Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration

High-risk pregnancy (HRP) is a pregnancy complicated by factors that can adversely affect outcomes of the mother or the infant. Health insurers use algorithms to identify members who would benefit from additional clinical support. We aimed to build machine learning algorithms to identify pregnant patients and triage them by risk of complication to assist care management. In this retrospective study, we trained a hybrid Lasso regularized classifier to predict whether a patient is currently pregnant using claims data from 36735 insured members of Independence Blue Cross (IBC), a health insurer in Philadelphia. We then train a linear classifier on a subset of 12,243 members to predict whether a patient will develop gestational diabetes or gestational hypertension. These algorithms were developed in cooperation with the care management team at IBC and integrated into the dashboard. In small user studies with the nurses, we evaluated the impact of integrating our algorithms into their workflow. We find that the proposed model predicts an earlier pregnancy start date for 3.54% (95% CI 3.05-4.00) for patients with complications compared to only using a set of pre-defined codes that indicate the start of pregnancy and never later at the expense of a 5.58% (95% CI 4.05-6.40) false positive rate. The classifier for predicting complications has an AUC of 0.754 (95% CI 0.764-0.788) using data up to the patient's first trimester. Nurses from the care management program expressed a preference for the proposed models over existing approaches. The proposed model 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.

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