Evidence map›Paper›PMID 41510261›Full record

ArticleResearch square2025

Identifying Gaps in Caries Prevention and Management: A Multi-Institutional Mixed-Methods Study.

Suhasini Bangar, Janelle Urata, Oluwabunmi Tokede, Sayali Tungare, Heather Weidner, Aaron Truong, Urvi Mehta, Alfa-Ibrahim Yansane, Gregory Olson, Donald Worley and 13 more

Abstract readPreprint
In one paragraph

Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Suhasini BangarThe University of Texas Health Science Center McWilliams School of Biomedical Informatics.
Janelle UrataUniversity of California San Francisco.
Oluwabunmi TokedeThe University of Texas Health Science Center at Houston School of Dentistry.
Sayali TungareThe University of Texas Health Science Center McWilliams School of Biomedical Informatics.
Heather WeidnerHealthPartners Institute.
Aaron TruongWillamette Dental.
Urvi MehtaUniversity of Alabama at Birmingham, School of Dentistry.
Alfa-Ibrahim YansaneUniversity of California, San Francisco.
Gregory OlsonThe University of Texas Health Science Center at Houston School of Dentistry.
Donald WorleyThe University of Texas Health Science Center at Houston School of Dentistry.
Emily W SedlockUTHealth Houston School of Dentistry.
Joanna MullinsWillamette Dental.
Ryan BrandonWillamette Dental.
D Brad RindalHealthPartners Institute.
Todd JohnsonThe University of Texas Health Science Center McWilliams School of Biomedical Informatics.
Krishna Kumar KookalThe University of Texas Health Science Center McWilliams School of Biomedical Informatics.
Nicholas SkourtesWillamette Dental.
Swaroop GantelaUniversity of Sydney.
Heiko SpallekThe University of Sydney, School of Dentistry.
Joel WhiteUniversity of California San Francisco.
Elsbeth KalenderianUniversity of Pretoria.
Muhammad WaljiUTHealth Houston School of Dentistry.
Ana NeumannThe University of Texas Health Science Center at Houston School of Dentistry.

Funding

Implementing Dental Quality Measures in PracticeR01DE024166 · NIDCR · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KALENDERIAN, ELSBETH, WALJI, MUHAMMAD · 2015 to 2025
$7.3M
NIDCR NIH HHS R01 DE024166
6 · The paper itself

Abstract

Background: Despite established evidence-based guidelines, the prevention and management of dental caries varies across clinical settings. This study aimed to identify and understand quality gaps in caries prevention and management across four dental institutions. Methods: A mixed-methods study was conducted across four large dental institutions. Three data sources were integrated: (1) structured chart reviews (n = 2,000) using six validated dental quality measures; (2) semi-structured interviews (n = 102) with patients, caregivers, staff, and dental providers; and (3) ethnographic observations of clinical care (n = 64) using the AEIOU framework. Data were triangulated using thematic coding and root cause analysis. Quality gaps were categorized as quantitative and qualitative gaps, and system-level challenges. Results: Three categories of gaps emerged. (1) Quantitative gaps included low sealant placement (33.7%-54.6% in ages 6-9; 17.5%-43.0% in ages 10-14), inconsistent caries risk documentation (50.4%-99.6%), and high rates of untreated (23.1%-56.4%) and no new decay rates (65.9%-80.6%). (2) Qualitative gaps included limited documentation of preventive therapies, oral hygiene instruction, and nutritional counseling. Providers cited time constraints, unclear CRA protocols, and EHR usability issues. Interviews revealed unclear team roles and limited patient awareness of preventive options. (3) System-level challenges included workflow inefficiencies, fragmented responsibilities, and poor integration of CRA and preventive codes into clinical routines. Observations confirmed misalignments between documented and delivered care and missed opportunities for risk communication and same-day preventive interventions. Conclusions: Substantial quality gaps persist in caries prevention and management despite institutional protocols. These gaps result from a complex interplay of systems, providers, and patient-level factors. Findings emphasize the need for coordinated data-informed strategies to improve the consistency and effectiveness of preventive care. This study highlights the value of integrating multiple data sources and user-centered methods to inform quality improvement in dental settings.

Indexed as

cariescaries detection/diagnosis/preventiondental informatics/bioinformaticsdental public healthelectronic dental records

Identifiers

PMID41510261
PMCPMC12776439

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Registered trials

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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.