CPT to RVU conversion improves model performance in the prediction of surgical case length.

Thumbnail Image



Journal Title

Journal ISSN

Volume Title

Repository Usage Stats


Citation Stats


Methods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT-RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model's predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.





Published Version (Please cite this version)


Publication Info

Garside, Nicholas, Hamed Zaribafzadeh, Ricardo Henao, Royce Chung and Daniel Buckland (2021). CPT to RVU conversion improves model performance in the prediction of surgical case length. Scientific reports, 11(1). p. 14169. 10.1038/s41598-021-93573-2 Retrieved from https://hdl.handle.net/10161/25507.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.



Ricardo Henao

Associate Professor in Biostatistics & Bioinformatics

Daniel Buckland

Assistant Professor of Emergency Medicine

Dr. Buckland is an Attending Physician at Duke University Hospital Emergency Department. He is also the Director of the Duke Acute Care Technology Lab (DACTL) where he leads research in developing technology for the diagnosis and treatment of acute disease in data science and robotics projects. Dr Buckland oversees several PhD, Masters, and Undergraduate engineer researchers as their primary advisor, as well as manages collaborative research projects between clinicians and engineering students. His work at DACTL also involves studying how advancements in technology affect the healthcare system. In addition, Dr. Buckland is the Deputy Chair of the Human System Risk Board of the Office of the Chief Health and Medical Officer via an Intergovernmental Personnel Act agreement with NASA, where he determines the human system risk of spaceflight and how standards, countermeasures, and mission design can mitigate risk.

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.