Developing and Testing Electronic Health Record-Derived Caries Indices

White, Joel M.; Mertz, Elizabeth; Mullins, Joanna; Even, Joshua; Blaga, Elena; Kottek, Aubri; Kumar, Shria; Bangar, Suhasini; Vaderhobli, Ram; Brandon, Ryan; Santo, William; Jenson, Larry; Gansky, Stuart A.

doi:10.1159/000499700

Cited by 5 publications

(7 citation statements)

References 29 publications

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“…Recently, the literature reported on the development and validation of automated tools to calculate caries indices from data available in the EHR, and can be adapted to implement eAoC measures. 55 The next steps in this research will be to investigate the relationship between eCRA, eAoC for prevention, and eAoC for incidence of new caries lesions.…”

Section: Discussionmentioning

confidence: 99%

Caries Risk Documentation And Prevention: eMeasures For Dental Electronic Health Records

et al. 2022

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Background Longitudinal patient level data available in the electronic health record (EHR) allows for the development, implementation, and validations of dental quality measures (eMeasures). Objective We report the feasibility and validity of implementing two eMeasures. The eMeasures determined the proportion of patients receiving a caries risk assessment (eCRA) and corresponding appropriate risk-based preventative treatments for patients at elevated risk of caries (appropriateness of care [eAoC]) in two academic institutions and one accountable care organization, in the 2019 reporting year. Methods Both eMeasures define the numerator and denominator beginning at the patient level, populations' specifications, and validated the automated queries. For eCRA, patients who completed a comprehensive or periodic oral evaluation formed the denominator, and patients of any age who received a CRA formed the numerator. The eAoC evaluated the proportion of patients at elevated caries risk who received the corresponding appropriate risk-based preventative treatments. Results EHR automated queries identified in three sites 269,536 patients who met the inclusion criteria for receiving a CRA. The overall proportion of patients who received a CRA was 94.4% (eCRA). In eAoC, patients at elevated caries risk levels (moderate, high, or extreme) received fluoride preventive treatment ranging from 56 to 93.8%. For patients at high and extreme risk, antimicrobials were prescribed more frequently site 3 (80.6%) than sites 2 (16.7%) and 1 (2.9%). Conclusion Patient-level data available in the EHRs can be used to implement process-of-care dental eCRA and AoC, eAoC measures identify gaps in clinical practice. EHR-based measures can be useful in improving delivery of evidence-based preventative treatments to reduce risk, prevent tooth decay, and improve oral health.

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Section: Discussionmentioning

confidence: 99%

Caries Risk Documentation And Prevention: eMeasures For Dental Electronic Health Records

et al. 2022

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“…We believe that these methods mitigate the "noise" that includes con rmation bias from data obtained from private dental practices. The purposive sampling used in this study followed methods utilized in the development, implementation and validation of clinical decision support, dental diagnostic terminology, quality measures, process of care, appropriateness of care and outcomes of care measurements (22)(23)(24)(25)(26)(27)(28).…”

Section: Discussionmentioning

confidence: 99%

A Generative Adversarial Inpainting Network to Enhance Prediction of Periodontal Clinical Attachment Level

Kearney¹,

Yansane

Brandon

et al. 2021

Preprint

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Deep learning algorithms has recently been used to determine clinical attachment levels (CAL) which aid in the diagnosis of periodontal disease. However, the limited field-of-view of dental bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were conducted using mean bias error (MBE), mean absolute error (MAE) and Dunn’s pairwise test comparing CAL at p=0.05. Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with a MAE of 1.04mm and 1.50mm respectively. The Dunn’s pairwise test indicated a statistically significant improvement in CAL prediction accuracy between both inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn’s pairwise value of -63.89. This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing images.

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“…AoCs include radiographic recall interval, radiograph planned/taken, fluoride varnish, and silver diamine fluoride (SDF) applications for cavitated carious lesions in dentin, and procedures in phase. OoCs are derived from patients with an exam and measured with a validated tool that calculates the recognized standard caries index outcome measures; number of decayed, missing, or filled permanent teeth and tooth surfaces (DMFT/S for permanent teeth and dmft/s for primary teeth) 23 . WDG adapted these caries indices to measure untreated decay and new decay, the OoC measures.…”

Section: Methodsmentioning

confidence: 99%

“…OoCs are derived from patients with an exam and measured with a validated tool that calculates the recognized standard caries index outcome measures; number of decayed, missing, or filled permanent teeth and tooth surfaces (DMFT/S for permanent teeth and dmft/s for primary teeth). 23…”

Section: Data Metrics and Analyticsmentioning

confidence: 99%

“…[18][19][20][21][22] This study describes the implementation of WDG's evidence-based dental caries prevention and management program and describes its performance measures over the first 6 years using EHR measures derived from dental diagnostic terms and a newly validated clinical outcome measure. 13,23 The study aim is to utilize HIT to track and report retrospective, cross-sectional, annual oral health measures in a large health-care system that has implemented current best practices to improve oral health care quality and effectiveness, and examine the value of this approach in managing caries in the dental clinic setting.…”

Section: Introductionmentioning

confidence: 99%

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Tracking oral health in a standardized, evidence‐based, prevention‐focused dental care system

White

Brandon

Mullins

et al. 2020

J Public Health Dent

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Objectives: Learning health-care systems are foundational for measuring and achieving value in oral health care. This article describes the components of a preventive dental care program and the quality of care in a large dental accountable care organization. Methods: A retrospective study design describes and evaluates the crosssectional measures of process of care (PoC), appropriateness of care (AoC), and outcomes of care (OoC) extracted from the electronic health record (EHR), between 2014 and 2019. Annual and composite measures are derived from EHR-based clinical decision support for risk determination, diagnostic and treatment terminology, and decayed-missing-filled-teeth (DMFT) measures. Results: Annually, 253,515 AE 27,850 patients were cared for with 618,084 AE 80,559 visits, 209,366 AE 22,300 exams, and 2,072,844 AE 300,363 clinical procedures. PoC metrics included provider adherence (98.3 percent) in completing caries risk assessments and patient receipt (96.9 percent) of a proactive dental care plan. AoC metrics included patients receiving prevention according to the risk-based protocol. The percent of patients at risk for caries receiving fluoride varnish was 95.4 AE 0.4 percent. OoC metrics included untreated decay and new decay. The 6-year average prevalence of untreated decay was 11.3 AE 0.3 percent, and average incidence of new decay was 13.6 AE 0.5 percent, increasing with risk level: low = 7.5 percent, medium = 18.8 percent, high = 29.4 percent, and extreme = 28.1 percent. Conclusions: The preventive dental care system demonstrates excellent provider adherence to the evidence-based prevention protocol, with measurably better dental outcomes by patient risk compared to national estimates. These achievements are enabled by a value-centric, accountable model of care and incentivized by a compensation model aligned with performance measures.

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Developing and Testing Electronic Health Record-Derived Caries Indices

Cited by 5 publications

References 29 publications

Caries Risk Documentation And Prevention: eMeasures For Dental Electronic Health Records

Caries Risk Documentation And Prevention: eMeasures For Dental Electronic Health Records

A Generative Adversarial Inpainting Network to Enhance Prediction of Periodontal Clinical Attachment Level

Tracking oral health in a standardized, evidence‐based, prevention‐focused dental care system

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