There are many hospitals (or groups of ORs allocated to the same specialty) where each OR averages at least 8 hours of cases and turnover times on each workday. We consider 8 hours as the shortest shift to which an anesthesiologist or nurse anesthetist would typically be scheduled to work. At such hospitals, 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.1 Some anesthesia groups have substantial involvement in OR management,2 yet many have limited involvement. The latter situation frequently exists when anesthesia group managers lack education in the scientific basis of OR management.3–6
In this Special Article, we rely on experimental and observational study results (e.g., of cognitive biases) to show why absence of an appropriate agreement of management principles and performance metrics between the hospital and the anesthesia group related to OR staffing and case scheduling can/should result in lack of an anesthesia group’s involvement in OR management. This is important because, in other industries, formal agreements between organizations are common to ensure that mutually beneficial processes are performed as designed to reduce costs, even when the processes are not necessarily perceived to be beneficial (e.g., from cognitive biases) to individuals within the organizations.
2. DECISION MAKING TO REDUCE OVERUTILIZED TIME
Decision making can be systematically performed shortly before the day of surgery, based on principles of reducing the hours of overutilized OR time and tardiness of case starts (i.e., patient waiting).7 For vocabulary terms, we refer readers to our review article Ref. (4). Definitions of relevant terms also are provided, for convenience, in Table 1 of this article.7–18
a. Previous Review Articles About Allocating OR Time
Hours worked exceeding allocated hours (i.e., the hours into which cases are scheduled) are referred to as “hours of overutilized OR time.”4–10 Allocated hours are calculated months before the day of surgery8–10 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 (Table 1).8–14,19,20 Surgical services’ cases are scheduled into these 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.21
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 an anesthesiologist or nurse anesthetist 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.22–26 Staff assignments are usually made the working day before surgery.
Cases often (although <50%) take longer than scheduled if the mean of historical case durations is (as appropriate) used for staff scheduling.16–18 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. The contribution is, by definition, the mean. A consequence of using the mean (e.g., versus median) is that most (>50%) cases take less time than scheduled18 (see Ref. (16) for a recent review of that topic).
Reference (9) is a full review from 2006 describing how to calculate OR allocations to minimize the inefficiency of use of OR time and reductions in labor costs (Table 2.a.i).12,27–29 Reference (30) is a book chapter examining the same material. Reference (10) shows that staffing for 8 hours rather than 10 hours is appropriate when the day averages 8.5 hours or less. When the length of the day approaches 9 hours, 10 hours of staffing should be scheduled.10 Reference (19) compares statistical methods for the calculation of OR allocations. Reference (7), from 2004, reviews corresponding decision making on the day of surgery (Table 2.a.ii). For derivations, readers can refer to the appendices of Refs. (8) and (11). Lectures covering these topics are available online at www.FranklinDexter.net/education.htm.a The calculation of OR allocations is a topic in itself,8–10,12,19,20 with a full review article and book chapters published;7,9,10 we refer readers to that prior work. The content and implications of this article depend on knowledge of this material, as in those reviews, not herein. At hospitals where anesthesiologists and nurse anesthetists care for all OR patients and reasonably are clinically interchangeable, staff scheduling and staff assignment should not influence the efficiency of use of OR time (see Example 2.a.1). We think that readers who do not understand why and/or would feel unsure about writing down the 3 simultaneous equations for the inefficiency of use of OR time (Table 1) would benefit from the review articles.
At some hospitals, the numbers of ORs in use are greater than that which minimizes the inefficiency of use of OR time (i.e., anesthesiologists and nurse anesthetists’ time).9,12,27–29 This is done to provide patients with desired morning start times31 and surgeons with less tardiness from scheduled start times.32–34 Analyses can quantify the cost sustained by the anesthesia group (or department) for staffing those extra ORs (e.g., for institutional support).27,35 Reference (35) is a prior review of this topic.9
b. Benefits of Making Decisions to Reduce Overutilized OR Time
Decisions can be made 1 to 2 days in advance and on the day of surgery with the goal of reducing overutilized OR time (Table 2.a.ii).8–10,36–38 However, in practice, decisions are often made that are inconsistent with this goal, even by those with some training in OR management (Table 2.b.iii).39 Knowledge of the principles underlying overutilized time is neither intuitive nor learned through experience working in ORs.39,40 This is unfortunate because reducing overutilized OR time reduces the hours that anesthesiologists and nurse anesthetists work late (see Example 2.b.2).
c. Treating All ORs as Having the Same Allocations Is Economically Suboptimal
Cases can be scheduled into overutilized time when schedulers conceptually simplify the problem of allocating OR time and scheduling cases by assuming that all ORs at the hospital have been allocated the same number of hours.3 This also can occur when surgeons’ blocks are used to represent fixed hours into which cases must be scheduled rather than as conceptual representations of ORs for the day.9,10,20,41–46 Unless proper (i.e., economically rational) allocations are calculated by analysts and entered into the scheduling system correctly for use by the schedulers, decisions often will not minimize the hours of overutilized time (see Figure 1 and Examples 2.c.3 and 2.c.4). If the screen of the scheduling system has not been configured with the actual number of hours allocated to each service on each day of the week (i.e., those calculated to minimize the inefficiency of use of OR time), the hours extending beyond the displayed allocated time, available for scheduling cases, do not represent overutilized OR time.
3. COGNITIVE BIASES INFLUENCE CASE SCHEDULING AND STAFF ASSIGNMENT
In the absence of education, anesthesiologists, schedulers, and other personnel making decisions often do so subject to cognitive biases (i.e., they apply rules based on beliefs, even when such beliefs are irrational; Table 1).
a. Cognitive Bias of Cases Not Finishing Early
Perceptions of anesthesiologists, surgeons, OR nurses, and administrators about the importance of all ORs starting precisely on time at the beginning of the workday and cases never finishing early are due to a combination of a lack of organizational knowledge and a cognitive bias (Table 3.a.i).39 Personnel generally fail to consider that most cases take less time than scheduled (see section 2.b., Benefits of Making Decisions to Reduce Overutilized OR Time).47 A delay in the start time of the first case of the day in an OR by a certain amount does not result in a similar delay of all other cases in the same OR.39,48–50 Small changes in first case start times are not detrimental to the efficiency of use of OR time (Table 3.a.ii).39,48,49 The same principles and bias influence the choice of times of patient fasting and arrival at the hospital on the day of surgery (Table 3.a.iii).47 Regardless, the result is a focus on achieving on-time starts for all patients rather than targeting ORs with the potential for overutilized OR time and/or with many cases so that a first case of the day start delay has a substantial cumulative impact.
b. Cognitive Bias of “Pull-to-Center”
A cognitive bias causes managers to underestimate the time needed to complete a service’s daily hours of cases.9,10,27 When sufficient time is available, negligible incremental hours worked late are created by case scheduling and sequencing in available OR time (see Table 3.b.iv for an analogy,41,51 choosing the size of a box that is slightly too small to accommodate the items to be packed, a common cognitive bias). Optimizing the size of the box is effectively accomplished by the process of appropriately allocating OR time (Table 3.b.v).
Targeting ORs with overutilized time requires objective recommendations to overcome a bias that causes managers to underestimate the time needed to complete a service’s daily hours of cases.40 OR information systems with electronic displays (“whiteboards”) showing information about surgical case progress can be used to facilitate OR managers’ decision making on the day of surgery, provided the overutilized OR times are calculated correctly. Many electronic displays provide information about case progress but not recommendations (e.g., as to what cases to consider moving to reduce overutilized time).52 Without recommendations, decisions made involving multiple ORs are significantly worse than if made by random chance (e.g., by flipping a coin).40 Education increases trust in displays’ recommendations and in the decisions made to reduce overutilized OR time.3,5,53
c. Cognitive Bias of Risk-Averse or Non- Risk-Averse
The decision maker at the OR control desk may also be psychologically risk-averse,54 determined either by psychologically testing54 the person or interviewing them while using decision scenarios created from the hospital’s OR information system data.55 The risk-averse decision maker postpones starting cases in afternoons to prevent overutilized OR time.54 This can be out of concern that the ongoing cases will take longer than scheduled and/or that there will be add-on case(s). Regardless, such decisions neglect the fact that the allocated OR time has been based on the workload from all performed cases (i.e., includes both predictive error in scheduled durations and the chance of add-on cases).54 The non-risk-averse decision maker considers the potential for unexpected events including add-on cases and works aggressively at preventing a queue. When measured, decisions made by non-risk-averse decision makers result in less overutilized OR time than decisions made by risk-averse mangers.54 That is, the managers who often prevent cases from starting in the mid-to-late afternoon to avoid “overtime” tend, on average, to make the ORs less efficient (i.e., a result opposite of their intention).54 The implication for anesthesia groups is that, if decision makers at the OR control desk will not be chosen based on psychological tests (i.e., to choose individuals who are not risk-averse),54 electronic displays with recommendations40 and education5 are needed.
d. Cognitive Bias of Increasing Clinical Work Done
A fourth cognitive bias is to make decisions and actions that effectively increase the clinical work done per unit time during the hours assigned to the decision maker (Table 3.d.vi).40,56–61 This is a bias because the decision making, appropriate for single ORs, is then applied inappropriately by anesthesiologists and other decision makers to situations involving more than one OR.40 This behavior can result in decisions that increase overutilized OR time, as well as tardiness from scheduled start times61,62 (see Example 3.d.1).
As expected scientifically, application of a brief, systematic educational program did not alter behavior resulting from this cognitive bias.56 This behavior also was unaffected by paying anesthesiologists more for each hour worked late (or paying them a set fee for being scheduled to work late and stay as long as necessary).63 The bias was mitigated by displays with recommendations (i.e., such displays improve decisions for the ORs with overutilized time because those ORs are targeted more often).40 The role of education is that it increases trust in statistical recommendations (“cues”) provided by the displays.3,53
4. LEADERSHIP PRINCIPLES TO COMPENSATE FOR THE COGNITIVE BIASES
In the setting of the earlier summarized cognitive biases,3,39,40,47,54–57,61 management decisions on the day of surgery or in the scheduling office to reduce overutilized OR time do not appear to be improved significantly by collaborative decision making, education, and/or frequent feedback within hospitals.3,5 Rather, decision support systems help when they provide recommendations.3,40 For most decisions, all that is needed is a computerized prompt when a case is about to be scheduled into overutilized OR time. Occasional questions of use can be answered by e-mail with an expert advisor.64
a. Autocratic Decision Making Related to OR Management Is Appropriate
Education increases leaders’ trust in statistical recommendations and their skill at evaluating when a recommendation may be faulty because of incomplete data.4,40,53,65 The anesthesia group leadership, perioperative medical director, and so on, need to be evaluated based on setting up these management systems.1 That generally is not achieved by leaders collaborating with people in their organization who lack education in OR management science.5 Although group members unfamiliar with the scientific literature may provide useful information to the leader (e.g., provincial labor laws), such group members make decisions that are inferior (Tables 4.a.i and 4.a.ii).5,44,66,67 Suppose a group member (e.g., an educated perioperative medical director)4,5 holds key shared information that points to a superior alternative unsupported by the majority of group members. The manager can share a problem with an analyst who, after reviewing the data, provides recommendation(s) (e.g., appropriate patient arrival times to balance patient waiting versus OR waiting causing overutilized OR time).47,50,68 The manager then makes the decision. If the other individuals who would be involved in group-level decision making do not share the knowledge of the leader and analyst, decision making by the educated leader alone has the best chance of producing quality decisions.5
b. Nursing Directors and Perioperative Medical Directors
The structure of OR nursing directors’ positions often make it challenging to lead in decisions that reduce overutilized OR time.69 Nursing directors’ jobs are foremost about nursing, although their decisions influence overutilized OR times and anesthesia groups’ productivity (Table 4.b.iii).70–72 In addition, many OR nursing directors’ salaries are based principally on the director’s operational budget (Table 4.b.iv).69 A consequence is that many have a financial incentive to increase OR nursing staff scheduled hours (i.e., not decisions that increase anesthesia group productivity).
These findings69 can assist anesthesiologists in strategizing how to reduce overutilized OR time and/or patient waiting on the day of surgery. Suppose that the perioperative medical director has identified opportunities to hire additional nonphysician providers who will reduce overutilized time.1 Examples include not only (1) having more housekeeping teams,61,73 but also hiring more (2) postanesthesia care unit nurses62,74–78; (3) transporters to reduce delays in postanesthesia care unit exit79; and (4) nurses to setup, assist anesthesiologists, and monitor patients after peripheral nerve blocks.80 An appropriate strategy69 is to encourage hospital administrators to expand the nursing and/or OR budget to hire the additional personnel, not to hire using the existing budget. The medical director takes the lead in getting more hospital resources for the OR nursing director, not vice versa.
c. Importance and Structure of Anesthesia Group and Hospital Agreement
From earlier section, 3.d. Cognitive Bias of Increasing Clinical Work Done, in the evenings, many anesthesiologists do not assign cases to their colleagues who are scheduled to work late but send them home even when there is a case(s) queued that could be started in allocated OR time56,57,61 (see Example 3.d.1). Instead of performing a case at 7:30 PM, the case is supervised at 10 PM by the on-call anesthesiologist assigning cases.56,57 This has the effect that the assigning anesthesiologists increase their clinical work per time unit during the hours to which they are assigned. The consequence, however, is that wait times of patients and surgeons are increased. Again, as mentioned above, the behavior appears to be immutable to education (i.e., it reflects a cognitive bias).3,5,56
One appropriate solution to this problem is for anesthesia groups and hospitals to agree mutually to the management principle expressed in the following sentence:81
The anesthesia group and hospital will ensure, hourly, that, when there are case(s) waiting to start, the number of ORs in use for each service will be at least the number that maximizes the efficiency of use of OR time.
Both the hospital and the anesthesia group are included because anesthesiologists cannot run more ORs than there are surgical technologists and OR nurses available (e.g., one of each per OR). In the absence of such an agreement, individual people will make decisions that are not economically rational for the organization(s) (see Example 4.c.1).46,82–84 Effective management control can be by automatic e-mail.85
The effect of such an agreement is that, to be satisfied, the OR allocations need to be calculated appropriately (e.g., anesthesiologists and nurse anesthetists would not have to work late beyond their 8-hour shifts because OR nursing did not plan an OR appropriately for 10 hours, as in Example 4.c.1).54 Absent the decision making process stipulated by the agreement, personnel more often work late and the scheduling office ineffectively uses extra (flexible) ORs within a few days of the day of surgery.21,86–89
This mutual agreement can also influence the economic incentive of the anesthesia group for activities reducing overutilized OR time (Table 4.c.v). Even when OR allocations have been calculated to minimize the inefficiency of use of OR time, the anesthesia group may negotiate institutional support35 for its sustained underutilized time or overutilized time (see Example 4.c.2). Yet, at no hospital do cases take precisely as long as predicted, nor do scheduled durations perfectly pack into allocated time.16 In addition, the mean absolute percentage predictive error in case durations is principally a function of the specialty18 and of appropriate decisions at surgical suites to reduce the hours of overutilized time.16 Underutilized and overutilized time is an inevitable consequence of perioperative care. If the anesthesia group receives incremental institutional support for the hours of underutilized or overutilized time, the payment generally results in a (counterproductive) net loss for activities that result in increased production by the anesthesia group.35 This is shown in Example 4.c.3.
Even without direct payments for working in overutilized time, the anesthesia group and hospital agreement affect the economic incentive of the hospital for activities reducing overutilized OR time (e.g., informatics purchases).35 This is shown in Example 4.c.4. To satisfy the (abovementioned) single sentence agreement, the anesthesia group and hospital must assign sufficient staff to be performing cases in at least the allocated OR time for each service on each day while case(s) are waiting.35 One additional principle is sufficient conceptually for both the anesthesia group and the hospital not to suffer from decreases in productivity:35,b
The anesthesia group and hospital will ensure, hourly, that, when there are case(s) waiting to start, the number of ORs in use for each service will be at least the number that maximizes the efficiency of use of OR time. Neither the anesthesia group nor the hospital will be expected to run more than that number of ORs without mutual agreement.35
We write “conceptually” because the sentences are brief, being subject to the topics discussed and definitions in Section 2. Although adoption of such an agreement has daily implications,57,81 reconsideration of such arrangements generally occurs no greater than annually.
Decision making soon before the day of surgery can be made systematically based on reducing the hours of overutilized OR time and tardiness of case starts (i.e., patient waiting)7 (see the above section 2.a., Previous Review Articles about Allocating OR Time). We subsequently considered in 2008 that such decision making depends on rational anesthesia–hospital agreements for purposes of providing anesthesia staffing. For example, suppose that a hospital seeks to have more ORs in use than that which maximizes the efficiency of use of OR time. How to calculate the incremental cost sustained by the anesthesia group (i.e., necessary institutional support) was described in that article.9,27,35
Since that prior study, there has been a substantial increase in understanding of the timing of decision making to reduce overutilized OR time. Most decisions substantively influencing overutilized OR time are those made within 1 workday before the day of surgery and on the day of surgery, because only then are ORs sufficiently full that case scheduling and staff assignment decisions affect overutilized OR time.21,36–38 Consequently, anesthesiologists can easily be engaged in such decisions, because generally they must be involved to ensure that the corresponding anesthesia staff assignments are appropriate. Despite this, at hospitals with >8 hours of OR time in use daily in each OR (i.e., the subject of this Special Article), computerized recommendations are superior to intuition because of cognitive biases.3,40 Decisions need to be made by a perioperative medical director who is familiar with the principles published in the scientific literature and/or relying on an expert rather than by a committee lacking this knowledge.4,5,64 The medical director is responsible for assuring review of reports of lack of use of the ordered priorities (Table 2.a.ii).57,81 Education in the scientific literature, including the use of appropriate analytical methods, is important.3–5,53
The addition that we made in this article is to show that an agreement between the anesthesia group and the hospital, that specifies the number of ORs in use when cases are queued at the end of the workday, can both reduce overutilized OR time and patient waiting. Agreements assure that processes mutually beneficial to organizations but not necessarily to individuals at any one moment are performed as designed, especially in the setting of cognitive biases.
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 receives no funds personally other than his salary and allowable expense reimbursements from the University of Iowa, and has tenure with no incentive program. Income from the Division’s consulting work is used to fund Division research.
Name: Richard H. Epstein, MD, CPHIMS.
Contribution: This author helped design the study, conduct the study, and write the manuscript. This author is the archival author.
Attestation: Richard H. Epstein has approved the final manuscript.
Conflicts of Interest: Richard H. Epstein is the President of Medical Data Applications, Ltd., whose CalculatOR™ software 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.
The author thank Professor Ruth Wachtel, PhD, MBA, and Ms. Jennifer Espy of the University of Iowa’s Department of Anesthesia edited parts of the article.
Dr. Franklin Dexter is the Statistical Editor and Section Editor for Education, Economics, and Policy for Anesthesia & Analgesia. This manuscript was handled by Tong J. Gan, Section Editor for Ambulatory Anesthesiology and Perioperative Management, and Dr. Dexter was not involved in any way with the editorial process or decision.
a The lectures are available so non-experts can determine the specific vocabulary words needed to find the article(s) relevant to a managerial question.4 It is (paradoxically) not possible to search the OR management literature successfully using PubMed without first knowing the corresponding vocabulary.4 Each set of slides also contains reference(s) so that citation pearl growing can alternatively be used to find relevant article(s).4
b Our Special Article provides the scientific principles needed for an agreement. As shown throughout (e.g., Table 1), use can depend in practice on definitions.
1. 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
2. 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. A&A Case Reports. 2015;5:206–11
3. Wachtel RE, Dexter F. Review article: review of behavioral operations experimental studies of newsvendor problems for operating room management. Anesth Analg. 2010;110:1698–710
4. Wachtel RE, Dexter F. Difficulties and challenges associated with literature searches in operating room management, complete with recommendations. Anesth Analg. 2013;117:1460–79
5. 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
6. Cline KM, Roopani R, Kash BA, Vetter TR. Residency board certification requirements and preoperative surgical home activities in the United States: comparing anesthesiology, family medicine, internal medicine, and surgery. Anesth Analg. 2015;120:1420–5
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
13. Dexter F, Epstein RH. Optimizing second shift OR staffing. AORN J. 2003;77:825–30
14. 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
15. 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
16. 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
17. 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;117:205–10
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. Freytag S, Dexter F, Epstein RH, Kugler C, Schnettler R. Allocating and scheduling operating room time based on maximizing operating room efficiency at a German university hospital. Der Chirurg. 2005;76:71–9
29. 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
30. 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 University Press:46–66
31. Dexter F, Birchansky L, Bernstein JM, Wachtel RE. Case scheduling preferences of one Surgeon’s cataract surgery patients. Anesth Analg. 2009;108:579–82
32. Wachtel RE, Dexter F. Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg. 2009;108:1889–901
33. 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
34. 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
35. Dexter F, Epstein RH. Calculating institutional support that benefits both the anesthesia group and hospital. Anesth Analg. 2008;106:544–53
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. Dexter F, Macario A, Traub RD, Lubarsky DA. Operating room utilization alone is not an accurate metric for the allocation of operating room block time to individual surgeons with low caseloads. Anesthesiology. 2003;98:1243–9
46. 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
47. 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
48. Dexter F, Macario A, Ledolter J. Identification of systematic underestimation (bias) of case durations during case scheduling would not markedly reduce overutilized operating room time. J Clin Anesth. 2007;19:198–203
49. 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
50. 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
51. Vanberkel PT, Boucherie RJ, Hans EW, Hurink JL, van Lent WA, van Harten WH. Accounting for inpatient wards when developing master surgical schedules. Anesth Analg. 2011;112:1472–9
52. 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
53. 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
54. 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
55. 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
56. Dexter F, Lee JD, Dow AJ, Lubarsky DA. A psychological basis for anesthesiologists’ operating room managerial decision-making on the day of surgery. Anesth Analg. 2007;105:430–4
57. Ledolter J, Dexter F, Wachtel RE. Control chart monitoring of the numbers of cases waiting when anesthesiologists do not bring in members of call team. Anesth Analg. 2010;111:196–203
58. Shapiro LE, Alfille PH, Sandberg WS. Robots with a social memory. Anesth Analg. 2010;111:19–20
59. Masursky D, Dexter F, Isaacson SA, Nussmeier NA. Surgeons’ and anesthesiologists’ perceptions of turnover times. Anesth Analg. 2011;112:440–4
60. Arakelian E, Gunningberg L, Larsson J. How operating room efficiency is understood in a surgical team: a qualitative study. Int J Qual Health Care. 2011;23:100–6
61. 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
62. Ehrenfeld JM, Dexter F, Rothman BS, Minton BS, Johnson D, Sandberg WS, Epstein RH. Lack of utility of a decision support system to mitigate delays in admission from the operating room to the postanesthesia care unit. Anesth Analg. 2013;117:1444–52
63. Masursky D, Dexter F, Garver MP, Nussmeier NA. Incentive payments to academic anesthesiologists for late afternoon work did not influence turnover times. Anesth Analg. 2009;108:1622–6
64. Prahl A, Dexter F, Swol LV, Braun MT, Epstein RH. E-mail as the appropriate method of communication for the decision-maker when soliciting advice for an intellective decision task. Anesth Analg. 2015;121:669–77
65. 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
66. Greitemeyer T, Schulz-Hardt S. Preference-consistent evaluation of information in the hidden profile paradigm: beyond group-level explanations for the dominance of shared information in group decisions. J Pers Soc Psychol. 2003;84:322–39
67. Sargis EG, Larson JR Jr. Informational centrality and member participation during group decision making. Group Process Intergroup Relat. 2002;5:333–47
68. Smallman B, Dexter F. Optimizing the arrival, waiting, and NPO times of children on the day of pediatric endoscopy procedures. Anesth Analg. 2010;110:879–87
69. Masursky D, Dexter F, Nussmeier NA. Operating room nursing directors’ influence on anesthesia group operating room productivity. Anesth Analg. 2008;107:1989–96
70. United States Government Accountability Office. VA Health Care. . Many medical facilities have challenges in recruitment and retention of nurse anesthetists. United States Government Accountability Office. 2007
71. Nissen S. Practical steps for boosting staff retention. OR Manager. 2003;19:18–9
72. Mohr DC, Burgess JF Jr, Young GJ. The influence of teamwork culture on physician and nurse resignation rates in hospitals. Health Serv Manage Res. 2008;21:23–31
73. 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
74. Dexter F, Epstein RH, Penning DH. Statistical analysis of postanesthesia care unit staffing at a surgical suite with frequent delays in admission from the operating room–a case study. Anesth Analg. 2001;92:947–9
75. Dexter F, Epstein RH, Marcon E, de Matta R. Strategies to reduce delays in admission into a postanesthesia care unit from operating rooms. J Perianesth Nurs. 2005;20:92–102
76. Schoenmeyr T, Dunn PF, Gamarnik D, Levi R, Berger DL, Daily BJ, Levine WC, Sandberg WS. A model for understanding the impacts of demand and capacity on waiting time to enter a congested recovery room. Anesthesiology. 2009;110:1293–304
77. 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
78. 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
79. Marcon E, Kharraja S, Smolski N, Luquet B, Viale JP. Determining the number of beds in the postanesthesia care unit: a computer simulation flow approach. Anesth Analg. 2003;96:1415–23
80. Russell RA, Burke K, Gattis K. Implementing a regional anesthesia block nurse team in the perianesthesia care unit increases patient safety and perioperative efficiency. J Perianesth Nurs. 2013;28:3–10
81. Stepaniak PS, Dexter F. Monitoring anesthesiologists’ and anesthesiology departments’ managerial performance. Anesth Analg. 2013;116:1198–200
82. 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
83. 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
84. 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
85. Epstein RH, Dexter F, Patel N. Influencing anesthesia provider behavior using anesthesia information management system data for near real-time alerts and post hoc reports. Anesth Analg. 2015;121:678–92
86. Dexter F, Macario A. When to release allocated operating room time to increase operating room efficiency. Anesth Analg. 2004;98:758–62
87. 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
88. van Essen JT, Hans EW, Hurink JL, Oversberg A, van Essen JT, Hans EW, Hurink JL, Oversberg A. Minimizing the waiting time for emergency surgery. Operations Research for Health Care. 2012;1:34–44
89. Tancrez JS, Roland B, Cordier JP, Riane F. Assessing the impact of stochasticity for operating theater sizing. Decision Support Systems. 2013;55:616–28