Operating list composition and surgical performance

Objective: To use a routinely collected large surgical procedures dataset to understand the relationship between case list order and operative performance. Summary Background Data: We propose that the analysis of large clinical datasets can facilitate our understanding of the conditions necessary for optimal surgical performance. Recent reviews suggest that how surgeons prepare for a procedure (“warm up”) can affect performance. Operating lists present a natural experiment to explore this phenomenon. Methods: Case lists involving the 35 most frequently performed procedures by senior surgeons across 38 private hospitals in the UK over 26 months were examined. A linear mixed effects model (on 255,757 procedures) and matched analysis (including 48,632 pairs of procedures) were used to estimate the impact of list order and the cost of switching between procedures on a list whilst controlling for key prognosticators (age, ASA-PS). The influence of procedure modality (open vs. minimally invasive) and complexity was also explored. Results: Repeating the same procedure in a list resulted in an overall time reduction of 0.98% for each increase in list position. Switching between procedures increased operating time duration by an average of 6.48%. We show an overall reduction in operating time from completing the second procedure relative to the first is 6.18%. This pattern of results was consistent across procedure modality and complexity. Conclusions: There is a robust relationship between operating list composition and surgical performance (indexed by operating time). An evidence based approach to structuring a case list could reduce the total time spent performing procedures.