  A high-risk pregnancy is a pregnancy complicated by factors that can
adversely affect the outcomes of the mother or the infant. Health insurers use
algorithms to identify members who would benefit from additional clinical
support. This work presents the implementation of a real-world ML-based system
to assist care managers in identifying pregnant patients at risk of
complications. In this retrospective evaluation study, we developed a novel
hybrid-ML classifier to predict whether patients are pregnant and trained a
standard classifier using claims data from a health insurance company in the US
to predict whether a patient will develop pregnancy complications. These models
were developed in cooperation with the care management team and integrated into
a user interface with explanations for the nurses. The proposed models
outperformed commonly used claim codes for the identification of pregnant
patients at the expense of a manageable false positive rate. Our risk
complication classifier shows that we can accurately triage patients by risk of
complication. Our approach and evaluation are guided by human-centric design.
In user studies with the nurses, they preferred the proposed models over
existing approaches.
