Jennifer Schmitt
Mayo Clinic in Florida, Twitter (United States), Mayo Clinic, Mayo Clinic in Arizona, Allina Health
Papers
7
Total Citations
110
H-Index
5
About
Jennifer Schmitt is a leading researcher in gynecologic surgery, with a primary focus on optimizing the route of hysterectomy for benign indications. Her major contribution lies in the development and clinical implementation of evidence-based decision-tree algorithms designed to increase the rate of vaginal hysterectomy—the safest, most cost-effective approach—while reducing unnecessary robotic or abdominal procedures. Her landmark 2016 study, "Determining Optimal Route of Hysterectomy for Benign Indications" (48 citations), and its prospective follow-up in 2020 (30 citations) demonstrate how structured algorithms can transform surgical practice, achieving higher vaginal hysterectomy rates and improved patient outcomes. Schmitt’s work directly addresses the declining use of vaginal hysterectomy in the U.S. and provides a practical tool for surgeons to overcome common contraindications, as explored in her 2017 study on vaginal versus robotic hysterectomy. Beyond algorithm development, she has also contributed to innovative surgical techniques, such as holmium laser ablation for vesicovaginal fistula management. With cumulative citations exceeding 100, Schmitt’s research has shaped clinical guidelines and empowered surgeons to make evidence-based decisions, ultimately enhancing patient recovery and reducing healthcare costs.
Research Focus
Key Achievements
Top Papers
- 1Determining Optimal Route of Hysterectomy for Benign Indications48 citations · 2016
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- 5Management of a vesicovaginal fistula using holmium laser ablation6 citations · 2016
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