Evidence map›Paper›PMID 38659337›Full record

ArticleJournal of public health dentistry2024

Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.

Krishna Kumar Kookal, Muhammad F Walji, Ryan Brandon, Ferit Kivanc, Elizabeth Mertz, Aubri Kottek, Joanna Mullins, Shuang Liang, Larry E Jenson, Joel M White

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of public health dentistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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

10 authors.

Krishna Kumar KookalTechnology Services and Informatics, School of Dentistry, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Muhammad F WaljiDepartment of Clinical and Health Informatics, D. Bradley McWIlliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Ryan BrandonWillamette Dental Group and Skourtes Institute, Hillsboro, Oregon, USA.ORCID 0000-0003-1335-2438
Ferit KivancWillamette Dental Group and Skourtes Institute, Hillsboro, Oregon, USA.
Elizabeth MertzDepartment of Preventive and Restorative Dental Sciences, University of California, San Francisco, California, USA.ORCID 0000-0001-5897-835X
Aubri KottekDepartment of Preventive and Restorative Dental Sciences, University of California, San Francisco, California, USA.
Joanna MullinsWillamette Dental Group and Skourtes Institute, Hillsboro, Oregon, USA.
Shuang LiangDepartment of Preventive and Restorative Dental Sciences, University of California, San Francisco, California, USA.
Larry E JensonDepartment of Preventive and Restorative Dental Sciences, University of California, San Francisco, California, USA.
Joel M WhiteDepartment of Preventive and Restorative Dental Sciences, University of California, San Francisco, California, USA.ORCID 0000-0001-8950-697X

Funding

Reducing Oral Health Disparities in Children: Assessing the Multilevel Impact of a Standardized Preventive Dental Care SystemR01MD013719 · NIMHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MERTZ, ELIZABETH, WHITE, JOEL M · 2019 to 2023
$3.4M
NIMHD NIH HHS R01 MD013719
6 · The paper itself

Abstract

objectivesThis work describes the process by which the quality of electronic health care data for a public health study was determined. The objectives were to adapt, develop, and implement data quality assessments (DQAs) based on the National Institutes of Health Pragmatic Trials Collaboratory (NIHPTC) data quality framework within the three domains of completeness, accuracy, and consistency, for an investigation into oral health care disparities of a preventive care program.

methodsElectronic health record data for eligible children in a dental accountable care organization of 30 offices, in Oregon, were extracted iteratively from January 1, 2014, through March 31, 2022. Baseline eligibility criteria included: children ages 0-18 with a baseline examination, Oregon home address, and either Medicaid or commercial dental benefits at least once between 2014 and 2108. Using the NIHPTC framework as a guide, DQAs were conducted throughout data element identification, extraction, staging, profiling, review, and documentation.

resultsThe data set included 91,487 subjects, 11 data tables comprising 75 data variables (columns), with a total of 6,861,525 data elements. Data completeness was 97.2%, the accuracy of EHR data elements in extracts was 100%, and consistency between offices was strong; 29 of 30 offices within 2 standard deviations of the mean (s = 94%).

conclusionsThe NIHPTC framework proved to be a useful approach, to identify, document, and characterize the dataset. The concepts of completeness, accuracy, and consistency were adapted by the multidisciplinary research team and the overall quality of the data are demonstrated to be of high quality.

Indexed as

Data AccuracyElectronic Health RecordsHealthcare DisparitiesAdolescentChildChild, PreschoolFemaleHumansInfantInfant, NewbornMaleOral HealthOregonUnited Statesdata qualitydental informaticselectronic health recordhealth care disparitiespublic health dentistry

Identifiers

PMID38659337
PMCPMC11499288

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.