Forecasting Preventive Dental Quality Measures

Abstract

Dental quality measures objectively measure the efficiency and performance of dental providers and organizations. While these measures in conjunction with established benchmarks are used routinely for self-assessment, forecasting them ahead of time in a data-driven and evidence-based manner has the potential to assist in assessing future dental treatment needs, oral disease burden, care utilization patterns, and strategic decision making for sustained performance improvement complementing traditional descriptive visualization dashboards. The present study modeled the temporal trends of four key preventive dental quality measures related to caries prevention (Adult New Caries, Sealants (6-9yrs.), Sealants (12-15yrs.), Fluoride Varnish) sampled monthly from (Dec. 2010 to July 2017) averaged across ten Family Health Center Dental Centers (FQHC), Wisconsin, using auto-regressive integrated moving average time series models. Five-month ahead forecasts along with their 95% confidence levels and mean absolute percentage error were determined across the four measures (Adult New Caries: 1.8%, Sealants (6-9yrs.): 0.90%, Sealants (12-15yrs.): 0.30%, Fluoride Varnish: 0.15%). Model diagnostics revealed auto-regressive integrated moving average models to sufficiently capture the temporal patterns of these measures and the forecast estimates of Adult New Caries and Sealants (12-15yrs.) revealed the need for increased efforts for improved preventive care utilization. Forecasting preventive dental quality measures can provide insights into expected treatment needs ahead of time and can assist in optimal resource and staff allocation with potential to prescribe suitable interventions to shift the trajectory from predicted outcomes to desired outcomes in a targeted manner. While the present study investigated organization level preventive dental quality measures, the time series approach presented is as such generic and expected to translate across similar settings.