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The goal of the research project is to create a blue-print of a robot-assisted hysterectomy procedure to support design and evaluation of technology to enhance system performance. To create this blue-print, we will conduct a task analysis, model the cognitive task flow and decision making, and develop a simulation of the hysterectomy procedure. The surgical simulation will be used as a platform to train surgeons on robotic-assisted hysterectomies, as well as to assess learning and performance. Additionally, it will be used to design and develop techniques and novel technology to support surgeons in their performance of the surgery. Current research efforts are focused on the task analysis step. Data collection included observations in the hospital operating room, interviews with surgeons and nurses, analysis of surgery instructional videos and textbooks. A hierarchical task decomposition has been conducted. Thus far, results of the task analysis reveal several different types of hysterectomies and large variance in surgical techniques based on each surgeon’s preference. These findings will be validated by expert surgeons, and supplemented with a cognitive task analysis. In the next phase of the research project, we will identify several critical decision points within the surgical procedure that include variations in the use of surgical tools or variations in the sequence of actions. For example, the use of a uterine manipulator during the hysterectomy procedure seems to have an impact on the surgeon’s ease, speed, and accuracy while performing the procedure. These variations will be modeled and incorporated into the surgical simulation during development. Ultimately, the simulator will be used to train and assess the physician’s performance. It will also allow us to analyze the difference in techniques and how that affects patient outcome. A surgical simulation that has been designed and developed based on a systematic task analysis and cognitive model will allow us to more accurately study the requirements and constraints of the surgical environment, and support future innovate to enhance surgical performance and patient safety.
Biomedical Devices and Instrumentation | Biomedical Engineering and Bioengineering | Medicine and Health Sciences
Colleges & Schools
Engineering and Computer Science
Axiopoulou , A., Cao , C. G., Lin , K., & Watson , K. (2020). How Do Surgeon Preferences and Technique Variances Affect Outcome?. .
Faculty Advisor Name
Dr. Caroline Cao