factors and select the best way to realize the automatic prediction of evidence-based clinical indicators and pathways and finally realize the complete intellectualization of clinical diagnosis and treatment processes will be one of the directions that dental clinicians need to strive for.

Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications / 中国医学科学杂志(英文版)

Electrocardiogram (ECG) is a low-cost, simple, fast, and non-invasive test. It can reflect the heart's electrical activity and provide valuable diagnostic clues about the health of the entire body. Therefore, ECG has been widely used in various biomedical applications such as arrhythmia detection, disease-specific detection, mortality prediction, and biometric recognition. In recent years, ECG-related studies have been carried out using a variety of publicly available datasets, with many differences in the datasets used, data preprocessing methods, targeted challenges, and modeling and analysis techniques. Here we systematically summarize and analyze the ECG-based automatic analysis methods and applications. Specifically, we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes. Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications. Finally, we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

Proposed public policies to improve outcomes in vascular surgery: an experts' forum

ABSTRACT Objective To evaluate outcomes of vascular surgeries and identify strategies to improve public vascular care. Methods This was a descriptive, qualitative, and cross-sectional survey involving 30 specialists of the Hospital Israelita Albert Einstein via Zoom. The outcomes of vascular procedures performed in the Public Health System extracted through Big Data analysis were discussed, and 53 potential strategies to improve public vascular care to improve public vascular care. Results There was a consensus on mandatory reporting of some key complications after complex arterial surgeries, such as stroke after carotid revascularization and amputations after lower limb revascularization. Participants agreed on the recommendation of screening for diabetic feet and infrarenal abdominal aortic aneurysms. The use of Telemedicine as a tool for patient follow-up, auditing of centers for major arterial surgeries, and the concentration of complex arterial surgeries in reference centers were also points of consensus, as well as the need to reduce the values of endovascular materials. Regarding venous surgery, it was suggested that there should be incentives for simultaneous treatment of both limbs in cases of varicose veins of the lower limbs, in addition to the promotion of ultrasound-guided foam sclerotherapy in the public system. Conclusion After discussing the data from the Brazilian Public System, proposals were defined for standardizing measures in population health care in the area of vascular surgery.

Construction of a caries diagnosis model based on microbiome novelty score / 华西口腔医学杂志

OBJECTIVES@#This study aimed to analyze the bacteria in dental caries and establish an optimized dental-ca-ries diagnosis model based on 16S ribosomal RNA (rRNA) data of oral flora.@*METHODS@#We searched the public databa-ses of microbiomes including NCBI, MG-RAST, EMBL-EBI, and QIITA and collected data involved in the relevant research on human oral microbiomes worldwide. The samples in the caries dataset (1 703) were compared with healthy ones (20 540) by using the microbial search engine (MSE) to obtain the microbiome novelty score (MNS) and construct a caries diagnosis model based on this index. Nonparametric multivariate ANOVA was used to analyze and compare the impact of different host factors on the oral flora MNS, and the model was optimized by controlling related factors. Finally, the effect of the model was evaluated by receiver operating characteristic (ROC) curve analysis.@*RESULTS@#1) The oral microbiota distribution obviously differed among people with various oral-health statuses, and the species richness and species diversity index decreased. 2) ROC curve was used to evaluate the caries data set, and the area under ROC curve was AUC=0.67. 3) Among the five hosts' factors including caries status, country, age, decayed missing filled tooth (DMFT) indices, and sampling site displayed the strongest effect on MNS of samples (P=0.001). 4) The AUC of the model was 0.87, 0.74, 0.74, and 0.75 in high caries, medium caries, low caries samples in Chinese children, and mixed dental plaque samples after controlling host factors, respectively.@*CONCLUSIONS@#The model based on the analysis of 16S rRNA data of oral flora had good diagnostic efficiency.

Analysis on the Key Points of Clinical Research Management Based on the Scientific Research Big Data Platform of a Tertiary Hospital / 中国医学伦理学

With the rapid development of healthcare big data and artificial intelligence technology, how to utilize the massive medical data generated based on clinical diagnosis and treatment has become an important issue to be solved in the field of clinical research. Clinical diagnosis and treatment data is an essential part of healthcare big data, and also the main field of healthcare big data research. With the continuous deepening and extensive development of informatization, hospitals have accumulated a large number of patient-centered clinical diagnosis and treatment data. Deeply mining and analyzing these data through big data technology can provide reference for precise diagnosis and treatment, and standardized prevention and control of diseases. However, conducting relevant research still faces many difficulties and blockages, such as the increased risk of data leakage or abuse, and the difficulty in implementing informed consent. To safely, legally and efficiently utilize clinical diagnosis and treatment data to conduct clinical research and fully tap into the value of these precious medical resources, a tertiary hospital in Beijing has built a research big data platform and developed relevant systems to effectively solve the problems of blockages and difficulties in the application of rich clinical resources to clinical research, and improve the service quality of medical institutions and the conversion rate of scientific research achievements. By introducing the key points and management methods in the implementation of clinical research based on the scientific research big data platform, analyzing and exploring the existing problems and improvement measures, this paper aimed to provide theoretical basis and system reference for high-quality and efficient health and medical big data clinical research, inspire and promote the continuous improvement of medical research management, and promote the development of medical and health science and technology innovation.

Research Ideas and Challenge of Real World Study and Artificial Intelligence Based On Clinical Diagnosis and Treatment Data of Traditional Chinese Medicine / 中医杂志

With the continuous progress of research methodology in the real world and the growing maturity of artificial intelligence technology， a method for conducting “quantitative” research to guide clinical practice based on traditional Chinese medicine （TCM） diagnosis and treatment data was gradually developed. However， there is still a need for further improvements in the overall design of studies and the transformation of findings into clinical practice. Based on this， we put forward a comprehensive overall design concept and application approach for real-world study and artificial intelligence research based on clinical diagnosis and treatment data of TCM. This approach consists of five steps： Constructing a research-based database with a large sample size and high data quality； Mining and classification of core prescriptions； Conducting cohort studies to evaluate the effectiveness of core prescriptions； Utilizing case-control studies to clarify the dominant population； Establishing predictive models to achieve precision medicine. Additionally， it is imperative for researchers to establish a standardized system for collecting TCM variables and processing data， optimize the determination and measurement methods of confounding factors， further improve and promote methodologies， and strengthen the training of interdisciplinary talents. By following this research method， we anticipate that the clinical translation of research findings will be facilitated， leading to advancements in TCM precision medicine. Real-world study and artificial intelligence research share similar research foundations， and clinical applications complement each other. In the future， the two will merge together.

Implication practice of “Internet Plus” new technology in chronic diseases management in community / 公共卫生与预防医学

Objective  To introduce and evaluate the practice of “Internet Plus” new technology for health management of chronic diseases in community in Yichang, and to provide reference for chronic disease patients' health management in community. Methods  Data of hypertensive patients were collected from the national basic public health service system, the big data intelligent sorting system for chronic disease patients in Yichang City, and the basic public health service system in urban areas in Yichang from 2016 to 2020. Data on the discovery, sorting and filing, standardized management rate and blood pressure control of urban hypertension patients were analyzed. The application effect of “Internet Plus” new technology in chronic disease community health management was evaluated. Results  From 2016 to 2020, 15 934 patients with hypertension were found and their health records were established through big and intelligent data in Yichang City, accounting for 93.54% (15 934 / 17 035) of the total. The rate of standardized management in each district increased year by year, with an increase of 8.71% in 2020 compared with 2016, and the difference was statistically significant (χ2=1273.30, P2=867.14, P<0.001). Conclusion  Data exchange and sharing among medical institutions at all levels can strengthen the health management of chronic diseases in the community. The “Internet Plus” new technology, integrating the Internet, big data, cloud computing and intelligent terminal technology, can effectively improve the detection, management and treatment rate of chronic diseases, and provide a new direction for the health management of chronic diseases.

O futuro das avaliações por meio das redes sociais / The future of assessments through social media / El futuro de las evaluaciones mediante las redes sociales

O objetivo geral da presente pesquisa é discutir as principais inovações em avaliação psicológica possibilitadas pela existência das redes sociais. Mais especificamente, iremos caracterizar as práticas (e fragilidades) da avaliação psicológica convencionais, os métodos mais modernos de psicometria computacional e exemplos de aplicações de psicometria computacional a partir de dados provenientes de redes sociais. De forma geral, as práticas convencionais da avaliação psicológica da psicometria foram criadas no século XIX e muitas de suas práticas, desenvolvidas próximo à metade do século XX, continuam sendo utilizadas de forma pouco crítica. Como alternativa, a psicometria computacional, uma abordagem da psicometria que combina métodos da ciência da computação orientados a dados e teoria psicométrica, tem sido utilizada para gerar inovações na área de avaliação. Por fim, discutimos algumas aplicações da psicometria computacional e como essas inovações irão, provavelmente, gerar mudanças profundas no contexto de avaliação.(AU)

The overall aim of this study was to discuss the main innovations in psychological assessment made possible by the existence of social networks. More specifically, we characterize the practices (and weaknesses) of conventional psychological assessment, the most modern methods of computational psychometry and examples of applications of computational psychometry using data from social networks. In general, the conventional practices of psychological assessment of psychometrics were created in the 19th century and many of its practices, developed around the middle of the 20th century, continue to be used in a non-critical way. As an alternative, computational psychometrics, an approach to psychometrics that combines data-driven methods of computer science and psychometric theory, has been used to generate innovations in the area of assessment. Finally, we discuss some applications of computational psychometrics and how these innovations are likely to generate profound changes in the assessment context.(AU)

El objetivo general de esta investigación es discutir las principales innovaciones, en materia de evaluación psicológica, posibilitadas por la existencia de las redes sociales. Más específicamente, caracterizaremos las prácticas convencionales (y debilidades) de la evaluación psicológica, los métodos más modernos de psicometría computacional y ejemplos de aplicaciones de psicometría computacional a partir de los datos de redes sociales. En general, las prácticas convencionales de evaluación psicológica de la psicometría se crearon en el siglo XIX y muchas de sus prácticas, desarrolladas a mediados del siglo XX, continúan utilizándose de forma acrítica. Como alternativa, la psicometría computacional, un enfoque de la psicometría que combina métodos informáticos basados en datos y teoría psicométrica, se ha empleado para generar innovaciones en el área de la evaluación. Por último, discutimos algunas aplicaciones de la psicometría computacional y cómo es probable que estas innovaciones generen cambios profundos en el contexto de la evaluación.(AU)

Population-based analysis of the epidemiology of the surgical correction of hyperhidrosis in 1,216 patients over 11 years: a cross-sectional study

Abstract BACKGROUND: Endoscopic thoracic sympathectomy is the definitive surgical treatment for hyperhidrosis and a nationwide study has suggested that cultural and socioeconomic factors play a role in the numbers of operations performed. Thus, there is a need to evaluate local data in order to understand the local epidemiology and trends in hyperhidrosis treatment. OBJECTIVE: To study the epidemiology of sympathectomy for treating hyperhidrosis in São Paulo, the largest city in Brazil. DESIGN AND SETTING: Population-based retrospective cross-sectional study. METHODS: Data on sympathectomies for treating hyperhidrosis between 2008 and 2018 were assessed from the database of the Municipal Health Department of São Paulo, Brazil. RESULTS: 65.29% of the patients were female, 66.2% were aged between 20 and 39 years and 37.59% had registered with addresses outside São Paulo. 1,216 procedures were performed in the city of São Paulo from 2008 to 2018, and 78.45% of them were in only two public hospitals. The number of procedures significantly declined over the years (P = 0.001). 71.63% of the procedures were associated with 2-3 days of hospital stay, only 78 intensive care unit days were billed and we did not observe any intra-hospital death. CONCLUSION: The profile of patients operated on in São Paulo (young women) is similar to that described in other populations. Sympathectomy is a very safe procedure, with no mortality in our series. There was a decreasing trend in the number of surgeries over the years.

Clinical profile and demographic distribution of ophthalmia nodosa: An electronic medical record-driven big data analytics from a multitier eye care network

Purpose: To describe the demographics and clinical profile of ophthalmia nodosa in patients presenting to a multitier ophthalmology hospital network in India. Methods: This cross?sectional, hospital?based study included 3,082,727 new patients presenting between August 2010 and December 2021. Patients with a clinical diagnosis of ophthalmia nodosa in at least one eye were included as cases. The data were collected using an electronic medical record system. Results: Overall, 434 (0.014%) patients were diagnosed with ophthalmia nodosa. Most of the patients were male (71.43%) and had unilateral (97.7%) affliction. The most common age group at presentation was during the third decade of life with 116 (26.73%) patients. The overall prevalence was higher in patients from a higher socioeconomic status (0.015%) presenting from the urban geography (0.019%) and in professionals (0.027%). The setae were identified and removed at presentation in 287 (66.13%) patients. The most common location of the setae was conjunctiva (45.72%) followed by cornea (39.64%). Most of the eyes (355; 79.95%) had mild or no visual impairment (<20/70). The most documented ocular signs were eye lid edema (35.81%), conjunctival congestion (73.87%), and corneal abrasion (29.05%). Less than one?tenth of the eyes required a surgical intervention for removal of the setae, corneal foreign body removal was performed in 10 (2.25%) eyes and conjunctival foreign body removal in four (0.90%) eyes. Conclusion: Ophthalmia nodosa more commonly affects males presenting during the third decade of life and is predominantly unilateral. The setae are most commonly lodged in the conjunctiva followed by the cornea, and the majority of the eyes have mild or no visual impairment

A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la / The scientific research in the age of Big data: five ways that show how the Big data harms the science, and how we can save it / La investigación científica en la era del Big data: cinco maneras que muestran como el Big data perjudica la ciencia, y como la salvar

O livro A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la, de Sabina Leonelli, publicado pela Editora Fiocruz em 2022, explora em seus capítulos as definições do termo Big data e os seus impactos negativos na pesquisa científica. Em seguida, a autora revela uma nova abordagem epistemológica para o Big data e, por fim, apresenta um conjunto de propostas para a pesquisa científica. A revisão e atualização de definições, tanto quanto as importantes reflexões e os questionamentos por um uso consciente do Big data na pesquisa científica fazem com que a obra adicione importantes contribuições à biblioteca do pesquisador de informação e comunicação em saúde

The book titled A pesquisa científica na era do Big Data: cinco maneiras que mostram como o Big Data prejudica a ciência, e como podemos salvá-la [The scientific research in the age of Big Data: five ways that show how the Big Data harms the science, and how we can save it], by Sabina Leonelli, published in 2002, by Editora Fiocruz, explores in its chapters the definitions of Big Data and its negative impacts on scientific research. Then, the author reveals a new epistemological approach to Big data and finally she presents a set of proposals for developing a good scientific research. The literature review and updating of definitions as well as the important reflections and questions for a conscious use of Big data in scientific research make the work an important contribution to the researcher's library of the information and communication about health.

El libro denominado A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la [La investigación científica en la era del Big data: cinco maneras que muestran como el Big data perjudica la ciencia, y como la salvar], de Sabina Leonelli, publicado en 2002, por la Editora Fiocruz, explora em sus capítulos las definiciones de Big data y sus impactos negativos en la investigación científica. A continuación, la autora revela un nuevo enfoque epistemológico del Big data y, al fin y al cabo, presenta un conjunto de propuestas para desarrollar una investigación científica de cualidad. La revisión de literatura y la actualización de las definiciones, así como las importantes reflexiones y discusiones para un uso consciente del Big data en la investigación científica, hacen de la obra un aporte importante a la biblioteca del investigador de la información y la comunicación acerca de la salud

Impactos da insuficiência cardíaca no sistema de saúde e previdenciário brasileiro: qual é o custo da doença? / Impacts of heart failure on the Brazilian health and pension system: what is the cost of the disease?

Objetivo: Estimar os principais custos indiretos da insuficiência cardíaca (IC) na população brasileira, sobre o sistema de saúde, o custo previdenciário e o quanto se perde em produtividade pelas complicações da doença. Métodos: Estudo ecológico desenvolvido com dados secundários, para a série histórica de 2018 a 2021, minerados do Departamento de Informática do Sistema Único de Saúde (Datasus), do Instituto Brasileiro de Geografia e Estatística (IBGE), e indicadores previdenciários coletados da Previdência Social e Instituto Nacional do Seguro Social (INSS). Resultados: Foram registrados 77.290 óbitos por IC no Brasil para o período, distribuídos uniformemente em relação ao sexo. A taxa de mortalidade foi diversificada entre as regiões brasileiras, com ênfase para Sudeste e Nordeste. As projeções indicam um gasto total de mais de R$ 1 bilhão com hospitalizações, com custo médio hospitalar de R$ 1.725,27 por pessoa. O custo médio por internação ultrapassou os R$ 2 bilhões de reais. Aproximadamente 3% das despesas federais são destinadas a pagamentos de benefícios relacionados a IC. Do total de afastamentos, 65% correspondem a homens e 35%, a mulheres, com custos que podem chegar a R$ 6 bilhões perdidos por ano. Conclusão: Os resultados sugerem um aumento do afastamento de portadores de IC da força de trabalho, o que acarreta maiores dispêndios para o sistema de saúde e pagamentos de benefícios previdenciários, como auxílio-doença e aposentadoria por incapacidade de longa duração. Este é o primeiro estudo que estima e correlaciona os dados socioepidemiológicos e os custos de saúde e previdenciários da IC no Brasil.

Objective: To estimate the main indirect costs of heart failure (HF) in the Brazilian population, on the health system, social security cost, and how much is lost in productivity due to the complications of the disease. Methods: Ecological study developed with secondary data, for the historical series from 2018 to 2021, mined from the Department of Informatics of the Unified Health System (Datasus), from the Brazilian Institute of Geography and Statistics (IBGE), and social security indicators collected from Social Security and the National Social Security Institute (INSS). Results: There were 77,290 deaths from HF in Brazil for the period, evenly distributed according to sex. The mortality rate was diversified among Brazilian regions, with emphasis on the Southeast and Northeast. Projections indicate a total expenditure of more than BRL 1 billion with hospitalizations, with an average hospital cost of BRL 1,725.27 per person. The average cost per hospitalization exceeded BRL 2 billion. Approximately 3% of federal expenditures are earmarked for IC benefit payments. Of the total number of absences, 65% correspond to men and 35% to women, with costs that can reach R$ 6 billion lost per year. Conclusion: The results suggest an increase in the removal of HF patients from the workforce, which leads to higher expenditures for the health system and payments of social security benefits, such as sick pay and retirement due to long-term disability. This is the first study that estimates and correlates socio-epidemiological data, health and social security costs of HF in Brazil.

Clinical profile and demographic distribution of Fuchs’ endothelial dystrophy: An electronic medical record–driven big data analytics from an eye care network in India

Purpose: To describe the demographics and clinical profile of Fuchs’ endothelial corneal dystrophy (FECD) in patients presenting to a multi?tiered ophthalmology hospital network in India. Methods: This cross?sectional hospital?based study included 3,082,727 new patients presenting between August 2010 and December 2021. Patients with a clinical diagnosis of FECD in at least one eye were included as cases. The data were collected using an electronic medical record system. Results: Overall, 2570 (0.08%) patients were diagnosed with FECD. The majority of the patients were female (65.53%) and were predominantly adults (99.92%). The most common age group at presentation was during the seventh decade of life with 867 patients (33.74%). The overall prevalence was higher in patients from a higher socioeconomic status (0.1%) presenting from the urban geography (0.09%) and in retired individuals (0.4%). About half of the 5,140 eyes had mild or no visual impairment (< 20/70) in 2643 eyes (51.42%) followed by moderate visual impairment (>20/70 to 20/200) in 708 eyes (13.77%). The average logMAR was 0.61 ± 0.81 at presentation. The most documented corneal signs were guttae (76.63%), corneal scar (23%) and stromal edema (21.73%). The most associated ocular comorbidity was cataract (47.32%) followed by glaucoma (5.39%). More than a tenth of the affected eyes required a surgical intervention of endothelial keratoplasty (15.58%). Conclusion: FECD more commonly affects females presenting during the seventh decade of life. Majority of the eyes had mild or no visual impairment and endothelial keratoplasty is warranted in a tenth of the affected eyes

Retinitis pigmentosa in Usher syndrome in India: Electronic medical records driven big data analytics: Report III

Purpose: To describe the clinical presentation and demographic distribution of retinitis pigmentosa (RP) in patients with Usher syndrome (USH). Methods: This is a cross?sectional observational hospital?based study including patients presenting between March 2012 and October 2020. In total, 401 patients with a clinical diagnosis of USH and RP in at least one eye were included as cases. The data were retrieved from the electronic medical record database. For better analysis, all 401 patients were reclassified into three subtypes (type 1, type 2, and type 3) based on the USH criteria. Results: In total, there were 401 patients with USH and RP, with a hospital?based prevalence rate of 0.02% or 2/10,000 population. Further, 353/401 patients were subclassified, with 121 patients in type 1, 146 patients in type 2, and 86 patients in the type 3 USH group. The median age at presentation was 27 years (IQR: 17.5–38) years. There were 246 (61.35%) males and 155 (38.65%) females. Males were more commonly affected in all three subtypes. Defective night vision was the predominant presenting feature in all types of USH (type 1: 43 (35.54%), type 2: 68 (46.58%), and type 3: 40 (46.51%) followed by defective peripheral vision. Patients with type 2 USH had more eyes with severe visual impairment. Conclusion: RP in USH is commonly bilateral and predominantly affects males in all subtypes. Patients with USH and RP will have more affection of peripheral vision than central vision. The key message of our study is early visual and hearing rehabilitation in USH patients with prompt referral to otolaryngologists from ophthalmologists and vice versa.