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1.
Cardiol Young ; 31(11): 1829-1834, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1526031

ABSTRACT

BACKGROUND: Multicentre research databases can provide insights into healthcare processes to improve outcomes and make practice recommendations for novel approaches. Effective audits can establish a framework for reporting research efforts, ensuring accurate reporting, and spearheading quality improvement. Although a variety of data auditing models and standards exist, barriers to effective auditing including costs, regulatory requirements, travel, and design complexity must be considered. MATERIALS AND METHODS: The Congenital Cardiac Research Collaborative conducted a virtual data training initiative and remote source data verification audit on a retrospective multicentre dataset. CCRC investigators across nine institutions were trained to extract and enter data into a robust dataset on patients with tetralogy of Fallot who required neonatal intervention. Centres provided de-identified source files for a randomised 10% patient sample audit. Key auditing variables, discrepancy types, and severity levels were analysed across two study groups, primary repair and staged repair. RESULTS: Of the total 572 study patients, data from 58 patients (31 staged repairs and 27 primary repairs) were source data verified. Amongst the 1790 variables audited, 45 discrepancies were discovered, resulting in an overall accuracy rate of 97.5%. High accuracy rates were consistent across all CCRC institutions ranging from 94.6% to 99.4% and were reported for both minor (1.5%) and major discrepancies type classifications (1.1%). CONCLUSION: Findings indicate that implementing a virtual multicentre training initiative and remote source data verification audit can identify data quality concerns and produce a reliable, high-quality dataset. Remote auditing capacity is especially important during the current COVID-19 pandemic.


Subject(s)
COVID-19 , Data Accuracy , Humans , Infant, Newborn , Pandemics , Retrospective Studies , SARS-CoV-2
2.
Elife ; 102021 06 01.
Article in English | MEDLINE | ID: covidwho-1513078

ABSTRACT

A voucher is a permanently preserved specimen that is maintained in an accessible collection. In genomics, vouchers serve as the physical evidence for the taxonomic identification of genome assemblies. Unfortunately, the vast majority of vertebrate genomes stored in the GenBank database do not refer to voucher specimens. Here, we urge researchers generating new genome assemblies to deposit voucher specimens in accessible, permanent research collections, and to link these vouchers to publications, public databases, and repositories. We also encourage scientists to deposit voucher specimens in order to recognize the work of local field biologists and promote a diverse and inclusive knowledge base, and we recommend best practices for voucher deposition to prevent taxonomic errors and ensure reproducibility and legality in genetic studies.


Subject(s)
Biological Specimen Banks , Databases, Genetic , Genomics , Specimen Handling , Animals , Data Accuracy , Humans , Phylogeny , Reproducibility of Results
3.
J Med Internet Res ; 23(10): e28924, 2021 10 28.
Article in English | MEDLINE | ID: covidwho-1496827

ABSTRACT

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.


Subject(s)
Patient Portals , Caregivers , Data Accuracy , Health Personnel , Humans , Ontario
4.
PLoS One ; 16(10): e0259179, 2021.
Article in English | MEDLINE | ID: covidwho-1496531

ABSTRACT

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%.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Algorithms , COVID-19/metabolism , Data Accuracy , Deep Learning , Humans , Neural Networks, Computer , ROC Curve , Radiography/methods , SARS-CoV-2/pathogenicity , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
6.
Sci Rep ; 11(1): 21413, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1493222

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Models, Statistical , Neural Networks, Computer , Pandemics/prevention & control , SARS-CoV-2 , COVID-19/transmission , COVID-19/virology , Data Accuracy , Forecasting/methods , Humans , Nigeria/epidemiology , Prevalence , South Africa/epidemiology , Uncertainty
8.
Biomed Res Int ; 2021: 5122962, 2021.
Article in English | MEDLINE | ID: covidwho-1467752

ABSTRACT

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.


Subject(s)
Algorithms , COVID-19 Testing/methods , COVID-19/diagnostic imaging , Neural Networks, Computer , Tomography, X-Ray Computed/methods , COVID-19/metabolism , COVID-19/pathology , COVID-19/virology , Data Accuracy , Databases, Factual , Deep Learning , Humans , Image Processing, Computer-Assisted/methods , Models, Theoretical , Reproducibility of Results , Research Design , SARS-CoV-2/isolation & purification , Sensitivity and Specificity
9.
Yearb Med Inform ; 30(1): 75-83, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1392941

ABSTRACT

OBJECTIVES: To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations that can address the identified gaps and challenges. METHODS: A literature review of relevant peer-reviewed and grey literature published from January 2020 to December 2020 was conducted to inform the paper. RESULTS: The literature revealed several themes regarding health information management and health informatics challenges and gaps: information systems and information technology infrastructure; data collection, quality, and standardization; and information governance and use. These challenges and gaps were often driven by public policy and funding constraints. CONCLUSIONS: COVID-19 exposed complexities related to responding to a world-wide, fast moving, quickly spreading novel virus. Longstanding gaps and ongoing challenges in the local, national, and global health and public health information systems and data infrastructure must be addressed before we are faced with another global pandemic.


Subject(s)
COVID-19 , Information Management , Medical Informatics , Data Accuracy , Data Collection/standards , Humans , Public Health Administration , Public Health Practice/legislation & jurisprudence , United States
10.
Aust N Z J Public Health ; 45(5): 526-530, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1388133

ABSTRACT

OBJECTIVE: To conduct a real-time audit to assess a Continuous Quality Improvement (CQI) activity to improve the quality of public health data in the Sydney Local Health District (SLHD) Public Health Unit during the first wave of COVID-19. METHODS: A real-time audit of the Notifiable Conditions Information Management System was conducted for positive cases of COVID-19 and their close contacts from SLHD. After recording missing and inaccurate data, the audit team then corrected the data. Multivariable regression models were used to look for associations with workload and time. RESULTS: A total of 293 cases were audited. Variables measuring completeness were associated with improvement over time (p<0.0001), whereas those measuring accuracy reduced with increased workload (p=0.0003). In addition, the audit team achieved 100% data quality by correcting data. CONCLUSION: Utilising a team, separate from operational staff, to conduct a real-time audit of data quality is an efficient and effective way of improving epidemiological data. Implications for public health: Implementation of CQI in a public health unit can improve data quality during times of stress. Auditing teams can also act as an intervention in their own right to achieve high-quality data at minimal cost. Together, this can result in timely and high-quality public health data.


Subject(s)
COVID-19/diagnosis , Contact Tracing , Management Audit , Quality Improvement , Australia/epidemiology , COVID-19/epidemiology , Data Accuracy , Humans , Management Information Systems , Public Health , Workload
15.
Proc Natl Acad Sci U S A ; 118(33)2021 08 17.
Article in English | MEDLINE | ID: covidwho-1356601

ABSTRACT

Contact tracing has for decades been a cornerstone of the public health approach to epidemics, including Ebola, severe acute respiratory syndrome, and now COVID-19. It has not yet been possible, however, to causally assess the method's effectiveness using a randomized controlled trial of the sort familiar throughout other areas of science. This study provides evidence that comes close to that ideal. It exploits a large-scale natural experiment that occurred by accident in England in late September 2020. Because of a coding error involving spreadsheet data used by the health authorities, a total of 15,841 COVID-19 cases (around 20% of all cases) failed to have timely contact tracing. By chance, some areas of England were much more severely affected than others. This study finds that the random breakdown of contact tracing led to more illness and death. Conservative causal estimates imply that, relative to cases that were initially missed by the contact tracing system, cases subject to proper contact tracing were associated with a reduction in subsequent new infections of 63% and a reduction insubsequent COVID-19-related deaths of 66% across the 6 wk following the data glitch.


Subject(s)
COVID-19/epidemiology , Contact Tracing/statistics & numerical data , Pandemics , SARS-CoV-2 , COVID-19/mortality , COVID-19/prevention & control , COVID-19 Testing/statistics & numerical data , Contact Tracing/methods , Cooperative Behavior , Data Accuracy , Data Collection , England/epidemiology , Humans , Incidence , Information Storage and Retrieval , Program Evaluation , Software , Time Factors
17.
BMJ Open ; 11(7): e051823, 2021 07 29.
Article in English | MEDLINE | ID: covidwho-1334584

ABSTRACT

INTRODUCTION: Front-line health workers in remote health facilities are the first contact of the formal health sector and are confronted with life-saving decisions. Health information systems (HIS) support the collection and use of health related data. However, HIS focus on reporting and are unfit to support decisions. Since data tools are paper-based in most primary healthcare settings, we have produced an innovative Paper-based Health Information System in Comprehensive Care (PHISICC) using a human-centred design approach. We are carrying out a cluster randomised controlled trial in three African countries to assess the effects of PHISICC compared with the current systems. METHODS AND ANALYSIS: Study areas are in rural zones of Côte d'Ivoire, Mozambique and Nigeria. Seventy health facilities in each country have been randomly allocated to using PHISICC tools or to continuing to use the regular HIS tools. We have randomly selected households in the catchment areas of each health facility to collect outcomes' data (household surveys have been carried out in two of the three countries and the end-line data collection is planned for mid-2021). Primary outcomes include data quality and use, coverage of health services and health workers satisfaction; secondary outcomes are additional data quality and use parameters, childhood mortality and additional health workers and clients experience with the system. Just prior to the implementation of the trial, we had to relocate the study site in Mozambique due to unforeseen logistical issues. The effects of the intervention will be estimated using regression models and accounting for clustering using random effects. ETHICS AND DISSEMINATION: Ethics committees in Côte d'Ivoire, Mozambique and Nigeria approved the trials. We plan to disseminate our findings, data and research materials among researchers and policy-makers. We aim at having our findings included in systematic reviews on health systems interventions and future guidance development on HIS. TRIAL REGISTRATION NUMBER: PACTR201904664660639; Pre-results.


Subject(s)
Health Information Systems , Child , Cote d'Ivoire , Data Accuracy , Humans , Mozambique , Nigeria , Primary Health Care , Randomized Controlled Trials as Topic , Systematic Reviews as Topic
18.
Rev Med Interne ; 42(8): 583-590, 2021 Aug.
Article in French | MEDLINE | ID: covidwho-1318949

ABSTRACT

The present article details the publication process and the vicissitudes of three articles about SARS-CoV-2 and its related disease (COVID-19). The three articles were published one month apart between March and May 2020. Their mediatization led French health authorities to intervene. Our article does not focus on and does not assess the scientific quality of the articles presented, but only aims to open the reflection on medical publication. Beyond the description of these three specific cases, this article raises issues about article retraction, peer-reviewing, preprints, authorship and the dissemination of scientific medical information, including through the mass media. It discusses new publishing modes and the dissemination of published information in clinical research.


Subject(s)
COVID-19 , Communications Media , Information Dissemination , Public Opinion , Publishing , COVID-19/epidemiology , Data Accuracy , Decision Making , France/epidemiology , Humans , Public Health Administration/standards , Publications/standards , Publications/statistics & numerical data , Publishing/standards , Publishing/statistics & numerical data , SARS-CoV-2/physiology
19.
BMC Infect Dis ; 21(1): 617, 2021 Jun 29.
Article in English | MEDLINE | ID: covidwho-1285993

ABSTRACT

BACKGROUND: Seasonal influenza leads to significant morbidity and mortality. Rapid self-tests could improve access to influenza testing in community settings. We aimed to evaluate the diagnostic accuracy of a mobile app-guided influenza rapid self-test for adults with influenza like illness (ILI), and identify optimal methods for conducting accuracy studies for home-based assays for influenza and other respiratory viruses. METHODS: This cross-sectional study recruited adults who self-reported ILI online. Participants downloaded a mobile app, which guided them through two low nasal swab self-samples. Participants tested the index swab using a lateral flow assay. Test accuracy results were compared to the reference swab tested in a research laboratory for influenza A/B using a molecular assay. RESULTS: Analysis included 739 participants, 80% were 25-64 years of age, 79% female, and 73% white. Influenza positivity was 5.9% based on the laboratory reference test. Of those who started their test, 92% reported a self-test result. The sensitivity and specificity of participants' interpretation of the test result compared to the laboratory reference standard were 14% (95%CI 5-28%) and 90% (95%CI 87-92%), respectively. CONCLUSIONS: A mobile app facilitated study procedures to determine the accuracy of a home based test for influenza, however, test sensitivity was low. Recruiting individuals outside clinical settings who self-report ILI symptoms may lead to lower rates of influenza and/or less severe disease. Earlier identification of study subjects within 48 h of symptom onset through inclusion criteria and rapid shipping of tests or pre-positioning tests is needed to allow self-testing earlier in the course of illness, when viral load is higher.


Subject(s)
Influenza A virus/immunology , Influenza B virus/immunology , Influenza, Human/diagnosis , Mobile Applications , Self-Testing , Adult , Cross-Sectional Studies , Data Accuracy , Enzyme-Linked Immunosorbent Assay/methods , Feasibility Studies , Female , Humans , Influenza, Human/virology , Male , Middle Aged , Sensitivity and Specificity
20.
J Diabetes Sci Technol ; 15(5): 1181-1187, 2021 09.
Article in English | MEDLINE | ID: covidwho-1280566

ABSTRACT

Complications of Coronavirus Disease 2019 (COVID-19) occur with increased frequency in people admitted to the hospital with diabetes or hyperglycemia. The increased risk for COVID-19 infections in the presence of these metabolic conditions is in part due to overlapping pathophysiologic features of COVID-19, diabetes, and glucose control. Various antiviral treatments are being tested in COVID-19 patients. We believe that in these trials, it will be useful to evaluate treatment effect differences in patients stratified according to whether they have diabetes or hyperglycemia. In this way, it will be possible to better facilitate development of antiviral treatments that are most specifically beneficial for the large subset of COVID-19 patients who have diabetes or hyperglycemia.


Subject(s)
COVID-19/epidemiology , COVID-19/therapy , Clinical Trials as Topic , Diabetes Mellitus , Hyperglycemia , Antiviral Agents/therapeutic use , Blood Glucose/metabolism , COVID-19/blood , COVID-19/complications , Clinical Trials as Topic/methods , Clinical Trials as Topic/statistics & numerical data , Data Accuracy , Diabetes Mellitus/blood , Diabetes Mellitus/epidemiology , Diabetes Mellitus/therapy , Hospitalization/statistics & numerical data , Humans , Hyperglycemia/blood , Hyperglycemia/epidemiology , Hyperglycemia/therapy , Research Design , Risk Factors , SARS-CoV-2
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