John E Jelovsek
Instructor, Temporary in the Department of Obstetrics and Gynecology

Dr. Jelovsek is the Vice Chair of Education and the Director of Data Science for Women’s Health in Department of Obstetrics & Gynecology (OBGYN) at Duke University. He is Board Certified in OBGYN by the American Board of OBGYN and Board Certified in Female Pelvic Medicine & Reconstructive Surgery by the American Board of OBGYN and American Board of Urology. He currently practices Female Pelvic Medicine and Reconstructive Surgery (FPMRS). He has expertise in the development and validation of “individualized” patient-centered prediction tools to improve patient and clinician decision-making around development and possible prevention of pelvic floor disorders after childbirth, predicting the risk of de novo stress urinary incontinence after surgery for pelvic organ prolapse, wound infection after Cesarean delivery, transfusion during gynecologic surgery and efficacy and risk of undergoing pelvic organ prolapse surgery. These tools, although established in other fields, are innovative in the field of FPMRS and part of efforts to “individualize” medicine to patients with pelvic floor disorders by providing estimates of their risk for the conditions. He spends a significant proportion of time devoted to one on one mentoring of learners who are interested in learning how to build, code and test prediction models. He has significant clinical research experience as an investigator in the NICHD Pelvic Floor Disorders Network. He is the principle investigator on CAPABLe (Clinical trials.gov Identifier NCT02008565), one of the largest multi-center trials for fecal incontinence in the Pelvic Floor Disorders Network (PFDN)(U10 HD054215) studying anal exercises with biofeedback and loperamide for the treatment of fecal incontinence, and the principle investigator of E-OPTIMAL (Clinical trials.gov Identifier NCT01166373), describing the long-term follow up sacrospinous ligament fixation compared to uterosacral ligament suspension for apical vaginal prolapse. 

Current Research Interests

Pelvic floor disorders, urinary incontinence, fecal incontinence, pelvic organ prolapse, data science, predictive analytics, predictive model, machine learning, surgical education

Current Appointments & Affiliations

Contact Information

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