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.

IF 1.3 Q4 HEALTH CARE SCIENCES & SERVICES BMJ Open Quality Pub Date : 2024-11-13 DOI:10.1136/bmjoq-2024-002947

James Harvey Jones, Neal Fleming

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Abstract

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.

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利用蒙特卡洛方法进行模拟，将质量改进工作的重点放在最有可能缩短 PACU 住院时间的干预措施上：一项横断面观察研究。

背景：时间和金钱是实现质量改进（QI）目标的有限资源。使用蒙特卡洛方法进行计算机模拟可能有助于集中资源，采取最有效的干预措施：这项观察性横断面研究分析了一家大型学术医疗中心麻醉后护理病房（PACU）中美国麻醉医师协会（ASA）1-3级成人患者的住院时间（LOS）。该研究回顾性地收集了 2023 年 4 月 1 日至 2024 年 3 月 31 日期间的数据。采用蒙特卡洛方法进行统计分析，模拟了消除术后恶心呕吐（PONV）、低体温（初始体温）后 PACU LOS 的减少百分比：本研究纳入了 7345 名患者的 PACU LOS。10.29%的患者出现过 PONV，与之相关的 PACU 平均 LOS 为 96.64 分钟（±33.98 分钟）。体温过低是最不常见的并发症，有 8.93% 的患者出现过体温过低，导致 PACU 平均 LOS 为 83.55 分钟（±35.99 分钟）。分别有 34.05% 和 40.83% 的患者出现重度疼痛和中度阿片类药物使用，与 PACU LOS 相比，PONV 患者的 PACU LOS 更短。蒙特卡洛模拟显示，不使用中度阿片类药物对 PACU LOS 的影响最大（12.5% (95% CI 12.0% to 13.0%)）：讨论：虽然 PONV 与最长的 PACU LOS 有关，但使用蒙特卡洛方法进行的统计模拟显示，取消中度阿片类药物的使用可最大程度地减少 PACU LOS，从而指明了最有效的项目：结论：使用蒙特卡洛方法进行统计模拟，有助于指导质量改进小组选择最有效的项目或干预措施。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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