BigMouth: development and maintenance of a successful dental data repository

Walji, Muhammad F.; Spallek, Heiko; Kookal, Krishna Kumar; Barrow, Jane; Magnuson, Britta; Tiwari, Tamanna; Oyoyo, Udochukwu; Brandt, Michael T.; Howe, Brian J; Anderson, Gary Clayton; White, Joel M.; Kalenderian, Elsbeth

doi:10.1093/jamia/ocac001

Cited by 11 publications

(8 citation statements)

References 35 publications

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“…Although in the United States, the National Institute of Dental and Craniofacial Research (2022) provides $337.2 million annually for dental research grants supporting 728 projects, open dental data sets are still rare. In the cases where dental data are available, the data are complex (Tachalov et al 2021), have restricted access (Walji et al 2022), or are in formats that limit their secondary use (Liu et al 2013; Morris 2019). Currently, machine learning (ML) algorithms (a subset of artificial intelligence) can generate further output data that may differ from the initial input data.…”

Section: Introductionmentioning

confidence: 99%

“…In the cases where dental data are available, the data are complex ( Tachalov et al. 2021 ), have restricted access ( Walji et al 2022 ), or are in formats that limit their secondary use ( Liu et al 2013 ; Morris 2019 ). Currently, machine learning (ML) algorithms (a subset of artificial intelligence) can generate further output data that may differ from the initial input data.…”

Section: Introductionmentioning

confidence: 99%

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Dental Research Data Availability and Quality According to the FAIR Principles

et al. 2022

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According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable—for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dental Research Data Availability and Quality According to the FAIR Principles

et al. 2022

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“…BigMouth Dental Data Repository is an example of a centralized platform used for analytics that collects, stores and manages patient data from multiple sources 9,10 . BigMouth currently contains data from over 4.5 million patients (as of 01/2023) derived from 11 large academic dental institutions and is updated quarterly.…”

Section: Bigmouth Dental Data Repository: An Informatics Platform For...mentioning

confidence: 99%

Informatics approaches to improve the quality of dental care

Walji

2023

Orthod Craniofacial Res

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Despite technological advances, challenges exist in US dental care, including variations in quality of care, access and untreated dental needs. The implementation of learning health systems (LHSs) in dentistry can help to address these challenges. LHSs use robust informatics infrastructure including data and technology to continuously measure and improve the quality and safety of care and can help to reduce costs and improve patient outcomes. The use of EHRs and standardized diagnostic terminologies are highlighted, as they allow for the storage and sharing of patient data, providing a comprehensive view of a patient's medical and dental history, and can be used to identify patterns and trends to improve the delivery of care. The BigMouth Dental Data Repository is an example of an informatic platform that aggregates patient data from multiple institutions and is being used to for scientific inquiry to improve oral health.

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“…14,15 BigMouth, 16 a dental data repository launched in 2012, currently with >4 million patients, is comprised of 11 US dental schools in the Consortium for Oral Health Research and Informatics (COHRI) to assess quality measures. [17][18] Schools can access their own local EHR database-without requiring Institutional Review Board (IRB) approval-in addition to the COHRI's structured data. BigMouth has two search methods: (1) "Navigate Terms" via existing categories (folders, e.g., diagnosis, procedure, insurance, provider); and (2) "Find" option which names and codes can be queried (Figure 1).…”

Section: Problemmentioning

confidence: 99%