Evaluating quality of dental care among patients with diabetes

Neumann, Ana; Kalenderian, Elsbeth; Ramoni, Rachel L.; Yansane, Alfa; Tokede, Oluwabunmi; Etolue, Jini; Vaderhobli, Ram; Simmons, Kristen; Even, Joshua; Mullins, Joanna; Kumar, Shria; Bangar, Suhasini; Kookal, Krishna Kumar; White, Joel M.; Walji, Muhammad F.

doi:10.1016/j.adaj.2017.04.017

Cited by 21 publications

(13 citation statements)

References 19 publications

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“… Step 3 Measure validation We validated the automated query performance through a manual chart review, which served as the gold standard. In our earlier studies [ 13 , 14 , 36 , 37 ], we effectively calibrated two trained reviewers (dentists or dental hygienists) at each institution, by calculating the interrater reliability using 50 manual chart audits. When both reviewers achieved > 80% agreement [ 38 ], we proceeded with single reviews to complete the remaining charts, calculated the sensitivity, specificity, positive predictive value, and negative predictive value of the automated query.…”

Section: Methodsmentioning

confidence: 99%

Assessing the completeness of periodontal disease documentation in the EHR: a first step in measuring the quality of care

et al. 2021

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Background Our objective was to measure the proportion of patients for which comprehensive periodontal charting, periodontal disease risk factors (diabetes status, tobacco use, and oral home care compliance), and periodontal diagnoses were documented in the electronic health record (EHR). We developed an EHR-based quality measure to assess how well four dental institutions documented periodontal disease-related information. An automated database script was developed and implemented in the EHR at each institution. The measure was validated by comparing the findings from the measure with a manual review of charts. Results The overall measure scores varied significantly across the four institutions (institution 1 = 20.47%, institution 2 = 0.97%, institution 3 = 22.27% institution 4 = 99.49%, p-value < 0.0001). The largest gaps in documentation were related to periodontal diagnoses and capturing oral homecare compliance. A random sample of 1224 charts were manually reviewed and showed excellent validity when compared with the data generated from the EHR-based measure (Sensitivity, Specificity, PPV, and NPV > 80%). Conclusion Our results demonstrate the feasibility of developing automated data extraction scripts using structured data from EHRs, and successfully implementing these to identify and measure the periodontal documentation completeness within and across different dental institutions.

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Section: Methodsmentioning

confidence: 99%

Assessing the completeness of periodontal disease documentation in the EHR: a first step in measuring the quality of care

et al. 2021

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“…We applied each monitoring method to the specifications for the annual diabetic oral exam[2] and the annual caries risk assessment[3] measures. Since each site in this study used the same Electronic Dental Record (EDR) system, we developed and validated SQL scripts for each method at one site.…”

Section: Methodsmentioning

confidence: 99%

“…Once-a-year measures are those that require each eligible patient to meet a condition one time in the reporting year. In this paper, we focus on two measures: an annual diabetic oral exam[1,2] and an annual caries risk assessment. [3] Other similar measures in the overall health arena include annual falls screening for elderly patients[4] and annual flu vaccination.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Methods for Monitoring Annual Quality Measures by Month to Predict Year-End Values

Johnson

Kookal

Applegate

et al. 2021

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ObjectiveOur objective was to evaluate four different methods for monitoring annually defined quality measures on a monthly basis, so as to best predict year-end performance throughout the reporting year.MethodsWe developed four methods for monitoring annual measures by month: 1) Monthly Proportion: The proportion of patients with one or more encounters in the month who still needed to meet the measure at their first encounter of the month and met the measure by the end of the month; (2) Monthly Lookback Proportion: The proportion of patients seen in the month who met the measure by the end of the month, regardless of whether it was met in that month or previously in the reporting year; (3) Rolling 12 Month: The annual measure reported as if each month was the twelfth month of a twelve-month reporting period; and (4) YTD (Year-to-Date) Cumulative: The proportion of patients with one or more visits from the start of the reporting year through the month who satisfy the measure. We applied each method to two annual dental quality measures using data from two reporting years, and four different dental sites. We used mean squared error (MSE) to evaluate year-end predictive performance.ResultsMethod 3 (Rolling 12 Month) had the lowest MSE in 11 out of 16 cases (2 measures X 2 years X 4 sites) and lowest total MSE across all 16 cases. In 5 of the 16 cases, YTD Cumulative had the lowest MSE.ConclusionOur results demonstrate that the Rolling 12 Month method was best for predicting the year-end value across both measures and all four sites.

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“…Our research team, through grant funding from the National Institute of Dental and Craniofacial Research (NIDCR) grant number 1R01DE024166, has been working to refine and transform dental quality measures into e-measures that are diagnosis-centered and can be easily deployed through the electronic health records (EHRs) at dental offices [24, 25]. Through this work, we have observed some unintended consequences and challenges with quality measurement in dentistry.…”

Section: Main Textmentioning

confidence: 99%

Unintended consequences and challenges of quality measurements in dentistry

et al. 2019

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Background In recent years, several state dental programs, researchers and the Dental Quality Alliance (DQA) have sought to develop baseline quality measures for dentistry as a way to improve health outcomes, reduce costs and enhance patient experiences. Some of these measures have been tested and validated for various population groups. However, there are some unintended consequences and challenges with quality measurement in dentistry as observed from our previous work on refining and transforming dental quality measures into e-measures. Main body Some examples of the unintended consequences and challenges associated with implementing dental quality measures include: a de-emphasis on patient-centeredness with process-based quality measures, an incentivization of unethical behavior due to fee-for-service reimbursement systems, the risk of compromising patient and provider autonomy with plan-level measures, a disproportionate benefits of dental quality measurement going toward payers, and the risk of alienating smaller dental offices due to the resource-intensive nature of quality measurement. Conclusion As our medical counterparts have embraced quality measurement for improved health outcomes, so too must the dental profession. Our ultimate goal is to ensure the delivery of high quality, patient-centered dental care and effective quality measurement is the first step. By continuously monitoring the performance of dental quality measures and their continued refinement when unintended consequences are observed, we can improve patient and population health outcomes.

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Evaluating quality of dental care among patients with diabetes

Abstract: Detailed clinical patient-level data in dental EHRs may be useful to dentists in evaluating the quality of dental care provided to patients with diabetes.

Cited by 21 publications

References 19 publications

Assessing the completeness of periodontal disease documentation in the EHR: a first step in measuring the quality of care

Assessing the completeness of periodontal disease documentation in the EHR: a first step in measuring the quality of care

An Evaluation of Methods for Monitoring Annual Quality Measures by Month to Predict Year-End Values

Unintended consequences and challenges of quality measurements in dentistry

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