  Tooth loss from periodontal disease is a major public health burden in the
United States. Standard clinical practice is to recommend a dental visit every
six months; however, this practice is not evidence-based, and poor dental
outcomes and increasing dental insurance premiums indicate room for
improvement. We consider a tailored approach that recommends recall time based
on patient characteristics and medical history to minimize disease progression
without increasing resource expenditures. We formalize this method as a dynamic
treatment regime which comprises a sequence of decisions, one per stage of
intervention, that follow a decision rule which maps current patient
information to a recommendation for their next visit time. The dynamics of
periodontal health, visit frequency, and patient compliance are complex, yet
the estimated optimal regime must be interpretable to domain experts if it is
to be integrated into clinical practice. We combine non-parametric Bayesian
dynamics modeling with policy-search algorithms to estimate the optimal dynamic
treatment regime within an interpretable class of regimes. Both simulation
experiments and application to a rich database of electronic dental records
from the HealthPartners HMO shows that our proposed method leads to better
dental health without increasing the average recommended recall time relative
to competing methods.
