Journal of clinical anesthesia
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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. ⋯ 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.