Free
Perioperative Medicine  |   March 2012
Influence of Supervision Ratios by Anesthesiologists on First-case Starts and Critical Portions of Anesthetics
Author Affiliations & Notes
  • Richard H. Epstein, M.D., C.P.H.I, M.S.
    *
  • Franklin Dexter, M.D., Ph.D.
  • *Professor, Department of Anesthesiology, Jefferson Medical College, Philadelphia, Pennsylvania. Professor, Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, Iowa.
Article Information
Perioperative Medicine / Ambulatory Anesthesia / Cardiovascular Anesthesia / Practice Management / Respiratory System / Technology / Equipment / Monitoring
Perioperative Medicine   |   March 2012
Influence of Supervision Ratios by Anesthesiologists on First-case Starts and Critical Portions of Anesthetics
Anesthesiology 3 2012, Vol.116, 683-691. doi:10.1097/ALN.0b013e318246ec24
Anesthesiology 3 2012, Vol.116, 683-691. doi:10.1097/ALN.0b013e318246ec24
What We Already Know about This Topic
  • The most appropriate ratio of anesthesiologists to providers would avoid lapses of supervision during critical portions of anesthetic cases. A simulation study suggested this occurs most commonly with simultaneous first starts.

What This Article Tells Us That Is New
  • In a review of 1 yr of data from a tertiary hospital, lapses occurred commonly during first-case starts even with a 1:2 supervision ratio.

  • These data suggest that either staggered starts or additional anesthesiologists working at the start of the day would be needed to reduce lapses during critical periods.

ANESTHESIOLOGISTS often function in anesthesia care teams (e.g.  , supervising concurrently two or more certified registered nurse anesthetists).1  7 Many anesthesia groups perceive an incentive to decrease their supervision ratio.8  10 Because a ratio lower than 1:2 does not satisfy accreditation requirements of the American College of Graduate Medical Education, ratios lower than 1:2 apply to nurse anesthetists, not anesthesia residents.1Because many hospitals focus on tardiness of first-case starts11,12 and offer many such starts,13  16 anesthesiologist staffing is sensitive to the supervision ratio.
The number of operating rooms (ORs) that an anesthesiologist can supervise is limited by the probability of occurrence of two or more simultaneous events (i.e.  , critical portions) requiring either physical presence or a time-sensitive, nonpreemptive interaction. The probability of supervision lapses is also influenced by the availability of other anesthesiologists to cross-cover. The consequence might be limited to a case delay, but patient safety could be affected when there are coincident critical physiologic events.
In the United States, invoicing Medicare for professional anesthesia services requires that the anesthesiologist “personally participates in the most demanding procedures in the anesthesia plan, including induction and emergence, where indicated.”2However, to reduce the risk of substandard care,17 many institutions do not reveal patient insurance information. Consequently, all patients are supervised in accordance with Medicare rules. Furthermore, anesthesiologists' time before induction likely will increase from implementation of the World Health Organization surgical safety checklist.18 
Paoletti and Marty19 used simulation to estimate the risk of a supervision lapse in surgical suites with various numbers of ORs (2–18) performing a mix of elective cases of various durations (0.8–4.5 h) and a range of anesthesiologist supervision ratios (1:1, 1:2, 1:3). Their model parameters were based on data from several French hospitals. The simulated risk of a supervision lapse peaked at the start of the day. Risks ranged from 14% to 87% for inability to supervise all critical portions of cases at a 1:2 ratio, depending on case length (higher with shorter cases) and the size of the suite (lower with more ORs). Increasing the supervision ratio to 1:3 markedly increased the risk. Providing an unassigned “floater” anesthesiologist greatly reduced the risk.
We explored predictions of the French simulation study using real data captured from an anesthesia information management system to determine the incidence and timing of simultaneous critical portions of cases.
Our first hypothesis was that, as predicted,19 on one-third of days, there would be supervision lapses even with a supervision ratio of 1:2.
Our second hypothesis was that, as predicted,19 the peak incidence of supervision lapses occurred at the start of the day (e.g.  , not during lunch breaks). If true, a supervision ratio less than 1:2 would require an increase in first-case start delays; first-case starts staggered sufficiently to allow the later first case to start on schedule20; additional anesthesiologists available at the start of the day; or anesthesiologists not present for all critical portions of cases.
If the first and second hypotheses were true, then the mean anesthesia release time would determine the average delay when two patients, supervised by the same anesthesiologist, were simultaneously ready for induction and all other anesthesiologists were occupied. We previously published how to use such mean times for anesthesia group economic analyses of first-case starts.12,13 
Our third hypothesis was that anesthesia release times for first-case starts would average 22 min, in the midrange of values determined at Yale-New Haven Hospital.21 
Materials and Methods
After Thomas Jefferson University Institutional Review Board (Philadelphia, Pennsylvania) approval with waiver of informed consent, we reviewed all 15,656 records in the hospital's anesthesia information management system on nonholiday weekdays between May 3, 2010 and May 1, 20113that took place in the 24 ORs comprising the two largest surgical suites. Inpatient and outpatient procedures are performed in these suites, but not cardiac surgery or diagnostic gastrointestinal procedures. The times of events and descriptive information listed in table 1were obtained. Heart rate, oxygen saturation, and invasive and noninvasive blood pressure values were retrieved from the anesthesia information management system database, recorded at 1-min intervals. Actual room locations where procedures took place were determined as previously described.22 
Table 1. Data Obtained from Cases
Image Not Available
Table 1. Data Obtained from Cases
×
We considered the anesthesia providers (i.e.  , those individuals delivering direct anesthesia care) to be busy during the interval from the beginning to the end of anesthesia. The duration of breaks and lunch relief was considered as the interval from the documented start of the break to the documented end of the break, or lasting the mean duration of documented breaks if only the start time of the break was recorded in the anesthesia information management system, which is typical practice (72% of cases) for our providers. Where the end time of the break was not documented, the mean lunch break duration (30 min, based on 1,998 documented breaks) was substituted (presumed for breaks occurring between 11:00 AM and 1:30 PM, which is when lunch is offered). For breaks outside this period with a missing end time, the duration was set at the mean duration of such breaks (i.e.  , 15 min, based on 2,776 documented breaks).
Each day was divided into 1,440 1-min intervals, during each of which the total number of providers who were busy was determined. We considered anesthesiologists to be occupied in tasks that cannot be preempted (i.e.  , unable to leave the patient being cared for) during the periods listed in table 2. For each day, the number of anesthesiologists who were occupied as specified was determined during each 1-min interval.
Table 2. Tasks Considered as Critical Portions of the Anesthetic
Image Not Available
Table 2. Tasks Considered as Critical Portions of the Anesthetic
×
Table 3lists the physiologic events (hypoxemia, hypotension, and hypertension) considered critical portions of cases. The physiologic event definitions were based on published manuscripts demonstrating adverse outcomes and represent prolonged alarm conditions, as opposed to transient or false alarms. The duration of each such event corresponded to when the threshold for the critical event occurred (e.g.  , after 10 min with systolic blood pressure less than 70 mmHg), until when the alarm trigger no longer was in effect (e.g.  , systolic blood pressure ≥70 mmHg). The events we included deliberately underestimated the critical portions of cases to take a conservative approach with respect to the incidence of supervision lapses, increasing the chance of rejecting Hypothesis 1 (discussed in the Statistical Methods section). For example, a blood pressure of 220/140 lasting 20 min during a case scheduled for 1 h was not classified as a critical physiologic event in our analysis, although such instances would almost certainly trigger a call to the supervising anesthesiologist. The same goes for a systolic blood pressure of 75 in a patient undergoing carotid endarterectomy, or a progressive drop in oxygen saturation measured by pulse oximetry from 100% to 90% in a patient undergoing robotic prostatectomy. Our approach was also conservative because there are other physiologic perturbations where the anesthesiologist would likely be notified that we did not include (e.g.  , ST segment depression, hypercapnia not responding to an increase in minute ventilation, or runs of supraventricular tachycardia). In addition, we did not include “false alarm” conditions (e.g.  , disconnection of an electrocardiogram electrode, kinking of the blood pressure tubing, or plugging of the capnograph sampling tubing) that may generate a call to the attending to help troubleshoot and/or resolve the problem.
Table 3. Evidence-based Physiologic Events Considered as Critical Portions of Cases
Image Not Available
Table 3. Evidence-based Physiologic Events Considered as Critical Portions of Cases
×
For each minute of the day, we determined the total number of critical portions of cases that occurred simultaneously (fig. 1). For example, if at 8:40 AM there was a patient being extubated, a patient ready for induction of general anesthesia, and a patient with hypoxemia due to severe bronchospasm, there would be three critical portions of cases in the interval from 8:40:00 AM to 8:40:59 AM. Consequently, the total number of providers needed would equal the number of ORs with cases running plus three anesthesiologists.
Fig. 1. Example of overlapping critical portions of cases. Critical portions of cases are noted by the thick red lines  , and other portions by the thin green lines  . During critical portions of cases, a supervising anesthesiologist would be expected to be present. A six operating room (OR) suite is staffed by two anesthesiologists, Drs. Smith and Jones. Dr. Smith is medically directing ORs 1 to 3 and Dr. Jones ORs 4 to 6. At time 1 (7:15), induction takes place in OR 2 and 6, staffed by the two anesthesiologists in their own rooms with no lapse in supervision. At time 2 (7:30), Dr. Smith has two cases to induce in OR 1 and 3, but Dr. Jones is available and performs the simultaneous induction in OR 3, preventing a lapse in supervision. At time 3 (8:35), Dr. Jones is helping treat a patient with hypoxemia and severe bronchospasm in OR 5, and Dr. Smith is cross-covering the extubation of the patient in OR 6. The patient in OR 4 has to wait for induction, as both anesthesiologists are busy. There has been a supervision lapse due to the occurrence of three simultaneous critical portions of cases.
Image Not Available
Fig. 1. Example of overlapping critical portions of cases. Critical portions of cases are noted by the thick red lines  , and other portions by the thin green lines  . During critical portions of cases, a supervising anesthesiologist would be expected to be present. A six operating room (OR) suite is staffed by two anesthesiologists, Drs. Smith and Jones. Dr. Smith is medically directing ORs 1 to 3 and Dr. Jones ORs 4 to 6. At time 1 (7:15), induction takes place in OR 2 and 6, staffed by the two anesthesiologists in their own rooms with no lapse in supervision. At time 2 (7:30), Dr. Smith has two cases to induce in OR 1 and 3, but Dr. Jones is available and performs the simultaneous induction in OR 3, preventing a lapse in supervision. At time 3 (8:35), Dr. Jones is helping treat a patient with hypoxemia and severe bronchospasm in OR 5, and Dr. Smith is cross-covering the extubation of the patient in OR 6. The patient in OR 4 has to wait for induction, as both anesthesiologists are busy. There has been a supervision lapse due to the occurrence of three simultaneous critical portions of cases.
×
Statistical Methods
Hypothesis 1.
For each minute of each workday excluding Thursdays, the running minimum number of anesthesia providers during overlapping 5 min was calculated (i.e.  , to determine the number of ORs with cases). Thursdays were excluded because the OR starts 1 h later on this day and we were assessing supervision as a function of time of day. Over the same overlapping intervals, the minimum number of simultaneous critical portions of cases was calculated (i.e.  , to determine the number of anesthesiologists needed). For each workday, the number of ORs was calculated as the maximum of the running minimums of the number of simultaneous providers. The number of anesthesiologists needed daily was the maximum of the running minimums of simultaneous critical portions of cases. The ratio of the number of ORs to number of anesthesiologists needed was then calculated for each day. This was most commonly simply 24 ORs divided by the maximum number of anesthesiologists needed for at least 5 min. For hypothetical ratios from 1.0 to 3.0 (i.e.  , one anesthesiologist supervising from one to three ORs), the percentage of workdays for which the daily ratio was smaller was calculated. The use of overlapping 5-min intervals deliberately resulted in underestimation of this ratio (i.e.  , increasing the chance of rejecting Hypothesis 1). For the ratio of 2.0, the lower 95% confidence limit was calculated for the percentage of workdays for which at least one supervision lapse would have occurred. The 95% confidence interval (CI) was calculated using the method of Blyth-Still-Casella (StatXact-9, Cytel Software Corporation, Cambridge, MA).
Hypothesis 2.
For each minute of each of the 202 workdays, excluding Thursdays, the total number of providers needed was calculated = provider in the operating room + anesthesiologist (if a critical portion of a case occurred) + and person on break (if applicable). Next, for each workday, the minute of the day with the largest total number of providers was calculated. That minute was then classified as “first case” if it occurred at 8:00 AM or earlier, otherwise “morning” if before 10:56 AM, otherwise “lunch” if before 1:31 PM, and otherwise “afternoon.” We calculated the percentage of days for which a minute at or before 8:00 AM had the largest total number of providers for the day, along with the 95% lower confidence limit. We tested whether the percentage exceeded half (i.e.  , most) of the days. The calculations were performed twice, once with ties for the time of the day being assigned to the earlier time of day and once to the later time of day. For example, if the daily maximum of 35 anesthesia providers were needed on a day both at 7:58 AM and at 8:02 AM, then first the maximum would be attributed to the 7:58 AM “first case” and next attributed to the 8:02 AM “morning.” The calculations were also repeated using anesthesiologists' critical portions instead of the total number of providers needed.
Hypothesis 3.
For all combinations of the 253 workdays and OR first cases of the day, the time from each OR entrance to anesthesia release was known from the anesthesia information management system data. The probability distribution of the n = 5,769 times to release were not normally distributed with or without inverse squared, inverse, inverse square root, logarithmic, square root, or squared transformations of the release time durations (all Lilliefors tests P  < 0.00001, Systat 13, SYSTAT Software, Chicago, IL). Therefore, the mean was taken for each day. The 253 means followed a normal distribution (Lilliefors test P  = 0.42). The means had neither statistically significant Pearson auto-correlation from 1 day to the next (Pearson r = −0.01, P  = 0.94) nor from 1 week to the next (r = 0.11 P  = 0.08). Therefore, the 95% two-sided CI for the mean release time was calculated using the Student t  distribution, with the sample size being the 253 workdays. Similarly, the overall mean was compared with the anesthesia release time of 22 min determined at Yale-New Haven Hospital21 using Student one group two-sided t  test.
Results
Hypothesis 1: Staffing Lapses
The percentage of days during which there would have been at least one 5-min interval with too few anesthesiologists to supervise all critical portions of cases at varying ratios of ORs to anesthesiologists is shown in figure 2. Even at a ratio of 1:2, there would have been at least one such lapse in supervision for 35% of days (lower 95% confidence limit = 30%). At a ratio of 1:3, there would be supervision lapses on 99% of days (lower 95% confidence limit = 96%).
Fig. 2. Risk of supervision lapses based on number of rooms supervised by each anesthesiologist. A supervision lapse is defined as a critical portion of a case (see tables 1and 2) where there are insufficient anesthesiologists available. For each of the 202 weekdays (excluding Thursday, when the operating room [OR] starts late) in the study interval, the minimum number of providers busy during the five previous 1-min intervals was calculated for each minute of the case. The maximum of this series equals the number of ORs that were running simultaneously at any point in the day (typically 24, but occasionally smaller if any OR were closed for the day). Similarly, the minimum number of critical portions during consecutive overlapping 5-min intervals was determined. The maximum of this series equals the number of anesthesiologists required to supervise all critical portions of cases. The ratio of maximum rooms divided by maximum anesthesiologists was then computed for each day. The value on the y-axis corresponds to the cumulative probability among the 202 days where the ratio listed on the x-axis would be exceeded for at least one interval during the day. For example, suppose each anesthesiologist is supervising two rooms, then on 35% of days, there would be at least one interval when a supervision lapse would occur.
Image Not Available
Fig. 2. Risk of supervision lapses based on number of rooms supervised by each anesthesiologist. A supervision lapse is defined as a critical portion of a case (see tables 1and 2) where there are insufficient anesthesiologists available. For each of the 202 weekdays (excluding Thursday, when the operating room [OR] starts late) in the study interval, the minimum number of providers busy during the five previous 1-min intervals was calculated for each minute of the case. The maximum of this series equals the number of ORs that were running simultaneously at any point in the day (typically 24, but occasionally smaller if any OR were closed for the day). Similarly, the minimum number of critical portions during consecutive overlapping 5-min intervals was determined. The maximum of this series equals the number of anesthesiologists required to supervise all critical portions of cases. The ratio of maximum rooms divided by maximum anesthesiologists was then computed for each day. The value on the y-axis corresponds to the cumulative probability among the 202 days where the ratio listed on the x-axis would be exceeded for at least one interval during the day. For example, suppose each anesthesiologist is supervising two rooms, then on 35% of days, there would be at least one interval when a supervision lapse would occur.
×
Extrapolating from figure 5b of the French simulation study19 with 24 ORs, a staffing ratio of 1:2, and one additional floater anesthesiologist (i.e.  , effective supervision ratio of 1:1.8), the expected incidence of supervision lapses is 12%. We observed a 12% incidence with a supervision ratio of 1:1.7.
The first hypothesis that supervision lapses would take place on one-third of days and that our results would be similar to the simulation study was confirmed.
Hypothesis 2: Time of Day with Largest Number of Providers Needed
The average peak activity (total providers needed) during cases occurred at the start of the workday for most days (fig. 3, table 4, P  < 0.0001). This was especially true for critical portions of cases (i.e.  , times that would influence anesthesiologist staffing; table 3). The second hypothesis was confirmed.
Fig. 3. Average daily workload by hours of the day. During each hour of the workday between 6:00 AM and 11:00 PM, the average numbers of staff required (providers, anesthesiologists, and break personnel) were determined. Operating rooms (green line  ) equals the number of providers, and critical portions (red line  ) are as defined in tables 1and 2, indicating the number of supervising anesthesiologists required. Breaks (purple line  ) represent staff relieving providers for lunch and bathroom breaks. The total number of providers needed (blue line  ) is the sum of the other three quantities. The peak activity occurred at 7:30 AM, as did the number of critical portions of cases. Some operating rooms have scheduled start times of 6:30 AM and others at 7:30 AM, based on surgical specialty; this has no bearing on the results.
Image Not Available
Fig. 3. Average daily workload by hours of the day. During each hour of the workday between 6:00 AM and 11:00 PM, the average numbers of staff required (providers, anesthesiologists, and break personnel) were determined. Operating rooms (green line  ) equals the number of providers, and critical portions (red line  ) are as defined in tables 1and 2, indicating the number of supervising anesthesiologists required. Breaks (purple line  ) represent staff relieving providers for lunch and bathroom breaks. The total number of providers needed (blue line  ) is the sum of the other three quantities. The peak activity occurred at 7:30 AM, as did the number of critical portions of cases. Some operating rooms have scheduled start times of 6:30 AM and others at 7:30 AM, based on surgical specialty; this has no bearing on the results.
×
Table 4. Percentages of n = 202 Days for which the Time of Day Had the Largest Total Number of Providers and/or Critical Portions for Any Minute of the Day
Image Not Available
Table 4. Percentages of n = 202 Days for which the Time of Day Had the Largest Total Number of Providers and/or Critical Portions for Any Minute of the Day
×
Hypothesis 3: Anesthesia Release Time
The mean number of minutes of critical portions of first-case starts was 22.2 min (95% CI 21.8–22.8 min, SD 2.8 min). This observation matched observational findings reported previously from Yale-New Haven Hospital21 (P  = 0.29). Thus, the third hypothesis that the mean number of critical minutes for first-case starts would match the anesthesia release time measured by observers21 was confirmed.
Effect of Providing Higher Supervision Ratios or Staggered First-case Starts on Supervision Lapses
Because the three hypotheses were satisfied, as a sensitivity analysis, we examined the effect on supervision lapses of either lowering the supervision ratio from 1:2 at the start of the day to 1:3 after first cases had begun or supervising at a 1:3 ratio throughout the day with staggered first-case start times. The former strategy would be possible only if there were anesthesiologists with nonclinical assignments (e.g.  , academic institutions), whereas the latter approach could be instituted anywhere. When critical portions of cases occurring at or before 8:00 AM and breaks were excluded, at least one supervision lapse would occur on 14% of days at the 1:3 supervision ratio (95% lower confidence limit = 10%). However, when breaks were included, supervision lapses increased to 62% of days (95% lower confidence limit = 56%; fig. 4). The breaks affecting the maximum supervision ratio were principally lunch reliefs (see fig. 2and table 4). These findings indicate that at a 1:3 supervision ratio, additional providers (e.g.  , certified registered nurse anesthetists) would be needed to provide breaks. In contrast, if supervision were maintained at 1:2 throughout the day, there would be supervision lapses on only 0% and 2% of days, excluding and including breaks, respectively. Thus, additional providers would not be necessary at a 1:2 supervision ratio. Overall, the financial benefit of decreasing the supervision ratio from 1:2 to 1:3 is offset by the need for additional nonanesthesiologist providers.
Fig. 4. Risk of supervision lapses excluding critical portions of cases on or before 8 AM. This graph was constructed as described in the legend for figure 2, with the exception that critical portions of cases occurring on or before 8 AM were excluded. Excluding supervision lapses during first-case starts represents a strategy of either staggering the start times of first cases or providing additional anesthesiologists at the start of the day. The blue circles  and regression line  represent the cumulative percentage of days with at least one supervision lapse when lunch reliefs and breaks after 8 AM were excluded. The red squares  and regression line  represent the cumulative percentage of days with at least one supervision lapse when lunch reliefs and breaks after 8 AM were included. The large increase in staffing lapses at a supervision ratio of 1:3 (13.9%–61.9%) indicates that additional staff would need to be present if lunch relief is to be provided. At a supervision ratio of 1:2, minimal additional staff would be needed, because the increase in days with staffing lapses is small (0% to 2%). Thus, the potential financial benefit of reducing the anesthesiologist staffing ratio will be offset by the need to provide additional providers for lunch relief.
Image Not Available
Fig. 4. Risk of supervision lapses excluding critical portions of cases on or before 8 AM. This graph was constructed as described in the legend for figure 2, with the exception that critical portions of cases occurring on or before 8 AM were excluded. Excluding supervision lapses during first-case starts represents a strategy of either staggering the start times of first cases or providing additional anesthesiologists at the start of the day. The blue circles  and regression line  represent the cumulative percentage of days with at least one supervision lapse when lunch reliefs and breaks after 8 AM were excluded. The red squares  and regression line  represent the cumulative percentage of days with at least one supervision lapse when lunch reliefs and breaks after 8 AM were included. The large increase in staffing lapses at a supervision ratio of 1:3 (13.9%–61.9%) indicates that additional staff would need to be present if lunch relief is to be provided. At a supervision ratio of 1:2, minimal additional staff would be needed, because the increase in days with staffing lapses is small (0% to 2%). Thus, the potential financial benefit of reducing the anesthesiologist staffing ratio will be offset by the need to provide additional providers for lunch relief.
×
Discussion
In this study, we confirmed results of the French simulation study,19 showing that even at a supervision ratio of one anesthesiologist for every two anesthesia providers, all simultaneous critical portions of cases could not be supervised on one-third of days without occasionally waiting for the anesthesiologist. We also confirmed that the largest number of providers is needed at the start of the day, and that is also when there was the highest incidence of critical portions of cases. The mean anesthesia release time (22 min) we measured was close to that measured at Yale-New Haven Hospital.21 That time represents the average expected delay in starting the second case when an anesthesiologist has two patients who are ready for induction simultaneously and there is not another anesthesiologist who is available to cross-cover.
Our findings and the simulation results19 are in contrast to the study of Wright et al.  ,23 which found that cases with a start time after 3 PM had the highest proportion of adverse events. We obtained different results because our focus was on the time of the day with the largest total number of critical portions among all ORs. Wright et al.  23 considered when each individual case had the highest risk.
Administrators who want to reduce their anesthesia group's costs24 by encouraging them to decrease their anesthesiologist supervision ratios need to consider the effect of our findings on the timeliness of first-case starts, which is often a major institutional focus.11,12 At a ratio of one anesthesiologist to three anesthesia providers, it will not be possible to start all ORs simultaneously and have sufficient anesthesiologists to supervise all critical portions of cases on most days. Either the administrators will need to accept the fact that the additional OR often will be delayed from its scheduled start time, or agree to rearrange the OR schedule so that first cases supervised simultaneously by each anesthesiologist will have staggered start times.20 The former approach can lead to discontent, because such delays are publicly visible.25 The use of staggered starts has a built-in expectation that some ORs will start later than other ORs. For some organizations this may be advantageous (e.g.  , surgeons running multiple ORs or who simply prefer to start somewhat later than the “official” start time may embrace this change). Provided the ORs selected for the staggered start times20 are those with the most expected underutilized OR time, this has no economic disadvantage.12,13,26,27 
Another potential approach to the problem of supervision lapses during first cases of the day is for the anesthesia group to make additional anesthesiologists available at the start of the day. Then, once the ORs have been started, some of these individuals are released to perform other duties important to the department (e.g.  , research, informatics, and management and administrative duties). The importance of Hypothesis 2 is in knowing that lunch breaks are not the bottleneck; rather, it is the first case starts that must be considered economically.12,24 However, the importance of our sensitivity analysis is in showing that this approach then necessitates adding additional nonanesthesiologists for breaks, which may nullify the economic benefit.
The fact that some organizations do not routinely provide breaks is not a limitation of our study to such practices, because our results of the importance of the start of the workday with respect to the peak incidence of staffing lapses would then be even stronger  . Similarly, the fact that we studied a tertiary hospital with many long cases rather than an outpatient surgery center with short cases is not a limitation because, from the simulation study,19 our results would be even stronger for short cases. Instead, the principal limitations of our study relate to the definitions of critical portions of anesthetics. Although we relied on process times recorded in an anesthesia information management system, such times recorded by nurses in an operating room information system could be used equivalently, as shown by Sandberg et al.  28 
During our analysis, we assumed, as did Paoletti and Marty,19 that any anesthesiologist can go into any OR when a critical portion of the case occurs and provide supervision equivalent to the anesthesiologist who is otherwise occupied and cannot be interrupted. If complex patients are involved or an extended discussion about management has taken place, such substitution may provide suboptimal patient care. To the extent that all anesthesiologists are not equivalent and thus not able to supervise every critical portion of cases (e.g.  , a patient to receive a regional block that the available anesthesiologist does not feel qualified to perform), the percentage of days with a lapse in supervision with a 1:2 supervision ratio would be even larger than the observed 35%.
There are aspects of our analysis related to our definitions of critical portions of cases (tables 1and 2) that could result in some readers viewing our conclusions as too conservative. Several of our colleagues offered feedback that they do not think that it is necessary for the supervising anesthesiologist to be physically present for induction or emergence in straightforward cases with experienced certified registered nurse anesthetists, as long as they are immediately available. The extent to which anesthesiologist presence is required during and soon after the anesthesia release time varies highly among countries because of varying regulatory requirements and within countries among institutions (e.g.  , depending on local requirements for participation in the preoperative briefing). Because the intraoperative briefing including the surgeon and all anesthesia providers reduces mortality,18 likely its inclusion will be increasingly prevalent.
In summary, we showed that the start of the OR day is the period of time when the anesthesiologist supervision requirement is greatest. Even with lunch breaks included, this result is so robust that changes in the anesthesiologist supervision ratio can be described to administrators simply in terms of the effect on first-case starts. This finding is useful because the psychology of first-case starts is already understood (e.g.  , how they are interpreted economically).11 Decreasing the supervision ratio by anesthesiologists from 1:2 to 1:3 will have a great effect on the timeliness of the start of the first cases of the day due to the high incidence of simultaneous critical portions of cases peaking at that time. As the economics of first-case starts are also fully developed, the decision to stagger first- case starts appropriately1113,26,27 versus  having more anesthesiologists can be modeled for each facility.11,12,24 Unless one of these options is chosen, the consequence will be a marked increase in the incidence of supervision lapses.
References
Rolly G, MacRae WR, Blunnie WP, Dupont M, Scherpereel P: Anaesthesiological manpower in Europe. Eur J Anaesthesiol 1996; 13:325–32
Freund PR, Posner KL: Sustained increases in productivity with maintenance of quality in an academic anesthesia practice. Anesth Analg 2003; 96:1104–8
Demeere JL: Anaesthesia manpower in Belgium until 2020: Nurse anaesthetists—a possible solution? Eur J Anaesthesiol 2002; 19:755–9
Posner KL, Freund PR: Trends in quality of anesthesia care associated with changing staffing patterns, productivity, and concurrency of case supervision in a teaching hospital. ANESTHESIOLOGY 1999; 91:839–47
Kheterpal S, Tremper KK, Shanks A, Morris M: Brief report: Workforce and finances of the United States anesthesiology training programs: 2009–2010. Anesth Analg 2011; 112:1480–6
Abenstein JP, Long KH, McGlinch BP, Dietz NM: Is physician anesthesia cost-effective? Anesth Analg 2004; 98:750–7
Baker AB: Anaesthesia workforce in Australia and New Zealand. Anaesth Intensive Care 1997; 25:60–7
Schubert A, Eckhout G, Cooperider T, Kuhel A: Evidence of a current and lasting national anesthesia personnel shortfall: Scope and implications. Mayo Clin Proc 2001; 76:995–1010
Bayman EO, Dexter F, Laur JJ, Wachtel RE: National incidence of use of monitored anesthesia care. Anesth Analg 2011; 113:165–9
Pontone S, Brouard N: Despite corrective measures, will there still be a lack of anaesthetists and intensive care physicians in France by 2020? Annales Françaises d'Anesthésie et de Réanimation 2010; 29:862–7
Dexter EU, Dexter F, Masursky D, Garver MP, Nussmeier NA: Both bias and lack of knowledge influence organizational focus on first case of the day starts. Anesth Analg 2009; 108:1257–61
Dexter F, Epstein RH: Typical savings from each minute reduction in tardy first case of the day starts. Anesth Analg 2009; 108:1262–7
McIntosh C, Dexter F, Epstein RH: The impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: Tutorial using data from an Australian hospital. Anesth Analg 2006; 103:1499–516
Abouleish AE, Dexter F, Epstein RH, Lubarsky DA, Whitten CW, Prough DS: Labor costs incurred by anesthesiology groups because of operating rooms not being allocated and cases not being scheduled to maximize operating room efficiency. Anesth Analg 2003; 96:1109–13
Dexter F, Weih LS, Gustafson RK, Stegura LF, Oldenkamp MJ, Wachtel RE: Observational study of operating room times for knee and hip replacement surgery at nine US community hospitals. Health Care Manag Sci 2006; 9:325–39
Wachtel RE, Dexter F: Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg 2009; 108:1889–901
Burstin HR, Lipsitz SR, Brennan TA: Socioeconomic status and risk for substandard medical care. JAMA 1992; 268:2383–7
Haynes AB, Weiser TG, Berry WR, Lipsitz SR, Breizat AH, Dellinger EP, Herbosa T, Joseph S, Kibatala PL, Lapital MC, Merry AF, Moorthy K, Reznick RK, Taylor B, Gawande AA, Safe Surgery Saves Lives Study Group: A surgical safety checklist to reduce morbidity and mortality in a global population. N Engl J Med 2009; 360:491–9
Paoletti X, Marty J: Consequences of running more operating theatres than anaesthetists to staff them: A stochastic simulation study. Br JAnaesth 2007; 98:462–9
Koenig T, Neumann C, Ocker T, Kramer S, Spies C, Schuster M: Estimating the time needed for induction of anaesthesia and its importance in balancing anaesthetists' and surgeons' waiting times around the start of surgery. Anaesthesia 2011; 66:556–62
Escobar A, Davis EA, Ehrenwerth J, Watrous GA, Fisch GS, Kain ZN, Barash PG: Task analysis of the preincision surgical period: An independent observer-based study of 1558 cases. Anesth Analg 2006; 103:922–7
Epstein RH, Dexter F, Piotrowski E: Automated correction of room location errors in anesthesia information management systems. Anesth Analg 2008; 107:965–71
Wright MC, Phillips-Bute B, Mark JB, Stafford-Smith JB, Grichnik KP, Andregg BC, Taekman JM: Time of day effects on the incidence of anesthetic adverse events. QualSaf Health Care 2006; 15:258–63
Dexter F, Epstein RH: Calculating institutional support that benefits both the anesthesia group and hospital. Anesth Analg 2008; 106:544–53
Masursky D, Dexter F, Isaacson SA, Nussmeier NA: Surgeons' and anesthesiologists' perceptions of turnover times. Anesth Analg 2011; 112:440–4,
Dexter F, Epstein RH, Traub RD, Xiao Y: Making management decisions on the day of surgery based on operating room efficiency and patient waiting times. ANESTHESIOLOGY 2004; 101:1444–53
Dexter F, Willemsen-Dunlap A, Lee JD: Operating room managerial decision-making on the day of surgery with and without computer recommendations and status displays. Anesth Analg 2007; 105:419–29
Sandberg WS, Daily B, Egan M, Stahl JE, Goldman M, Wiklund RA, Rattner D: Deliberate perioperative systems design improves operating room throughput. ANESTHESIOLOGY 2005; 103:406–18
Ehrenfeld JM, Funk LM, Van Schalkwyk J, Merry AF, Sandberg WS, Gawande A: The incidence of hypoxemia during surgery: Evidence from two institutions. Can J Anesth 2010; 57:888–97
Reich DL, Bennett-Guerrero E, Bodian CA, Hossain S, Winfree W, Krol M: Intraoperative tachycardia and hypertension are independently associated with adverse outcome in noncardiac surgery of long duration. Anesth Analg 2002; 95:273–7
Reich DL, Hossain S, Krol M, Baez B, Patel P, Bernstein A, Bodian CA: Predictors of hypotension after induction of general anesthesia. Anesth Analg 2005; 622–8
Fig. 1. Example of overlapping critical portions of cases. Critical portions of cases are noted by the thick red lines  , and other portions by the thin green lines  . During critical portions of cases, a supervising anesthesiologist would be expected to be present. A six operating room (OR) suite is staffed by two anesthesiologists, Drs. Smith and Jones. Dr. Smith is medically directing ORs 1 to 3 and Dr. Jones ORs 4 to 6. At time 1 (7:15), induction takes place in OR 2 and 6, staffed by the two anesthesiologists in their own rooms with no lapse in supervision. At time 2 (7:30), Dr. Smith has two cases to induce in OR 1 and 3, but Dr. Jones is available and performs the simultaneous induction in OR 3, preventing a lapse in supervision. At time 3 (8:35), Dr. Jones is helping treat a patient with hypoxemia and severe bronchospasm in OR 5, and Dr. Smith is cross-covering the extubation of the patient in OR 6. The patient in OR 4 has to wait for induction, as both anesthesiologists are busy. There has been a supervision lapse due to the occurrence of three simultaneous critical portions of cases.
Image Not Available
Fig. 1. Example of overlapping critical portions of cases. Critical portions of cases are noted by the thick red lines  , and other portions by the thin green lines  . During critical portions of cases, a supervising anesthesiologist would be expected to be present. A six operating room (OR) suite is staffed by two anesthesiologists, Drs. Smith and Jones. Dr. Smith is medically directing ORs 1 to 3 and Dr. Jones ORs 4 to 6. At time 1 (7:15), induction takes place in OR 2 and 6, staffed by the two anesthesiologists in their own rooms with no lapse in supervision. At time 2 (7:30), Dr. Smith has two cases to induce in OR 1 and 3, but Dr. Jones is available and performs the simultaneous induction in OR 3, preventing a lapse in supervision. At time 3 (8:35), Dr. Jones is helping treat a patient with hypoxemia and severe bronchospasm in OR 5, and Dr. Smith is cross-covering the extubation of the patient in OR 6. The patient in OR 4 has to wait for induction, as both anesthesiologists are busy. There has been a supervision lapse due to the occurrence of three simultaneous critical portions of cases.
×
Fig. 2. Risk of supervision lapses based on number of rooms supervised by each anesthesiologist. A supervision lapse is defined as a critical portion of a case (see tables 1and 2) where there are insufficient anesthesiologists available. For each of the 202 weekdays (excluding Thursday, when the operating room [OR] starts late) in the study interval, the minimum number of providers busy during the five previous 1-min intervals was calculated for each minute of the case. The maximum of this series equals the number of ORs that were running simultaneously at any point in the day (typically 24, but occasionally smaller if any OR were closed for the day). Similarly, the minimum number of critical portions during consecutive overlapping 5-min intervals was determined. The maximum of this series equals the number of anesthesiologists required to supervise all critical portions of cases. The ratio of maximum rooms divided by maximum anesthesiologists was then computed for each day. The value on the y-axis corresponds to the cumulative probability among the 202 days where the ratio listed on the x-axis would be exceeded for at least one interval during the day. For example, suppose each anesthesiologist is supervising two rooms, then on 35% of days, there would be at least one interval when a supervision lapse would occur.
Image Not Available
Fig. 2. Risk of supervision lapses based on number of rooms supervised by each anesthesiologist. A supervision lapse is defined as a critical portion of a case (see tables 1and 2) where there are insufficient anesthesiologists available. For each of the 202 weekdays (excluding Thursday, when the operating room [OR] starts late) in the study interval, the minimum number of providers busy during the five previous 1-min intervals was calculated for each minute of the case. The maximum of this series equals the number of ORs that were running simultaneously at any point in the day (typically 24, but occasionally smaller if any OR were closed for the day). Similarly, the minimum number of critical portions during consecutive overlapping 5-min intervals was determined. The maximum of this series equals the number of anesthesiologists required to supervise all critical portions of cases. The ratio of maximum rooms divided by maximum anesthesiologists was then computed for each day. The value on the y-axis corresponds to the cumulative probability among the 202 days where the ratio listed on the x-axis would be exceeded for at least one interval during the day. For example, suppose each anesthesiologist is supervising two rooms, then on 35% of days, there would be at least one interval when a supervision lapse would occur.
×
Fig. 3. Average daily workload by hours of the day. During each hour of the workday between 6:00 AM and 11:00 PM, the average numbers of staff required (providers, anesthesiologists, and break personnel) were determined. Operating rooms (green line  ) equals the number of providers, and critical portions (red line  ) are as defined in tables 1and 2, indicating the number of supervising anesthesiologists required. Breaks (purple line  ) represent staff relieving providers for lunch and bathroom breaks. The total number of providers needed (blue line  ) is the sum of the other three quantities. The peak activity occurred at 7:30 AM, as did the number of critical portions of cases. Some operating rooms have scheduled start times of 6:30 AM and others at 7:30 AM, based on surgical specialty; this has no bearing on the results.
Image Not Available
Fig. 3. Average daily workload by hours of the day. During each hour of the workday between 6:00 AM and 11:00 PM, the average numbers of staff required (providers, anesthesiologists, and break personnel) were determined. Operating rooms (green line  ) equals the number of providers, and critical portions (red line  ) are as defined in tables 1and 2, indicating the number of supervising anesthesiologists required. Breaks (purple line  ) represent staff relieving providers for lunch and bathroom breaks. The total number of providers needed (blue line  ) is the sum of the other three quantities. The peak activity occurred at 7:30 AM, as did the number of critical portions of cases. Some operating rooms have scheduled start times of 6:30 AM and others at 7:30 AM, based on surgical specialty; this has no bearing on the results.
×
Fig. 4. Risk of supervision lapses excluding critical portions of cases on or before 8 AM. This graph was constructed as described in the legend for figure 2, with the exception that critical portions of cases occurring on or before 8 AM were excluded. Excluding supervision lapses during first-case starts represents a strategy of either staggering the start times of first cases or providing additional anesthesiologists at the start of the day. The blue circles  and regression line  represent the cumulative percentage of days with at least one supervision lapse when lunch reliefs and breaks after 8 AM were excluded. The red squares  and regression line  represent the cumulative percentage of days with at least one supervision lapse when lunch reliefs and breaks after 8 AM were included. The large increase in staffing lapses at a supervision ratio of 1:3 (13.9%–61.9%) indicates that additional staff would need to be present if lunch relief is to be provided. At a supervision ratio of 1:2, minimal additional staff would be needed, because the increase in days with staffing lapses is small (0% to 2%). Thus, the potential financial benefit of reducing the anesthesiologist staffing ratio will be offset by the need to provide additional providers for lunch relief.
Image Not Available
Fig. 4. Risk of supervision lapses excluding critical portions of cases on or before 8 AM. This graph was constructed as described in the legend for figure 2, with the exception that critical portions of cases occurring on or before 8 AM were excluded. Excluding supervision lapses during first-case starts represents a strategy of either staggering the start times of first cases or providing additional anesthesiologists at the start of the day. The blue circles  and regression line  represent the cumulative percentage of days with at least one supervision lapse when lunch reliefs and breaks after 8 AM were excluded. The red squares  and regression line  represent the cumulative percentage of days with at least one supervision lapse when lunch reliefs and breaks after 8 AM were included. The large increase in staffing lapses at a supervision ratio of 1:3 (13.9%–61.9%) indicates that additional staff would need to be present if lunch relief is to be provided. At a supervision ratio of 1:2, minimal additional staff would be needed, because the increase in days with staffing lapses is small (0% to 2%). Thus, the potential financial benefit of reducing the anesthesiologist staffing ratio will be offset by the need to provide additional providers for lunch relief.
×
Table 1. Data Obtained from Cases
Image Not Available
Table 1. Data Obtained from Cases
×
Table 2. Tasks Considered as Critical Portions of the Anesthetic
Image Not Available
Table 2. Tasks Considered as Critical Portions of the Anesthetic
×
Table 3. Evidence-based Physiologic Events Considered as Critical Portions of Cases
Image Not Available
Table 3. Evidence-based Physiologic Events Considered as Critical Portions of Cases
×
Table 4. Percentages of n = 202 Days for which the Time of Day Had the Largest Total Number of Providers and/or Critical Portions for Any Minute of the Day
Image Not Available
Table 4. Percentages of n = 202 Days for which the Time of Day Had the Largest Total Number of Providers and/or Critical Portions for Any Minute of the Day
×