ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
Quantifying clinical data quality using relative gold standards.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance.Learning health systems · 2025Article
- Impact of Interhospital Transfer on Outcomes in Acute Pancreatitis: Implications for Healthcare Quality.Journal of clinical medicine · 2024Article
- How is Big Data reshaping preclinical aging research?Lab animal · 2023Review
- Establishing Trust in Pharmaceutical Data with an Independent Verification and Validation Methodology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository.Applied clinical informatics · 2021Article
- The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Feasibility analysis of conducting observational studies with the electronic health record.BMC medical informatics and decision making · 2019Article
- Usability Testing of an Interactive Dashboard for Surgical Quality Improvement in a Large Congenital Heart Center.Applied clinical informatics · 2019Article
- Accuracy of the medication list in the electronic health record-implications for care, research, and improvement.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Electronic medical records as a replacement for prospective research data collection in postoperative pain and opioid response studies.International journal of medical informatics · 2018Article
- The Reliability of Electronic Health Record Data Used for Obstetrical Research.Applied clinical informatics · 2018Article
- Quantifying the Effect of Data Quality on the Validity of an eMeasure.Applied clinical informatics · 2017Article
- A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data.EGEMS (Washington, DC) · 2016Article
- A Data Quality Ontology for the Secondary Use of EHR Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
- Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project.EGEMS (Washington, DC) · 2013Article
- Diabetes screening with hemoglobin A1c prior to a change in guideline recommendations: prevalence and patient characteristics.BMC family practice · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
As the use of detailed clinical data expands for strategic planning, clinical quality measures, and research, the quality of the data contained in source systems, such as electronic medical records, becomes more critical. Methods to quantify and monitor clinical data quality in large operational databases involve a set of predefined data quality queries that attempt to detect data anomalies such as missing or unrealistic values based on meta-knowledge about a data domain. However, descriptive data elements, such as patient race, cannot be assessed using these methods. We present a novel approach leveraging existing intra-institutional databases with differing data quality for the same data element to quantify data quality for descriptive data. Using the concept of a relative gold standard, we show how this method can be used to assess data quality in enterprise clinical databases.
Indexed as
Identifiers
21347000PMC3041459What 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.