Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration
This work addresses the challenge of improving care for high-risk pregnancies in health insurance settings, though it is incremental as it builds on existing ML methods with human-AI collaboration.
The paper tackled the problem of identifying high-risk pregnancies by implementing a machine learning system that assists care managers, showing that the proposed models outperformed existing claim codes for identifying pregnant patients with a manageable false positive rate and accurately triaged patients by complication risk.
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.