Surgery

Liposuction is a common invasive procedure. Liposuction is performed for cosmetic and non-cosmetic reasons. Its use in regenerative medicine has been increasing. Its invasive nature renders it to have complications which can cause limitations in patients' recovery and patient lives. This thesis’s aim is to create an analytical framework to assess the liposuction procedure and its outcomes. The fundamental requirement to create this framework is to have a complete understanding of the procedure which includes preparation and planning of the procedure, correctly performing the procedure and ensuring patient safety on day 0, week 2, week 4, and week 12 of the procedure. 54 patient’s liposuction outcomes were followed till week 12. Data collection is the first part of the framework, which involves understanding the complex surgical outcomes. Algorithms that have been previously studied to assess morbidities and mortalities have been used in this framework to assess if they can assess liposuction outcomes. In this framework algorithms like decision tree, XG boost, random forest, support vector classifier, k nearest neighbor, k means, k fold validation have been used. XG boost performed best to assess liposuction outcomes without validation. However, after cross validation other algorithms which are random forest, support vector machine and KNN classifier outperformed XG boost. This framework allows to assess liposuction outcomes based on the performance of the algorithms. In future, researchers can use this framework to assess liposuction as well as other surgical outcome.

While the complexities and challenges facing healthcare continue to grow, the focus on improving surgical practices remains constant. Possessing a strong influence over patient referral patterns, public reputation/prominence, and financial performance, surgical practices command heightened attention on operational performance and clinical outcomes. Executive leadership cannot support (nor improve) a surgical practice without comprehending the importance of team dynamics in the operating room (OR) environment.
Previous literature offers mixed and incomplete results on themes of team familiarity and OR efficiency, frequently citing handoffs, late starts, and task disruptions as catalysts for negative performance. Studies routinely use historical interaction counts to measure team familiarity, which often neglect the degree of participation (engagement) across prior experiences. Similarly, counts of handoffs or individuals entering an OR do not offer an accurate assessment of team performance. Guided by historical studies, four hypotheses are presented and argue that enhancing surgical team dynamics yield favorable improvements for operational performance and clinical outcomes. Utilizing data from 9,049 neurologic surgery cases performed at two separate campuses (belonging to the same organization) over a three-year timeframe (March 2019 to November 2021), this study measures surgical team dynamics in a highly complex setting through the lens of case continuity and surgeon familiarity to assess key outputs: case scheduling errors (proxy for operational performance) and post-operative complications within 30-days of surgery (proxy for clinical outcomes).