Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation

• Main Authors: Joshua Guedalia (Author), Michal Lipschuetz (Author), Sarah M Cohen (Author), Yishai Sompolinsky (Author), Asnat Walfisch (Author), Eyal Sheiner (Author), Ruslan Sergienko (Author), Joshua Rosenbloom (Author), Ron Unger (Author), Simcha Yagel (Author), Hila Hochler (Author)
• Format: Book
• Published: JMIR Publications, 2021-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Research using artificial intelligence (AI) in medicine is expected to significantly influence the practice of medicine and the delivery of health care in the near future. However, for successful deployment, the results must be transported across health care facilities. We present a cross-facilities application of an AI model that predicts the need for an emergency caesarean during birth. The transported model showed benefit; however, there can be challenges associated with interfacility variation in reporting practices.

Item Description

1438-8871
10.2196/28120