Simulation with Monte Carlo methods to focus quality improvement efforts on interventions with the greatest potential for reducing PACU length of stay: a cross-sectional observational study.

Background: Time and money are limited resources to pursue quality improvement (QI) goals. Computer simulation using Monte Carlo methods may help focus resources towards the most efficacious interventions to pursue.

Methods: This observational, cross-sectional study analysed the length of stay (LOS) for adult American Society of Anesthesiologists (ASA) 1-3 patients in the postanaesthesia care unit (PACU) at a major academic medical centre. Data were collected retrospectively from 1 April 2023 to 31 March 2024. Statistical analysis with Monte Carlo methods simulated the per cent reduction in PACU LOS following the elimination of postoperative nausea and vomiting (PONV), hypothermia (initial temperature<36°C), severe pain (pain score≥7) or moderate opioid use (≥ 50 mcg fentanyl or≥0.4 mg hydromorphone).

Results: The PACU LOS of 7345 patients were included in this study. PONV was experienced by 10.29% of patients and was associated with a mean PACU LOS of 96.64 min (±33.98 min). Hypothermia was the least frequent complication, experienced by 8.93% of patients and was associated with a mean PACU LOS of 83.55 min (±35.99 min). Severe pain and moderate opioid use were seen in 34.05% and 40.83% of patients, respectively and were associated with PACU LOS that were shorter than those experienced by patients with PONV. Monte Carlo simulations demonstrated that the greatest impact on PACU LOS (12.5% (95% CI 12.0% to 13.0%)) would result from the elimination of moderate opioid use.

Discussion: Although PONV was associated with the longest PACU LOS, statistical simulation with Monte Carlo methods demonstrated the greatest per cent reduction in PACU LOS would result from the elimination of moderate opioid use, thus indicating the most efficacious project to pursue.

Conclusion: Statistical simulation with Monte Carlo methods can help guide QI teams to the most efficacious project or intervention to pursue.