Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
Joshua Guedalia, Michal Lipschuetz, S. M. Cohen, Yishai Sompolinsky, Asnat Walfisch, Eyal Sheiner, Ruslan Sergienko, Joshua I. Rosenbloom, Ron Unger, Simcha Yagel, Hila Hochler
Abstract
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.
Topics & Concepts
Software deploymentMedical emergencyHealth careVariation (astronomy)MedicineEmergency medicineComputer scienceAstrophysicsOperating systemEconomicsEconomic growthPhysicsTrauma and Emergency Care StudiesArtificial Intelligence in Healthcare and EducationEmergency and Acute Care Studies