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Economics, Education, and Policy: Special Article

Decreasing the Hours That Anesthesiologists and Nurse Anesthetists Work Late by Making Decisions to Reduce the Hours of Over-Utilized Operating Room Time

Dexter, Franklin MD, PhD; Wachtel, Ruth E. PhD, MBA; Epstein, Richard H. MD, CPHIMS

Author Information
doi: 10.1213/ANE.0000000000001136
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In this special article, we consider operational methods to reduce the hours that anesthesiologists and nurse anesthetists work later than their scheduled hours in operating rooms (ORs). For convenience, we will refer to this as “working late.” Working late increases long-term costs and thereby reduces productivity (Table 1).1 We review decisions that reduce the hours of Over-Utilized OR time. Such decisions can be made rationally and systematically. Coordination of OR patients and personnel (e.g., anesthesiologists and nurse anesthetists), including evidence-based staff scheduling, case scheduling, and staff assignment, is 1 of the 2 principal economic opportunities for cost reduction from the perioperative surgical home.2

Table 1
Table 1:
Definitions of Terms Relevant to This Article

We consider surgical suites (or groups of ORs of the same specialty) for which each OR averages at least 8 hours of cases and turnover times on each regular workday. We do so based on considering “8 hours” to be the shortest shift to which an anesthesiologist or nurse anesthetist would typically be scheduled to work. If each OR averages substantively <8 hours of cases daily, anesthesiologists and nurse anesthetists would rarely work late, provided no large gaps occur between cases. The percentages of ORs with >8 hours of cases vary markedly not only among hospitals3–6 but also among services and days of the week at individual hospitals.7 Thus, the effectiveness of the managerial interventions described in this article differs both among hospitals and among ORs at the same hospital.8


Decision making can be systematically performed shortly before the day of surgery, based on the principles of reducing the hours of Over-Utilized OR time.9 For vocabulary terms, we refer readers to our review article Ref. 10. Definitions of the relevant terms also are provided, for convenience, in Table 1 of this article.11–22 We recently published another special article on anesthesia-hospital agreements, and for that paper, we used a similar Table 1 and summary of the previous review articles.23

a. Previous Review Articles About Over-Utilized OR Time

Hours worked exceeding “allocated hours” (i.e., the hours into which cases are scheduled) are referred to as “hours of Over-Utilized OR time.”10–13 Allocated hours are calculated months before the day of surgery11–13 for each combination of service and day of the week, based on the historical workload and the total hours of cases, including add-ons and turnovers.11–13,15–17,24,25 Surgical services schedule into the same allocated hours. Calculated OR allocations include add-on cases because they contribute to the historical OR workload. Cases of patients who are inpatients preoperatively often are add-on cases.26,27

OR allocations are needed for staff scheduling, the process that determines which individual anesthesiologists and nurse anesthetists will work each future day. Usually, staff scheduling is done months in advance. In contrast, staff assignment is the process of choosing the individual OR in which a nurse anesthetist or anesthesiologist will personally administer anesthesia or the ORs in which the anesthesiologist will supervise anesthesia residents or nurse anesthetists. By supervision, we refer to the process of overseeing patient care.28–36 Staff assignments are usually made the day prior to surgery.

Cases often (although <50%) take longer than scheduled if the mean of historical case durations is (appropriately) used for staff scheduling.20–22 This is because case durations, for given combinations of surgeon and scheduled procedure(s), follow right skewed distributions.20 The mean is used because OR allocations are calculated based on actual OR workloads, and thus, cases should be scheduled using an unbiased estimator of the contribution of each case’s duration to the total workload. That contribution is, by definition, the mean. A consequence of using the mean (e.g., versus median) is that most cases (≅56%) will take less time than scheduled.22 See Ref. 20 for a recent review of that topic. Most Over-Utilized OR time is caused by relatively large hours of scheduled cases, not many cases taking longer than scheduled (see next section, 2.b).37

Ref. 12 is a full review describing how to calculate OR allocations to minimize the inefficiency of use of OR time (i.e., the weighted combination of the hours of under-utilized and Over-Utilized OR time).12 Ref. 38 is a book chapter reviewing the same material.38 Ref. 13 shows that staffing for 8 hours rather than 10 hours is appropriate when the day averages 8 hours 30 minutes or less. When the length of the day approaches 9 hours, 10 hours of staffing should be scheduled.13 Ref. 24 compares statistical methods.24 Ref. 9 reviews corresponding decision making on the day of surgery.9 For derivations, readers can refer to the Appendices of Refs. 11 and 14. Lectures covering these topics are online at The calculation of OR allocations is a topic in itself,11–13,15,24,25 with a full-review article and book chapters published,9,12,13 and we refer readers to the prior work. The content and implications of the current special article depends on knowledge of the material in those reviews, not repeated here. For readers who would feel unsure about writing the 3 simultaneous equations for the inefficiency of use of OR time (Table 1), we recommend the review articles.

b. Cases Are Performed in Over-Utilized Time Instead of Being Cancelled

The limit to the hours of cases scheduled daily generally should not be based on avoiding Over-Utilized time but rather patient safety.9,12–14 Cancelling a case on the day of surgery (and/or the postponing of an add-on case until the next day), even if the case would cause Over-Utilized OR time, causes a net increase in costs, even if anesthesiologists and nurse anesthetists work late (Table 2.b.i).39,40 Nearly all elective (scheduled) cases are subsequently performed on a later date.41,42 Regardless of how clinical production is measured economically (Table 2.b.ii),3,43,44 anesthesiologists and nurse anesthetists are not considered a bottleneck. During periods when there are modestly fewer anesthesiologists than desired (e.g., one has an unexpected medical absence), the other anesthesiologists in the group each work more hours or locums tenens providers are hired.

Table 2
Table 2:
Examples (1–3) and Details (i–ii) for Section 2

Efforts should be made to reduce Over-Utilized time. However, this should not be done at the expense of not performing cases (i.e., cancelling cases on the day of surgery). The reason is that if no cases were performed, both the inefficiency of use of OR time and Over-Utilized time would be 0.11–13 Each reduction in the hours of Over-Utilized OR time then contributes to an expected reduction in the hours that the anesthesiologists and nurse anesthetists work late. See Example 2.b.1. Hours worked late could always be reduced by having every anesthesiologist and nurse anesthetist scheduled for >12 hours daily. However, such a trivial “solution” to the problem of working late is dismissed based on consideration of productivity. Staff scheduling should be planned for hours that just slightly exceed allocated time, because that choice increases productivity. See Table 2’s Example 2.b.2. Reducing the hours of Over-Utilized OR time consequently reduces the hours worked late and increases productivity. See Example 2.b.3.


Decisions can be made 1–2 days in advance and on the day of surgery with the goal of reducing Over-Utilized OR time.11–13,41 Knowledge of the principles underlying Over-Utilized time is neither intuitive nor learned through experience working in ORs.45,46

a. Timing of Decisions in the Scheduling Office to Reduce Over-Utilized Time

Most decisions made by the scheduling office that substantively reduce the hours worked late are made 1 working day prior to the day of surgery and on the day of surgery. Slightly more than half of ORs have a change in 1 or more cases within 1 workday of surgery (Table 3.a.i).47 Thus, anesthesiologists (e.g., as Medical Directors2,23,48) can wait to make decisions in the scheduling office until the late afternoon 2 workdays before surgery and/or 1 workday before surgery. However, there is little difference between making decisions at the end of the day 2 workdays before surgery versus at the start of the workday 1 day before surgery (Table 3.a.ii.).47 Because anesthesiologists are busy caring for patients early in the workday, often these decisions are made at the end of the day 2 workdays and 1 workday before surgery.49,76 Cases can be rearranged, and start times changed to reduce Over-Utilized time. Subsequent changes will nonetheless occur because of multiple additions of cases and cancellations (i.e., cases scheduled that are not performed or performed on a different date).41,42,47 See Table 3.a.iii.

Table 3
Table 3:
Examples (1–6) and Details (i–x) for Section 3

When OR allocations are calculated appropriately by service and day of the week, it is not evident which days will have the largest hours of Over-Utilized time until the working day before surgery or the day of surgery (see section “3.b. Add-on Cases Cause Much of the Over-Utilized Time”).26,47 Similarly, provided the OR allocations have been calculated appropriately based on minimizing the inefficiency of use of OR time, the days with the most under-utilized time are not apparent until the day prior to surgery or the day of surgery.26,47

Because of cancellations, schedulers can make decisions to fill, but not exceed, the allocated hours for the specific OR into which the case is placed (Table 3.a.iv).47 Cancellations within 1 workday of surgery (slightly) reduce variability in services’ workloads among days (P < 0.0001).41,47 This is not because cancellations reduce the total hours of cases and turnovers to close to 0 (i.e., resulting in no variability at all).47 Rather, cancellations soon before the day of surgery are associated with (slightly) increased net hours of cases scheduled.47 This apparent paradox occurs because when cases are scheduled many weeks in advance, the workload for the date of surgery is unknown, because few cases have been scheduled and there may be many interim changes. When a cancelled case is replaced close to the day of surgery, the schedulers have a better idea of the anticipated workload and can make an informed substitution to stay within the allocated hours (Table 3.a.v).24,47,50–52

b. Add-on Cases Cause Much of the Over-Utilized Time

Many ORs have at least 1 change in case(s) because, by the end of the day of surgery, approximately 20% of OR scheduled hours arise from patients who are inpatient preoperatively.52 The heterogeneity in numbers and hours of add-on cases extensively influences the end of the workday (Table Correlations between (1) the daily numbers of elective (scheduled) cases and (2) the times when a hospital has a certain number of ORs ongoing are so small that using this knowledge produces a negligible reduction in the absolute error of the forecasted end of workdays for anesthesiologists.52 This is even more so for hospitals with more ORs than needed to minimize the inefficiency of use of OR time (Table 3.b.vii).37,53

The difference typically is <1 case between whether a 2 OR service has overall under-utilized or Over-Utilized time (Table 3.b.viii).47,53 This remarkable observation explains why the OR allocation decision is so important to reduce Over-Utilized OR time, whereas the case scheduling decision is usually obvious (and, by corollary, absolute differences between scheduled and actual case durations are unimportant). Example 3.b.1 highlights that effective decision making depends on the OR allocations having been calculated based on minimizing the inefficiency of use of OR time.54

Each add-on case should be assigned to an OR in which the case can be performed safely (e.g., the personnel in the room have the requisite skills to do the case), and, to the extent possible, without creating additional Over-Utilized time.9,47,55 This is done by scheduling in descending sequence of duration.9,55 See Example 3.b.2 and Table 3.b.ix.56

c. Moving Cases Can Reduce Hours of Over-Utilized Time

Sometimes a case in an OR with Over-Utilized OR time can be moved to an OR with a cancellation, thereby reducing expected Over-Utilized time.9,57 Cases may also be moved between ORs allocated for 8 hours and those allocated for 10 hours. See Example 3.c.3.58

Decision making on the day of surgery to reduce the hours of Over-Utilized time depends in part on predicting the mean (expected) time remaining in ongoing cases that are taking longer than originally scheduled.21,59 Figure 1 and Table 1 of Ref. 59 show a detailed example and corresponding OR display for assigning an add-on case to an OR with ongoing cases.59 By using the expected time remaining in each of the ORs, the expected under-utilized and Over-Utilized OR time can be calculated and used to guide decisions.9,14 The need to use the mathematics is not obvious, because, for example, it is not intuitive a case scheduled for 4 hours that has already been ongoing for 3 hours will have an expected time remaining >1 hour (e.g., 1.5 hours).59 The Bayesian statistical method to predict the mean (expected) times remaining in late-running cases is accurate.59–61 See Table 3.c.x.62–64

d. Targeting Specific ORs to Reduce Over-Utilized OR Time

Hospitals can plan which ORs to target to reduce case durations and turnovers before the day of surgery47 and on the day of surgery. For each OR, the expected hours of Over-Utilized time are calculated based on the cases scheduled.9,14,46 Decision makers should target the ORs with Over-Utilized time. This targeting can involve decreasing turnover times by assigning additional personnel in the middle of the day or scheduling them to arrive earlier, reducing surgical duration (e.g., by not having the medical student or inexperienced intern do the closure), decreasing OR times by selective use of experienced personnel, choosing anesthetic techniques that facilitate rapid emergence, and by scheduling add-on cases into rooms to result in the least expected Over-Utilized time (see section 3.b. “Add-on Cases Cause Much of the Over-Utilized Time”).9 See Example 3.d.4.

e. Staff Assignment Decisions to Reduce Over-Utilized OR Time

Modifying case assignments can influence Over-Utilized OR time. See Example 3.e.5. Staff assignments can be modified dynamically, knowing the OR entry and exit times and surgical start and stop times.65 The principles can (and should) be applied not only to anesthesiologists and nurse anesthetists but also to housekeepers, OR nurses, managers, postanesthesia care unit nurses, etc.9,12,66 All decision making on the day of surgery that has “improving efficiency” as the goal can revolve around reducing the hours of Over-Utilized OR time. See Example 3.e.6.67–70

Anesthesiologists’ physical presence in ORs when supervising does not appear to reduce Over-Utilized OR time substantively.4,71–73 Consider OR times segmented into surgical times (start of positioning to application of dressing) plus nonsurgical times (i.e., “anesthesia-controlled,” from entering the OR until the patient is ready for positioning, plus the time from application of dressing until the patient leaves the OR).71,74–76 The OR times are affected principally by surgical times, not anesthesia-controlled times,71–73 because means of anesthesia-controlled times are generally brief relative to SDs of surgical times.71,77 At an academic outpatient surgical suite, anesthesia-controlled times were no briefer (<1 minute) with anesthesiologist supervision of nurse anesthetists (i.e., 2 providers present) than when anesthesiologists personally provided care.78 Among joint replacement cases at several community hospitals, there was no association between increases in the physical presence of the anesthesiologist from 0% to 21% of the case and reductions in OR times.4

f. Sequencing Cases to Avoid Constraints from Equipment or Personnel

The scheduling office also can identify 1 to 2 days before surgery when there may be conflicts over equipment or personnel (e.g., surgical assistants) and resequence cases to mitigate such conflicts.21,79

First, suppose that the OR information system detects an overlap of a resource (e.g., an operating microscope) between ORs based on the cases taking precisely as long as scheduled.80 Then, there is a >50% chance of contention over equipment or personnel, and thus, the case sequences within each OR should be changed. The extent to which the percentage exceeds 50% depends on the proportional overlap and the predictive error in case durations, but contention should be expected.

In contrast, suppose that no overlap is detected. However, a conflict over equipment or personnel would occur if 1 or more cases took slightly longer than scheduled (e.g., by 1 hour). Then, the sensitivity of the risk of a conflict to the inaccuracy in the case duration estimates should be evaluated.9,62,81,82 If no time buffer is used when cases have a substantive chance of overlap (10%–50%), surgeons and patients should expect delays. Statistical methods can be used to learn whether there is a substantive probability (e.g., >10%) of overlap, incorporating uncertainty in actual OR times. To use the statistical methods, both historical case duration data and the surgeon’s (scheduled) estimates are combined.21,22,62 This is done for both of the cases in the 2 ORs. For each procedure or combination of procedures that the surgeon has scheduled many (e.g., >15) times before,21 the variability around the median (e.g., from the scheduled duration)21 is estimated principally from the corresponding historical data.62,81 For implementation, refer to Equations 9 to 12 of Ref. 62, Appendices 1 to 2 of Ref. 61, and parameter estimation as described in Ref. 21.


Appropriate decision making months before the day of surgery depends on the OR allocations, because the hours of cases and turnovers differ markedly among ORs.7

a. Variation in Workloads Among Services, Days of the Week, and Seasons

Some anesthesia groups have different anesthesiologists for some services (e.g., those specially trained in regional anesthesia for orthopedics).83 For staff scheduling to be by service, there must then be different numbers of specially trained anesthesiologists scheduled among days of the week.84 Staff scheduling should also incorporate the duration of preoperative procedures into anesthesiologists’ arrival times.85 See Example 4.a.1.

OR workload varies among different days of the week due to differences in the hours of cases performed by different services (Table 4.a.i).7,52,86–88 There is also substantial variability in hours of cases among ORs at the same facility on the same day (Table 4.a.ii). Consequently, anesthesia groups with different anesthesiologists for some services should plan different durations of staff schedules among days of the week. See Example 4.a.2.

Table 4
Table 4:
Examples (1–5) and Details (i–ix) for Section 4

Variation in hours worked late among seasons of the year can be modeled and used to adjust staff scheduling (Table 4.a.iii).17 Seasonal variation is sufficiently large that it needs to be considered when performing statistical analyses of OR/anesthesia information system data.12,52 However, the magnitude of the seasonal effect usually is sufficiently small not to warrant different staff scheduling (Table 4.a.iv).17,89,90 We, therefore, do not consider seasonal variability further.

b. Increasing First Case Starts Reduces Hours of Over-Utilized Time

We consider the hours into which a surgeon can book cases with a guaranteed first-case start to be block time.10,18,91–94 By definition, allocated time must be at least as long as the corresponding block time.25,95

OR allocations typically are 8 or 10 hours.12,13,96 Cross-sectional and longitudinal studies of hospital decisions show that ORs tend to be added when upper percentiles of hours of cases and turnovers per OR exceed 8 hours per workday.43,94,97 Therefore, the number of surgeon/service blocks, or first case of the day starts (i.e., ORs with assigned anesthesiologists at the start of the workday), is often greater than the number of ORs that maximizes the efficiency of use of OR time.12,15,98,99 Increasing the number of first-case starts has the advantage, from a surgeon’s perspective, of substantial reductions in tardiness from the scheduled start time of the surgeon’s list of cases (Table 4.b.v).18 Additional first case starts can reduce Over-Utilized time at hospitals with many ORs with >1 surgeon scheduling cases.12 See Example 4.b.3. Analyses can be used to quantify the cost sustained by the anesthesia group (or department) for staffing those extra ORs (e.g., for institutional support).98,100Table and Example 4.b.4 show that the analysis can be used to assure that extra OR time is given to the service that can best use it.

c. Targeting ORs When First Case of the Day Starts Are Staggered

Hospitals adopt concerted efforts to reduce tardiness in first case starts.101–105 However, small changes in first case start times do not disproportionately cause greater Over-Utilized OR time.45,62,106 Thus, specific services and days of the week with greater expected Over-Utilized OR time can be targeted for on-time first-case starts (Table 4.c.vii).

At some hospitals, anesthesiologists supervise multiple ORs simultaneously (i.e., >1 resident physician and/or nurse anesthetist). The largest number of anesthesia providers needed (e.g., anesthesiologists + resident physicians + nurse anesthetists) occurs at the start of the workday for most days (P < 0.0001).76 Without the effective use of planned staggered starts,108 risks of anesthesiologists being unable to be present for all critical portions of cases at a 1:2 ratio of anesthesiologist to those being supervised range from 14% to 87%, depending on the case duration (higher with shorter cases) and the number of ORs (lower with more ORs because more anesthesia providers are available to go to any OR; Table 4.c.viii).49 Consequently, provided the anesthesiologist is present in the OR at critical portions of cases, then regardless of the listed case start times, the ORs will have staggered times of induction of anesthesia if anesthesiologists supervise multiple ORs.49,76 This staggering should be planned to reduce resulting Over-Utilized OR time.108 The surgeons who will be starting second can be informed the day before surgery that their first case will be starting slightly (e.g., 15 minutes) later.107 Although this could be done on an ad hoc basis, for communication purposes, we find it easier also to change the final OR schedule.108 This is an issue of planning well based on reality (i.e., not a change, because cases cannot start simultaneously if an anesthesiologist is required to be present at induction).

d. Turnovers and Planning Staff Scheduling for Breaks

Staff scheduling can facilitate decision making on the day of or the day before surgery to reduce Over-Utilized OR time on the day of surgery.109 Such efforts include adding an extra team, adding an additional OR, and/or using individual induction rooms, all for use by surgeons each with >8 hours of scheduled OR time.109–111 The impact of these interventions is insensitive to the specific changes in workflow.109 Even when there are increases in the hours of under-utilized OR time (e.g., a 25% increase from 4 to 5 ORs), anesthesia group productivity can increase from the reductions in the more expensive hours of Over-Utilized OR time.112–114

Because of variation in workloads among services and days of the week, reducing prolonged turnovers does not necessarily decrease hours worked late. See Example 4.d.5. Most prolonged turnovers occur in the middle of day.18,54 This is not because the mean turnover times are longer (e.g., due to lunches).54 Rather, it is because, when many ORs have 2 cases per day, the turnover will typically occur in the middle of the day (Table 4.d.ix).115 Except for first cases of the day, the middle of the day is also the period of the day with the largest number of anesthesia providers, because of the lunch breaks.116 Therefore, staff scheduling should include having enough anesthesia providers to provide breaks in all the ORs for which there will be a break, so that turnovers are not used for the personnel to take a break but as an opportunity to start a case, originally scheduled in that OR or a different OR.109–113 (This is a statement of “should” because, as emphasized in the Introduction, we consider only surgical suites for which ORs have at least 8 hours of cases). The same process applies to other personnel such as surgical technologists and housekeepers.53,115 The largest opportunities to reduce tardiness of starts are by interventions early and/or in the middle of the day, because then subsequent cases in other ORs benefit.18 See section 3.d, above, where staff assignment was considered.


Decisions to reduce the hours of Over-Utilized OR time can result in reductions in the hours that anesthesiologists and nurse anesthetists work late. OR allocations are based on historical workloads and are specific to each surgical service on each day of the week and include time for add-on cases. Decisions made months in advance to decrease Over-Utilized OR time and hours worked late include increasing the number of first case starts, staggering first-case starts when an anesthesiologist supervises >1 resident or nurse anesthetist simultaneously, and staffing for turnovers and lunch breaks during the busiest times of the day. These decisions are predicated on expected Over-Utilized OR time being calculated appropriately (i.e., OR allocations based on maximizing the efficiency of use of OR time). Our recent Special Article23 emphasizes the importance of the anesthesia-hospital agreement to ensure that OR allocations are calculated correctly.

Most decisions substantively influencing Over-Utilized OR time are made within 1 workday before the day of surgery and on the day of surgery, because only then are ORs sufficiently full that changes to reduce Over-Utilized OR time can be identified. The consequence is that anesthesiologists’ involvement in the scheduling office within 1 workday of surgery is exceedingly important. Decisions to reduce Over-Utilized time include targeting ORs with expected Over-Utilized OR time, effective staff assignments, and scheduling of add-on cases.

We emphasize that making decisions to reduce over-utilized time is distinctly different from a strategy to delay the start of cases in order to ensure that only a fixed number of ORs will be in use at specified times of the day (e.g., 20 ORs at 3:00 PM and 15 ORs at 5:00 PM). Readers can verify this assertion by examining each of the sections and focusing on the decisions that reduce Over-Utilized hours. They will see that not a single one overlaps with a decision based on targeting the predicted number of ORs in use at some time in the future. Making predictive decisions on the day of surgery to avoid having more than a prespecified number of ORs in use is a statistically suboptimal approach to reducing the hours that anesthesiologists work late.


Name: Franklin Dexter, MD, PhD.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

Attestation: Franklin Dexter has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Conflicts of Interest: The University of Iowa, Department of Anesthesia, Division of Management Consulting, performs some of the analyses described in this article for hospitals. Franklin Dexter has tenure and receives no funds personally, including honoraria, other than his salary and allowable expense reimbursements from the University of Iowa. Income from the Division’s consulting work is used to fund Division research.

Name: Ruth E. Wachtel, PhD, MBA.

Contribution: This author helped write the manuscript.

Attestation: Ruth E. Wachtel has approved the final manuscript.

Conflicts of Interest: The author declares no conflicts of interest.

Name: Richard H. Epstein, MD, CPHIMS.

Contribution: This author helped design the study, conduct the study, and write the manuscript, and is the archival author.

Attestation: Richard H. Epstein has approved the final manuscript.

Conflicts of Interest: Richard H. Epstein is President of Medical Data Applications, Ltd., whose CalculatOR™ software (Atlantic City, NJ) includes some of the analyses used to perform the study. The University of Iowa pays licensing fees to use the software for hospital consultations performed by its Division of Management Consulting.

RECUSE NOTEDr. Franklin Dexter is the Statistical Editor for Anesthesia & Analgesia. This manuscript was handled by Dr. Steven L. Shafer, Editor-in-Chief, and Dr. Dexter was not involved in any way with the editorial process or decision.


Ms. Jennifer Espy of the University of Iowa’s Department of Anesthesia edited parts of the article.


a The lectures are available so readers not already very familiar with the scientific literature can determine the specific vocabulary words needed to find the article(s) relevant to a managerial question.10 It is (paradoxically) not possible to search the OR management literature successfully using PubMed without first knowing the corresponding vocabulary.10 Each set of slides also contains reference(s) so that citation pearl growing can alternatively be used to find relevant article(s).10
Cited Here


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2. Dexter F, Wachtel RE. Strategies for net cost reductions with the expanded role and expertise of anesthesiologists in the perioperative surgical home. Anesth Analg. 2014;118:1062–71
3. Abouleish AE, Prough DS, Whitten CW, Zornow MH, Lockhart A, Conlay LA, Abate JJ. Comparing clinical productivity of anesthesiology groups. Anesthesiology. 2002;97:608–15
4. 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 U.S. community hospitals. Health Care Manag Sci. 2006;9:325–39
5. Berry M, Berry-Stölzle T, Schleppers A. Operating room management and operating room productivity: the case of Germany. Health Care Manag Sci. 2008;11:228–39
6. Dexter F, Dutton RP, Kordylewski H, Epstein RH. Anesthesia workload nationally during regular workdays and weekends. Anesth Analg. 2015;121:1600–3
7. Marcon E, Dexter F. An observational study of surgeons’ sequencing of cases and its impact on postanesthesia care unit and holding area staffing requirements at hospitals. Anesth Analg. 2007;105:119–26
8. Dexter F, Epstein RH. Typical savings from each minute reduction in tardy first case of the day starts. Anesth Analg. 2009;108:1262–7
9. 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
10. Wachtel RE, Dexter F. Difficulties and challenges associated with literature searches in operating room management, complete with recommendations. Anesth Analg. 2013;117:1460–79
11. Strum DP, Vargas LG, May JH, Bashein G. Surgical suite utilization and capacity planning: a minimal cost analysis model. J Med Syst. 1997;21:309–22
12. 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: a tutorial using data from an Australian hospital. Anesth Analg. 2006;103:1499–516
13. Pandit JJ, Dexter F. Lack of sensitivity of staffing for 8-hour sessions to standard deviation in daily actual hours of operating room time used for surgeons with long queues. Anesth Analg. 2009;108:1910–5
14. Dexter F, Traub RD. How to schedule elective surgical cases into specific operating rooms to maximize the efficiency of use of operating room time. Anesth Analg. 2002;94:933–42
15. Dexter F, Epstein RH, Marsh HM. A statistical analysis of weekday operating room anesthesia group staffing costs at nine independently managed surgical suites. Anesth Analg. 2001;92:1493–8
16. Dexter F, Epstein RH. Optimizing second shift OR staffing. AORN J. 2003;77:825–30
17. Dexter F, Traub RD. Determining staffing requirements for a second shift of anesthetists by graphical analysis of data from operating room information systems. AANA J. 2000;68:31–6
18. Wachtel RE, Dexter F. Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg. 2009;108:1889–901
19. Kynes JM, Schildcrout JS, Hickson GB, Pichert JW, Han X, Ehrenfeld JM, Westlake MW, Catron T, Jacques PS. An analysis of risk factors for patient complaints about ambulatory anesthesiology care. Anesth Analg. 2013;116:1325–32
20. Dexter F, Epstein RH, Bayman EO, Ledolter J. Estimating surgical case durations and making comparisons among facilities: identifying facilities with lower anesthesia professional fees. Anesth Analg. 2013;116:1103–15
21. Dexter F, Ledolter J, Tiwari V, Epstein RH. Value of a scheduled duration quantified in terms of equivalent numbers of historical cases. Anesth Analg. 2013;117:204–9
22. Dexter F, Dexter EU, Ledolter J. Influence of procedure classification on process variability and parameter uncertainty of surgical case durations. Anesth Analg. 2010;110:1155–63
23. Dexter F, Epstein RH. Associated roles of perioperative medical directors and anesthesia—hospital agreements for operating room management. Anesth Analg. 2015;121:1469–78
24. He B, Dexter F, Macario A, Zenios S. The timing of staffing decisions in hospital operating rooms: incorporating workload heterogeneity into the newsvendor problem. Manuf Serv Oper Manag. 2012;14:99–114
25. Dexter F, Macario A. Changing allocations of operating room time from a system based on historical utilization to one where the aim is to schedule as many surgical cases as possible. Anesth Analg. 2002;94:1272–9
26. Dexter F, Maxbauer T, Stout C, Archbold L, Epstein RH. Relative influence on total cancelled operating room time from patients who are inpatients or outpatients preoperatively. Anesth Analg. 2014;118:1072–80
27. Epstein RH, Dexter F. Management implications for the perioperative surgical home related to inpatient case cancellations and add-on case scheduling on the day of surgery. Anesth Analg. 2015;121:206–18
28. de Oliveira Filho GR, Dal Mago AJ, Garcia JH, Goldschmidt R. An instrument designed for faculty supervision evaluation by anesthesia residents and its psychometric properties. Anesth Analg. 2008;107:1316–22
29. De Oliveira GS Jr, Rahmani R, Fitzgerald PC, Chang R, McCarthy RJ. The association between frequency of self-reported medical errors and anesthesia trainee supervision: a survey of United States anesthesiology residents-in-training. Anesth Analg. 2013;116:892–7
30. Hindman BJ, Dexter F, Kreiter CD, Wachtel RE. Determinants, associations, and psychometric properties of resident assessments of anesthesiologist operating room supervision. Anesth Analg. 2013;116:1342–51
31. Dexter F, Logvinov II, Brull SJ. Anesthesiology residents’ and nurse anesthetists’ perceptions of effective clinical faculty supervision by anesthesiologists. Anesth Analg. 2013;116:1352–5
32. Dexter F, Ledolter J, Smith TC, Griffiths D, Hindman BJ. Influence of provider type (nurse anesthetist or resident physician), staff assignments, and other covariates on daily evaluations of anesthesiologists’ quality of supervision. Anesth Analg. 2014;119:670–8
33. Hindman BJ, Dexter F, Smith TC. Anesthesia residents’ global (departmental) evaluation of faculty anesthesiologists’ supervision can be less than their average evaluations of individual anesthesiologists. Anesth Analg. 2015;120:204–8
34. De Oliveira GS Jr, Dexter F, Bialek JM, McCarthy RJ. Reliability and validity of assessing subspecialty level of faculty anesthesiologists’ supervision of anesthesiology residents. Anesth Analg. 2015;120:209–13
35. Dexter F, Masursky D, Hindman BJ. Reliability and validity of the anesthesiologist supervision instrument when certified registered nurse anesthetists provide scores. Anesth Analg. 2015;120:214–9
36. Dexter F, Hindman BJ. Quality of supervision as an independent contributor to an anesthesiologist’s individual clinical value. Anesth Analg. 2015;121:507–13
37. Dexter F, Macario A, Lubarsky DA, Burns DD. Statistical method to evaluate management strategies to decrease variability in operating room utilization: application of linear statistical modeling and Monte Carlo simulation to operating room management. Anesthesiology. 1999;91:262–74
38. Dexter F, Epstein RHKaye AD, Fox CJ, Urman RD. Influence of staffing and scheduling on operating room productivity. In: Operating Room Leadership and Management. 2012 Cambridge, United Kingdom Cambridge University Press:46–66
39. Tessler MJ, Kleiman SJ, Huberman MM. A “zero tolerance for overtime” increases surgical per case costs. Can J Anaesth. 1997;44:1036–41
40. Stepaniak PS, Mannaerts GH, de Quelerij M, de Vries G. The effect of the Operating Room Coordinator’s risk appreciation on operating room efficiency. Anesth Analg. 2009;108:1249–56
41. Epstein RH, Dexter F. Rescheduling of previously cancelled surgical cases does not increase variability in operating room workload when cases are scheduled based on maximizing efficiency of use of operating room time. Anesth Analg. 2013;117:995–1002
42. Epstein RH, Dexter F. Management implications for the perioperative surgical home related to inpatient case cancellations and add-on case scheduling on the day of surgery. Anesth Analg. 2015;121:206–18
43. Masursky D, Dexter F, O’Leary CE, Applegeet C, Nussmeier NA. Long-term forecasting of anesthesia workload in operating rooms from changes in a hospital’s local population can be inaccurate. Anesth Analg. 2008;106:1223–31
44. Dexter F, Masursky D, Ledolter J, Wachtel RE, Smallman B. Monitoring changes in individual surgeon’s workloads using anesthesia data. Can J Anaesth. 2012;59:571–7
45. 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
46. 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
47. Dexter F, Shi P, Epstein RH. Descriptive study of case scheduling and cancellations within 1 week of the day of surgery. Anesth Analg. 2012;115:1188–95
48. Dexter F, Wachtel RE, Todd MM, Hindman BJ. The “fourth mission:” the time commitment of anesthesiology faculty for management is comparable to their time commitments to education, research, and indirect patient care. AA Case Rep. 2015;5:206–11
49. Paoletti X, Marty J. Consequences of running more operating theatres than anaesthetists to staff them: a stochastic simulation study. Br J Anaesth. 2007;98:462–9
50. Dexter F, Macario A. When to release allocated operating room time to increase operating room efficiency. Anesth Analg. 2004;98:758–62
51. Dexter F, Traub RD, Macario A. How to release allocated operating room time to increase efficiency: predicting which surgical service will have the most underutilized operating room time. Anesth Analg. 2003;96:507–12
52. Dexter F, Epstein RH, Elgart RL, Ledolter J. Forecasting and perception of average and latest hours worked by on-call anesthesiologists. Anesth Analg. 2009;109:1246–52
53. Wang J, Dexter F, Yang K. A behavioral study of daily mean turnover times and first case of the day start tardiness. Anesth Analg. 2013;116:1333–41
54. Dexter F, Epstein RH, Marcon E, Ledolter J. Estimating the incidence of prolonged turnover times and delays by time of day. Anesthesiology. 2005;102:1242–8
55. Dexter F, Macario A, Traub RD. Which algorithm for scheduling add-on elective cases maximizes operating room utilization? Use of bin packing algorithms and fuzzy constraints in operating room management. Anesthesiology. 1999;91:1491–500
56. Van Houdenhoven M, van Oostrum JM, Hans EW, Wullink G, Kazemier G. Improving operating room efficiency by applying bin-packing and portfolio techniques to surgical case scheduling. Anesth Analg. 2007;105:707–14
57. Dexter F. A strategy to decide whether to move the last case of the day in an operating room to another empty operating room to decrease overtime labor costs. Anesth Analg. 2000;91:925–8
58. Dexter F, Smith TC, Tatman DJ, Macario A. Physicians’ perceptions of minimum time that should be saved to move a surgical case from one operating room to another: internet-based survey of the Association of Anesthesia Clinical Directors’ (AACD) members. J Clin Anesth. 2003;15:206–10
59. Tiwari V, Dexter F, Rothman BS, Ehrenfeld JM, Epstein RH. Explanation for the near-constant mean time remaining in surgical cases exceeding their estimated duration, necessary for appropriate display on electronic white boards. Anesth Analg. 2013;117:487–93
60. Stepaniak PS, Dexter F. Monitoring anesthesiologists’ and anesthesiology departments’ managerial performance. Anesth Analg. 2013;116:1198–200
61. Dexter F, Epstein RH, Lee JD, Ledolter J. Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and “instant messaging” updates from anesthesia providers. Anesth Analg. 2009;108:929–40
62. Dexter F, Ledolter J. Bayesian prediction bounds and comparisons of operating room times even for procedures with few or no historic data. Anesthesiology. 2005;103:1259–167
63. Zhou J, Dexter F. Method to assist in the scheduling of add-on surgical cases—upper prediction bounds for surgical case durations based on the log-normal distribution. Anesthesiology. 1998;89:1228–32
64. Dexter F, Yue JC, Dow AJ. Predicting anesthesia times for diagnostic and interventional radiological procedures. Anesth Analg. 2006;102:1491–500
65. Epstein RH, Dexter F. Mediated interruptions of anaesthesia providers using predictions of workload from anaesthesia information management system data. Anaesth Intensive Care. 2012;40:803–12
66. Xiao Y, Jones A, Zhang BB, Bennett M, Mears SC, Mabrey JD, Kennerly D. Team consistency and occurrences of prolonged operative time, prolonged hospital stay, and hospital readmission: a retrospective analysis. World J Surg. 2015;39:890–6
67. Dexter F, Bayman EO, Epstein RH. Statistical modeling of average and variability of time to extubation for meta-analysis comparing desflurane to sevoflurane. Anesth Analg. 2010;110:570–80
68. Masursky D, Dexter F, Kwakye MO, Smallman B. Measure to quantify the influence of time from end of surgery to tracheal extubation on operating room workflow. Anesth Analg. 2012;115:402–6
69. Epstein RH, Dexter F, Brull SJ. Cohort study of cases with prolonged tracheal extubation times to examine the relationship with duration of workday. Can J Anaesth. 2013;60:1070–6
70. Dexter F, Epstein RH. Increased mean time from end of surgery to operating room exit in a historical cohort of cases with prolonged time to extubation. Anesth Analg. 2013;117:1453–9
71. Dexter F, Coffin S, Tinker JH. Decreases in anesthesia-controlled time cannot permit one additional surgical operation to be reliably scheduled during the workday. Anesth Analg. 1995;81:1263–8
72. Abouleish AE, Dexter F, Whitten CW, Zavaleta JR, Prough DS. Quantifying net staffing costs due to longer-than-average surgical case durations. Anesthesiology. 2004;100:403–12
73. Silber JH, Rosenbaum PR, Zhang X, Even-Shoshan O. Estimating anesthesia and surgical procedure times from medicare anesthesia claims. Anesthesiology. 2007;106:346–55
74. 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
75. Kodali BS, Kim KD, Flanagan H, Ehrenfeld JM, Urman RD. Variability of subspecialty-specific anesthesia-controlled times at two academic institutions. J Med Syst. 2014;38:11
76. Epstein RH, Dexter F. Influence of supervision ratios by anesthesiologists on first-case starts and critical portions of anesthetics. Anesthesiology. 2012;116:683–91
77. Dexter F, Abouleish AE, Epstein RH, Whitten CW, Lubarsky DA. Use of operating room information system data to predict the impact of reducing turnover times on staffing costs. Anesth Analg. 2003;97:1119–26
78. Urman RD, Sarin P, Mitani A, Philip B, Eappen S. Presence of anesthesia resident trainees in day surgery unit has mixed effects on operating room efficiency measures. Ochsner J. 2012;12:25–9
79. Molina-Pariente JM, Fernandez-Viagas V, Framinan JM. Integrated operating room planning and scheduling problem with assistant surgeon dependent surgery durations. Comput Ind Eng. 2015;82:8–20
80. Bayman EO, Dexter F, Laur JJ, Wachtel RE. National incidence of use of monitored anesthesia care. Anesth Analg. 2011;113:165–9
81. Dexter F, Traub RD. Sequencing cases in the operating room: predicting whether one surgical case will last longer than another. Anesth Analg. 2000;90:975–9
82. Dexter F, Xiao Y, Dow AJ, Strader MM, Ho D, Wachtel RE. Coordination of appointments for anesthesia care outside of operating rooms using an enterprise-wide scheduling system. Anesth Analg. 2007;105:1701–10
83. Lubarsky DA, Reves JG. Effect of subspecialty organization of an academic department of anesthesiology on faculty perceptions of the workplace. J Am Coll Surg. 2005;201:434–7
84. Dexter F, Wachtel RE, Epstein RH, Ledolter J, Todd MM. Analysis of operating room allocations to optimize scheduling of specialty rotations for anesthesia trainees. Anesth Analg. 2010;111:520–4
85. Chelly JE, Horne JL, Hudson ME, Williams JP. Factors impacting on-time transfer to the operating room in patients undergoing peripheral nerve blocks in the preoperative area. J Clin Anesth. 2010;22:115–21
86. Moore IC, Strum DP, Vargas LG, Thomson DJ. Observations on surgical demand time series: detection and resolution of holiday variance. Anesthesiology. 2008;109:408–16
87. Wachtel RE, Dexter F. A simple method for deciding when patients should be ready on the day of surgery without procedure-specific data. Anesth Analg. 2007;105:127–40
88. Tiwari V, Furman WR, Sandberg WS. Predicting case volume from the accumulating elective operating room schedule facilitates staffing improvements. Anesthesiology. 2014;121:171–83
89. Dexter F, Traub RD. The lack of systematic month-to-month variation over one-year periods in ambulatory surgery caseload—application to anesthesia staffing. Anesth Analg. 2000;91:1426–30
90. Mariano ER, Chu LF, Ramamoorthy C, Macario A. Scheduling elective pediatric procedures that require anesthesia: the perspective of parents. Anesth Analg. 2006;103:1426–31
91. O’Neill L, Dexter F. Tactical increases in operating room block time based on financial data and market growth estimates from data envelopment analysis. Anesth Analg. 2007;104:355–68
92. Dexter F, Ledolter J, Wachtel RE. Tactical decision making for selective expansion of operating room resources incorporating financial criteria and uncertainty in subspecialties’ future workloads. Anesth Analg. 2005;100:1425–32
93. Wachtel RE, Dexter F. Tactical increases in operating room block time for capacity planning should not be based on utilization. Anesth Analg. 2008;106:215–26
94. Sulecki L, Dexter F, Zura A, Saager L, Epstein RH. Lack of value of scheduling processes to move cases from a heavily used main campus to other facilities within a health care system. Anesth Analg. 2012;115:395–401
95. Dexter F, Macario A, O’Neill L. Scheduling surgical cases into overflow block time—computer simulation of the effects of scheduling strategies on operating room labor costs. Anesth Analg. 2000;90:980–8
96. Lehtonen JM, Torkki P, Peltokorpi A, Moilanen T. Increasing operating room productivity by duration categories and a newsvendor model. Int J Health Care Qual Assur. 2013;26:80–92
97. Dexter F, Marco AP. Rationale for anesthesia groups to run additional flexible operating rooms for multiple surgeons who have scheduled more than 8 hours of cases. Anesth Analg. 2011;113:1295–7
98. 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
99. Lehtonen JM, Kujala J, Kouri J, Hippelainen M. Cardiac surgery productivity and throughput improvements. Int J Health Care Qual Assur. 2007;20:40–52
100. Dexter F, Epstein RH. Calculating institutional support that benefits both the anesthesia group and hospital. Anesth Analg. 2008;106:544–53
101. Ernst C, Szczesny A, Soderstrom N, Siegmund F, Schleppers A. Success of commonly used operating room management tools in reducing tardiness of first case of the day starts: evidence from German hospitals. Anesth Analg. 2012;115:671–7
102. van Veen-Berkx E, Elkhuizen SG, Kalkman CJ, Buhre WF, Kazemier G. Successful interventions to reduce first-case tardiness in Dutch university medical centers: results of a nationwide operating room benchmark study. Am J Surg. 2014;207:949–59
103. Wright JG, Roche A, Khoury AE. Improving on-time surgical starts in an operating room. Can J Surg. 2010;53:167–70
104. Panni MK, Shah SJ, Chavarro C, Rawl M, Wojnarwsky PK, Panni JK. Improving operating room first start efficiency—value of both checklist and a pre-operative facilitator. Acta Anaesthesiol Scand. 2013;57:1118–23
105. Scalea TM, Carco D, Reece M, Fouche YL, Pollak AN, Nagarkatti SS. Effect of a novel financial incentive program on operating room efficiency. JAMA Surg. 2014;149:920–4
106. Dexter F, Macario A, Ledolter J. Identification of systematic under-estimation (bias) of case durations during case scheduling would not markedly reduce over-utilized operating room time. J Clin Anesth. 2007;19:198–203
107. 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
108. Wachtel RE, Dexter F. Reducing tardiness from scheduled start times by making adjustments to the operating room schedule. Anesth Analg. 2009;108:1902–9
109. Marjamaa RA, Torkki PM, Hirvensalo EJ, Kirvelä OA. What is the best workflow for an operating room? A simulation study of five scenarios. Health Care Manag Sci. 2009;12:142–6
110. Mulier JP, De Boeck L, Meulders M, Beliën J, Colpaert J, Sels A. Factors determining the smooth flow and the non-operative time in a one-induction room to one-operating room setting. J Eval Clin Pract. 2015;21:205–14
111. Austin TM, Lam HV, Shin NS, Daily BJ, Dunn PF, Sandberg WS. Elective change of surgeon during the OR day has an operationally negligible impact on turnover time. J Clin Anesth. 2014;26:343–9
112. Torkki PM, Marjamaa RA, Torkki MI, Kallio PE, Kirvelä OA. Use of anesthesia induction rooms can increase the number of urgent orthopedic cases completed within 7 hours. Anesthesiology. 2005;103:401–5
113. Hanss R, Buttgereit B, Tonner PH, Bein B, Schleppers A, Steinfath M, Scholz J, Bauer M. Overlapping induction of anesthesia: an analysis of benefits and costs. Anesthesiology. 2005;103:391–400
114. Shi P, Dexter F, Epstein RH. Comparing policies for case scheduling within 1 day of surgery by Markov chain models. Anesth Analg. 2016;122:526–38
115. Dexter F, Marcon E, Aker J, Epstein RH. Numbers of simultaneous turnovers calculated from anesthesia or operating room information management system data. Anesth Analg. 2009;109:900–5
116. Smallman B, Dexter F, Masursky D, Li F, Gorji R, George D, Epstein RH. Role of communication systems in coordinating supervising anesthesiologists’ activities outside of operating rooms. Anesth Analg. 2013;116:898–903
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