title: Evaluating the C-section rate of different physician practices: using machine learning to model standard practice.
abstract: The C-section rate of a population of 22,175 expectant mothers is 16.8%; yet the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 13% to 23%. Our goal is to determine retrospectively if the variations in the observed rates can be attributed to variations in the intrinsic risk of the patient sub-populations (i.e. some groups contain more "high-risk C-section" patients), or differences in physician practice (i.e. some groups do more C-sections). We apply machine learning to this problem by training models to predict standard practice from retrospective data. We then use the models of standard practice to evaluate the C-section rate of each physician practice. Our results indicate that although there is variation in intrinsic risk among the groups, there also is much variation in physician practice.
authors.affiliation: Cornell University, Computer Science, Ithaca, NY 14853, USA. caruana@cs.cornell.edu
authors.forename: Rich
authors.initials: R
authors.lastname: Caruana
authors.forename: Radu S
authors.initials: RS
authors.lastname: Niculescu
authors.forename: R Bharat
authors.initials: RB
authors.lastname: Rao
authors.forename: Cynthia
authors.initials: C
authors.lastname: Simms
mesh_terms: D001185:Artificial Intelligence; D002585:Cesarean Section / Q000706:statistics & numerical data*; D003663:Decision Trees; D005260:Female; D006801:Humans; D015233:Models, Statistical; D009774:Obstetrics / Q000706:statistics & numerical data*; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D011247:Pregnancy; D012189:Retrospective Studies; D012307:Risk Factors