Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929322487447552 |
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| author | Mozannar, Hussein Utsumi, Yuria Chen, Irene Y. Gervasi, Stephanie S. Ewing, Michele Smith-McLallen, Aaron Sontag, David |
| author_facet | Mozannar, Hussein Utsumi, Yuria Chen, Irene Y. Gervasi, Stephanie S. Ewing, Michele Smith-McLallen, Aaron Sontag, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_17261 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration Mozannar, Hussein Utsumi, Yuria Chen, Irene Y. Gervasi, Stephanie S. Ewing, Michele Smith-McLallen, Aaron Sontag, David Machine Learning Human-Computer Interaction 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. |
| title | Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2305.17261 |