Development of quality measures to assess tooth decay outcomes from electronic health record data.

OBJECTIVES: To develop outcomes of care quality measures derived from the dental electronic health record (EHR) to assess the occurrence and timely treatment of tooth decay. METHODS: Quality measures were developed to assess whether decay was treated within 6 months and if new decay occurred in patients seen. Using EHR-derived data of the state of each tooth surface, algorithms compared the patient's teeth at different dates to determine if decay was treated or new decay had occurred. Manual chart reviews were conducted at three sites to validate the measures. The measures were implemented and scores were calculated for three sites over four calendar years, 2016 through 2019. RESULTS: About 954 charts were manually reviewed for the timely treatment of tooth decay measure, with measure performance of sensitivity 97%, specificity 85%, positive predictive value (PPV) 91%, negative predictive value (NPV) 95%. About 739 charts were reviewed for new decay measure, with sensitivity 94%, specificity 99%, PPV 99%, and NPV 94%. Across all sites and years, 52.8% of patients with decay were fully treated within 6 months of diagnosis (n = 247,959). A total of 23.8% of patients experienced new decay, measured at an annual exam (n = 640,004). CONCLUSION: Methods were developed and validated for assessing timely treatment of decay and occurrence of new decay derived from EHR data, creating effective outcome measures. These EHR-based quality measures produce accurate and reliable results that support efforts and advancement in quality assessment, quality improvement, patient care and research.

A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level.

OBJECTIVES: Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disease. However, the limited field-of-view of 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. METHODS: Retrospective purposive sampling of cases with healthy periodontium and diseased periodontium with bitewing and periapical radiographs and clinician recorded CAL were utilized. Data utilized was from July 1, 2016 through January 30, 2020. 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 mean bias error (MBE), mean absolute error (MAE) and Dunn's pairwise test comparing CAL at p=0.05. RESULTS: Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with MAE of 1.04mm and 1.50mm respectively. The Dunn's pairwise test indicated statistically significant improvement in CAL prediction accuracy between inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn's pairwise value of -63.89. CONCLUSIONS: This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing and periapical images. CLINICAL SIGNIFICANCE: Artificial intelligence was developed and utilized to predict clinical attachment level compared to clinical measurements. A generative adversarial inpainting network with partial convolutions was developed, tested and validated to predict clinical attachment level. The inpainting approach was found to be superior to non-inpainted methods and within the 1mm clinician-determined measurement standard.

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

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 cross-sectional 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 ± 27,850 patients were cared for with 618,084 ± 80,559 visits, 209,366 ± 22,300 exams, and 2,072,844 ± 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 ± 0.4 percent. OoC metrics included untreated decay and new decay. The 6-year average prevalence of untreated decay was 11.3 ± 0.3 percent, and average incidence of new decay was 13.6 ± 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.