Time and Expertise in Open Structural Rhinoplasty: A Task-Based Analysis Using Hierarchical Task Analysis and Machine Learning

Rhinoplasty consistsof specific surgical tasks performed in order and executed at specific times. Hierarchical task analysis (HTA) is an essential tool for developing performance metrics to help evaluate surgeries. The authors aimed to determine if there is a correlation with experience and time required for task completion. We developed an HTA for open structural rhinoplasty, then performed a survey to gather surgeons’ self-reported time to complete tasks. Surgeons were grouped according to the number of rhinoplasty cases they have performed; those who performed <100 were considered “non-expert,” and those who performed more than 100 cases were considered “expert.” Statistical analysis was done. Machine learning (ML) was utilized as well to help evaluate the comparison of two groups. Responses from 25 surgeons were analyzed. The surgical steps that showed statistically significant differences between the two surgeon groups included the elevation of (septal) mucoperichondrial-mucoperiosteal flaps, cephalic trim, septoplasty closure, and rhinoplasty closure, with significantly shorter time required by the expert surgeons. According to ML model, rhinoplasty closure, injection, transcolumellar incisions, dorsal hump reduction, dorsal surgery-lateral osteotomies, assessment of lower lateral cartilage, and dorsal hump bone reduction were the steps where the 2 groups of surgeons had significantly different time frames. These tasks may be accepted as more prone to benefits from time and surgical volume. The number of cases observed had no significant effect, therefore, the benefits from time and surgical volume are most noted with hands-on practice and performing the procedure.