Facilities studied were all sites of the United States health system at which Surginet had been in use for at least N = 5 eight-week periods. We used eight-week periods since that duration achieved at least 50 scheduled cases for all such periods at all 21 facilities. Each facility had at least 1 cancellation for at least 4 of the 5 periods. We did not use >5 eight-week periods because doing so reduced the number of evaluable facilities. The facilities studied were from 6 states. Both hospitals and ambulatory surgery facilities were studied because our focus is scheduling (see Introduction), and, in health systems, cases can be scheduled systematically between a hospital and adjacent ambulatory facility.
Cases studied were those with scheduled dates that were nonholiday weekdays and that were performed at main (tertiary) surgical suites or ambulatory surgery centers. Anesthetics administered in other locations, such as the gastrointestinal endoscopy and the magnetic resonance imaging suites, were excluded. One facility performed electroconvulsive therapy procedures in ORs (for logistical reasons), and those cases were excluded.
Inpatient or outpatient preoperative status was determined from the American Hospital Association’s National Uniform Billing Committee UB-04 specification “Priority (Type) of Visit.”
Scheduled durations excluded turnover times, because assignments to specific ORs often were not made until the working day before surgery. Scheduled durations were calculated by Surginet as the facility’s trimmed mean OR time of the primary scheduled procedure.23–25 Specifically, when there were 10 or more cases, the briefest and longest were trimmed, and the mean of the remaining cases used. Otherwise, the mean was used.
As above, a case was considered cancelled if the date of surgery was changed or the case was not performed (i.e., not if only the scheduled OR and/or start time was changed). For example, if a patient with one case identifier had a procedure scheduled for one day, that record was cancelled, and the patient was rescheduled with a new case identifier for a different time on the same day, that was not considered a cancellation. Cases were considered as having been cancelled after 7:00 PM on the working day before surgery if the timestamp when the cancellation was posted was later than this time.
Each outpatient either attended a preoperative clinic or had a preoperative phone interview. Facilities used the same set of questions developed with anesthesiologists. Data collected included medications, allergies, presence of an advance directive, and preferred language. Anesthesia protocols such as fasting periods for solids and liquids were communicated to patients. However, anesthesiologists evaluated patients for the first time on the day of surgery. Among the 47,894 physical preoperative evaluations, the maximum professional level of the preadmission documenting provider was nurse practitioner 3.4%, registered nurse 93.4%, or nursing assistant including licensed practical nurse 3.1%. However, anesthesiologists and/or certified registered nurse anesthetists were available at all facilities for questions.
Hypothesis #1 was that at least half of cancelled minutes were attributable to inpatients. For each of the 5 eight-week periods, a percentage was calculated, as follows. The numerator was the total scheduled minutes among all facilities’ cancelled cases that were for inpatients. The denominator was the total scheduled minutes of cancelled cases. The mean and the SEM were calculated for the N = 5 percentages.18 To test “at least half,” the mean was compared to half (0.50) by using the 2-sided Student t test.16 All analyses were performed using batches of thousands of cases (Tables 1 and 2 and footnote b in Results section).10,15–17,26
Hypothesis #2 was that the cancellation rate among outpatients was insensitive to the preoperative clinic evaluation rate. In Figure 1, along the vertical axis was plotted the percentage of scheduled minutes that were cancelled among outpatients for each combination of facility and eight-week period. Along the horizontal axis was plotted the percentage of scheduled minutes among outpatients evaluated in a preoperative clinic within 8 weeks of the scheduled date of surgery. Linear mixed effects modeling was performed to test the association between the preoperative clinical evaluation rate and the cancellation rate while treating the facility as a random effect (i.e., compensating for the repeated measures). Since there were 21 facilities and 5 eight-week periods, there were 83 degrees of freedom, where 83 = 21 facilities × (5 periods –1 estimate per facility) – 1.
For one sensitivity analysis, both axes were changed to the number of cases instead of scheduled minutes. For another, the horizontal axis was changed to the percentage of scheduled minutes among outpatients evaluated in a preoperative clinic within 2 weeks of the scheduled date of surgery, rather than 8 weeks.
Academic Hospital, Hypothesis #3
Data from all cases scheduled to be performed between Sunday July 3, 2011, and Saturday, June 29, 2013, were retrieved from the OR case scheduling system (ORSOS®, McKesson, San Francisco, CA). We used a longer period (13 weeks) than for the health system because the data were available and we needed larger denominators to measure cancellation rates among the few outpatient cases scheduled only a few days before the day of surgery.
Cases studied were those with scheduled dates that were nonholiday weekdays. Cases performed in nonoperating room locations were excluded (e.g., endoscopy, labor and delivery, and electroconvulsive therapy).
Inpatient or outpatient preoperative status was determined from dates and times of hospital admission and case scheduling. A case was considered inpatient: (a) if the case was scheduled after the patient was admitted to the hospital, (b) if the case was scheduled 1 working day before the day of surgery and the patient was admitted to the hospital earlier than 4:00 AM on the day of surgery, or (c) if the case was scheduled on the day of surgery (e.g., cadaveric kidney transplant).
Scheduled durations were unbiased Bayesian estimates calculated for each case by using the surgeon’s (scheduler’s) estimated duration and the historical data of cases of the same scheduled procedure code(s) and surgeon.27–29
The original date that each case was first entered into the scheduling system was used to calculate the number of workdays between scheduling and the scheduled date of surgery, including when the case was cancelled and rescheduled for another date.10 Whereas primary scheduling tables were used to determine whether the case was performed, the audit table was the source of data on changes to the date of surgery (i.e., timestamps of the entire scheduling/rescheduling/cancellation history of each case). This table records all changes made to all cases including the name of the changed field, the date and time when the change was made, the previous value of the field before the change, and the new value after the change. For cases created on a Saturday or Sunday, the interval was calculated functionally as if the case had been entered on the previous Friday. Thus, pairs considered 1 workday were Sunday for Monday, Monday for Tuesday, …, Thursday for Friday, Friday for Monday, Saturday for Monday, and Sunday for Monday.
Hypothesis #3 was that most of the minutes of cancelled cases were attributable to the patients being inpatients preoperatively, not simply that cases had been scheduled within a few working days before surgery. For each combination of thirteen-week period and inpatient or outpatient, the cumulative probability distribution for scheduled minutes of cancelled cases was calculated as a function of the workdays before surgery that the case was scheduled. The inpatient distribution was then multiplied by the period’s overall fraction of scheduled minutes attributable to inpatient cases, and vice-versa. The result was that the far right of the cumulative distributions summed to 100% for the period (Fig. 2). Then, the N = 8 periods were combined. For each number of workdays ahead, the means and standard deviations (SDs) were taken among periods for the percentages of inpatients and of outpatients. Inpatient results for 0 and 1 workdays before surgery were compared to half (0.5) by using Student 1-group 2-sided t test, to test the hypothesis that “most” minutes of surgery were due to these intervals between the initial scheduling event and the date of surgery.
Nonacademic Health System, Hypothesis #1
Whereas among outpatients, 1.6% ± 0.1% of scheduled minutes were cancelled, among inpatients, the rate was 8.1% ± 0.4%.b Consequently, even though inpatients accounted for far less than half of the total scheduled minutes of surgery (16.2% ± 0.5%, P < 0.0001), they accounted for 49% ± 2% (P = 0.55 compared to 50%) of the total cancelled minutes. This rate included 5 facilities with no inpatients, making the estimate conservative. Among the 7 facilities (hospitals) with at least 20% of scheduled minutes (and cases) being for inpatients, inpatients also accounted for approximately half of the cancelled minutes (57% ± 3%, P = 0.062 compared with 50%).
Nonacademic Health System, Hypothesis #2
The patients having a physical preoperative clinic visit within 8 weeks of the scheduled date of surgery, rather than only receiving a detailed preoperative phone call (i.e., a “virtual” preoperative visit), accounted for 64% ± 4% of outpatients’ total scheduled minutes of surgery. Cancelled cases were 1.6% ± 0.1% of outpatients’ total scheduled minutes. There was no association between having a physical or virtual preoperative evaluation while controlling for facility and period (P = 0.58, Fig. 1). Each 10% increase in the percentage use of preoperative clinic was associated with a 0.0% ± 0.1% absolute decrease in cancelled minutes.
The clinic visit rate was 62% ± 4% of outpatient cases. The cancellation rate was 1.8% ± 0.1% of outpatient cases. There was no association between physical or virtual evaluation and cancellation rate (P = 0.22, 0.0% ± 0.1%).
A preoperative clinic visit within 2 weeks of the scheduled date of surgery was performed for 62% ± 3% of the scheduled minutes of outpatient surgery. There also was no association between physical or virtual visits with the cancellation rate of outpatients, measured using minutes (P = 0.53, 0.0% ± 0.1%).
Academic Hospital, Hypothesis #3
At the academic hospital, the overall cancellation rate, measured using scheduled minutes, was 12.1% ± 0.5%. The percentage of performed cases for which the patient was an inpatient was 22.3% ± 0.4%. Similar to the University of Pennsylvania,11 inpatient cases accounted for most of the total cancelled minutes (70% ± 2%, P < 0.0001, Fig. 2).
Slightly more than half of the total cancelled minutes were due to inpatient cases scheduled within 1 workday of surgery (55% ± 2%, P = 0.018). Important for intervention, slightly more than half the total inpatient cancelled minutes (54% ± 1%, P = 0.006) were due to cases scheduled within 1 workday of the day of surgery but before the day of surgery (e.g., Friday for Monday).
Figure 2 shows that outpatient cases scheduled soon before surgery accounted for so few of the total cancelled minutes (e.g., 1.0% ± 0.1% within 1 workday) that their cancellation rates were unimportant (Fig. 2). Nevertheless, using our long time series with many cases, we could determine that the inpatient cancellation rates, measured in minutes, were several-fold larger than outpatient rates regardless of the workdays in advance that the cases were scheduled (P < 0.0001, Fig. 3).
In this article, we made three observations about cancelled OR time, differentiating between patients who were admitted before surgery (“inpatients”) or not (“outpatients”). First, although most scheduled minutes of OR time were for outpatients, at least half of cancelled OR time was attributable to inpatients. At the two academic hospitals (Ref. 11 and Fig. 2), inpatients accounted for much more than half of cancelled time. Second, community hospitals and ambulatory surgery centers can achieve very low (≤ 2%) rates of cancellations among outpatients, even with many patients undergoing a virtual (phone) preoperative evaluation (Fig. 1). Third, the reason why inpatient cases have much higher cancellation rates than outpatient cases is not simply from the cases being scheduled within a couple of days of surgery, although this is how they are typically scheduled (Fig. 3).
Hypotheses #1 and #3
A limitation of our findings is that we do not know the percentage of the inpatient cancellations in our study that were medically preventable. Counting from qualitative reports is unreliable because many cancellations can be attributed to multiple categories of causes.14 For example, Tung et al.14 presented “an inpatient … scheduled for surgery on Monday but the procedure was … done urgently on the preceding Sunday.” The category of cause was surgeon, because the surgeon failed to cancel her Monday case after performing it on Sunday.14 The category was also hospital, because software and clerk failed to detect a duplicate case booking on Sunday and Monday.14 The category was, yet too, patient (medical), because of the change in the underlying disease prompting urgent surgery.14
Based on hip fracture patients,30 future studies should evaluate whether earlier patient evaluation reduces cancellations. Schiff et al.31 showed that during regular workdays, when preoperative clinics are open, many inpatients can be seen at some hospitals’ clinics. Specifically, use of the clinic significantly reduced anesthesia time for evaluation without reducing patient satisfaction.31 On nights and weekends, Dy et al.30 evaluated having an on-call medical diagnostic technologist (e.g., for echocardiogram) and hospitalist physician to conduct preoperative medical evaluations promptly for patients with hip fracture. The incremental cost (U.S. 2009) was small, only $2300 per quality adjusted life year.30
There is a likely explanation for the difference between our findings and those of the previous four observational studies that showed reduced cancellation rates with preoperative evaluation.19–22 The community hospitals’ outpatient cancellation rate was ≤2% (Fig. 1). At the Hospital of the University of Pennsylvania, it was 5%, with more than half the cancellations among patients not showing up on the day of surgery. Two hospitals have previously reported that the best predictor of outpatient cancellation is whether the patient shows up for a planned preoperative clinic visit.13,14 A substantial proportion of the benefit in reducing cancellations attributed in the 4 studies19–22 to the preoperative clinic may have been that they showed up for the clinic visit. These investigators likely could not have known this relationship because their 4 studies of preoperative clinics and cancellations19–22 predated understanding that no-shows in ORs are predicted by no-show at clinic.13,14 Each of the 4 studies compared cases between patients who attended or did not attend the clinic (i.e., patients were not randomized in an intention to “treat” design nor was propensity score modeling used to control for group assignment).19–22 Completion of an extensive preoperative virtual interview may achieve the same effect as attending clinic both to confirm the patient’s plan to have surgery and to learn of reasons the patient may not show (e.g., anticipated difficulty with transportation).
Our study is limited in not comparing a preoperative clinic visit to no evaluation but in comparing a physical clinic visit to a virtual visit (i.e., a detailed, completed phone call). Figure 1 suggests that when nurses complete nursing evaluations by phone with patients, and the nurses include discussion of advanced directive and postoperative home care, likely no-show patients are identified (e.g., patient relates being unaware that surgery has been scheduled). However, testing our explanation for the difference between our results and those of previous studies19–22 would be challenging. A suitable randomized trial would need to randomize at the level of facility, not patient.
Recently, in an editorial about our article on management of preoperative clinics,32 Hooper33 encouraged research in understanding the impact of the model of care delivery of preoperative assessment on outcome. Our results suggest that, as Hooper discussed, a model of principally registered nurses evaluating patients is sufficient for low (2%) cancellations when anesthesiologists are available for questions. Limitations to applying Figure 1 alone are that we have no data on patient morbidity or mortality, recovery, or delays on the day of surgery. Still, perhaps anesthesiology residents’ required rotations in preoperative evaluation should include work in being consultants to and/or leaders of nursing teams.
Another limitation of our study is that we have no data on the use of preoperative laboratory studies and consultations (i.e., both may be ordered more at the facilities with virtual [phone] evaluations). When anesthesiologists in preoperative clinics manage which laboratory studies are ordered, the incidence of ordering is less.34 When preoperative clinics are staffed by anesthesiologists, referrals are less20 (e.g., to family medicine physicians).35 Since nearly a quarter of patients undergoing low-risk surgery are sent for physician consultation preoperatively,35,36 likely some, or perhaps most, of the facilities that we studied have this practice. Without regional health system data, as available for Ontario, we would not know.35,36
Applying Results to Surgeon, Anesthesia Provider, and OR Productivity
From the low (2%) rates of cancellations among the outpatients, it cannot be that inpatient cases being scheduled within 1 workday before surgery routinely cause the cancellation of outpatient cases. This is rational because cancellation of outpatients increases net direct cost from patient, physician, hospital, and societal perspectives.37,38 Cost accrues from the rework of getting cases rescheduled,37,38 as 90% of cancelled procedures subsequently are performed at the hospital.10 Since the vast majority of outpatient cases are scheduled at least 1 week before surgery, it also cannot be that a cancelled inpatient case can be replaced by a newly scheduled outpatient case. Consequently, revenue (production) of surgeons and anesthesiologists cannot be increased by reducing cancellations. This result from Figure 2 is new.
We previously showed that the costs of cancellation for the OR, surgeons, and anesthesia providers are exquisitely sensitive to whether the scheduling office working 0 to 2 workdays before surgery makes decisions to reduce the hours of over-utilized OR time.2,10 OR time is over-utilized if it exceeds that which has been forecasted based on minimizing a weighted combination of under-utilized and over-utilized time.2,10 Suppose that a hospital is making decisions soon before surgery based on these statistical forecasts. Then, cancellations cause little or no increase in anesthesia, surgeon, and/or OR nursing labor costs or variability in work hours. There are two reasons. First, most cases that both are cancelled and rescheduled for later days are rescheduled to days that surgeons have slightly less than their median workload (P = 0.022).10 Second, the cancellations themselves slightly reduce variability in surgical services’ hours of cases among days (P < 0.0001).2 The reason is that the closer one is to the day of surgery, the more the knowledge of the expected hours of under-utilized and over-utilized OR time in each OR.,2,39–43 Schedulers can (and in practice do) fully fill but not exceed the allocated hours for the specific OR into which the case is placed. This information was known before the current study.
Suppose that, in contrast, a hospital is not making decisions based on reducing the hours of over-utilized hours of OR time. Then, a large cost is being realized from the inpatient cases in Figure 2. Again, hours are over-utilized only when the allocated hours have been calculated (including all cases performed such as those of the inpatients) based on minimizing the optimal weighted combination of under-utilized (empty) and over-utilized hours (i.e., the allocated hours are not that set by a committee).5–9,44 Trained and untrained managers perform poorly at this optimization problem without applying the mathematics.45 The value of education is to provide the vocabulary so that leaders can search the evidence-based management literature for the appropriate statistical methods to implement.46 The value of education also is to increase leaders’ trust in the use of statistical methods and skill at evaluating when a recommendation may be based on incomplete information.46–48 If a hospital treats the OR schedule as of 4 PM the workday before surgery as what the day of surgery will be, rather than relying on statistical forecasts, then by definition, cancellations increase variability (predictive error from the schedule) because the schedule has changed. Given the (new) findings of Figure 2, we urge hospitals to provide statistical information to the anesthesia providers, nurses, etc., on end of the day times and for that information to include probabilities of cancellations and add-on cases (i.e., not to treat the OR schedule as literally what will happen).43 Unless cancellation rates are unusually high, management should be evaluated not based on those rates, but on the quality of the scheduling offices’ decisions based on these disruptions to the schedules.4 This is especially important when considering (the new finding) that at least half of the cancelled time is from inpatients. It is unclear to us whether preventing these cancellations would benefit the patients (e.g., not cancelling a patient who developed new onset atrial fibrillation on the morning of surgery).
Facilities can achieve a ≤2% cancellation rate for patients who are outpatient preoperatively with very few attending a preoperative clinic, provided a virtual evaluation is carried out by phone. At least half the cancelled time at health systems and hospitals is due to inpatients, and these patients principally are scheduled within 1 workday of the day of surgery. This is why there are so many changes to the OR schedule within 1 workday of the day of surgery. Hospitals should evaluate the cost-effectiveness of earlier assessments of inpatients. In addition, scheduling office decision-making within 1 workday before surgery should be based on statistical forecasts that include the risks of cancellation and of inpatient add-on cases being scheduled. Hospitals should monitor the performance of their perioperative managers with respect to such behavior.
Dr. Franklin Dexter is the Statistical Editor and Section Editor for Economics, Education, and Policy for the Journal. This article 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.
Name: Franklin Dexter, MD, PhD.
Contribution: This author helped design and 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 some of the analyses described in this article for hospitals. He 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 research.
Name: Tina Maxbauer.
Contribution: This author helped design and conduct the study. This author is the archival author for the dataset from the nonacademic health system.
Attestation: Tina Maxbauer has approved the final manuscript.
Conflicts of Interest: The author has no conflicts of interest to declare.
Name: Carole Stout, MS, BSN, CNOR.
Contribution: This author helped conduct the study.
Attestation: Carole Stout has approved the final manuscript.
Conflicts of Interest: The author has no conflicts of interest to declare.
Name: Laura Archbold, BSN, MBA.
Contribution: This author helped design the study.
Attestation: Laura Archbold has 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 and conduct the study and write the manuscript. This author is the archival author for the dataset from the academic hospital.
Attestation: Richard Epstein has approved the final manuscript.
Conflicts of Interest: Richard Epstein is President of Medical Data Applications, Ltd., whose CalculatOR™ software includes some of the analyses described in discussion. The University of Iowa pays licensing fees to use the software for hospital consultations performed by its Division of Management Consulting.
a Personal communication, RenYu Liu, University of Pennsylvania, RenYu.Liu@uphs.upenn.edu, July 24, 2013
b As described in the Methods, analyses were performed in batches of eight-week periods.15 As an example why batches were used, we examined cases on days that the surgeon had at least 1 cancelled case. All of these surgeons’ other outpatient cases had cancellation rates of 5.0% ± 0.2% by minutes and 4.6% ± 0.3% by cases, vs the overall 1.6% ± 0.1% and 1.8% ± 0.1%, respectively. Among the inpatient cases, the rates were 10.0% ± 0.7% by minutes and 11.4% ± 1.0% by cases, in comparison with 8.1% ± 0.4% and 8.4% ± 0.5%, respectively.
1. 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
2. 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
3. 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
4. Stepaniak PS, Dexter F. Monitoring anesthesiologists’ and anesthesiology departments’ managerial performance. Anesth Analg. 2013;116:1198–200
5. 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
6. 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
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: a tutorial using data from an Australian hospital. Anesth Analg. 2006;103:1499–516
8. 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
9. 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
10. 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
11. Xue W, Yan Z, Barnett R, Fleisher L, Liu R. Dynamics of Elective Case Cancellation for Inpatient and Outpatient in an Academic Center. J Anesth Clin Res. 2013;4:314
12. Argo JL, Vick CC, Graham LA, Itani KM, Bishop MJ, Hawn MT. Elective surgical case cancellation in the Veterans Health Administration system: identifying areas for improvement. Am J Surg. 2009;198:600–6
13. Basson MD, Butler TW, Verma H. Predicting patient nonappearance for surgery as a scheduling strategy to optimize operating room utilization in a veterans’ administration hospital. Anesthesiology. 2006;104:826–34
14. 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
15. 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
16. Dexter F, Marcon E, Epstein RH, Ledolter J. Validation of statistical methods to compare cancellation rates on the day of surgery. Anesth Analg. 2005;101:465–73, table of contents
17. 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; discussion 6A
18. Ehrenfeld JM, Dexter F, Rothman BS, Johnson AM, Epstein RH. Case cancellation rates measured by services differ if based on the number of cases or the number of minutes cancelled. Anesth Analg. 2013;117:711–6
19. Pollard JB, Zboray AL, Mazze RI. Economic benefits attributed to opening a preoperative evaluation clinic for outpatients. Anesth Analg. 1996;83:407–10
20. Fischer SP. Development and effectiveness of an anesthesia preoperative evaluation clinic in a teaching hospital. Anesthesiology. 1996;85:196–206
21. van Klei WA, Moons KG, Rutten CL, Schuurhuis A, Knape JT, Kalkman CJ, Grobbee DE. The effect of outpatient preoperative evaluation of hospital inpatients on cancellation of surgery and length of hospital stay. Anesth Analg. 2002;94:644–9; table of contents
22. Ferschl MB, Tung A, Sweitzer B, Huo D, Glick DB. Preoperative clinic visits reduce operating room cancellations and delays. Anesthesiology. 2005;103:855–9
23. Dexter F, Traub RD, Qian F. Comparison of statistical methods to predict the time to complete a series of surgical cases. J Clin Monit Comput. 1999;15:45–51
24. Macario A, Dexter F. Estimating the duration of a case when the surgeon has not recently scheduled the procedure at the surgical suite. Anesth Analg. 1999;89:1241–5
25. Stepaniak PS, Heij C, Mannaerts GH, de Quelerij M, de Vries G. Modeling procedure and surgical times for current procedural terminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: a multicenter study. Anesth Analg. 2009;109:1232–45
26. Ledolter J, Dexter F, Epstein RH. Analysis of variance of communication latencies in anesthesia: comparing means of multiple log-normal distributions. Anesth Analg. 2011;113:888–96
27. 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
28. Dexter F, Macario A, Ledolter J. Identification of systematic underestimation (bias) of case durations during case scheduling would not markedly reduce overused operating room time. J Clin Anesth. 2007;19:198–203
29. 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
30. Dy CJ, McCollister KE, Lubarsky DA, Lane JM. An economic evaluation of a systems-based strategy to expedite surgical treatment of hip fractures. J Bone Joint Surg Am. 2011;93:1326–34
31. Schiff JH, Frankenhauser S, Pritsch M, Fornaschon SA, Snyder-Ramos SA, Heal C, Schmidt K, Martin E, Böttiger BW, Motsch J. The Anesthesia Preoperative Evaluation Clinic (APEC): a prospective randomized controlled trial assessing impact on consultation time, direct costs, patient education and satisfaction with anesthesia care. Minerva Anestesiol. 2010;76:491–9
32. Dexter F, Ahn HS, Epstein RH. Choosing which practitioner sees the next patient in the preanesthesia evaluation clinic based on the relative speeds of the practitioner. Anesth Analg. 2013;116:919–23
33. Hooper VD. Who staffs the perioperative surgical home? Anesth Analg. 2013;116:754–5
34. Katz RI, Dexter F, Rosenfeld K, Wolfe L, Redmond V, Agarwal D, Salik I, Goldsteen K, Goodman M, Glass PS. Survey study of anesthesiologists’ and surgeons’ ordering of unnecessary preoperative laboratory tests. Anesth Analg. 2011;112:207–12
35. Thilen SR, Bryson CL, Reid RJ, Wijeysundera DN, Weaver EM, Treggiari MM. Patterns of preoperative consultation and surgical specialty in an integrated healthcare system. Anesthesiology. 2013;118:1028–37
36. Wijeysundera DN, Austin PC, Beattie WS, Hux JE, Laupacis A. Variation in the practice of preoperative medical consultation for major elective noncardiac surgery: a population-based study. Anesthesiology. 2012;116:25–34
37. Tessler MJ, Kleiman SJ, Huberman MM. A “zero tolerance for overtime” increases surgical per case costs. Can J Anaesth. 1997;44:1036–41
38. 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
39. Dexter F, Traub RD, Macario A. How to release allocated operating room time to increase efficiency: predicting which surgical service will have the most underused operating room time. Anesth Analg. 2003;96:507–12, table of contents
40. Dexter F, Macario A. When to release allocated operating room time to increase operating room efficiency. Anesth Analg. 2004;98:758–62
41. 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
42. 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 Op. 2012;14:99–114
43. 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
44. 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
45. Wachtel RE, Dexter F. Review of behavioral operations experimental studies of newsvendor problems for operating room management. Anesth Analg. 2010;110:1698–710
46. Wachtel RE, Dexter F. Difficulties and challenges associated with literature searches in operating room management, complete with recommendations. Anesth Analg. 2013;117:1460–79
47. Wachtel RE, Dexter F. Curriculum providing cognitive knowledge and problem-solving skills for anesthesia systems-based practice. J Grad Med Educ. 2010;2:624–32
© 2014 International Anesthesia Research Society
48. Prahl A, Dexter F, Braun MT, Van Swol L. Review of experimental studies in social psychology of small groups when an optimal choice exists and application to operating room management decision-making. Anesth Analg. 2013;117:1221–9