ArticleEGEMS (Washington, DC)2017
Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions.
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 18 papers, 3 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
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 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
- Reusability challenges of livestock production data to improve animal health.Scientific data · 2025Article
- Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.Journal of public health dentistry · 2024Article
- Systematic data quality assessment of electronic health record data to evaluate study-specific fitness: Report from the PRESERVE research study.PLOS digital health · 2024Article
- Barriers to healthcare data quality and recommendations in public health facilities in Dire Dawa city administration, eastern Ethiopia: a qualitative study.Frontiers in digital health · 2024Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- An interactive fitness-for-use data completeness tool to assess activity tracker data.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- The IeDEA harmonist data toolkit: A data quality and data sharing solution for a global HIV research consortium.Journal of biomedical informatics · 2022Article
- Levels of Trimethylamine N-Oxide Remain Elevated Long Term After Left Ventricular Assist Device and Heart Transplantation and Are Independent From Measures of Inflammation and Gut Dysbiosis.Circulation. Heart failure · 2021Article
- Developing a Data Quality Standard Primer for Cardiovascular Risk Assessment from Electronic Health Record Data Using the DataGauge Process.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Gut microbiota, endotoxemia, inflammation, and oxidative stress in patients with heart failure, left ventricular assist device, and transplant.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2020Article
- Clinical data quality: a data life cycle perspective.Biostatistics & epidemiology · 2020Article
- A tale of three subspecialties: Diagnosis recording patterns are internally consistent but Specialty-Dependent.JAMIA open · 2019Article
- Adapting data management education to support clinical research projects in an academic medical center.Journal of the Medical Library Association : JMLA · 2019Article
- Lost in Translation: Diagnosis Records Show More Inaccuracies After Biopsy in Oncology Care EHRs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Leveraging healthcare professionals' insights to enhance data quality in medical big data platforms: A qualitative study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionElectronic health record (EHR) data are known to have significant data quality issues, yet the practice and frequency of assessing EHR data is unknown. We sought to understand current practices and attitudes towards reporting data quality assessment (DQA) results by data professionals.
methodsThe project was conducted in four Phases: (1) examined current DQA practices among informatics/CER stakeholders via engagement meeting (07/2014); (2) characterized organizations conducting DQA by interviewing key personnel and data management professionals (07-08/2014); (3) developed and administered an anonymous survey to data professionals (03-06/2015); and (4) validated survey results during a follow-up informatics/CER stakeholder engagement meeting (06/2016).
resultsThe first engagement meeting identified the theme of unintended consequences as a primary barrier to DQA. Interviewees were predominantly medical groups serving distributed networks with formalized DQAs. Consistent with the interviews, most survey (N=111) respondents utilized DQA processes/programs. A lack of resources and clear definitions of how to judge the quality of a dataset were the most commonly cited individual barriers. Vague quality action plans/expectations and data owners not trained in problem identification and problem-solving skills were the most commonly cited organizational barriers. Solutions included allocating resources for DQA, establishing standards and guidelines, and changing organizational culture. DISCUSSION: Several barriers affecting DQA and reporting were identified. Community alignment towards systematic DQA and reporting is needed to overcome these barriers.
conclusionUnderstanding barriers and solutions to DQA reporting is vital for establishing trust in the secondary use of EHR data for quality improvement and the pursuit of personalized medicine.
Identifiers
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.