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1.
Braz. j. oral sci ; 20: e211076, jan.-dez. 2021. tab
Artigo em Inglês | LILACS, BBO - Odontologia | ID: biblio-1253739

RESUMO

Aim: to evaluate the intra and inter-device reliability of two intraoral spectrophotometers in measuring the Commission Internationale de l'Éclairage (CIE) L*a*b* color coordinates and to compare the color difference (ΔE) between both devices. Methods: the central region of the labial surface of the maxillary central incisor of 31 participants was measured twice by each of the devices (VITA EasyShade and Degudent Shadepilot) by one examiner. CIE L*a*b* color coordinates were obtained for all teeth and ΔE was measured and compared. Intraclass correlation coefficient (ICC) and Mann-whitney U test were used to analyze the data (p<0.05). Results: inter-device reliability ICCs in measuring CIE L*a*b* color coordinates ranged between 0.08-0.49 with significant difference between devices only concerning the b coordinate (p<0.05). While intra device reliability ICCs ranged between 0.86-0.89 for VITA EasyShade and 0.81-0.86 for Degudent Shadepilot. The mean ΔE for CIE L*a*b* color coordinates of VITA EasyShade was 3.61 (±1.93) compared to 3.60 (± 1.45) for Degudent Shadepilot with insignificant difference between both devices (p>0.05). Conclusions: high intra device reliability in measuring CIE L*a*b* color coordinates was achieved particularly of Vita EasyShade, and both devices had clinically acceptable color difference (ΔE <3.7) however, inter device reliability was low to moderate. Consequently, the same spectrophotometer should be used throughout the steps of performing any tooth- colored restoration


Assuntos
Humanos , Masculino , Adulto , Espectrofotometria , Cor , Confiabilidade dos Dados
2.
BMC Med Inform Decis Mak ; 21(1): 302, 2021 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-34724930

RESUMO

BACKGROUND: Data quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues. OBJECTIVES: To explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task. METHODS: We started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods' results and corresponding outcome data (data that indicated the data's suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without. RESULTS: Our self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods' results. CONCLUSIONS: Our data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods' results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task.


Assuntos
Confiabilidade dos Dados , Projetos de Pesquisa , Humanos
3.
Trials ; 22(1): 845, 2021 Nov 25.
Artigo em Inglês | MEDLINE | ID: mdl-34823566

RESUMO

BACKGROUND: Registries are powerful clinical investigational tools. Although in hospitals registries may be mandated, industry-sponsored, international registries are voluntary and therefore can require clearer objectives and more planning. The registry also needs sufficient resources and appropriate measurement tools to motivate long-term participation and ensure success. METHODS: We summarize our learnings from 10 years of running a medical device registry that surveys patient-reported benefits of hearing implants. RESULTS: We enlisted 77 participating clinics globally, who actively recruited a total of more than 1500 hearing implant users. We identified the stages in developing a registry specific to hearing loss. Furthermore, we report the challenges and successes in design and implementation and make recommendations for future registries. CONCLUSIONS: Data collection infrastructure needs to be kept up to date throughout the defined registry lifetime, and it is essential to oversee data quality and completeness. Compliance at registry sites is important for data quality and needs to be weighed against the cost of site monitoring. To motivate sites to enter data accurately and expeditiously, we facilitated easy access to their own data which helped to support their clinical routine. TRIAL REGISTRATION: ClinicalTrials.gov NCT02004353. 9th December 2013.


Assuntos
Confiabilidade dos Dados , Perda Auditiva , Humanos , Próteses e Implantes , Sistema de Registros , Inquéritos e Questionários
4.
Sci Rep ; 11(1): 21413, 2021 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-34725416

RESUMO

In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on the COVID-19 prevalence and mortality data from 28 February 2020 to 27 June 2020 in South Africa and Nigeria. By comparing the accuracy level of forecasting measurements with the basic ARIMA and NARANN models, it was shown that this novel data-driven hybrid model did a better job of capturing the dynamic changing trends of the target data than the others used in this work. Our proposed mixture technique can be deemed as a helpful policy-supportive tool to plan and provide medical supplies effectively. The overall confirmed cases and deaths were estimated to reach around 176,570 [95% uncertainty level (UL) 173,607 to 178,476] and 3454 (95% UL 3384 to 3487), respectively, in South Africa, along with 32,136 (95% UL 31,568 to 32,641) and 788 (95% UL 775 to 804) in Nigeria on 12 July 2020 using this data-driven EEMD-ARIMA-NARANN hybrid technique. The contributions of this study include three aspects. First, the proposed hybrid model can better capture the dynamic dependency characteristics compared with the individual models. Second, this new data-driven hybrid model is constructed in a more reasonable way relative to the traditional mixture model. Third, this proposed model may be generalized to estimate the epidemic patterns of COVID-19 in other regions.


Assuntos
COVID-19/epidemiologia , COVID-19/mortalidade , Modelos Estatísticos , Redes Neurais de Computação , Pandemias/prevenção & controle , SARS-CoV-2 , COVID-19/transmissão , COVID-19/virologia , Confiabilidade dos Dados , Previsões/métodos , Humanos , Nigéria/epidemiologia , Prevalência , África do Sul/epidemiologia , Incerteza
5.
J Med Internet Res ; 23(11): e28915, 2021 11 09.
Artigo em Inglês | MEDLINE | ID: mdl-34751657

RESUMO

BACKGROUND: High-frequency patient-reported outcome (PRO) assessments are used to measure patients' symptoms after surgery for surgical research; however, the quality of those longitudinal PRO data has seldom been discussed. OBJECTIVE: The aim of this study was to determine data quality-influencing factors and to profile error trajectories of data longitudinally collected via paper-and-pencil (P&P) or web-based assessment (electronic PRO [ePRO]) after thoracic surgery. METHODS: We extracted longitudinal PRO data with 678 patients scheduled for lung surgery from an observational study (n=512) and a randomized clinical trial (n=166) on the evaluation of different perioperative care strategies. PROs were assessed by the MD Anderson Symptom Inventory Lung Cancer Module and single-item Quality of Life Scale before surgery and then daily after surgery until discharge or up to 14 days of hospitalization. Patient compliance and data error were identified and compared between P&P and ePRO. Generalized estimating equations model and 2-piecewise model were used to describe trajectories of error incidence over time and to identify the risk factors. RESULTS: Among 678 patients, 629 with at least 2 PRO assessments, 440 completed 3347 P&P assessments and 189 completed 1291 ePRO assessments. In total, 49.4% of patients had at least one error, including (1) missing items (64.69%, 1070/1654), (2) modifications without signatures (27.99%, 463/1654), (3) selection of multiple options (3.02%, 50/1654), (4) missing patient signatures (2.54%, 42/1654), (5) missing researcher signatures (1.45%, 24/1654), and (6) missing completion dates (0.30%, 5/1654). Patients who completed ePRO had fewer errors than those who completed P&P assessments (ePRO: 30.2% [57/189] vs. P&P: 57.7% [254/440]; P<.001). Compared with ePRO patients, those using P&P were older, less educated, and sicker. Common risk factors of having errors were a lower education level (P&P: odds ratio [OR] 1.39, 95% CI 1.20-1.62; P<.001; ePRO: OR 1.82, 95% CI 1.22-2.72; P=.003), treated in a provincial hospital (P&P: OR 3.34, 95% CI 2.10-5.33; P<.001; ePRO: OR 4.73, 95% CI 2.18-10.25; P<.001), and with severe disease (P&P: OR 1.63, 95% CI 1.33-1.99; P<.001; ePRO: OR 2.70, 95% CI 1.53-4.75; P<.001). Errors peaked on postoperative day (POD) 1 for P&P, and on POD 2 for ePRO. CONCLUSIONS: It is possible to improve data quality of longitudinally collected PRO through ePRO, compared with P&P. However, ePRO-related sampling bias needs to be considered when designing clinical research using longitudinal PROs as major outcomes.


Assuntos
Qualidade de Vida , Cirurgia Torácica , Confiabilidade dos Dados , Humanos , Internet , Medidas de Resultados Relatados pelo Paciente
6.
Anticancer Res ; 41(11): 5377-5391, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34732407

RESUMO

BACKGROUND/AIM: To assess the quantity and quality of systematic reviews of in vitro cancer studies. MATERIALS AND METHODS: PubMed, MEDLINE, Embase, Web of Knowledge and PROSPERO databases were searched. Articles described as systematic reviews of in vitro studies, focused on or relevant to cancer and published in English were selected and appraised using an adapted version of AMSTAR 2 'critical domains'. RESULTS: From 4,021 records, 41 reviews described as systematic and cancer-related were identified. Publication dates indicate increasing frequency of systematic review conduct. Mean number of databases searched was three (range=1-8). Thirty-six reviews (88%) reported search methods, 35 (85%) specified inclusion criteria, 26 (63%) reported study selection methods, and 21 (51%) used reporting guidelines. Only 13 reviews (32%) involved formal quality assessment. CONCLUSION: Detailed investigation of reviews of cancer-relevant in vitro studies indicates need for further development and use of robust search strategies, appropriate quality assessment tools, and researchers with relevant skills.


Assuntos
Pesquisa Biomédica/normas , Confiabilidade dos Dados , Oncologia/normas , Publicações Periódicas como Assunto/normas , Projetos de Pesquisa/normas , Revisões Sistemáticas como Assunto/normas , Animais , Guias como Assunto/normas , Humanos , Controle de Qualidade
7.
Gesundheitswesen ; 83(S 01): S54-S59, 2021 Nov.
Artigo em Alemão | MEDLINE | ID: mdl-34731894

RESUMO

OBJECTIVE: The German Federal Ministry of Education and Research funded a project accompanying a funding initiative for registries in health services research. The aim was to provide cross-registry support initially for 16 and later 6 projects with regard to methodological, technical and structural standards. METHODS: The 16 projects were initially guided in concept development, e. g., providing a template for a registry protocol. Furthermore, an expert consultation was organized and carried out. To assist in the selection of an IT solution, a challenge workshop was hosted where different vendors presented their software for registries. The catalogs of data elements of the projects were migrated into a metadata catalog and transferred to the standard model of ISO/IEC 11179. A set of quality indicators was defined for a cross-registry quality management approach to be implemented during the operational phase. To improve data quality, the indicators were to be transmitted and evaluated on a regular basis. RESULTS: The template for a registry protocol was used by the majority of projects when applying for funding of their operational phase. At the workshop on IT solutions, 12 products for registry software were presented; however, the projects opted for other solutions for different reasons. Transferring the catalogs of data elements into a standard model enabled a comparison of attributes and value sets, which in turn enabled formulation of recommendations for important elements. A set of five quality indicators was defined for quality management, for which an initial evaluation was carried out for 2020. CONCLUSION: The template of a registry protocol serves a systematic development of a concept. The use of a uniformly structured catalog of data elements supports compliance with the FAIR principles. Monitoring of data quality can be achieved by regularly identifying quality indicators across registries.


Assuntos
Confiabilidade dos Dados , Metadados , Alemanha , Pesquisa sobre Serviços de Saúde , Sistema de Registros
8.
Sensors (Basel) ; 21(21)2021 Oct 29.
Artigo em Inglês | MEDLINE | ID: mdl-34770517

RESUMO

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human-robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human-robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human-robot interaction.


Assuntos
Robótica , Algoritmos , Confiabilidade dos Dados , Eletrônica , Humanos , Monitorização Fisiológica
9.
PLoS One ; 16(10): e0255949, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34705833

RESUMO

BACKGROUND: A well designed Health management information system is necessary for improving health service effectiveness and efficiency. It also helps to produce quality information and conduct evidence based monitoring, adjusting policy implementation and resource use. However, evidences show that data quality is poor and is not utilized for program decisions in Ethiopia especially at lower levels of the health care and it remains as a major challenge. METHOD: Facility based cross sectional study design was employed. A total of 18 health centers and 302 health professionals were selected by simple random sampling using lottery method from each selected health center. Data was collected by health professionals who were experienced and had training on HMIS tasks after the tools were pretested. Data quality was assessed using accuracy, completeness and timeliness dimensions. Seven indicators from national priority area were selected to assess data accuracy and monthly reports were used to assess completeness and timeliness. Statistical software SPSS version 20 for descriptive statistics and binary logistic regression was used for quantitative data analysis to identify candidate variable. RESULT: A total of 291 respondents were participated in the study with response rate of 96%. Overall average data quality was 82.5%. Accuracy, completeness and timeliness dimensions were 76%, 83.3 and 88.4 respectively which was lower than the national target. About 52.2% respondents were trained on HMIS, 62.5% had supervisory visits as per standard and only 55.3% got written feedback. Only 11% of facilities assigned health information technicians. Level of confidence [AOR = 1.75, 95% CI (0.99, 3.11)], filling registration or tally completely [AOR = 3.4, 95% CI (1.3, 8.7)], data quality check, supervision AOR = 1.7 95% CI (0.92, 2.63) and training [AOR = 1.89 95% CI (1.03, 3.45)] were significantly associated with data quality. CONCLUSION: This study found that the overall data quality was lower than the national target. Over reporting of all indicators were observed in all facilities. It needs major improvement on supervision quality, training status to increase confidence of individuals to do HMIS activities.


Assuntos
Confiabilidade dos Dados , Atenção à Saúde/estatística & dados numéricos , Instalações de Saúde/estatística & dados numéricos , Sistemas de Informação Administrativa/estatística & dados numéricos , Qualidade da Assistência à Saúde/estatística & dados numéricos , Adulto , Estudos Transversais , Etiópia , Feminino , Pessoal de Saúde/estatística & dados numéricos , Registros de Saúde Pessoal , Humanos , Masculino , Inquéritos e Questionários , Adulto Jovem
10.
BMC Med Inform Decis Mak ; 21(1): 297, 2021 10 30.
Artigo em Inglês | MEDLINE | ID: mdl-34717599

RESUMO

BACKGROUND: The use of general practice electronic health records (EHRs) for research purposes is in its infancy in Australia. Given these data were collected for clinical purposes, questions remain around data quality and whether these data are suitable for use in prediction model development. In this study we assess the quality of data recorded in 201,462 patient EHRs from 483 Australian general practices to determine its usefulness in the development of a clinical prediction model for total knee replacement (TKR) surgery in patients with osteoarthritis (OA). METHODS: Variables to be used in model development were assessed for completeness and plausibility. Accuracy for the outcome and competing risk were assessed through record level linkage with two gold standard national registries, Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) and National Death Index (NDI). The validity of the EHR data was tested using participant characteristics from the 2014-15 Australian National Health Survey (NHS). RESULTS: There were substantial missing data for body mass index and weight gain between early adulthood and middle age. TKR and death were recorded with good accuracy, however, year of TKR, year of death and side of TKR were poorly recorded. Patient characteristics recorded in the EHR were comparable to participant characteristics from the NHS, except for OA medication and metastatic solid tumour. CONCLUSIONS: In this study, data relating to the outcome, competing risk and two predictors were unfit for prediction model development. This study highlights the need for more accurate and complete recording of patient data within EHRs if these data are to be used to develop clinical prediction models. Data linkage with other gold standard data sets/registries may in the meantime help overcome some of the current data quality challenges in general practice EHRs when developing prediction models.


Assuntos
Confiabilidade dos Dados , Registros Eletrônicos de Saúde , Adulto , Austrália , Medicina de Família e Comunidade , Humanos , Pessoa de Meia-Idade , Modelos Estatísticos , Prognóstico
11.
BMJ ; 375: n2202, 2021 10 13.
Artigo em Inglês | MEDLINE | ID: mdl-34645600

RESUMO

OBJECTIVE: To systematically review the conduct and reporting of formula trials. DESIGN: Systematic review. DATA SOURCES: Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) were searched from 1 January 2006 to 31 December 2020. REVIEW METHODS: Intervention trials comparing at least two formula products in children less than three years of age were included, but not trials of human breast milk or fortifiers of breast milk. Data were extracted in duplicate and primary outcome data were synthesised for meta-analysis with a random effects model weighted by the inverse variance method. Risk of bias was evaluated with Cochrane risk of bias version 2.0, and risk of undermining breastfeeding was evaluated according to published consensus guidance. Primary outcomes of the trials included in the systematic review were identified from clinical trial registries, protocols, or trial publications. RESULTS: 22 201 titles were screened and 307 trials were identified that were published between 2006 and 2020, of which 73 (24%) trials in 13 197 children were prospectively registered. Another 111 unpublished but registered trials in 17 411 children were identified. Detailed analysis was undertaken for 125 trials (23 757 children) published since 2015. Seventeen (14%) of these recently published trials were conducted independently of formula companies, 26 (21%) were prospectively registered with a clear aim and primary outcome, and authors or sponsors shared prospective protocols for 11 (9%) trials. Risk of bias was low in five (4%) and high in 100 (80%) recently published trials, mainly because of inappropriate exclusions from analysis and selective reporting. For 68 recently published superiority trials, a pooled standardised mean difference of 0.51 (range -0.43 to 3.29) was calculated with an asymmetrical funnel plot (Egger's test P<0.001), which reduced to 0.19 after correction for asymmetry. Primary outcomes were reported by authors as favourable in 86 (69%) trials, and 115 (92%) abstract conclusions were favourable. One of 38 (3%) trials in partially breastfed infants reported adequate support for breastfeeding and 14 of 87 (16%) trials in non-breastfed infants confirmed the decision not to breastfeed was firmly established before enrolment in the trial. CONCLUSIONS: The results show that formula trials lack independence or transparency, and published outcomes are biased by selective reporting. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2018 CRD42018091928.


Assuntos
Ensaios Clínicos como Assunto , Fórmulas Infantis , Projetos de Pesquisa , Aleitamento Materno/estatística & dados numéricos , Ensaios Clínicos como Assunto/ética , Ensaios Clínicos como Assunto/métodos , Ensaios Clínicos como Assunto/normas , Confiabilidade dos Dados , Humanos , Lactente , Fórmulas Infantis/classificação , Fórmulas Infantis/normas , Projetos de Pesquisa/normas , Projetos de Pesquisa/estatística & dados numéricos
12.
Int J Med Inform ; 156: 104584, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34634526

RESUMO

INTRODUCTION: Administrative hospital databases represent an important tool for hospital financing in many national health systems and are also an important data source for clinical, epidemiological and health services research. Therefore, the data quality of such databases is of utmost importance. This paper aims to present a systematic review of root causes of data quality problems affecting administrative hospital data, creating a catalogue of potential issues for data quality analysts to explore. METHODS: The MEDLINE and Scopus databases were searched using inclusion criteria based on two following concept blocks: (1) administrative hospital databases and (2) data quality. Studies' titles and abstracts were screened by two reviewers independently. Three researchers independently selected the screened studies based on their full texts and then extracted the potential root causes inferred from them. These were subsequently classified according to the Ishikawa model based on 6 categories: "Personnel", "Material", "Method", "Machine", "Mission" and "Management". RESULTS: The result of our investigation and the contribution of this paper is a classification of the potential (105) root causes found through a systematic review of the 77 relevant studies we have identified and analyzed. The result was represented by an Ishikawa diagram. Most of the root causes (25.7%) were associated with the category "Personnel" - people's knowledge, preferences, education and culture, mostly related to clinical coders and health care providers activities. The quality of hospital documentation, within category "Material", and aspects related to financial incentives or disincentives, within category "Mission", were also frequently cited in the literature as relevant root causes for data quality issues. CONCLUSIONS: The resultant catalogue of root causes, systematized using the Ishikawa framework, provides a compilation of potential root causes of data quality issues to be considered prior to reusing these data and that can point to actions aimed at improving data quality.


Assuntos
Confiabilidade dos Dados , Documentação/normas , Administração Hospitalar , Atenção à Saúde , Pessoal de Saúde , Pesquisa sobre Serviços de Saúde , Hospitais , Humanos
13.
PLoS One ; 16(10): e0259179, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34710175

RESUMO

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.


Assuntos
COVID-19/classificação , COVID-19/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , COVID-19/metabolismo , Confiabilidade dos Dados , Aprendizado Profundo , Humanos , Redes Neurais de Computação , Curva ROC , Radiografia/métodos , SARS-CoV-2/patogenicidade , Sensibilidade e Especificidade , Tomografia Computadorizada por Raios X/métodos
14.
BMC Health Serv Res ; 21(1): 1054, 2021 Oct 05.
Artigo em Inglês | MEDLINE | ID: mdl-34610844

RESUMO

BACKGROUND: Capacity Building and Mentorship Partnership (CBMP) is a flagship program designed by the Ethiopian Ministry of Health in collaboration with six local universities to strengthen the national health information system and facilitate evidence-informed decision making through various initiatives. The program was initiated in 2018. This evaluation was aimed to assess the outcome of CBMP on health data quality in the public health facilities of Amhara National Regional State, Ethiopia. METHODS: A matched comparison group evaluation design with a sequential explanatory mixed-method was used to evaluate the outcome of CBMP on data quality. A total of 23 health facilities from the intervention group and 17 comparison health facilities from a randomly selected district were used for this evaluation. The Organization for Economic Cooperation and Development (OECD) evaluation framework with relevance, effectiveness, and impact dimensions was used to measure the program's outcome using the judgment parameter. The program's average treatment effect on data quality was estimated using propensity score matching (PSM). RESULTS: The overall outcome of CBMP was found to be 90.75 %. The mean data quality in the intervention health facility was 89.06 % [95 %CI: 84.23, 93.88], which has a significant mean difference with the comparison health facilities (66.5 % [95 % CI: 57.9-75]). In addition, the CBMP increases the data quality of pilot facilities by 27.75 % points [95 %CI: 17.94, 37.58] on the nearest neighboring matching. The qualitative data also noted that there was a data quality problem in the health facility and CBMP improved the data quality gap among the intervention health facilities. CONCLUSIONS: The outcome of the CBMP was highly satisfactory. The program effectively increased the data quality in the health facilities. Therefore, the finding of this evaluation can be used by policymakers, program implementers, and funding organizations to scale the program at large to improve the overall health data quality for health outcome improvement.


Assuntos
Fortalecimento Institucional , Mentores , Confiabilidade dos Dados , Etiópia , Instalações de Saúde , Humanos , Avaliação de Resultados em Cuidados de Saúde , Melhoria de Qualidade
15.
Biomed Res Int ; 2021: 5122962, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34651046

RESUMO

In recent years, almost every country in the world has struggled against the spread of Coronavirus Disease 2019. If governments and public health systems do not take action against the spread of the disease, it will have a severe impact on human life. A noteworthy technique to stop this pandemic is diagnosing COVID-19 infected patients and isolating them instantly. The present study proposes a method for the diagnosis of COVID-19 from CT images. The method is a hybrid method based on convolutional neural network which is optimized by a newly introduced metaheuristic, called marine predator optimization algorithm. This optimization method is performed to improve the system accuracy. The method is then implemented on the chest CT scans with the COVID-19-related findings (MosMedData) dataset, and the results are compared with three other methods from the literature to indicate the method's performance. The final results indicate that the proposed method with 98.11% accuracy, 98.13% precision, 98.66% sensitivity, and 97.26% F1 score has the highest performance in all indicators than the compared methods which shows its higher accuracy and reliability.


Assuntos
Algoritmos , Teste para COVID-19/métodos , COVID-19/diagnóstico por imagem , Redes Neurais de Computação , Tomografia Computadorizada por Raios X/métodos , COVID-19/metabolismo , COVID-19/patologia , COVID-19/virologia , Confiabilidade dos Dados , Bases de Dados Factuais , Aprendizado Profundo , Humanos , Processamento de Imagem Assistida por Computador/métodos , Modelos Teóricos , Reprodutibilidade dos Testes , Projetos de Pesquisa , SARS-CoV-2/isolamento & purificação , Sensibilidade e Especificidade
16.
Sensors (Basel) ; 21(20)2021 Oct 18.
Artigo em Inglês | MEDLINE | ID: mdl-34696105

RESUMO

The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p<0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency.


Assuntos
Confiabilidade dos Dados , Aprendizado de Máquina , Coleta de Dados
17.
J Med Internet Res ; 23(10): e28924, 2021 10 28.
Artigo em Inglês | MEDLINE | ID: mdl-34709195

RESUMO

BACKGROUND: Comprehensive multi-institutional patient portals that provide patients with web-based access to their data from across the health system have been shown to improve the provision of patient-centered and integrated care. However, several factors hinder the implementation of these portals. Although barriers and facilitators to patient portal adoption are well documented, there is a dearth of evidence examining how to effectively implement multi-institutional patient portals that transcend traditional boundaries and disparate systems. OBJECTIVE: This study aims to explore how the implementation approach of a multi-institutional patient portal impacted the adoption and use of the technology and to identify the lessons learned to guide the implementation of similar patient portal models. METHODS: This multimethod study included an analysis of quantitative and qualitative data collected during an evaluation of the multi-institutional MyChart patient portal that was deployed in Southwestern Ontario, Canada. Descriptive statistics were performed to understand the use patterns during the first 15 months of implementation (between August 2018 and October 2019). In addition, 42 qualitative semistructured interviews were conducted with 18 administrative stakeholders, 16 patients, 7 health care providers, and 1 informal caregiver to understand how the implementation approach influenced user experiences and to identify strategies for improvement. Qualitative data were analyzed using an inductive thematic analysis approach. RESULTS: Between August 2018 and October 2019, 15,271 registration emails were sent, with 67.01% (10,233/15,271) registered for an account across 38 health care sites. The median number of patients registered per site was 19, with considerable variation (range 1-2114). Of the total number of sites, 55% (21/38) had ≤30 registered patients, whereas only 2 sites had over 1000 registered patients. Interview participants perceived that the patient experience of the portal would have been improved by enhancing the data comprehensiveness of the technology. They also attributed the lack of enrollment to the absence of a broad rollout and marketing strategy across sites. Participants emphasized that provider engagement, change management support, and senior leadership endorsement were central to fostering uptake. Finally, many stated that regional alignment and policy support should have been sought to streamline implementation efforts across participating sites. CONCLUSIONS: Without proper management and planning, multi-institutional portals can suffer from minimal adoption. Data comprehensiveness is the foundational component of these portals and requires aligned policies and a key base of technology infrastructure across all participating sites. It is important to look beyond the category of the technology (ie, patient portal) and consider its functionality (eg, data aggregation, appointment scheduling, messaging) to ensure that it aligns with the underlying strategic priorities of the deployment. It is also critical to establish a clear vision and ensure buy-ins from organizational leadership and health care providers to support a cultural shift that will enable a meaningful and widespread engagement.


Assuntos
Portais do Paciente , Cuidadores , Confiabilidade dos Dados , Pessoal de Saúde , Humanos , Ontário
18.
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
19.
Artigo em Inglês | MEDLINE | ID: mdl-34682600

RESUMO

The benefits of rapport between interviewers and respondents, in terms of recruiting the latter and motiving them to participate in research, have been generally endorsed. However, there has been less clarity with regard to the association between rapport and data quality. In theory, rapport could be beneficial if it motivates people to give complete and honest responses. On the other hand, efforts to maintain rapport by exhibiting pleasing and socially desirable behaviour could well be detrimental to data quality. In a large longitudinal epidemiological sample, generalized estimating equations (GEE) analyses were used to examine the association between rapport and the following three quality indicators: missing responses, responses to sensitive questions, and consistency of responses. The results of these analyses indicate an association between a high level of rapport and fewer missing responses. In contrast, we found more socially desirable responses for the high-rapport group. Finally, the high-rapport group did not differ from the low-rapport group in terms of the consistency of their responses.


Assuntos
Confiabilidade dos Dados , Relações Interpessoais , Humanos
20.
Cad Saude Publica ; 37(10): e00317020, 2021.
Artigo em Português | MEDLINE | ID: mdl-34644764

RESUMO

Deadly police force is a public health problem. Although the Mortality Information System (SIM) is the most reliable record of deaths from violence, the same is not true for cases of deadly police force, which displays a high degree of underreporting when compared to data from the São Paulo Department of Law Enforcement (SSP-SP). The current study aimed to estimate underreporting in the two official data sources (SIM and SSP-SP), identifying the ICD-10 categories used in cases of incorrectly classified deadly police force and mortality rates in the years 2014 and 2015 in the city of São Paulo, Brazil. Using linkage of data from the SIM and SSP-SP databases, we describe the use of underlying causes of death in cases of deadly police force, estimating underreporting in the SIM and the SSP-SP with the capture-recapture methodology and mortality rates in the city. Based on the database linkage, most of the deaths from deadly police force were classified incorrectly (53%) as other underlying causes of death in the SIM. Both the SIM and SSP-SP underreported the deaths committed by police officers, with different magnitudes (53.2% in the SIM and 7.9% in the SSP-SP). Reclassification of the deaths via linkage added a gain in the SIM, which now had the same mean mortality rate as the SSP-SP (3.44/100,000), thereby decreasing the underreporting in comparison to the initial scenario. Correct recording of death is the first step to the ensuring the right to justice and truth. Recording with quality means to guarantee the right to information, which is not an end per se, but the start in the task of prevention. Data-sharing and inter-sector work are urgently needed.


Assuntos
Confiabilidade dos Dados , Polícia , Brasil/epidemiologia , Causas de Morte , Humanos , Aplicação da Lei
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