  We partner with a leading European healthcare provider and design a mechanism
to match patients with family doctors in primary care. We define the
matchmaking process for several distinct use cases given different levels of
available information about patients. Then, we adopt a hybrid recommender
system to present each patient a list of family doctor recommendations. In
particular, we model patient trust of family doctors using a large-scale
dataset of consultation histories, while accounting for the temporal dynamics
of their relationships. Our proposed approach shows higher predictive accuracy
than both a heuristic baseline and a collaborative filtering approach, and the
proposed trust measure further improves model performance.
