ArticleApplied clinical informatics2017
Quantifying the Effect of Data Quality on the Validity of an eMeasure.
Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Manifestations and Potential Consequences of Information Distortion in Electronic Nursing Records: Qualitative Study.JMIR nursing · 2026Article
- Beyond Missingness: Systematizing Methods for Comprehensive Data Fitness Assessment in Clinical Research.Journal of medical Internet research · 2026Article
- Sharing EHR data of patients with rare diseases for research - The role of quality assessments in a national federated research data infrastructure.BMC medical informatics and decision making · 2026Article
- Factors Influencing Information Distortion in Electronic Nursing Records: Qualitative Study.Journal of medical Internet research · 2025Article
- Data Quality in Health Research: Integrative Literature Review.Journal of medical Internet research · 2023Review
- Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives.Applied clinical informatics · 2022Article
- Tailoring Rule-Based Data Quality Assessment to the Patient-Centered Outcomes Research Network (PCORnet) Common Data Model (CDM).AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- A data driven learning approach for the assessment of data quality.BMC medical informatics and decision making · 2021Article
- Clinical data sharing improves quality measurement and patient safety.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges.International journal of environmental research and public health · 2021Review
- A Rule-Based Data Quality Assessment System for Electronic Health Record Data.Applied clinical informatics · 2020Article
- Harmonization of the ICHOM Quality Measures to Enable Health Outcomes Measurement in Multimorbid Patients.Frontiers in digital health · 2020Article
- Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR.Applied clinical informatics · 2020Article
- A Framework for Visualizing Data Quality for Predictive Models and Clinical Quality Measures.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Using Clinical Data Standards to Measure Quality: A New Approach.Applied clinical informatics · 2018Article
- Utility of an Electronic Health Record Report to Identify Patients with Delays in Testing for Poorly Controlled Diabetes.Joint Commission journal on quality and patient safetyArticle
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 at 1 institution in 1 country.
Funding
Abstract
Objective The objective of this study was to demonstrate the utility of a healthcare data quality framework by using it to measure the impact of synthetic data quality issues on the validity of an eMeasure (CMS178—urinary catheter removal after surgery). Methods Data quality issues were artificially created by systematically degrading the underlying quality of EHR data using two methods: independent and correlated degradation. A linear model that describes the change in the events included in the eMeasure quantifies the impact of each data quality issue. Results Catheter duration had the most impact on the CMS178 eMeasure with every 1% reduction in data quality causing a 1.21% increase in the number of missing events. For birth date and admission type, every 1% reduction in data quality resulted in a 1% increase in missing events. Conclusion This research demonstrated that the impact of data quality issues can be quantified using a generalized process and that the CMS178 eMeasure, as currently defined, may not measure how well an organization is meeting the intended best practice goal. Secondary use of EHR data is warranted only if the data are of sufficient quality. The assessment approach described in this study demonstrates how the impact of data quality issues on an eMeasure can be quantified and the approach can be generalized for other data analysis tasks. Healthcare organizations can prioritize data quality improvement efforts to focus on the areas that will have the most impact on validity and assess whether the values that are reported should be trusted.
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.