Research Opportunities within the Minimally Invasive Gynecologic Surgery Program
Under the supervision of the Program Director, Dr. As-Sanie, trainees will be expected to complete one independent research project in collaboration with the MIGS faculty and/or other faculty within Michigan Medicine. These projects should result in a presentation at a national meeting and at least one publication in a peer-reviewed journal. Research time will be allocated on a weekly basis according to the service month, and semi-weekly MIGS division research meetings will serve as the forum to discuss and review the progress of faculty and trainee projects. Examples of research areas include:
- The role of the central nervous system in endometriosis-associated chronic pelvic pain
- Neurobiological predictors of persistent pelvic pain in women undergoing hysterectomy for chronic pelvic pain
- Predictors of acute and chronic post-surgical pain
- Surgical simulation and methods of teaching laparoscopy in gynecologic surgery
- Patient knowledge and decision making in minimally invasive surgery
- Non-pharmacologic treatments for chronic pelvic pain
- Predictors of sexual dysfunction following hysterectomy
- Surgical outcomes and quality improvement
- Strategies to reduce postoperative opioid prescribing and use
Sample Research Timeline
A sample timeline for appropriate progression for our two-year program is as follows:
Phase 1: Year 1, August – September
- Identify Research Question
- Review literature and establish a relevant and important research question
- State testable hypothesis and specific aims of the study
Phase 2: Year 1, October – November
- Design research study and methods
- Submit IRB
Phase 3: Year 1-2, January – December
- Collect data
Phase 4: Year 2, January – June
- Analyze results
- Prepare manuscripts for publication
Structured Statistical Curriculum
The University of Michigan has created a structured statistical curriculum developed for ACGME and non-ACGME fellows. This course in applied statistics provides learners the skills necessary to carry out common statistical analyses employed in health sciences research. This course is equivalent to a three-credit hour graduate-level applied biostatistics course and allows learners to study at their own schedule. After completing the course modules, learners will be able to:
- Understand the data management and statistical analysis techniques used in health sciences research
- Implement various data management and statistical analysis techniques using statistical software
- Identify advanced statistical issues that require further study or statistical consultation
The department provides an educational fund for each fellow to help cover the cost of approved research courses. This coursework will provide the foundation to complete an independent clinical research project that will result in presentation at a national meeting and publication in a peer-review journal. Protected research time is granted throughout the fellowship, and ample research opportunities are available within the division.