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1.
J Public Health Manag Pract ; 28(1): E16-E22, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34016907

RESUMO

Disease investigation and contact tracing are long-standing public health strategies used to control the spread of infectious disease. Throughout the COVID-19 pandemic, health departments across the country have lacked the internal workforce capacity and technology needed to efficiently isolate positive cases and quarantine close contacts to slow the spread of SARS-CoV-2. This article describes an innovative disease investigation and contact tracing program developed through a formalized community partnership between a local county health department and local university. This innovative new program added 108 contact tracers to the county's public health workforce, as well as enabled these contact tracers to work remotely using a call center app and secure cloud-based platform to manage the county's caseload of cases and contacts. An overview of the requirements needed to develop this program (eg, hiring, health data security protocols, data source management), as well as lessons learned is discussed.


Assuntos
COVID-19 , Pandemias , Busca de Comunicante , Gerenciamento de Dados , Humanos , Pandemias/prevenção & controle , SARS-CoV-2
2.
Medicine (Baltimore) ; 100(41): e27464, 2021 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-34731122

RESUMO

OBJECTIVE: This study aimed to determine the effectiveness of using total, individual serum, or urinary bile acids (BA) as potential markers of liver dysfunction. METHODS: We searched the PubMed and Web of Science databases using the following keywords- "serum bile acids," "liver dysfunction," "liver injury," "liver disease," "traditional liver function tests," "Chronic liver disease," "acute liver injury". The search was complemented by manual screening of the list of references for relevant articles. We selected only English-language manuscripts for adult patients based on predetermined inclusion and exclusion criteria. Animal studies and studies on neonates and children were not included. OUTCOME MEASURES: Changes in BA concentrations or ratios at or prior to changes in liver function tests. RESULTS: A total of 547 studies were identified, of which 28 were included after reading the entire manuscript. These studies included 1630 patients and 836 controls published between 1990 and 2017. The methods used in BA assays varied significantly, and the studies did not agree. on specific individual BA or BA ratios as biomarkers of specific liver injury or dysfunction. Except for the prognostic value of BA in intrahepatic cholestasis of pregnancy (ICP), studies have failed to provide evidence for BA as a liver biomarker. CONCLUSIONS: Despite the research conducted on BA for over 27 years, there are inconsistencies in the reported results and a lack of solid evidence to support the use of individual BA or BA ratios as biomarkers of liver injury. Adequately conducted studies needed to resolve this limitation in the literature.


Assuntos
Ácidos e Sais Biliares/sangue , Ácidos e Sais Biliares/urina , Hepatopatias/metabolismo , Fígado/lesões , Adulto , Biomarcadores/metabolismo , Estudos de Casos e Controles , Colestase Intra-Hepática/metabolismo , Gerenciamento de Dados , Feminino , Humanos , Fígado/metabolismo , Fígado/fisiopatologia , Hepatopatias/diagnóstico , Testes de Função Hepática/métodos , Testes de Função Hepática/estatística & dados numéricos , Masculino , Gravidez , Complicações na Gravidez/metabolismo , Sensibilidade e Especificidade
3.
Medicine (Baltimore) ; 100(41): e27467, 2021 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-34731123

RESUMO

BACKGROUND: Intra-hospital falls have become an important public health problem globally. The use of movement sensors with alarms has been studied as elements with predictive capacity for falls at hospital level. However, in spite of their use in some hospitals throughout the world, evidence is lacking about their effectiveness in reducing intra-hospital falls. Therefore, this study aims to develop a systematic review and meta-analysis of existing scientific literature exploring the impact of using sensors for fall prevention in hospitalized adults and the elderly population. METHODS: We explored literature based on clinical trials in Spanish, English, and Portuguese, assessing the impact of devices used for hospital fall prevention in adult and elderly populations. The search included databases such as IEEE Xplore, the Cochrane Library, Scopus, PubMed, MEDLINE, and Science Direct databases. The critical appraisal was performed independently by two researchers. Methodological quality was assessed based on the ratings of individual biases. We performed the sum of the results, generating an estimation of the grouped effect (Relative Risk, 95% CI) for the outcome first fall for each patient. We assessed heterogeneity and publication bias. The study followed PRISMA guidelines. RESULTS: Results were assessed in three randomized controlled clinical trials, including 29,691 patients. A total of 351 (3%) patients fell among 11,769 patients assigned to the intervention group, compared with 426 (2.4%) patients who fell among 17,922 patients assigned to the control group (general estimation RR 1.20, 95% CI 1.04, 1.37, P = .02, I2 = 0%; Moderate GRADE). CONCLUSION: Our results show an increase of 19% in falls among elderly patients who are users of sensors located in their bed, bed-chair, or chair among their hospitalizations. Other types of sensors such as wearable sensors can be explored as coadjutants for fall prevention care in hospitals.


Assuntos
Acidentes por Quedas/prevenção & controle , Arquitetura Hospitalar/instrumentação , Prevenção Primária/instrumentação , Equipamentos de Proteção/efeitos adversos , Acidentes por Quedas/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Estudos de Casos e Controles , Gerenciamento de Dados , Feminino , Hospitalização , Humanos , Masculino , Pessoa de Meia-Idade , Prevenção Primária/métodos , Equipamentos de Proteção/estatística & dados numéricos , Ensaios Clínicos Controlados Aleatórios como Assunto
4.
Stud Health Technol Inform ; 285: 159-164, 2021 Oct 27.
Artigo em Inglês | MEDLINE | ID: mdl-34734868

RESUMO

The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the entirety of what is needed for knowledge representation purposes. Based on the lack of computable semantics in frequently used Common Data Models, there appears to be a gap between knowledge representation requirements and these models. In this use-case oriented approach, we explore how a system-theoretic, architecture-centric, ontology-based methodology can help to better understand this gap. We show how using the Generic Component Model helps to analyze the data management system in a way that allows accounting for data management procedures inside the system and knowledge representation of the real world at the same time.


Assuntos
Ontologias Biológicas , Semântica , Gerenciamento de Dados
5.
Artigo em Inglês | MEDLINE | ID: mdl-34769956

RESUMO

To counteract the COVIC-19 pandemic, many governments have introduced social distancing measures. While these restrictions helped contain the virus, it had adverse effects on individuals' mental and physical health-especially children. The aim of the present study is to review the evidence on the effects of COVID-19 restrictions on children's physical activity and their determinants. A scoping review was conducted in the databases PubMed, Web of Science, SportDiscus, and BISp-Surf. Inclusion criteria were empirical and peer-reviewed studies, youth samples, investigation of COVID-19 restrictions, and investigating changes and/or determinants of physical activity before and during the COVID-19 pandemic. Risk of bias was assessed using the checklist by Downs and Black. The search resulted in 1672 studies, of which 84 studies were included in the analysis. The results highlighted a decrease in physical activity during the pandemic, ranging between -10.8 min/day and -91 min/day. If an increase was detected, it related to unstructured and outdoor activities. The main determinants of children's physical activity during the pandemic were age, gender, socioeconomic background, and the outdoor environment. The results imply that governments need to consider the negative effects that restrictive measures have on children's physical activity and act to ensure high levels of physical activity.


Assuntos
COVID-19 , Pandemias , Adolescente , Criança , Gerenciamento de Dados , Exercício Físico , Humanos , SARS-CoV-2
6.
Sensors (Basel) ; 21(21)2021 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-34770333

RESUMO

Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective and dependent to the language besides stepping in late in the schooling. We propose a pre-diagnosis tool for dysgraphia using drawings called graphomotor tests. These tests are recorded using graphical tablets. We evaluate several machine-learning models and compare them to build this tool. A database comprising 305 children from the region of Grenoble, including 43 children with dysgraphia, has been established and diagnosed by specialists using the BHK test, which is the gold standard for the diagnosis of dysgraphia in France. We performed tests of classification by extracting, correcting and selecting features from the raw data collected with the tablets and achieved a maximum accuracy of 73% with cross-validation for three models. These promising results highlight the relevance of graphomotor tests to diagnose dysgraphia earlier and more broadly.


Assuntos
Agrafia , Agrafia/diagnóstico , Algoritmos , Criança , Gerenciamento de Dados , Escrita Manual , Humanos , Aprendizado de Máquina
7.
Stud Health Technol Inform ; 287: 57-58, 2021 Nov 18.
Artigo em Inglês | MEDLINE | ID: mdl-34795080

RESUMO

The One Digital Health framework aims at transforming future health ecosystems and guiding the implementation of a digital technologies-based systemic approach to caring for humans' and animals' health in a managed surrounding environment. To integrate and to use the data generated by the ODH data sources, "FAIRness" stands as a prerequisite for proper data management and stewardship.


Assuntos
Gerenciamento de Dados , Ecossistema , Animais , Humanos
8.
Stud Health Technol Inform ; 287: 99-103, 2021 Nov 18.
Artigo em Inglês | MEDLINE | ID: mdl-34795090

RESUMO

The process of maintenance of an underlying semantic model that supports data management and addresses the interoperability challenges in the domain of telemedicine and integrated care is not a trivial task when performed manually. We present a methodology that leverages the provided serializations of the Health Level Seven International (HL7) Fast Health Interoperability Resources (FHIR) specification to generate a fully functional OWL ontology along with the semantic provisions for maintaining functionality upon future changes of the standard. The developed software makes a complete conversion of the HL7 FHIR Resources along with their properties and their semantics and restrictions. It covers all FHIR data types (primitive and complex) along with all defined resource types. It can operate to build an ontology from scratch or to update an existing ontology, providing the semantics that are needed, to preserve information described using previous versions of the standard. All the results based on the latest version of HL7 FHIR as a Web Ontology Language (OWL-DL) ontology are publicly available for reuse and extension.


Assuntos
Nível Sete de Saúde , Telemedicina , Gerenciamento de Dados , Registros Eletrônicos de Saúde , Semântica
9.
Comput Methods Programs Biomed ; 212: 106496, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34740063

RESUMO

BACKGROUND AND OBJECTIVES: In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these clinical tools have design limitations, since they are currently not able to cover the needs of adaptation to the continuous changes in the practice of the trials due to the heterogeneous and dynamic nature of the clinical research data. These systems are usually proprietary solutions provided by vendors for specific tasks. In this work, we propose FIMED, a software solution for the flexible management of clinical data from multiple trials, moving towards personalized medicine, which can contribute positively by improving clinical researchers quality and ease in clinical trials. METHODS: This tool allows a dynamic and incremental design of patients' profiles in the context of clinical trials, providing a flexible user interface that hides the complexity of using databases. Clinical researchers will be able to define personalized data schemas according to their needs and clinical study specifications. Thus, FIMED allows the incorporation of separate clinical data analysis from multiple trials. RESULTS: The efficiency of the software has been demonstrated by a real-world use case for a clinical assay in Melanoma disease, which has been indeed anonymized to provide a user demonstration. FIMED currently provides three data analysis and visualization components, guaranteeing a clinical exploration for gene expression data: heatmap visualization, clusterheatmap visualization, as well as gene regulatory network inference and visualization. An instance of this tool is freely available on the web at https://khaos.uma.es/fimed. It can be accessed with a demo user account, "researcher", using the password "demo". CONCLUSION: This paper shows FIMED as a flexible and user-friendly way of managing multidimensional clinical research data. Hence, without loss of generality, FIMED is flexible enough to be used in the context of any other disease where clinical data and assays are involved.


Assuntos
Gerenciamento de Dados , Software , Bases de Dados Factuais , Redes Reguladoras de Genes , Humanos , Internet , Interface Usuário-Computador
10.
J Med Internet Res ; 23(11): e29749, 2021 11 19.
Artigo em Inglês | MEDLINE | ID: mdl-34806996

RESUMO

BACKGROUND: Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD. OBJECTIVE: This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes. METHODS: The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed. RESULTS: We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning-based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%. CONCLUSIONS: This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.


Assuntos
Transtorno Bipolar , Algoritmos , Transtorno Bipolar/diagnóstico , Gerenciamento de Dados , Humanos , Aprendizado de Máquina , Processamento de Linguagem Natural
11.
PLoS One ; 16(11): e0259514, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34735523

RESUMO

INTRODUCTION: Famotidine is a competitive histamine H2-receptor antagonist most commonly used for gastric acid suppression but thought to have potential efficacy in treating patients with Coronavirus disease 2019 (COVID-19). The aims of this systematic review and meta-analysis are to summarize the current literature and report clinical outcomes on the use of famotidine for treatment of hospitalized patients with COVID-19. METHODS: Five databases were searched through February 12, 2021 to identify observational studies that reported on associations of famotidine use with outcomes in COVID-19. Meta-analysis was conducted for composite primary clinical outcome (e.g. rate of death, intubation, or intensive care unit admissions) and death separately, where either aggregate odds ratio (OR) or hazard ratio (HR) was calculated. RESULTS: Four studies, reporting on 46,435 total patients and 3,110 patients treated with famotidine, were included in this meta-analysis. There was no significant association between famotidine use and composite outcomes in patients with COVID-19: HR 0.63 (95% CI: 0.35, 1.16). Across the three studies that reported mortality separated from other endpoints, there was no association between famotidine use during hospitalization and risk of death-HR 0.67 (95% CI: 0.26, 1.73) and OR 0.79 (95% CI: 0.19, 3.34). Heterogeneity ranged from 83.69% to 88.07%. CONCLUSION: Based on the existing observational studies, famotidine use is not associated with a reduced risk of mortality or combined outcome of mortality, intubation, and/or intensive care services in hospitalized individuals with COVID-19, though heterogeneity was high, and point estimates suggested a possible protective effect for the composite outcome that may not have been observed due to lack of power. Further randomized controlled trials (RCTs) may help determine the efficacy and safety of famotidine as a treatment for COVID-19 patients in various care settings of the disease.


Assuntos
COVID-19/tratamento farmacológico , Famotidina/uso terapêutico , Hospitalização , Adulto , Idoso , Gerenciamento de Dados , Feminino , Antagonistas dos Receptores H2 da Histamina/uso terapêutico , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Observacionais como Assunto , Razão de Chances , Modelos de Riscos Proporcionais , Ensaios Clínicos Controlados Aleatórios como Assunto , Risco , SARS-CoV-2
12.
Front Public Health ; 9: 737269, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34616709

RESUMO

Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also attained. For the disallowed class, the scores were 89, 73, and 80% for precision, recall, and F1-measure, respectively. The performance of our proposed BPS is subdivided into two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age, etc. The proposed system is outstanding as none of the earlier revised works of literature described a recommender system of this kind.


Assuntos
Blockchain , Aprendizado Profundo , Internet das Coisas , Algoritmos , Gerenciamento de Dados , Humanos
13.
Anal Chim Acta ; 1183: 339001, 2021 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-34627524

RESUMO

Data fusion has gained much attention in the field of life sciences, and this is because analysis of biological samples may require the use of data coming from multiple complementary sources to express the samples fully. Data fusion lies in the idea that different data platforms detect different biological entities. Therefore, if these different biological compounds are then combined, they can provide comprehensive profiling and understanding of the research question in hand. Data fusion can be performed in three different traditional ways: low-level, mid-level, and high-level data fusion. However, the increasing complexity and amount of generated data require the development of more sophisticated fusion approaches. In that regard, the current study presents an advanced data fusion approach (i.e. proximities stacking) based on random forest proximities coupled with the pseudo-sample principle. Four different data platforms of 130 samples each (faecal microbiome, blood, blood headspace, and exhaled breath samples of patients who have Crohn's disease) were used to demonstrate the classification performance of this new approach. More specifically, 104 samples were used to train and validate the models, whereas the remaining 26 samples were used to validate the models externally. Mid-level, high-level, as well as individual platform classification predictions, were made and compared against the proximities stacking approach. The performance of each approach was assessed by calculating the sensitivity and specificity of each model for the external test set, and visualized by performing principal component analysis on the proximity matrices of the training samples to then, subsequently, project the test samples onto that space. The implementation of pseudo-samples allowed for the identification of the most important variables per platform, finding relations among variables of the different data platforms, and the examination of how variables behave in the samples. The proximities stacking approach outperforms both mid-level and high-level fusion approaches, as well as all individual platform predictions. Concurrently, it tackles significant bottlenecks of the traditional ways of fusion and of another advanced fusion way discussed in the paper, and finally, it contradicts the general belief that the more data, the merrier the result, and therefore, considerations have to be taken into account before any data fusion analysis is conducted.


Assuntos
Disciplinas das Ciências Biológicas , Interpretação Estatística de Dados , Gerenciamento de Dados , Humanos
14.
Sensors (Basel) ; 21(20)2021 Oct 14.
Artigo em Inglês | MEDLINE | ID: mdl-34696058

RESUMO

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.


Assuntos
Big Data , Gerenciamento de Dados , Bases de Dados Factuais , Prognóstico , Reprodutibilidade dos Testes
15.
Sensors (Basel) ; 21(20)2021 Oct 19.
Artigo em Inglês | MEDLINE | ID: mdl-34696155

RESUMO

Acne is a dermatosis that affects almost 90% of the adolescent population worldwide and its treatment is performed with retinoids, antimicrobials, acids, and topical or systemic antibiotics. Side effects such as skin irritation in addition to microbial resistance to antibiotics are the main side effects found. Phototherapy with blue light is being used as an alternative treatment. Our objective was to analyze the use of blue light to treat inflammatory acne. We conducted a systematic literature review, following the recommendation PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses), including in the sample randomized clinical trial studies that compared blue light with another intervention as control. The research was carried out in the PUBMED and WEB of SCIENCE databases and the methodological quality of the studies evaluated were made by the Cochrane Collaboration Bias Risk Scale. After the exclusion of duplicates, the titles and abstracts of 81 articles were evaluated, and 50 articles were selected for full reading, including in the review at the end 8 articles. Studies have shown significant improvements in the overall picture of acne. It is concluded that despite the great potential in its use in the treatment of acne, there is a need for more detailed trials on the effect of blue light on the treatment of inflammatory acne.


Assuntos
Acne Vulgar , Acne Vulgar/terapia , Adolescente , Antibacterianos , Gerenciamento de Dados , Humanos , Luz , Fototerapia , Ensaios Clínicos Controlados Aleatórios como Assunto , Resultado do Tratamento
16.
J Med Libr Assoc ; 109(3): 450-458, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-34629974

RESUMO

Objective: This study investigates research data management (RDM) services using a crosstab framework with the National Institutes of Health (NIH) Library as a case study to provide practical considerations for libraries seeking to improve their RDM services. Methods: We conducted semistructured interviews with four librarians who provide data services at the NIH Library regarding library user characteristics, RDM services provided, RDM infrastructure, and collaboration experiences. Through the analysis of interview transcripts, we identified and analyzed the NIH Library's RDM services according to Online Computer Library Center (OCLC)'s three categories of RDM services and the six stages of the data lifecycle. Results: The findings show that the two models' crosstab framework can provide an overview of an institution's current RDM services and identify service gaps. The NIH Library tends to take more responsibility in providing education and expertise services while relying more on information technology departments for curation services. The library provides significant support for data creation, analysis, and sharing stages to meet biomedical researchers' needs, suggesting areas for potential expansion of RDM services in the less supported stages of data description, storage, and preservation. Based on these findings, we recommend three key considerations for libraries: identify gaps in current services, identify services that can be supported via partnerships, and get regular feedback from users. Conclusion: These findings provide a deeper understanding of RDM support on the basis of RDM service categories and the data lifecycle and promote discussion of issues to be considered for future improvements in RDM services.


Assuntos
Pesquisa Biomédica , Bibliotecários , Bibliotecas Médicas , Serviços de Biblioteca , Gerenciamento de Dados , Humanos , National Institutes of Health (U.S.) , Estados Unidos
17.
Digital Transformation Toolkit; Technical ToolsPAHO/EIH/IS/21-030.
Monografia em Inglês | PAHO-IRIS | ID: phr-54962

RESUMO

The digital transformation toolkit was created with the aim of offering managerial, technical, knowledge, communication, and academic resources to all those health professionals, decision-makers, and institutions dedicated to strengthening health information systems, with the vision of achieving universal access to health and universal health coverage in the Region through access to good quality data, strategic information, and digital health tools for decision-making and well-being. The category of technical tools within the digital transformation toolkit is based on offering documents that facilitate an adequate implementation of policies, recommendations, data governance frameworks, monitoring and evaluation frameworks, analysis, and other rapid evaluation tools for the information systems for health of the countries.


Assuntos
Gestão do Conhecimento , Comunicação , Pessoal de Saúde , Gerenciamento de Dados , Sistemas de Informação , Sistemas de Informação em Saúde , Sistemas de Saúde
18.
Digital Transformation Toolkit; Technical ToolsPAHO/EIH/IS/21-029.
Monografia em Inglês | PAHO-IRIS | ID: phr-54961

RESUMO

The PAHO Digital Transformation Toolkit was created with the aim of offering managerial, technical, knowledge, communication, and academic resources to all those health professionals, decisionmakers, and institutions dedicated to strengthening health information systems. Its guiding vision is that of achieving universal access to health and universal health coverage in the Region through access to good-quality data, strategic information, and digital health tools for decision-making and well-being. The category of technical tools within the PAHO Digital Transformation Toolkit is based on offering documents that facilitate implementation of policies, recommendations, data governance frameworks, monitoring and evaluation frameworks, analysis, and other rapid evaluation tools for the Information Systems for Health initiative in countries.


Assuntos
Gestão do Conhecimento , Gerenciamento de Dados , Sistemas de Informação , Sistemas de Informação em Saúde , Comunicação , Pessoal de Saúde
19.
BMC Med Inform Decis Mak ; 21(1): 289, 2021 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-34670548

RESUMO

BACKGROUND: To describe an automated method for assessment of the plausibility of continuous variables collected in the electronic health record (EHR) data for real world evidence research use. METHODS: The most widely used approach in quality assessment (QA) for continuous variables is to detect the implausible numbers using prespecified thresholds. In augmentation to the thresholding method, we developed a score-based method that leverages the longitudinal characteristics of EHR data for detection of the observations inconsistent with the history of a patient. The method was applied to the height and weight data in the EHR from the Million Veteran Program Data from the Veteran's Healthcare Administration (VHA). A validation study was also conducted. RESULTS: The receiver operating characteristic (ROC) metrics of the developed method outperforms the widely used thresholding method. It is also demonstrated that different quality assessment methods have a non-ignorable impact on the body mass index (BMI) classification calculated from height and weight data in the VHA's database. CONCLUSIONS: The score-based method enables automated and scaled detection of the problematic data points in health care big data while allowing the investigators to select the high-quality data based on their need. Leveraging the longitudinal characteristics in EHR will significantly improve the QA performance.


Assuntos
Registros Eletrônicos de Saúde , Veteranos , Big Data , Confiabilidade dos Dados , Gerenciamento de Dados , Humanos
20.
Nutrients ; 13(10)2021 Oct 03.
Artigo em Inglês | MEDLINE | ID: mdl-34684504

RESUMO

Comprehensive food lists and databases are a critical input for programs aiming to alleviate undernutrition. However, standard methods for developing them may produce databases that are irrelevant for marginalised groups where nutritional needs are highest. Our study provides a method for identifying critical contextual information required to build relevant food lists for Indigenous populations. For our study, we used mixed-methods study design with a community-based approach. Between July and October 2019, we interviewed 74 participants among Batwa and Bakiga communities in south-western Uganda. We conducted focus groups discussions (FGDs), individual dietary surveys and markets and shops assessment. Locally validated information on foods consumed among Indigenous populations can provide results that differ from foods listed in the national food composition tables; in fact, the construction of food lists is influenced by multiple factors such as food culture and meaning of food, environmental changes, dietary transition, and social context. Without using a community-based approach to understanding socio-environmental contexts, we would have missed 33 commonly consumed recipes and foods, and we would not have known the variety of ingredients' quantity in each recipe, and traditional foraged foods. The food culture, food systems and nutrition of Indigenous and vulnerable communities are unique, and need to be considered when developing food lists.


Assuntos
Gerenciamento de Dados/métodos , Bases de Dados Factuais , Dieta/etnologia , Abastecimento de Alimentos , Grupo com Ancestrais do Continente Africano/etnologia , Cultura , Inquéritos sobre Dietas , Grupos Focais , Assistência Alimentar , Humanos , Povos Indígenas , População Rural , Meio Social , Uganda
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