ArticleEGEMS (Washington, DC)2017
DQ
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 10 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
10 citing papers in PubMed.
- A Query Engine for Self-controlled Case Series, with an application to COVID-19 EHR data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use.Learning health systems · 2022Article
- An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge.BMC medical informatics and decision making · 2021Article
- A method for interoperable knowledge-based data quality assessment.BMC medical informatics and decision making · 2021Article
- Workflow Differences Affect Data Accuracy in Oncologic EHRs: A First Step Toward Detangling the Diagnosis Data Babel.JCO clinical cancer informatics · 2020Article
- PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.Bioinformatics (Oxford, England) · 2019Article
- A tale of three subspecialties: Diagnosis recording patterns are internally consistent but Specialty-Dependent.JAMIA open · 2019Article
- Extending Achilles Heel Data Quality Tool with New Rules Informed by Multi-Site Data Quality Comparison.Studies in health technology and informatics · 2019Article
- DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data.EGEMS (Washington, DC) · 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
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
Data variability is a commonly observed phenomenon in Electronic Health Records (EHR) data networks. A common question asked in scientific investigations of EHR data is whether the cross-site and -time variability reflects an underlying data quality error at one or more contributing sites versus actual differences driven by various idiosyncrasies in the healthcare settings. Although research analysts and data scientists have commonly used various statistical methods to detect and account for variability in analytic datasets, self service tools to facilitate exploring cross-organizational variability in EHR data warehouses are lacking and could benefit from meaningful data visualizations. DQ
Indexed as
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.