Optimizing neurosurgical operating room schedule: Integrating machine learning models for operative time prediction and schedule design
Integrating ML models into operative time prediction can significantly improve the accuracy and efficiency of surgical schedules. This approach minimizes wasted OR time and enhances resource allocation, potentially improving patient outcomes and satisfaction by reducing waiting times.
Authors
- Nisha Jayasuriya (ORCID: https://orcid.org/0000-0003-4609-6063)
- Emily Feng (ORCID: https://orcid.org/0009-0006-0371-2400)
- Mohamad Bydon (ORCID: https://orcid.org/0000-0002-0543-396X)
- Rabih G. Tawk (ORCID: https://orcid.org/0000-0002-2695-0042)
- Paul Timm
- Karim Rizwan Nathani (ORCID: https://orcid.org/0000-0003-4134-7011)
- Sufyan Ibrahim (ORCID: https://orcid.org/0000-0001-9127-2738)
- Brett A. Freedman (ORCID: https://orcid.org/0000-0002-3408-0163)
- Rushna Ali (ORCID: https://orcid.org/0000-0001-7759-1468)
- Archis R. Bhandarkar (ORCID: https://orcid.org/0000-0001-6371-7496)
- Laura A. Machlab
- Ali-Muhammad Nathani
- Hendrik L. von Kentzinsky
Institutions
- Mayo Clinic (US)
- WinnMed (US)
- University of Chicago (US)
- Mayo Clinic in Florida (US)
- Southwest Minnesota State University (US)
Publication Details
- Journal
- Surgical Neurology International
- Published
- 2025-09-19
- DOI
- https://doi.org/10.25259/sni_626_2025
- Primary Topic
- Healthcare Operations and Scheduling Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00