ArticleEGEMS (Washington, DC)2017
A Data Quality Assessment Guideline for Electronic Health Record Data Reuse.
Article in EGEMS (Washington, DC), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 3 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
93 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Data quality assessment in healthcare, dimensions, methods and tools: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Where is the patient in the records? Evaluating physiotherapists' first visit in occupational health primary care pathway for low back pain.BMJ open quality · 2026Trial
- Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.Nature medicine · 2025Trial
- Using Electronic Health Records to Derive Control Arms for Early Phase Single-Arm Lung Cancer Trials: Proof-of-Concept in Randomized Controlled Trials.Clinical pharmacology and therapeutics · 2020Trial
- Availability and Quality of Electronic Health Record Data to Track Physical Function Across a Care Transition.Learning health systems · 2026Article
- QResearch primary care records linked to national hospital and cancer registry data: a validation study.ESMO real world data and digital oncology · 2026Article
- Article
- Why is the data quality of hospital morbidity so heterogeneous? A nationwide survey of Portuguese clinical coders.Archives of public health = Archives belges de sante publique · 2026Article
- A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality Study.JMIR medical informatics · 2026Article
- Beyond Missingness: Systematizing Methods for Comprehensive Data Fitness Assessment in Clinical Research.Journal of medical Internet research · 2026Article
- Estimating the observability of an outcome from an electronic health record data set using external data.American journal of epidemiology · 2025Article
- Assessing Data Quality in Heterogeneous Health Care Integration: Simulation Study of the AIDAVA Framework.JMIR medical informatics · 2025Article
- Integrating Artificial Intelligence, Electronic Health Records, and Wearables for Predictive, Patient-Centered Decision Support in Healthcare.Healthcare (Basel, Switzerland) · 2025Article
- Evaluation of the impact of defining observable time in real-world data on outcome incidence.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Data from Routine Primary Healthcare: Opportunities and Threats for Obesity Epidemiological Research.Portuguese journal of public health · 2025Article
- Addressing Note Bloat: Solutions for Effective Clinical Documentation.Journal of the American College of Emergency Physicians open · 2025Article
- The Venus score for the assessment of the quality and trustworthiness of biomedical datasets.BioData mining · 2025Article
- Electronic Health Records for Predicting Outcomes to Work-Related Musculoskeletal Disorders: A Scoping Review.Journal of occupational rehabilitation · 2024Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWe describe the formulation, development, and initial expert review of 3x3 Data Quality Assessment (DQA), a dynamic, evidence-based guideline to enable electronic health record (EHR) data quality assessment and reporting for clinical research.
methods3x3 DQA was developed through the triangulation results from three studies: a review of the literature on EHR data quality assessment, a quantitative study of EHR data completeness, and a set of interviews with clinical researchers. Following initial development, the guideline was reviewed by a panel of EHR data quality experts.
resultsThe guideline embraces the task-dependent nature of data quality and data quality assessment. The core framework includes three constructs of data quality: complete, correct, and current data. These constructs are operationalized according to the three primary dimensions of EHR data: patients, variables, and time. Each of the nine operationalized constructs maps to a methodological recommendation for EHR data quality assessment. The initial expert response to the framework was positive, but improvements are required. DISCUSSION: The initial version of 3x3 DQA promises to enable explicit guideline-based best practices for EHR data quality assessment and reporting. Future work will focus on increasing clarity on how and when 3x3 DQA should be used during the research process, improving the feasibility and ease-of-use of recommendation execution, and clarifying the process for users to determine which operationalized constructs and recommendations are relevant for a given dataset and study.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.