Using EHRs and Big Data for Measuring Oral Healthcare Quality

Objectives: Despite a plethora of proposed dental quality measures (DQMs), there is little understanding about oral healthcare quality. The goal of our research is to leverage the increasing adoption of electronic health records (EHRs) and Big Data in order to measure and compare oral health quality across multiple institutions.
Methods: Using a consensus-based process, we prioritized and identified 10 DQMs from an inventory containing over 100 measures proposed by national and state organizations. We assessed whether each DQM was implementable at 3 academic sites and 1 large private practice. As existing DQMs rely on insurance/claims data, we developed a process for adapting quality measures to use EHR data. We then developed a repeatable process for assessing the validity of automated EHR scripts in assessing quality compared to manual chart reviews that serve as a gold standard.
Results: The DQMs selected included process measures assessing caries and periodontal risk assessments, yearly oral evaluations for diabetics and pregnant women, and the provision of tobacco cessation. We also selected DQMs focused on appropriate treatments and outcomes for patients with caries and periodontal disease. To date, we have assessed the validity of three DQMS: 1) Percentage of diabetic patients who received an oral evaluation ranged from 81.2% to 84.2%, 2) Percentage of children (6-9 yrs old) at high risk of caries who received a sealant ranged from 25.0% to 39.1%, and 3) Percentage of patients who received a caries risk assessment varied widely from 2.6% to 91.1%. For all three measures, the automated query was able to achieve high (>0.8) specificity, sensitivity, positive and negative predictive values compared with manual chart reviews.
Conclusions: Our findings suggest the feasibility of using data contained in EHRs to measure the quality of oral healthcare delivered and the ability to compare data across sites.