Journal article
Treatment effect estimation via optimization in robotic-assisted surgery: Insights from the Southeastern U.S
IISE transactions on healthcare systems engineering, Vol.15(3), pp.287-302
07/03/2025
DOI: 10.1080/24725579.2025.2538013
Abstract
Robotic-assisted surgery (RAS) is a safe, effective, and rapidly growing minimally invasive surgical technology. This research investigates the average treatment outcomes of RAS and the factors that may influence its effectiveness by a novel optimization-based approach. Traditional causal effect estimation methods fall short of capturing the changing inclination of surgeons and its impact during the early stages of RAS adoption. By applying our optimization-based method, we uncovered several important findings: First, our analysis of Inguinal Hernia Repair, Ventral/Incisional Hernia Repair, Hysterectomy, Nephrectomy, and Colectomy procedures and other minimally invasive surgical procedures in the Southeast U.S. found no significant difference in treatment effect between RAS and non-RAS procedures. In addition, we identified worse treatment outcomes within subgroups undergoing RAS, such as older adults, certain racial groups, and people with lower socioeconomic status. Statistical tests for effect modification revealed that these factors negatively impact RAS outcomes. Faster adoption of RAS technology positively affects treatment outcomes, validating the potential interventions for improved RAS treatment effects through increased insurance coverage and professional training.
Details
- Title: Subtitle
- Treatment effect estimation via optimization in robotic-assisted surgery: Insights from the Southeastern U.S
- Creators
- Hanwen Liu - Clemson UniversityQi Luo - University of IowaAlfredo M. Carbonell - University of South CarolinaWes Love - Prisma HealthJackie Cha - Clemson University
- Resource Type
- Journal article
- Publication Details
- IISE transactions on healthcare systems engineering, Vol.15(3), pp.287-302
- DOI
- 10.1080/24725579.2025.2538013
- ISSN
- 2472-5579
- eISSN
- 2472-5587
- Publisher
- TAYLOR & FRANCIS LTD
- Grant note
- National Science Foundation: FW-HTF-P 2222806 The 2022 SAGES Robotics Grant
This work is supported by the National Science Foundation under Grant No. FW-HTF-P 2222806 and 2022 SAGES Robotics Grant.
- Language
- English
- Electronic publication date
- 08/04/2025
- Date published
- 07/03/2025
- Academic Unit
- Business Analytics
- Record Identifier
- 9984944727102771
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