Measuring provider-level differences in perioperative workflow using computer vision-based artificial intelligence
Theoren Loo, Brandon Mcglennen, Stephen Incavo, Nate Hilger · 2025
Read Measuring provider-level differences in perioperative workflow using computer vision-based artificial intelligence by Theoren Loo, Brandon Mcglennen, Stephen Incavo, Nate Hilger (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12718557.txt.
Description
Theoren Loo and co-authors use cameras and AI to time each stage of knee surgery. Their 2025 study covers 2,502 knee replacement cases at one regional health system from 2022 to 2025. Wall cameras recorded the room while models spotted people, gear, and key events.
Each case was split into eight phases, from numbing the patient to setting up the room again. Times varied by provider in nearly every phase, even after case mix was weighed. Surgeons varied most in the main operation phase. Nurse teams varied most in room setup. The authors say such fine timing can aim quality work where it counts most, surgeon by surgeon and team by team.
Catalog record
| CREATOR | Theoren Loo, Brandon Mcglennen, Stephen Incavo, Nate Hilger |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 25 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |