ArticleFrontiers in oral health2025
Evaluating the completeness of electronic health records in dental education: a big data study.
Article in Frontiers in oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- For the record: a narrative review of ambient voice technology in clinical documentation for dentistry and wider healthcare.British dental journal · 2026Review
- Prognostic Clinical Predictive Models for Dental Caries Using Artificial Intelligence: Methodological Considerations.Caries research · 2026Review
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
Objectives: The BigMouth Dental Data Repository is an oral health database developed from de-identified electronic health record (EHR) data from eleven dental schools within the United States. To better understand how this database can be used for further research, the repository must be analyzed for data quality, such as accuracy, consistency, and completeness. This study determined the completeness of all patient health records between 2017 and 2019, including demographic, dental, behavioral, and health history variables at the students, faculty, and resident level. Methods: This study analyzed demographic (age, gender, race/ethnicity, zip code, insurance), dental (pain ratings), behavioral (tobacco, alcohol, and drug use), and health history variables for completeness. ANOVA was conducted to detect differences in providers collecting data by year (using Tukey Results: Overall, the data showed high completeness in demographic variables (97.6%-99.9% for age, gender, and zip code) among the total sample of 543,363 patient visits. However, lower completeness rates were found in dental and behavioral variables (ranging from 1.5% to 66.1%), suggesting potential limitations for certain research applications. The study found significant differences in the completeness of records between students, faculty, and residents. In demographic variables, students demonstrated significantly higher completeness rates than faculty across the years 2017-2019, with 79.8%, 79%, and 78.8% completeness for race/ethnicity records, respectively. Furthermore, residents and faculty exhibited significantly higher completeness rates (76.8% and 86.7%, respectively) in insurance information compared to students (56.7%). Notably, students showcased greater completeness percentages in variables related to tobacco use, alcohol use, drug use, and health history compared to both faculty and residents. Conclusion: This study underscores significant variations in the completeness of EHR data among students, faculty, and residents across different schools. Despite these variances, the overall findings suggest a robust level of completeness in the demographic and health variables within the dataset.
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Registered trials
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