Papers
29
Total Citations
807
H-Index
13
About
Daniel Oh is a prominent surgical researcher whose work sits at the intersection of thoracic surgery, robotic-assisted procedures, and health outcomes research. His career has been defined by a sustained effort to evaluate and advance minimally invasive surgical techniques—particularly robotic-assisted lobectomy—through rigorous comparative effectiveness studies. His most cited work, a 2017 propensity-matched analysis of robotic, video-assisted thoracoscopic, and open lobectomy (191 citations), helped establish the clinical and economic case for robotic approaches in lung cancer surgery. Subsequent research comparing outcomes among high-volume thoracic surgeons (99 citations) and examining long-term clinical and economic trends (40 citations) further solidified his reputation as a leading voice in this space. Beyond outcomes research, Oh has made meaningful contributions to understanding surgical adoption and education. His analysis of CUSUM learning curves (82 citations) critically examines how surgeons acquire new technical skills, while his survey of port strategies for robotic lobectomy offers practical guidance for emerging practitioners. His work on public perceptions of robotic surgery (89 citations) adds a rare patient-facing dimension to the field. Collectively, Oh's research has shaped how surgeons, hospitals, and policymakers think about the transition to minimally invasive thoracic care.
Research Focus
Key Achievements
Top Papers
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- 4CUSUM learning curves: what they can and can’t tell us82 citations · 2023
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- 7The da Vinci Surgical System28 citations · 2019
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