Statistical methods for the study of safety in the operating room: analysis of compliance and complications risk using data from a local health unit in Italy

Surgical procedures carry intrinsic risks that can be exacerbated by deviations from safety protocols. The World Health Organization’s Surgical Safety Checklist (SSC) has been shown to improve patient outcomes, yet compliance and the factors influencing non-conformities (NCs) remain underexplored. This doctoral research develops and applies advanced statistical methods to evaluate surgical safety through SSC data from the Local Health Unit (LHU) of Modena, Emilia-Romagna (Italy), covering over 50,000 surgeries between 2018 and 2022. The thesis comprises three studies. The first investigates determinants of checklist NCs and incompleteness using modified Poisson regression models. Results showed that incomplete checklists, urgent and emergency surgeries, and higher surgical complexity were associated with significantly higher NC risks. The second study explores the relationship between SSC compliance and postoperative complications. Both checklist incompleteness and the presence of NCs were associated with a twofold increase in complication risk, independent of patient characteristics and surgery type. The final study develops predictive models for surgical complications using logistic and machine learning approaches, demonstrating the potential of SSC data as a tool for real-time risk prediction. Overall, the research highlights the critical role of checklist adherence in surgical safety and quantifies the impact of non-compliance on complication risk. The findings support the implementation of targeted interventions to improve checklist use and suggest that routinely collected SSC data can be leveraged to build predictive, data-driven safety monitoring systems.