METHODS: Data from a hospital in Australia are used throughout to illustrate the methods. OR allocation is a two-stage process. During the initial tactical stage of allocating OR time, OR capacity (“block time”) is adjusted. For operational decision-making on a shorter-term basis, the existing workload can be considered fixed. Staffing is matched to that workload based on maximizing the efficiency of use of OR time. RESULTS: Scheduling cases and making decisions on the day of surgery to increase OR efficiency are worthwhile interventions to increase anesthesia group productivity. However, by far, the most important step is the appropriate refinement of OR allocations (i.e., planning service-specific staffing) 2–3 mo before the day of surgery. CONCLUSIONS: Reducing surgical and/or turnover times and delays in first-case-of-the-day starts generally provides small reductions in OR labor costs. Results vary widely because they are highly sensitive both to the OR allocations (i.e., staffing) and to the appropriateness of those OR allocations....

We considered elective case scheduling at hospitals and surgical centers at which surgeons and patients choose the day of surgery, cases are not turned away, and anesthesia and nursing staffing are adjusted to maximize the efficiency of use of operating room (OR) time. We investigated scheduling a new case into an OR by using two patient-scheduling rules: Earliest Start Time or Latest Start Time. By using several scenarios, we showed that the use of Earliest Start Time is rational economically at such facilities. Specifically, it maximizes OR efficiency when a service has nearly filled its regularly scheduled hours of OR time. However, Latest Start Time will perform better at balancing workload among services’ OR time. We then used historical case duration data from two facilities in computer simulations to investigate the effect of errors in predicting case durations on the performance of these two heuristics. The achievable incremental reduction in overtime by having perfect information on case duration versus using historical case durations was only a few minutes per OR. The differences between Earliest Start Time and Latest Start Time were also only a few minutes per OR. We conclude that for facilities at which the goals are, in order of importance, safety, patient and surgeon access to OR time, and then efficiency, few restrictions need to be placed on patient scheduling to achieve an efficient use of OR time.

Background: The goal of this study was to identify interventions that anesthesiologists can make to decrease total costs of a postanesthesia care unit (PACU). Methods: Data were collected retrospectively from patients who underwent ambulatory surgery at our tertiary care center. Results: Supplies and medications accounted for only 2% of PACU charges. Personnel costs, which depend on the peak number of patients in the PACU, accounted for almost all PACU costs. If nausea and vomiting could have been eliminated in each patient who suffered this complication, without causing sedation, the total time to discharge for all patients would have been decreased by less than 4.8% (95% confidence interval < 7.3%). Arrival rates to and times to discharge from the PACU followed triangular and log-normal distributions, respectively. Computer simulations, using published times to discharge for drugs with "faster recovery," such as propofol, showed that the use of these drugs would only decrease PACU costs if operating rooms were consistently scheduled to run later each day. Such earlier discharge also might be beneficial if used at night, but only if the PACU could close after a single patient leaves. However, reasonably achievable decreases in the times to discharge for all patients undergoing general anesthesia are unlikely to substantively decrease PACU costs. In contrast, arranging an operating room schedule to optimize admission rates would greatly affect the number of PACU nurses needed. Conclusions: Anesthesiologists have little control over PACU economics via choice of anesthetic drugs. The major determinant of PACU costs is the distribution of admissions.

We describe an evidence-based approach for optimization of infection control and operating room management during the coronavirus disease 2019 (COVID-19) pandemic. Confirmed modes of viral transmission are primarily, but not exclusively, contact with contaminated environmental surfaces and aerosolization. Evidence-based improvement strategies for attenuation of residual environmental contamination involve a combination of deep cleaning with surface disinfectants and ultraviolet light (UV-C). (1) Place alcohol-based hand rubs on the intravenous (IV) pole to the left of the provider. Double glove during induction. (2) Place a wire basket lined with a zip closure plastic bag on the IV pole to the right of the provider. Place all contaminated instruments in the bag (eg, laryngoscope blades and handles) and close. Designate and maintain clean and dirty areas. After induction of anesthesia, wipe down all equipment and surfaces with disinfection wipes that contain a quaternary ammonium compound and alcohol. Use a top-down cleaning sequence adequate to reduce bioburden. Treat operating rooms using UV-C. (3) Decolonize patients using preprocedural chlorhexidine wipes, 2 doses of nasal povidone-iodine within 1 hour of incision, and chlorhexidine mouth rinse. (4) Create a closed lumen IV system and use hub disinfection. (5) Provide data feedback by surveillance of Enterococcus, Staphylococcus aureus, Klebsiella, Acinetobacter, Pseudomonas, and Enterobacter spp. (ESKAPE) transmission. (6) To reduce the use of surgical masks and to reduce potential COVID-19 exposure, use relatively long (eg, 12 hours) staff shifts. If there are 8 essential cases to be done (each lasting 1–2 hours), the ideal solution is to have 2 teams complete the 8 cases, not 8 first case starts. (7) Do 1 case in each operating room daily, with terminal cleaning after each case including UV-C or equivalent. (8) Do not have patients go into a large, pooled phase I postanesthesia care unit because of the risk of contaminating facility at large along with many staff. Instead, have most patients recover in the room where they had surgery as is done routinely in Japan. These 8 programmatic recommendations stand on a substantial body of empirical evidence characterizing the epidemiology of perioperative transmission and infection development made possible by support from the Anesthesia Patient Safety Foundation (APSF).

The authors review the scientific literature on operating room management operational decision making on the day of surgery. (1) Some decisions should rely on the expected (mean) duration of the scheduled case. Other decisions should use upper prediction bounds, lower prediction bounds, and other measures reflecting the uncertainty of case duration estimates. One single number cannot be used for good decision making, because durations are uncertain. (2) Operational decisions can be made on the day of surgery based on four ordered priorities. (3) Decisions to reduce overutilized operating room time rely on mean durations. Limited additional data are needed to make these decisions well, specifically, whether a patient is in each operating room and which cases are about to finish. (4) Decisions involving reducing patient (and surgeon) waiting times rely on quantifying uncertainties in case durations, which are affected highly by small sample sizes. Future studies should focus on using real-time display of data to reduce patient waiting.

Background: A common type of study performed by anesthesiologists determines the effect of an intervention on pain reported by groups of patients. The goal of this study was to evaluate the effectiveness of t, analysis of variance (ANOVA), Mann-Whitney, and Kruskal-Wallis tests to compare visual analog scale (VAS) measurements between two or among three groups of patients. These results may be particularly helpful during the design of studies that measure pain with a VAS. Methods: One VAS measurement was obtained from each of 480 nulliparous women in labor who were receiving oxytocin (149), nalbuphine (159), or epidural bupivacaine (172). Multiple simulated samples were then drawn from these data. These simulated samples were used in computer simulations of clinical trials comparing VAS measurements among groups. t and ANOVA tests were performed before and after an arcsin transformation was used, to make the data closer to a normal distribution. VAS measurements were also compared after they were divided into five ranked categories. Results: The statistical distributions of VAS measurements were not normal (P < 10(-7)). Arcsin transformation made the distributions closer to normal distributions. Nevertheless, no statistical test incorrectly suggested that a difference existed among groups, when there was no difference, more often than the expected rate. t or ANOVA tests had a slightly greater statistical power than the other tests to detect differences among groups. Because arcsin transformation both decreased differences among means and reduced the variance to a lesser extent, it decreased power to detect differences among groups. Statistical power to detect differences among groups was not less for a five-category VAS than for a continuous VAS. Conclusions: We conclude that t and ANOVA, without an accompanying arcsin transformation, are good tests to find differences in VAS measurements among groups.