The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data h...
The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health D...
Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on...
The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions...
The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executi...
Nutrient databases are a critical component of nutrition science and the basis of exciting new research in precision nutrition (PN). To identify the most critical components needed for improvement of ...
Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies a...
This article reviews recent methodological research that bears on the collection of egocentric network data. It begins with background on setting egocentric network boundaries and principal types of i...
Decision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure t...
Through an integrative literature review, the aim of this work was to identify and evaluate digital health technology interventions designed to support the conducting of health research based on data ...
A search was conducted in 6 electronic scientific databases in January 2022: PubMed, SCOPUS, Web of Science, Institute of Electrical and Electronics Engineers Digital Library, Cumulative Index of Nurs...
After analyzing and extracting the outcomes of interest, 33 papers were included in the review. The studies covered the period of 2017-2021 and were conducted in 22 countries. Key findings revealed va...
The main barriers to health data quality are technical, motivational, economical, political, legal, ethical, organizational, human resources, and methodological. The data quality process and technique...
RR2-10.1101/2022.05.31.22275804....
The characteristics of big data, including high volume, increased variety, and velocity, pose special challenges for data analysis. As these characteristics generally preclude manual data inspection a...
Source data verification (SDV) has been reported to account for up to 25% of the budget in clinical trials (CT) and cost-benefit of SDV has been questioned. Guidelines for risk-based monitoring (RBM) ...
Data on residual errors from four, large comparable randomised CTs were examined by post-trial SDV. Observed discrepancies were analysed in the categories of "overall" data, "major efficacy" and "majo...
A total of 1 716 087 data points were verified using CM and 323 174 using RBM. The overall error rate was 0.40% for RBM and 0.37% for CM (P < 0.01). For major efficacy, defined by risk assessment, the...
These empirical data, directly comparing RBM with CM, suggest that RBM improves data quality regarding data-points of major importance to trial outcomes, efficacy and major safety. Overall, the RBM ap...
This study is part of the U.S. Food and Drug Administration (FDA)'s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA's postmarket surveillance capabilities by using ...
The study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quali...
The volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by...
This study describes the platform's data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to ...
Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical intervent...
Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenge...
We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and qu...
The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including C...
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....
We describe NHLS data quality, particularly patient demographics among infants, and the effect this has on linking multiple test results to a single patient....
Retrospective descriptive analysis of NHLS data from 1...
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 ide...
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 identif...
