Anesthesia group and operating room (OR) managers must plan staffing months in advance, despite uncertainty in future daily demand for OR time. Costs are increased by both excess staffing, resulting in underutilized OR time, and by insufficient staffing, resulting in overutilized OR time.1 Balancing the underutilized and overutilized OR time optimizes the efficiency of use of OR time.1 Typically, resulting reductions in anesthesia group costs exceed 10%.2,3 Issues understood for application of the methods include:
- Types of data to be analyzed (e.g., either OR or anesthesia information management system data can be used),4
- Number of months of data to be analyzed (e.g., 9 months is often suitable),5
- Constraints from numbers of first-case starts (e.g., facility may be using more than needed for the cases for surgeon and patient preference),6–8
- Inclusion in anesthesia-hospital agreements (e.g., to assure anesthesia group does not have large increase in late workday cases because of an increase in scheduled gaps between successive cases in ORs),9–11
- Influence on appropriate staff scheduling (e.g., for resident anesthesiologists),12 and
- Influence of nursing directors, computer-human interface, and education on the methods' use (e.g., due to psychological biases).13–15
Issues of statistical assumptions that are understood include:
- Methods' typical insensitivity to the choice of probability distribution,1,16,17 and
- Insensitivity to method of calculation of turnover times,18 predictive error in case durations,9,19,20 surgeon blocks,9,20 and seasonal variation in workload.21
However, all the references, cited above, limited consideration to individual facilities (i.e., each facility independently planned staffing and performed case scheduling). Findings may not apply to health systems with the potential to move cases among the various affiliated hospitals.
Consider a health system with surgeons who practice at multiple hospitals and ambulatory surgery centers. The main campus' ORs have nearly 8 hours of cases, including turnovers, per OR per workday.22 The other (regional) facilities have substantial underutilized time. A surgeon wants to do one 3 hour case at the main campus, with an afternoon start. The anesthesia group's OR director could use the health systems' common OR information system to examine the surgeons' schedules at all facilities. If the surgeon is working at least 1 day at another facility that week, the case could possibly be performed in available time at the other facility. Moving the case would depend on many constraints such as appropriateness of the procedure for the alternative facility or credentialing of the surgeon at the facility.23–25 Nevertheless, the surgeon and patient would benefit, because the expected (mean) delay of the actual start time from the scheduled start time (i.e., tardiness) at the main campus would be considerable. Expected tardiness results from the surgeon's case following preceding cases in the OR and a low likelihood that her case will be moved to another OR to reduce tardiness, given that all the ORs typically are full.6 If the anesthesia group and scheduling office were able to find a suitable alternative slot for the case at another facility on the requested day or on a different day, they would be increasing the surgeons' productivity.
Although such a coordinated scheduling process seems beneficial in concept, there are practical and strategic limitations. First, central scheduling requires software, personnel expenses, and substantial time of OR managers. Second, routine use of the process would influence the calculations to optimize staffing. If the case were performed at a regional facility, the probability distributions of measured OR workloads at the main campus and the regional facility would both be altered. Thus, the observed distributions would no longer provide an unbiased estimate of the surgeons' preferred total OR workload at each facility.
For the effort to be worthwhile financially, the costs of implementation would need to be offset substantially by increases in productivity. Many hours of cases would need to be moved from the main campus. In this study, we quantify the percentage of OR hours that can practically be offloaded from a main campus with long duration workdays.
The investigation evaluated a health system with a main campus having many ORs full for nearly 8 hours, multiple regional facilities with low workload per OR per day and a common OR information system. The information system was not used for coordination of surgical scheduling among facilities during the study interval.
We use the word list to refer to a series of scheduled cases performed by the same surgeon on the same day and in the same OR. Table 2 of Ref. 6 indicates that, at a previously studied hospital, lists of cases starting after the first case of the day accounted for a mean ± SE of 19% ± 1% of OR hours including turnover times (n = 26 four-week periods).6 The SEs were calculated6 using the method of batch means,26 as previously validated for OR management studies.27–30 The lists had mean 3.3 hours of OR time, including turnover times. The range of durations of lists would be large because there was no arbitrary limit on the list duration (e.g., some lists would be >8 hour).
In the current study, we limited consideration to lists <4 hours, because 4 hours was necessarily longer than the mean of 3.3 hours but was sufficiently brief that, typically, such a surgeon could follow another in the same OR. Because some turnover times and longer lists were excluded, we hypothesized that 15% or fewer OR hours would be accounted for by surgeons with <4 hours of cases and no first-case start, as compared with the previously6 observed 19% of OR hours. In a previous study, we considered a 3.6% difference to be managerially significant when evaluating strategies to reduce cancellation rates.31 During the editorial review process for that paper,31 feedback provided was that the 3.6% value was too small. Therefore, we considered a priori that if fewer than 5.0% of cases could be moved, this would be sufficiently small that scheduling processes to move cases among facilities would not be implemented (see Discussion). Using Student's one-group t-test, to achieve α = 0.05 and β = 0.10 for detection of 5.0% with a SE of 5%, the necessary sample size was N = 13 four-week periods.
The health care system studied had 15 regional facilities sharing the same OR information system and all located within a 1-hour drive from the main campus (Table 1 column 2). All OR anesthetics performed in these facilities between August 30, 2010, through August 26, 2011 were studied (i.e., N = 13 four-week periods). Two facilities were excluded because they opened during the study interval (i.e., N <13 periods). Pain management cases were excluded because we could not determine reliably if an anesthesiologist was present to provide sedation or was there to perform the procedure. Nonelective (urgent) cases were excluded, as were cases performed on weekends and holidays. The number of remaining cases per day was tallied. Days with unusually low caseloads (e.g., US federal holidays) were excluded, identified as having a caseload <2 standard deviations below the facility's mean.
The count of ORs in use at each facility at each 30-minute time mark was totaled (e.g., 10:00 AM and 10:30 AM). The maximum number of ORs in use was determined for each combination of facility and workday. The 80th percentile was calculated for each combination of facility and 4-week period. The mean was taken among 4-week periods, giving the number of ORs (anesthetizing locations) in use simultaneously (Table 1, column 3). No standard errors are listed for this value because all the standard errors are (logically) negligibly small (≤0.1 ORs). The numbers of locations were used to find the mean hours of elective cases per workday per OR (Table 1 column 4).27,29
Two of the 13 regional facilities with data were excluded from further consideration because they had total hours of elective cases per OR per workday that exceeded 5.60 hours. The threshold of 5.60 hours was chosen because this is the breakeven point in which 2 specialties scheduling into the same shared OR time would result in the same inefficiency of use of OR time as each having its own 8 hour of allocated OR time.12,32,33 Applying this criterion left 11 facilities with sufficient underutilized time to be considered for cases to be moved from the main campus (Table 1). There was substantial flexibility in the availability of start times and dates of cases for the surgeons at each of these 11 studied regional facilities. Application of the methods described in the Introduction would have suggested reducing the number of staffed ORs at each of these 11 facilities due to their substantial underutilized OR time.12,32,33 All of the cases would have been performed on the same days as originally performed, and each of the surgeon's lists of cases in the same sequences; only the start times of some lists would have been different.3,8,9
The sum of OR hours was calculated for each surgeon-day combination for the 41,622 cases performed at the main campus. Each of the surgeon's daily sums was classified into 2 categories, <4 hour (see above) versus otherwise. For each 4-week period, the following ratio was calculated: total OR hours among surgeon-days with <4 hour of cases/total OR hours. The mean ± SE among 4-week periods is reported in Table 2, row 2. The calculations were repeated with respect to the number of cases and number of surgeons.34 For each 4-week period, the ratio by surgeon was number of surgeons having at least 1 day with <4 hours of cases/total number of surgeons.
Next, a subset of the surgeons at the main campus without first case starts was derived from the surgeon-day combinations with <4 hours of case. Each of these surgeon's daily sums was classified into 2 different categories: <4 hours and surgeon did not have a first case start versus otherwise. The new ratios displayed in Table 2, row 3 were calculated as for the row 2. A surgeon was considered to have a first case start if the case was scheduled to start within 1 hour of the start of the main campus OR workday of 7:30 AM. These are reliable on-time starts6, unlikely not to be used by the surgeon unless away (e.g., at a professional meeting).
Finally, each of the surgeon-day combinations of OR hours at the main campus was classified into 2 categories: <4 hour, the surgeon did not have a first case start, and the surgeon performed at least 1 case at any one of the 11 regional facilities between 6 days earlier and 6 days later versus otherwise. Hypothetically, the scheduling office could notify the surgeon or his/her designee that the surgeon is working at 1 of the relatively empty regional ORs and that the case could be performed at that site (see Introduction). Importantly, for the study, no consideration was made for patient preference,7,35–37 whether the patient had already been admitted to the main campus hospital, requirements for surgeon assistant(s), and/or suitability of the procedure for movement to the regional facility.23–24,25 Consequently, the calculated ratios in Table 2, row 4 deliberately overestimated practical percentage reductions in main campus' OR hours.
We had planned another subset of analyses with the addition of more (i.e., realistic) constraints on moving cases to regional facilities. However, the preceding study results were not as expected in the above power analysis, making additional study of the main campus data moot. We therefore performed a secondary study using different data from 21 facilities located in the US. The highly limited summary data were from assessments performed between 2008 and 2011. The purpose of this secondary study was to explore why there was a difference between surgeons' case scheduling at the main campus versus case scheduling at the facility used for the statistical power analysis.6
Along the horizontal axis of the Figure 1 was plotted the mean hours of cases including turnover times per anesthetizing location per 8-hour workday. The vast majority, but not all, of the anesthetizing locations studied would be ORs.
Along the vertical axis of the Figure 1 was plotted a ratio. The denominator was the number of anesthetizing locations per 2 weeks (i.e., daily number × 10). The numerator was the sum among surgeons of each surgeon's maximum number of 8-hour blocks per 2 weeks that the surgeon was forecasted always to fill.38
Positive correlation was expected in the Figure 1(see Discussion). A least-squares quadratic fit had residuals that were normally distributed (Shapiro-Wilk P = 0.23) and without correlation to the independent value along the horizontal axis. The Pearson (quadratic) r and its asymptotic SE were calculated.
The OR time was summed among surgeons without a first case start at the main campus that day, performing <4 hours of elective cases at the main campus that day, and doing at least 1 case at any of the regional facilities within the preceding or following week. This OR time was just 0.8% (95% CI, 0.7% to 0.8%) of the total OR time of the main campus, considerably less than the managerially important threshold of “≥5.0%” (P < 0.0001) (Table 2).
The principal reason for this result was that few (10%) OR hours at the main campus were used by surgeons performing <4 hours of cases that day (Table 2).a The green dotted line in the Figure 1 the fewer hours of cases per OR per workday at the facility used for the study design versus the blue dotted line with the hours observed from Table 1 for the main campus. Secondary assessment of 21 facilities nationwide showed that such larger hours of cases per OR per workday are associated with larger percentages of OR days for which single surgeons fill an OR for the day (r = 0.87 ± 0.05, Fig. 1).
Independent surgical case scheduling at each facility in a regional health system has been assumed in the extensive prior OR management literature for anesthesia staffing.1,3,5,8,16,18 Hypothetically, an active central scheduling office for a regional health system could work to move cases from a full facility to relatively empty facilities to increase the productivity of its surgeons. Our results suggest that such efforts are unlikely to increase either the efficiency of use of OR time or surgeon productivity. The maximum potential offloading of cases from the studied busy main campus was 0.8% of OR hours.
A limitation of this study is that it only includes data from a single health care system.b The main campus had many types of procedures (e.g., both extensive cardiac and cataract surgery practices), but the types potentially moveable to regional facilities were less physiologically complex (see footnote a in the Results and footnotes b and c in Table 2). Nevertheless, the value of 0.8% of OR hours that potentially could be moved is very small and overestimates what could be achievable in reality because patient and surgeon preferences,7,35–37 whether the patient had already been admitted to the main campus hospital, requirements for the presence of surgeon assistant(s), and/or suitability of the procedure at the regional facility23–25 were ignored.
We are unaware of studies comparing the effectiveness between: (1) OR schedulers looking among multiple days and facilities, seeking opportunities to increase OR and surgeon productivity, versus (2) just scheduling cases and looking 1 to 2 workdays ahead to coordinate staff assignment.41 Our findings suggest that the latter, relatively passive role in scheduling (i.e., addressing surgeons' concerns when asked), may be sufficient. At facilities that have many hours of cases per OR per workday (i.e., scheduling could limit OR, anesthesia, and surgeon productivity), the surgeons mostly fill ORs for the workday (i.e., there are few scheduling decisions to be made). At facilities that have few hours of cases per OR per workday, the surgeons are not filling ORs for the workday, and thus cases will typically be schedulable at whatever time the surgeon prefers. Thus, coordinating scheduling among the regional facilities would not be of value either.
In comparisons of many anesthesia groups, high productivity is obtained principally by having relatively long workdays per OR.42,43 The Figure 1 a correlate: High anesthesia productivity is obtained by having many ORs each filled with cases of a single surgeon. Two pieces of evidence suggest that high workload per OR per workday causes surgeons' scheduling behavior, not vice versa. First, at a hospital that was full, with little opportunity for a late starting surgeon to have his/her case moved to a different OR if the preceding cases run late, the only opportunity for having a brief mean (expected) tardiness (i.e., relatively high surgeon productivity) was to be assigned a first case start.6 Each increase in a surgeon's total hours of OR time was associated with the surgeon having an increased percentage rate of first case starts.6 That could not have been a negotiating position because the surgeons were allowed only to operate at the 1 hospital. Second, the 2 randomized trials of OR activities resulting in increased numbers of cases performed total (i.e., not just within 8 hours) both limited intervention to surgeons filling single ORs for the entire workday.44,45 Results were insensitive to how the workflow was increased.46 Yet, results were sensitive to there being 1 surgeon in each OR for the entire (long) workday.46
In conclusion, the economically most important anesthesia group and OR management decision is the choice made months before surgery of the allocated OR time (duration of the workday) for each service.1,3,9 Issues and reasons are summarized in the Introduction. Our results show that making this OR allocation decision for each facility independently is statistically reasonable. For many health systems, investing in the software and personnel to coordinate case scheduling among facilities is unlikely to be beneficial, either operationally or financially.
Franklin Dexter is the Statistical Editor and Section Editor for Economics, Education, and Policy for Anesthesia & Analgesia. This manuscript was handled by Steve Shafer, Editor-in-Chief, and Dr. Dexter was not involved in any way with the editorial process or decision.
Name: Louise Sulecki, BIE.
Contribution: This author helped design the study, conduct the study, and write the manuscript.
Attestation: Louise Sulecki approved the final manuscript.
Conflicts of Interest: The author has no conflicts of interest to declare.
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 approved the final manuscript.
Conflicts of Interest: The University of Iowa, Department of Anesthesia, Division of Management Consulting, performs the analyses described in this article for hospitals, including for some of the hospitals in this article. Franklin Dexter receives no funds personally from such activities. He has tenure and does not participate in any incentive programs. Income from the Division's consulting work is used to fund research.
Name: Andrew Zura, MD.
Contribution: This author helped conduct the study.
Attestation: Andrew Zura approved the final manuscript.
Conflicts of Interest: The author has no conflicts of interest to declare.
Name: Leif Saager, MD.
Contribution: This author helped write the manuscript.
Attestation: Leif Saager approved the final manuscript.
Conflicts of Interest: The author has no conflicts of interest to declare.
Name: Richard H. Epstein, MD, CPHIMS.
Contribution: This author helped design the study and write the manuscript.
Attestation: Richard Epstein approved the final manuscript.
Conflicts of Interest: Richard Epstein is President of Medical Data Applications, whose CalculatOR™ software includes some of the analyses used in this article. The University of Iowa pays licensing fees to use the software for hospital consultations performed by its Division of Management Consulting.
a The extra reduction from 10.0% to 0.8% OR hours was attributable to the types of procedures performed, based on the similarity of the relative distributions of types of procedures between cases of surgeons doing <4 hour of cases and either (a) having a first case start and/or no other case within a week at another facility or (b) either not having a first case start or case within a week at another facility. The similarity index is effectively a correlation coefficient (0 to 1), with standard errors calculated using Cramér's delta method.25 The similarity equals 0 when the types of procedures do not overlap and 1 when they match in equal relative proportions. The similarity between (a) and (b) was very small39,40 (0.15 ± 0.03, P < 0.0001 compared with 0.3). The cases were far from a random sample of those of all low workload surgeons at the main campus, because then the similarity index would have been high (>0.80).39,40 This was not an issue of case durations. Among surgeon days with <4 hours of OR time, the mean ± SE of case durations was 1.4 ± 0.02 hours. Among the subset of also not first case start and/or with a case at another facility within one week, durations were longer but negligibly so, 0.2 ± 0.04 hours (P = 0.0001 paired Student t-test among N = 13 periods). Durations averaged 3.1 ± 0.01 hours among all cases.
b A different health system, in a different US state, had a relatively full main (tertiary) surgical suite and ambulatory surgery center. The ambulatory center was being expanded and the question was what procedures could be targeted for movement. We performed a similar analysis. For each of 13 four-week periods, we calculated a ratio. The denominator was the total hours of cases. The numerator was the total hours among cases that were (1) elective, (2) patient hospital length of stay less than 24 hours, (3) surgeon performed <4 hours of cases in the main suite that day, and (4) surgeon did not have a first case start in the main suite that day. The mean ± SE equaled 2.2% ± 0.1%. Although the remaining cases accounted for a small overall percentage of workload, the 4 most common types of procedures by count were as expected: laparoscopic cholecystectomy, inguinal hernia repair, panendoscopy with flexible esophagoscopy, and unbilical hernia repair.
1. 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
2. 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
3. McIntosh C, Dexter F, Epstein RH. 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
4. Dexter F, Epstein RH. Optimizing second shift OR staffing. AORN J 2003;77:825–30
5. Epstein RH, Dexter F. Statistical power analysis to estimate how many months of data are required to identify operating room staffing solutions to reduce labor costs and increase productivity. Anesth Analg 2002;94:640–3
6. Wachtel RE, Dexter F. Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg 2009;108:1889–901
7. Dexter F, Birchansky L, Bernstein JM, Wachtel RE. Case scheduling preferences of one surgeon's cataract surgery patients. Anesth Analg 2009;108:579–82
8. 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
9. 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
10. Dexter F, Epstein RH. Calculating institutional support that benefits both the anesthesia group and hospital. Anesth Analg 2008;106:544–53
11. Wachtel RE, Dexter F. Reducing tardiness from scheduled start times by making adjustments to the operating room schedule. Anesth Analg 2009;108:1902–9
12. 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
13. Masursky D, Dexter F, Nussmeier NA. Operating room nursing directors' influence on anesthesia group operating room productivity. Anesth Analg 2008;107:1989–96
14. Wachtel RE, Dexter F. Review of behavioral operations experimental studies of newsvendor problems for operating room management. Anesth Analg 2010;110:1698–710
15. Wachtel RE, Dexter F. Curriculum providing cognitive knowledge and problem-solving skills for anesthesia systems-based practice. ACGME J Grad Med Educ 2010;2:624–32
16. 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
17. He PB, Dexter F, Macario A, Zenios S. The timing of staffing decisions in hospital operating rooms: incorporating workload heterogeneity into the newsvendor problem. Manuf Serv Op 2012;14:99–114
18. Epstein RH, Dexter F. Uncertainty in knowing the operating rooms in which cases were performed has little effect on operating room allocations or efficiency. Anesth Analg 2002;95:1726–30
19. 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
20. 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
21. 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
22. 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
23. Dexter F, Thompson E. Relative value guide basic units in operating room scheduling to ensure compliance with anesthesia group policies for surgical procedures performed at each anesthetizing location. AANA J 2001;69:120–3
24. Dexter F, Macario A, Penning DH, Chung P. Development of an appropriate list of surgical procedures of a specified maximum anesthetic complexity to be performed at a new ambulatory surgery facility. Anesth Analg 2002;95:78–82
25. Dexter F, Wachtel RE, Yue JC. Use of discharge abstract databases to differentiate among pediatric hospitals based on operative procedures—surgery in infants and young children in the State of Iowa. Anesthesiology 2003;99:480–7
26. Law AM, Kelton WD. Simulation Modeling and Analysis, 2nd ed. New York: McGraw-Hill, 1991:551–3
27. Dexter F, Macario A, Qian F, Traub RD. Forecasting surgical groups' total hours of elective cases for allocation of block time. Application of time series analysis to operating room management. Anesthesiology 1999;91:1501–8
28. 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
29. Dexter F, Macario A, Epstein RH, Ledolter J. Validity and usefulness of a method to monitor surgical services' average bias in scheduled case durations. Can J Anaesth 2005;52:935–9
30. 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
31. Tung A, Dexter F, Jakubczyk S, Glick DB. The limited value of sequencing cases based on their probability of cancellation. Anesth Analg 2010;111:749–56
32. Dexter F, Epstein RH. Typical savings from each minute reduction in tardy first case of the day starts. Anesth Analg 2009;108:1262–7
33. Dexter F, Wachtel RE, Epstein RH. Event-based knowledge elicitation of operating room management decision-making using scenarios adapted from information systems data. BMC Med Inform Decis Mak 2011;11:2
34. Masursky D, Dexter F, Isaacson SA, Nussmeier NA. Surgeons' and anesthesiologists' perceptions of turnover times. Anesth Analg 2011;112:440–4
35. Finlayson SRG, Birkmeyer JD, Tosteson AN, Nease RF Jr. Patient preferences for location of care: implications for regionalization. Med Care 1999;37:204–9
36. Schwartz LM, Woloshin S, Birkmeyer JD. How do elderly patients decide where to go for major surgery? Telephone interview survey. BMJ 2005;331:821
37. Schwappach DL, Strasmann TJ. Does location matter? A study of the public's preferences for surgical care provision. J Eval Clin Pract 2007;13:259–64
38. Dexter F, Macario A, Traub RD, Hopwood M, Lubarsky DA. An operating room scheduling strategy to maximize the use of operating room block time. Computer simulation of patient scheduling and survey of patients' preferences for surgical waiting time. Anesth Analg 1999;89:7–20
39. Wachtel RE, Dexter EU, Dexter F. Application of a similarity index to state discharge abstract data to identify opportunities for growth of surgical and anesthesia practices. Anesth Analg 2007;104:1157–70
40. Wachtel RE, Dexter F, Barry B, Applegeet C. Use of state discharge abstract data to identify hospitals performing similar types of operative procedures. Anesth Analg 2010;110:1146–54
41. 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
42. 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
43. 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
44. Sandberg WS, Daily B, Egan M, Stahl JE, Goldman JM, Wiklund RA, Rattner D. Deliberate perioperative systems design improves operating room throughput. Anesthesiology 2005;103:406–18
45. Smith MP, Sandberg WS, Foss J, Massoli K, Kanda M, Barsoum W, Schubert A. High-throughput operating room system for joint arthroplasties durably outperforms routine processes. Anesthesiology 2008;109:25–35
46. 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