University of Iowa, USA.
University of Miami, USA.
J Clin Anesth. 2024 Sep;96:111498. doi: 10.1016/j.jclinane.2024.111498. Epub 2024 May 17.
When choosing the anesthesia practitioner to operating room (OR) ratio for a hospital, objectives are applied to mitigate patient risk: 1) ensuring sufficient anesthesiologists to meet requirements for presence during critical intraoperative events (e.g., anesthesia induction) and 2) ensuring sufficient numbers to cover emergencies outside the ORs (e.g., emergent reintubation in the post-anesthesia care unit). At a 24-OR suite with each anesthesiologist supervising residents in 2 ORs, because critical events overlapped among ORs, ≥14 anesthesiologists were needed to be present for all critical events on >90% of days. The suitable anesthesia practitioner to OR ratio would be 1.58, where 1.58 = (24 + 14)/24. Our narrative review of 22 studies from 17 distinct hospitals shows that the practitioner to OR ratio needed to reduce non-operative time is reliably even larger. Activities to reduce non-operative times include performing preoperative evaluations, making prompt evidence-based decisions at the OR control desk, giving breaks during cases (e.g., lunch or lactation sessions), and using induction and block rooms in parallel to OR cases. The reviewed articles counted the frequency of these activities, finding them much more common than urgent patient-care events. Our review shows, also, that 1 anesthesiologist per OR, working without assistants, is often more expensive, from a societal perspective, than having a few more anesthesia practitioners (i.e., ratio > 1.00). These results are generalizable among hundreds of hospitals, based on managerial epidemiology studies. The implication of our narrative review is that existing studies have already shown, functionally, that artificial intelligence and monitoring technologies based on increasing the safety of intraoperative care have little to no potential to influence anesthesia or OR productivity. There are, in contrast, opportunities to use sensor data and decision-support to facilitate communication among anesthesiologists outside of ORs to choose optimal task sequences that reduce non-operative times, thereby increasing production and OR efficiency.
在为医院选择麻醉医师与手术室(OR)的配比时,目标是减轻患者风险:1)确保有足够的麻醉师在关键手术事件(例如麻醉诱导)期间在场;2)确保有足够的人员在手术室之外应对紧急情况(例如,在麻醉后恢复室进行紧急重新插管)。在一个拥有 24 个手术室的套房中,每位麻醉师在 2 个手术室监督住院医师,由于关键事件在手术室之间重叠,因此需要有≥14 名麻醉师在场,才能确保在>90%的天数中所有关键事件都有人在场。合适的麻醉医师与手术室的配比应为 1.58,其中 1.58=(24+14)/24。我们对来自 17 家不同医院的 22 项研究进行了叙述性综述,结果表明,减少非手术时间所需的医师与手术室的配比确实更大。减少非手术时间的活动包括进行术前评估、在手术室控制台做出及时的基于证据的决策、在手术期间休息(例如,午餐或哺乳时间)以及在手术室病例的同时使用诱导和阻滞室。综述文章计算了这些活动的频率,发现它们比紧急患者护理事件更为常见。我们的综述还表明,从社会角度来看,在没有助手的情况下,1 名麻醉师负责 1 个手术室,往往比有更多的麻醉师(即配比>1.00)更昂贵。这些结果基于管理流行病学研究,在数百家医院中具有普遍性。本叙述性综述的意义在于,现有研究已经从功能上表明,基于提高手术期间护理安全性的人工智能和监测技术几乎没有潜力影响麻醉或手术室的生产力。相比之下,有机会使用传感器数据和决策支持来促进手术室外麻醉师之间的沟通,选择减少非手术时间的最佳任务序列,从而提高生产效率和手术室效率。