ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
A Framework for Visualizing Data Quality for Predictive Models and Clinical Quality Measures.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.Journal of public health dentistry · 2024Article
- Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.Methods of information in medicine · 2023Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use.Learning health systems · 2022Article
- A method for interoperable knowledge-based data quality assessment.BMC medical informatics and decision making · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The ability to assess data quality is essential for secondary use of EHR data and an automated Healthcare Data Quality Framework (HDQF) can be used as a tool to support a healthcare organization's data quality initiatives. Use of a general purpose HDQF provides a method to assess and visualize data quality to quickly identify areas for improvement. The value of the approach is illustrated for two analytics use cases: 1) predictive models and 2) clinical quality measures. The results show that data quality issues can be efficiently identified and visualized. The automated HDQF is much less time consuming than a manual approach to data quality and the framework can be rerun repeatedly on additional datasets without much effort.
Identifiers
31259018PMC6568139What 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.