The underrepresentation of Blacks, Hispanics or Latinos, and American Indians or Alaska Natives among dentists raises concerns about the diversity of the dental workforce, disparities in access to dental care and in oral health status, and social justice. We quantified the shortage of underrepresented minority dentists and examined these dentists' practice patterns in relation to the characteristics of the communities they serve. The underrepresented minority dentist workforce is disproportionately smaller than, and unevenly distributed in relation to, minority populations in the United States. Members of minority groups represent larger shares of these dentists' patient panels than of the populations in the communities where the dentists are located. Compared to counties with no underrepresented minority dentists, counties with one or more such dentists are more racially diverse and affluent but also have greater economic and social inequality. Current policy approaches to improve the diversity of the dental workforce are a critical first step, but more must be done to improve equity in dental health.

Caries indices, the basis of epidemiologic caries measures, are not easily obtained in clinical settings. This study's objective was to design, test, and validate an automated program (Valid Electronic Health Record Dental Caries Indices Calculator Tool [VERDICT]) to calculate caries indices from an electronic health record (EHR). Synthetic use case scenarios and actual patient cases of primary, mixed, and permanent dentition, including decayed, missing, and filled teeth (DMFT/dmft) and tooth surfaces (DMFS/dmfs) were entered into the EHR. VERDICT measures were compared to a previously validated clinical electronic data capture (EDC) system and statistical program to calculate caries indices. Four university clinician-researchers abstracted EHR caries exam data for 45 synthetic use cases into the EDC and post-processed with SAS software creating a gold standard to compare the -VERDICT-derived caries indices. Then, 2 senior researchers abstracted EHR caries exam data and calculated caries indices for 24 patients, allowing further comparisons to VERDICT indices. Agreement statistics were computed among abstractors, and discrepancies were resolved by consensus. Agreement statistics between the 2 final-phase abstractors and the VERDICT measures showed extremely high concordance: Lin's concordance coefficients (LCCs) >0.99 for dmfs, dmft, DS, ds, DT, dt, ms, mt, FS, fs, FT, and ft; LCCs >0.95 for DMFS and DMFT; and LCCs of 0.92-0.93 for MS and MT. Caries indices, essential to developing primary health outcome measures for research, can be reliably derived from an EHR using VERDICT. Using these indices will enable population oral health management approaches and inform quality improvement efforts.