Development and Evaluation of an Operative Case Length Prediction Model in Adult Surgical Patients
A machine learning model leveraging comprehensive preoperative data significantly improved surgical case length prediction compared to an embedded EHR model. Future implementation has the potential to improve OR efficiency and patient and provider satisfaction.
Authors
- C. M. Bradford
- Gary E. Weissman (ORCID: https://orcid.org/0000-0001-9588-3819)
- Rachel Rapaport Kelz (ORCID: https://orcid.org/0000-0001-6635-5961)
- Armaan Nallicheri (ORCID: https://orcid.org/0000-0001-5993-5407)
- Isaac J. Perron (ORCID: https://orcid.org/0000-0002-2080-6189)
- J. Walker Rosenthal
- Bhavana Kunisetty
- Kaley Piersanti
- John H. Keogh
- Charles C. Horn
- Drew W. Goldberg
Institutions
- Hospital of the University of Pennsylvania (US)
- University of Pennsylvania Health System (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Annals of Surgery Open
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1097/as9.0000000000000652
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00