ReviewJournal of the American Medical Informatics Association : JAMIA2013
Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research.
Review in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 576 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
576 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Artificial Intelligence Approaches for Osteoporotic Fracture Risk Prediction Using Administrative Health Data: A Systematic Review.Calcified tissue international · 2026Pooled it
- Validating exacerbations of asthma in electronic health records: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.JMIR mental health · 2026Pooled it
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Data Quality-Driven Improvement in Health Care: Systematic Literature Review.Journal of medical Internet research · 2024Pooled 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
- A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trial.Nature communications · 2025Trial
- Comparing Fourteen Behavioral Science Electronic Health Record Deprescribing Tools in Older Adults: NUDGE-EHR Adaptive Trial.Journal of the American Geriatrics Society · 2025Trial
- Machine learning for missing data imputation in Alzheimer's research: predicting medial temporal lobe dynamic flexibility.Cognitive neurodynamics · 2026Article
- Availability and Quality of Electronic Health Record Data to Track Physical Function Across a Care Transition.Learning health systems · 2026Article
- SPELL: A scalable NLP method using regular expressions and large language models for clinical information extraction.Computer methods and programs in biomedicine · 2026Article
- Spatial bias in quality of estimated delivery date records in the electronic health record.JAMIA open · 2026Article
- Strengthening Rare Disease Data Readiness: a semantic approach to cross-registry interoperability.Scientific data · 2026Article
- Exploratory Implementation and Feasibility Report of CLASS (Clinical LLM Abstraction & Structuring System), A Large Language Model Pipeline for Extracting Unstructured Data From Clinical Notes.Journal of medical systems · 2026Article
- Patient-Transition Networks, Multiservice Trajectories, and Specialty Activity in a University Dental Clinic: A Retrospective Ecological Study.Healthcare (Basel, Switzerland) · 2026Article
- Large Language Models for Heterogeneous Data Mining in Liver Disease: Framework Development and Retrospective Validation Study.Journal of medical Internet research · 2026Article
- An Evaluation of Clinician and Informatician Perspectives on Medication Harm Risk Prediction Tools for Use in a Tertiary Hospital Setting.Journal of evaluation in clinical practice · 2026Article
- Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.PLOS digital health · 2026Article
- PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation.Journal of the American Medical Informatics Association : JAMIA · 2026Article
516 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
2 authors.
Funding
Abstract
objectiveTo review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. MATERIALS AND
methodsA review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used.
resultsFive dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. DISCUSSION: Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment.
conclusionThere is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.
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What OpenQuestion holds
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.