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2.
BMC Med Inform Decis Mak ; 22(1): 237, 2022 09 09.
Article in English | MEDLINE | ID: covidwho-2038728

ABSTRACT

BACKGROUND: Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders. METHODS: This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews. RESULTS: Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality-denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don't Matter: Just Another Tool in the Toolbox- reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword-the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care-broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care-elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation. CONCLUSION: The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care.


Subject(s)
Artificial Intelligence , Software , Clinical Competence , Data Accuracy , Humans , Primary Health Care
3.
Am J Infect Control ; 50(8): 960-962, 2022 08.
Article in English | MEDLINE | ID: covidwho-2000221

ABSTRACT

Case studies are utilized for training on National Healthcare Safety Network (NHSN) healthcare associated infection surveillance definitions. Item discrimination and item analysis were applied to case studies to identify questions that most accurately assess infection preventionists (IPs) application of surveillance definitions.


Subject(s)
Cross Infection , Cross Infection/epidemiology , Cross Infection/prevention & control , Data Accuracy , Health Facilities , Humans , Reproducibility of Results
4.
BMC Public Health ; 22(1): 1266, 2022 06 29.
Article in English | MEDLINE | ID: covidwho-1933129

ABSTRACT

BACKGROUND: South Africa's National Health Laboratory Service (NHLS), the only clinical laboratory service in the country's public health sector, is an important resource for monitoring public health programmes. OBJECTIVES: We describe NHLS data quality, particularly patient demographics among infants, and the effect this has on linking multiple test results to a single patient. METHODS: Retrospective descriptive analysis of NHLS data from 1st January 2017-1st September 2020 was performed. A validated probabilistic record-linking algorithm linked multiple results to individual patients in lieu of a unique patient identifier. Paediatric HIV PCR data was used to illustrate the effect on monitoring and evaluating a public health programme. Descriptive statistics including medians, proportions and inter quartile ranges are reported, with Chi-square univariate tests for independence used to determine association between variables. RESULTS: During the period analysed, 485 300 007 tests, 98 217 642 encounters and 35 771 846 patients met criteria for analysis. Overall, 15.80% (n = 15 515 380) of all encounters had a registered national identity (ID) number, 2.11% (n = 2 069 785) were registered without a given name, 63.15% (n = 62 020 107) were registered to women and 32.89% (n = 32 304 329) of all folder numbers were listed as either the patient's date of birth or unknown. For infants tested at < 7 days of age (n = 2 565 329), 0.099% (n = 2 534) had an associated ID number and 48.87% (n = 1 253 620) were registered without a given name. Encounters with a given name were linked to a subsequent encounter 40.78% (n = 14 180 409 of 34 775 617) of the time, significantly more often than the 21.85% (n = 217 660 of 996 229) of encounters registered with a baby-derivative name (p-value < 0.001). CONCLUSION: Unavailability and poor capturing of patient demographics, especially among infants and children, affects the ability to accurately monitor routine health programmes. A unique national patient identifier, other than the national ID number, is urgently required and must be available at birth if South Africa is to accurately monitor programmes such as the Prevention of Mother-to-Child Transmission of HIV.


Subject(s)
HIV Infections , Infectious Disease Transmission, Vertical , Child , Child Health , Data Accuracy , Data Warehousing , Female , HIV Infections/diagnosis , HIV Infections/epidemiology , HIV Infections/prevention & control , Humans , Infant , Infant, Newborn , Infectious Disease Transmission, Vertical/prevention & control , Retrospective Studies , South Africa/epidemiology
5.
Aust N Z J Public Health ; 46(3): 401-406, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1878979

ABSTRACT

OBJECTIVE: In this paper, we describe the design and baseline data of a study aimed at improving injury surveillance data quality of hospitals contributing to the Victorian Emergency Minimum Dataset (VEMD). METHODS: The sequential study phases include a baseline analysis of data quality, direct engagement and communication with each of the emergency department (ED) hospital sites, collection of survey and interview data and ongoing monitoring. RESULTS: In 2019/20, there were 371,683 injury-related ED presentations recorded in the VEMD. Percentage unspecified, the indicator of (poor) data quality, was lowest for 'body region' (2.7%) and 'injury type' (7.4%), and highest for 'activity when injured' (29.4%). In the latter, contributing hospitals ranged from 3.0-99.9% unspecified. The 'description of event' variable had a mean word count of 10; 16/38 hospitals had a narrative word count of <5. CONCLUSIONS: Baseline hospital injury surveillance data vary vastly in data quality, leaving much room for improvement and justifying intervention as described. IMPLICATIONS FOR PUBLIC HEALTH: Hospital engagement and feedback described in this study is expected to have a marked effect on data quality from 2021 onwards. This will ensure that Victorian injury surveillance data can fulfil their purpose to accurately inform injury prevention policy and practice.


Subject(s)
Emergency Service, Hospital , Hospitals , Data Accuracy , Data Collection , Humans
6.
Stud Health Technol Inform ; 294: 164-168, 2022 May 25.
Article in English | MEDLINE | ID: covidwho-1865418

ABSTRACT

One approach to verifying the quality of research data obtained from EHRs is auditing how complete and correct the data are in comparison with those collected by manual and controlled methods. This study analyzed data quality of an EHR-derived dataset for COVID-19 research, obtained during the pandemic at Hospital Universitario 12 de Octubre. Data were extracted from EHRs and a manually collected research database, and then transformed into the ISARIC-WHO COVID-19 CRF model. Subsequently, a data analysis was performed, comparing both sources through this convergence model. More concepts and records were obtained from EHRs, and PPV (95% CI) was above 85% in most sections. In future studies, a more detailed analysis of data quality will be carried out.


Subject(s)
COVID-19 , Data Accuracy , Databases, Factual , Electronic Health Records , Humans , Pandemics
7.
Proc Natl Acad Sci U S A ; 119(15): e2113561119, 2022 04 12.
Article in English | MEDLINE | ID: covidwho-1784075

ABSTRACT

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.


Subject(s)
COVID-19 , COVID-19/mortality , Data Accuracy , Forecasting , Humans , Pandemics , Probability , Public Health/trends , United States/epidemiology
8.
Age Ageing ; 51(3)2022 03 01.
Article in English | MEDLINE | ID: covidwho-1769119

ABSTRACT

This commentary discusses the role and value of qualitative data when undertaking quality improvement (QI) focussing on the care of older adults. To illustrate this, we reflect on our own experiences of planning a QI project to improve the documentation of Clinical Frailty Scale (CFS) scores in the emergency department (ED) during the coronavirus disease of 2019 (COVID-19) pandemic. National clinical guidance for COVID-19 states that all adults over the age of 65 should be given a CFS at the first point of contact during hospital admission. Therefore, there is a need to improve CFS documentation, specifically in acute care settings. We describe how qualitative methods facilitated an understanding of the barriers to CFS documentation in ED. Staff see the CFS as a useful tool for inter-professional communication, though there are tensions between clinical guidance and their beliefs. Staff had moral concerns about how an ED-allocated CFS might limit available treatment options for older adults. Our findings demonstrate how qualitative methods can illuminate the important social and moral dimensions of why improvement does or does not occur.


Subject(s)
COVID-19 , Frailty , Aged , Data Accuracy , Emergency Service, Hospital , Frailty/diagnosis , Frailty/therapy , Humans , Quality Improvement
10.
Health Aff (Millwood) ; 41(2): 296-303, 2022 02.
Article in English | MEDLINE | ID: covidwho-1686117

ABSTRACT

The Asian American health narrative reflects a long history of structural racism in the US and the complex interplay of racialized history, immigrant patterns, and policies regarding Asians in the US. Yet owing to systematic issues in data collection including missing or misclassified data for Asian Americans and practices that lead to indiscriminate grouping of unlike individuals (for example, Chinese, Vietnamese, and Bangladeshi) together in data systems and pervasive stereotypes of Asian Americans, the drivers and experiences of health disparities experienced by these diverse groups remain unclear. The perpetual exclusion and misrepresentation of Asian American experiences in health research is exacerbated by three racialized stereotypes-the model minority, healthy immigrant effect, and perpetual foreigner-that fuel scientific and societal perceptions that Asian Americans do not experience health disparities. This codifies racist biases against the Asian American population in a mutually reinforcing cycle. In this article we describe the poor-quality data infrastructure and biases on the part of researchers and public health professionals, and we highlight examples from the health disparities literature. We provide recommendations on how to implement systems-level change and educational reform to infuse racial equity in future policy and practice for Asian American communities.


Subject(s)
Asian Americans , Emigrants and Immigrants , Data Accuracy , Humans , Minority Groups
11.
BMJ Open ; 12(1): e057095, 2022 Jan 31.
Article in English | MEDLINE | ID: covidwho-1662318

ABSTRACT

OBJECTIVES: To clarify the definition of vignette-based methodology in qualitative research and to identify key elements underpinning its development and utilisation in qualitative empirical studies involving healthcare professionals. DESIGN: Scoping review according to the Joanna Briggs Institute framework and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. DATA SOURCES: Electronic databases: Academic Search Complete, CINAHL Plus, MEDLINE, PsycINFO and SocINDEX (January 2000-December 2020). ELIGIBILITY CRITERIA: Empirical studies in English or French with a qualitative design including an explicit methodological description of the development and/or use of vignettes to collect qualitative data from healthcare professionals. Titles and abstracts were screened, and full text was reviewed by pairs of researchers according to inclusion/exclusion criteria. DATA EXTRACTION AND SYNTHESIS: Data extraction included study characteristics, definition, development and utilisation of a vignette, as well as strengths, limitations and recommendations from authors of the included articles. Systematic qualitative thematic analysis was performed, followed by data matrices to display the findings according to the scoping review questions. RESULTS: Ten articles were included. An explicit definition of vignettes was provided in only half the studies. Variations of the development process (steps, expert consultation and pretesting), data collection and analysis demonstrate opportunities for improvement in rigour and transparency of the whole research process. Most studies failed to address quality criteria of the wider qualitative design and to discuss study limitations. CONCLUSIONS: Vignette-based studies in qualitative research appear promising to deepen our understanding of sensitive and challenging situations lived by healthcare professionals. However, vignettes require conceptual clarification and robust methodological guidance so that researchers can systematically plan their study. Focusing on quality criteria of qualitative design can produce stronger evidence around measures that may help healthcare professionals reflect on and learn to cope with adversity.


Subject(s)
Health Personnel , Text Messaging , Data Accuracy , Delivery of Health Care , Humans , Qualitative Research
12.
Sci Rep ; 12(1): 1614, 2022 01 31.
Article in English | MEDLINE | ID: covidwho-1661979

ABSTRACT

As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra.


Subject(s)
COVID-19/blood , COVID-19/diagnosis , Immunity , Lasers , Pandemics , SARS-CoV-2/immunology , Spectrophotometry, Atomic/methods , Barium/analysis , COVID-19/epidemiology , COVID-19/virology , Data Accuracy , Discriminant Analysis , False Negative Reactions , False Positive Reactions , Humans , Machine Learning , Reverse Transcriptase Polymerase Chain Reaction/methods , SARS-CoV-2/genetics , Sensitivity and Specificity , Zinc/analysis
14.
Pan Afr Med J ; 40: 206, 2021.
Article in English | MEDLINE | ID: covidwho-1614296

ABSTRACT

INTRODUCTION: among others, the objectives of Ghana's COVID-19 surveillance system are to rapidly detect, test, isolate and manage cases, to monitor trends in COVID-19 deaths and to guide the implementation and adjustment of targeted control measures. We therefore aimed to examine the operations of the COVID-19 surveillance system in New Juaben South Municipality, describe its attributes and explore whether its objectives were being met. METHODS: we utilized a mixed method descriptive study design to evaluate the COVID-19 surveillance system in the New Juaben South Municipality of the Eastern Region of Ghana. Desk review and key informant interviews were carried out from 1st February to 31st March 2021 to measure nine surveillance system attributes as an approximation of its performance using the CDC's 2013 updated surveillance system guidelines. RESULTS: while the COVID-19 surveillance system in New Juaben South (NJS) was highly representative of its population, it was rated 'moderate' for its stability, flexibility, sensitivity and acceptability. The system was however characterized by a low performance on data quality, simplicity, timeliness and predictive value positive. The sensitivity and predictive value positive (PVP) of the system were 55.6% and 31.3% respectfully. CONCLUSION: while the surveillance system is only partially meeting its objectives, it is useful in the COVID-19 response in New Juaben South Municipality. System performance could improve with stigma reduction especially among health care workers, timely testing and simplification of surveillance forms and software.


Subject(s)
COVID-19 , Cross-Sectional Studies , Data Accuracy , Ghana , Humans , Population Surveillance , SARS-CoV-2
17.
BMJ Open ; 11(12): e047623, 2021 12 06.
Article in English | MEDLINE | ID: covidwho-1555294

ABSTRACT

OBJECTIVES: High-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions. SETTINGS: On 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained. PARTICIPANTS: All COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June. PRIMARY AND SECONDARY OUTCOME MEASURES: Data completeness and consistency. RESULTS: DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable 'underlying conditions' had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily. CONCLUSIONS: Important quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed-for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers-as low data quality may lead to a deficient pandemic control.


Subject(s)
COVID-19 , Data Accuracy , Humans , Pandemics , Research , SARS-CoV-2
18.
Gigascience ; 10(11)2021 11 25.
Article in English | MEDLINE | ID: covidwho-1545941

ABSTRACT

BACKGROUND: The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19-affected UK population in terms of geographic, demographic, and temporal coverage. FINDINGS: The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. CONCLUSION: The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.


Subject(s)
COVID-19 , Cohort Studies , Data Accuracy , Humans , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed
19.
Sci Rep ; 11(1): 22914, 2021 11 25.
Article in English | MEDLINE | ID: covidwho-1537336

ABSTRACT

The COVID-19 pandemic has spurred controversies related to whether countries manipulate reported data for political gains. We study the association between accuracy of reported COVID-19 data and developmental indicators. We use the Newcomb-Benford law (NBL) to gauge data accuracy. We run an OLS regression of an index constructed from developmental indicators (democracy level, gross domestic product per capita, healthcare expenditures, and universal healthcare coverage) on goodness-of-fit measures to the NBL. We find that countries with higher values of the developmental index are less likely to deviate from the Newcomb-Benford law. The relationship holds for the cumulative number of reported deaths and total cases but is more pronounced for the death toll. The findings are robust for second-digit tests and for a sub-sample of countries with regional data. The NBL provides a first screening for potential data manipulation during pandemics. Our study indicates that data from autocratic regimes and less developed countries should be treated with more caution. The paper further highlights the importance of independent surveillance data verification projects.


Subject(s)
COVID-19/economics , COVID-19/epidemiology , Disease Notification/statistics & numerical data , Data Accuracy , Data Collection/trends , Delivery of Health Care , Developed Countries/economics , Developing Countries/economics , Gross Domestic Product , Humans , Models, Statistical , Pandemics , SARS-CoV-2 , Universal Health Insurance
20.
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
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