Personalized Uncertainty Quantification in Operating Room (PUQOR): Optimizing Surgical Time Estimation with Conformal Prediction

Presentation Time: 03:30 PM - 03:45 PM

Abstract Keywords: Clinical Decision Support, Healthcare Economics/Cost of Care, Machine Learning, Surgery, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

We developed a machine learning algorithm incorporating personalized uncertainty quantification through Conformal Prediction to estimate surgical duration, outperforming the existing Epic estimates and was nearly equivalent to surgeons’ estimates. We demonstrated the model’s superiority in various surgery subgroups, especially those historically misjudged by Epic, and showcased promising improvements. Our model substantiated its potential in supporting surgeons for decision-making in operating room management, and proposed a new path of optimizing personalized surgery planning in precision medicine.

Speaker(s):
Sylvia Cheng, Ph.D. Candidate
University of California, Berkeley

Author(s):
Vincent Liu, MD, MSc - Kaiser Permanente; Bradley Cohn, MD - Kaiser Permanente; Patricia Kipnis, PhD - Kaiser Permanente; Alejandro Schuler, PhD - University of California, Berkeley; Brian Lawson, PhD - Kaiser Permanente;