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*Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City;
Department of Anesthesiology, University of Texas Medical Branch, Galveston;
Department of Anesthesiology, Jefferson Medical College, Philadelphia; 
Medical Data Applications, Ltd., Jenkintown, Pennsylvania;
||Department of Anesthesiology and Pain Medicine, University of Texas Southwestern Medical Center, Dallas; and
¶Department of Anesthesiology, Perioperative Medicine, and Pain Management and School of Business, University of Miami, Florida
Address correspondence and reprint requests to Franklin Dexter, Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, IA 52242. Address e-mail to Franklin-Dexter{at}UIowa.edu
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Abstract |
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IMPLICATIONS: Many anesthesiologists work at hospitals where surgeons and/or operating room (OR) committees focus repeatedly on turnover time reduction. We developed a methodology by which the reductions in staffing cost as a result of turnover time reduction can be calculated for each facility using its own data. Staffing cost reductions are generally very small and would be achieved predominantly by reducing allocated OR time to the surgeons.
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Introduction |
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Analyses have studied the impact of reducing turnover times on increases in caseload (2,3) and on direct (4) and indirect (5,6) reductions in staffing costs. Achieved reductions from baseline average turnover times less than 40 min result in only small reductions in staffing costs, unless case durations are short (e.g., pediatric otolaryngology) (2–6).
At many surgical suites, there is a dichotomy between these scientific results (2–6) and the apparent importance attributed by surgeons (1) and other clinicians to turnover times. Many operating room (OR) committees are enamored of the topic. This dichotomy may be explained by the fact that clinicians are confronted with turnovers every workday but may not consider or understand concepts such as staffing costs and allocated OR time. Thus, to focus organizational attention toward quantitatively more important topics than turnover times, anesthesiologists and OR managers may need to evaluate the impact of changing each surgical suite’s turnover time using its own data. One of the two goals of this paper is to describe how to calculate the staffing cost reduction achievable from reduction in turnover time by using OR information system data from a surgical suite.
A second explanation for the dichotomy between scientific results and practice is that the scientific analysis of the direct effect of reducing turnover times on staffing costs considered small reductions in turnover time (4). This is appropriate for surgical suites with average turnover times of 40 min or less. Then, organizational efforts to reduce turnover times are often unsuccessful (1) or small. Average reductions of 7.1 min have been published but with the expense of changing from a standard OR to a minimally invasive one (7). A community hospital with an average turnover time of approximately 35 min achieved an average reduction of 8 to 10 min by using personal public recognition (a Tree of Excellence) and gift certificates to local restaurants and the hospital cafeteria donated by the surgeons and hospital (Personal communication with the Director of Perioperative Services, March 4, 2003).
In contrast, when average turnover times are longer than 40 min, larger reductions in turnover time have been described (e.g., 16-min average reduction from average of 57 min (8) and 13-min reduction from average of 65 min (9)). A second goal of this paper was to apply the turnover time analysis to four different tertiary hospitals with overall average turnover times ranging from 34 min to 66 min.
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Methods |
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We used the following definitions for the analysis:
Surgical service refers to a group of surgeons who share allocated OR time. In that we studied academic practices, most services were departments. However, one surgeon could represent a service. The services ranged from one to dozens of surgeons.
Regularly scheduled OR hours were the hours that individual OR team members plan on working (e.g., 7 AM to 5 PM). This excludes days when a team member is scheduled to work regular hours and to stay late if required "on call."
Allocated OR time is an interval of OR time with a specified start and end time on a specified day of the week that is assigned by the surgical suite to a service for scheduling its cases.
Case duration is defined as the time from when a patient enters an OR until he or she leaves the OR.
Turnover time is the time from when one patient exits an OR until the next patient on that day’s OR schedule enters the same OR on the same day (10). If the first patient was cared for by a different service than the next patient, the turnover time was attributed to the first service when calculating OR workload (10).
Turnover times include clean up times and set up times, but not delays between contiguous cases in an OR. An example of a delay would be when the first patient of the day is found to have widespread metastases, resulting in cancellation of resection. The second case of the day in the OR will be performed by a different surgeon who will be unavailable for 3 h. To exclude delays, a maximum amount of time was ascribed to the turnover time (10). For example, suppose that the maximum turnover is 1.5 h. If the end and start of the two successive elective cases performed on the same day in the same OR were separated by 2 h, then a turnover time of 1.5 h was used. Previous studies used maximum turnovers of 1.0 h (5,10) or 1.25 h (6). We used an unusually long maximum turnover, since, as explained in the final paragraph of the Introduction, we intentionally studied some surgical suites with long average turnover times. When the methodology described in this paper is used at other surgical suites, a maximum turnover of 1.0 h would often be used.
Elective OR workload of a service is its total hours of elective cases including turnover times on that workday (11–13). Separate OR time was allocated on all days of the week for urgent cases.
Under-utilized OR time is the positive difference between allocated OR time and the OR workload (12,13). For example, if the service "Gynecology" was allocated an OR for 10 h from 7 AM to 5 PM but finished cases at 3 PM, the under-utilized OR time would be 2 h.
Over-utilized OR time is the positive difference between OR workload and allocated OR time. When allocated OR time and the regularly scheduled OR hours are the same (e.g., a service is allocated an OR for 10 h from 7 AM to 5 PM) and allocated OR time has not released, then over-utilized OR time is the hours that ORs run past the regularly scheduled OR hours (12,13). Also, then, hourly employees receive overtime when working during over-utilized hours.
Inefficiency of use of OR time equals the sum of two products: hours of under-utilized OR time multiplied by the cost per hour of under-utilized OR time and hours of over-utilized OR time multiplied by the cost per hour of over-utilized OR time (12,13). The cost per hour of over-utilized OR time is invariably more expensive than the cost per hour of under-utilized OR time.
OR efficiency is the value that is maximized when the inefficiency of use of OR time has been minimized (12,13).
For example, suppose that the hypothetical service "Dr. Smith" does 10 h of cases every Monday. A 0 h allocation would have a lower OR efficiency than an 8 h allocation because of 10 versus 2 over-utilized hours each Monday. An 8 h allocation would have a lower OR efficiency than a 10 h allocation because of 2 versus 0 over-utilized hours each Monday. Finally, a 13 h allocation would have a lower OR efficiency than a 10 h allocation because of 3 under-utilized hours each Monday. Thus, the service Dr. Smith would be allocated 10 h of OR time.
Staffing cost equals the allocated OR time for a service multiplied by its cost per hour plus the number of over-utilized hours multiplied by its cost per hour (4).
The cost of an over-utilized hour was considered to equal 1.75 times the cost of an under-utilized hour and of a regularly scheduled hour. This reflects both the direct costs of overtime or bonus pay at "time and a half" (1.50), plus an increment (0.25) for indirect costs of employee dissatisfaction and resignation, resulting in increased recruitment and retention costs.
For example, an anesthesiologist and a certified registered nurse anesthetist are scheduled to work 8 h from 7 AM to 3 PM (i.e., these are their regularly scheduled hours). The OR time is allocated for 8 h. However, since cases continue to 4 PM, they work until 4 PM. Then, the staffing cost was considered to equal the cost of 9.75 regularly scheduled hours, where 9.75 = (8 regularly scheduled OR hours) + 1.75 x (1 over-utilized hour).
The anesthesia cost of a regularly-scheduled hour was estimated at $119, based on the 2001 median annual United States (US) compensation for academic anesthesiologists of $198,413 (14) and for certified registered nurse anesthetists of $89,160 (14), 26% benefits, 2000 clinical hours per year, and staffing of 1 anesthesiologist to 2 nurse anesthetists (15). If both anesthesia and OR nursing costs were considered, the hourly cost would simply be increased appropriately.
We estimated how reducing turnover times would reduce staffing costs in three steps.
First, OR workload was calculated for each service on each day of the week using the previously described method (11–13) (CalculatORTM, Medical Data Applications, Ltd., Jenkintown, PA). All possible allocations of OR time were evaluated for the one that would have resulted in the maximal OR efficiency, options being 0 h, 8 h, 10 h, 13 h, 16 h, 18 h, and so forth. If providing 0 h to a service for a day of the week would have resulted in increased OR efficiency than providing 8 h, no OR was assigned to that service for that day of the week.
Although some services had an insufficient OR workload to be allocated OR time, OR time still required to be provided for them to schedule their cases. So that every surgeon has access to OR time on every workday (see first paragraph of Discussion), at least 8 h of OR time was allocated for the OTHER services on all workdays. This was also required because the methodology considered every case to be performed on the workday on which it was previously performed. For each day of the week, all services not allocated OR time were combined into an OTHER service. Some surgical suites refer to the OTHER service as "open OR time," "unblocked OR time," or "first-come first-served OR time." The allocated OR time for the OTHER service was calculated as in the preceding paragraph.
Second, using the OR workload and the allocated OR time for each service from the first step, staffing costs were calculated by summing the staffing costs required among all services on all workdays of the studied year. All cases were considered to be scheduled on the same date that they were actually performed.
Third, the maximum turnover time was reduced for all cases from 90 min to 60, 50, 40, or 30 min. The first and second steps were repeated. The reduction in average turnover time and the difference in staffing costs between the 90-min maximum and the lower maximums were calculated.
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Results |
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Discussion |
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Applicability to Other Surgical Suites
The maximum potential reductions in staffing costs from reducing turnover times, while completing existing cases, were a few percent (Table 2). This is the same as that of other OR management interventions on the day of surgery (e.g., changing how add-on cases are scheduled (22)). However, such interventions do not cost anything, whereas efforts to reduce turnover times to an average less than approximately 30 minutes may require significant costs in and of themselves. Thus, we suspect that the principal benefit of our methodology to anesthesiologists will be to help them use their own data to show their OR committees why turnover time should not be their focus. Our experience is that the method may be most helpful when combined with benchmark data of turnover times.
We knew from previous simulation studies that the financial impact of achievable reductions in turnover time would be small (4). Our method, in fact, probably overestimates the reduction in staffing cost because it neglects any increase in cost that may be required to achieve the reduction in turnover times. The fact that the method generally overestimates the cost reduction can be put to advantage by heads of anesthesiology departments and OR managers using the methods.
When providing results to individuals focused on turnover times but not conversant in the science of OR management, it may be particularly useful to show that the financial benefit of reducing turnover times is achieved by reducing allocated OR time. An example is given in the last paragraph of the Results. The rationale is quite obvious in retrospect, and the implications for surgeons are clear.
The absolute reduction in staffing costs depends on the absolute reduction in the average turnover time. Ambulatory surgery centers will achieve smaller reductions in staffing costs than the hospitals in Table 2, because surgical suites starting with briefer turnover times can achieve smaller absolute reductions in average turnover time. Consequently, it is unlikely that our methodology can be of societal benefit unless it can be used to help focus organizational attention toward quantitatively more important topics than turnover times. Whether it can be is unknown, requiring future investigation.
Other Potential Benefits to Reducing Turnover Times
Advocates of reducing turnover time often state that doing so has additional benefits. Reducing turnover times could increase revenue by providing sufficient additional OR time to perform another elective case that would not otherwise have been economically sound to have been performed, prevent a case from being canceled out of concern that the case would finish after the end of regularly scheduled hours at a hospital aiming to reduce costs, permit a surgeon to perform another case in his or her OR that could not otherwise be completed, permit another add-on case to be performed that could not otherwise be completed within a fixed time period, reduce staffing costs, indirectly, by reducing the impact of erroneous turnover times on inaccurate allocations of OR time, and/or reduce case delays from scheduled start times. In the following paragraphs, we review why none of these arguments are true (2,3,5,6,23–25) (see Appendix). This is why our analysis focused on the direct effect of reducing turnover times on staffing costs.
Reducing turnover times could provide sufficient additional OR time to perform another elective case, thereby increasing revenue. The fallacy to this argument is that revenue enhancement is not an appropriate managerial goal. Rather, increasing contribution margin (revenue minus variable costs) is the relevant financial goal. Contribution margins per OR hour are consistently at least several hundred dollars and usually several thousand dollars (23,24). Consequently, if a case can be done safely, it would be irrational for a surgical suite concerned with contribution margin to perform a case if turnover times were reduced but not perform it if turnover times were not reduced. Even if OR teams were paid several hundred dollars per hour, it would make financial sense to perform the case, regardless of the turnover time, provided the case can be performed safely. For example, in the last paragraph of the Results, we describe how a reduction in turnover times for cystoscopy would result in a reduction in OR allocation. A false argument is that it would be better financially not to reduce staffing but instead to do more cases, because if increasing contribution margin was the goal, the additional cases would have been done even if turnover time had not been reduced.
Reducing turnover times could prevent a case from being canceled out of concern that the case would finish after the end of regularly scheduled hours and thus increase OR costs. The fallacy to this argument is that, then, presumably decision-making is being done to minimize costs. When OR time is allocated and cases are scheduled based on minimizing costs, then canceling a case to prevent small amounts of over-utilized OR time results in overall increased costs whether analyzed from a societal, hospital, physician, or patient perspective (25).
Reducing turnover times could permit a surgeon to perform another case in his or her OR that could not otherwise be completed within a fixed time period (not that there should be fixed time periods as explained in the preceding two paragraphs). The fallacy of this argument is that average turnover times are usually shorter than the mean absolute difference between scheduled and actual case durations (2). Thus, achievable turnover time reductions (4,7–9) would not permit another case to be scheduled and completed during the fixed time period, just performed on an add-on unscheduled basis (2). For example, suppose that Dr. Smith’s total hip replacements have case durations of 3.7 hours ± 0.9 hours (mean ± SD); the statistical distribution is log normal (26); the cases are scheduled for 3.7 hours (27); the turnover times are 0.5 hours; and the fixed time period is 8 hours. Even if turnover times were zero, a new 0.5-hour case could not be completed within 8 hours on 44% of workdays (2,4). In fact, for 29% of days the new case would not even be started since its start time would be after the end of the 8-hour workday. Note that reducing turnover times is of value for surgeons with very short cases because there are many turnover times in one OR in a day, and short cases have small mean absolute differences between scheduled and actual durations (2).
Reducing turnover times could permit another add-on case to be performed that could not otherwise be completed within a fixed time period. The latter could be based on a safety criterion or on a fixed, externally specified budget. This argument is that although reducing turnover time may not be sufficient to do an extra scheduled case in one OR (2), if turnovers are reduced in many ORs, then an add-on case can be done in one of them. The fallacy to this argument is that it presupposes that the additional case could not be scheduled unless turnover time was reduced (3). When a short add-on case is submitted, usually there will be some OR into which the case can be scheduled at the end of the day even without a reduction in turnover time. Rather, it is only very long add-on cases that cannot otherwise be scheduled without a reduction in OR time. Yet, reducing turnover times do not add up to enough OR time in any one OR to permit scheduling of a very long case that could not otherwise have been scheduled (3). For example, at 4 PM of the day before surgery, there are 5 add-on cases. To maximize OR utilization (22), the OR manager considers the add-on cases in descending order of mean historical case duration: coronary artery bypass graft (4.5 hours), total hip replacement (3.7 hours), cholecystectomy (3 hours), orchiopexy (2.3 hours), carpal tunnel release (1 hours), and adenoidectomy (1 hour). One OR has 3.1 hours open, so the cholecystectomy is scheduled. Two other ORs have 1.6 hours and 1.1 hours open, so the carpal tunnel and adenoidectomy are scheduled. All other ORs have 0.7 hour or less of time remaining. Now the question applies whether reducing turnover times in one OR can achieve the additional 1.6 hours (2.3 hours–0.7 hours) required to schedule the orchiopexy.
Reducing turnover times could indirectly reduce staffing costs by reducing the impact of erroneous turnover times on producing poor allocations of OR time. Often surgical suites have limited or erroneous data concerning in which OR each case was performed. However, incorrect (5) or unknown (6) OR assignments cause increases in some measured turnover times and reductions in others. The net effect is too small to be financially important (5,6).
Reducing turnover times could reduce delays in cases starting after their scheduled start times. Reducing all turnover times could reduce delays, since the shorter the average turnover time, the less the chance that a delay would be substantial. However, we show in the Appendix that previously reported reductions in turnover times (7–9) would then only reduce average delays by 1–2 minutes. Reducing turnover times selectively for ORs with longer than average turnover times would be more fruitful, reducing average delays by 4–7 minutes. However, this is negligible compared with the reductions in time achievable by appropriately choosing when patients are ready for surgery based on quantitative analysis of OR information systems data (28). Unlike reducing turnover times, achieving the latter costs very little.
The principal benefit to a surgical suite in reducing turnover times is to reduce staffing costs. That is why, in this study, we described a methodology by which an anesthesiologist and/or OR manager can show stake holders at his or her surgical suite what would be the effect of reducing turnover time on staffing costs at their own surgical suite. The method will also show, for each potential reduction in average turnover time, by how much the surgeons’ allocated OR time would be reduced.
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Appendix |
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TopAbstractIntroductionMethodsResultsDiscussionAppendixReferences |
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We define:
(z) Distribution function of a standard normal random variable z
f(c) Probability density function for normally distributed case (c) effect with 0 mean
The average underestimate of turnover time for a surgical suite equals
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We represent decreases in turnover times for a surgical suite by multiplying µ by a value gs, 0 < gs 
1. From Equation (4), the ratio of the mean turnover time before and after intervention equals
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From Equation (1), the ratio of the mean under-estimate of turnover time before and after intervention equals
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Thus, from Equation 1, the percentage decrease in the mean length of time that all turnovers finish after their scheduled times, before and after the intervention, equals the percentage change in the mean turnover time for all turnovers at the surgical suite before and after intervention. Results for Hospitals A–D, using previously reported reductions in turnover times (7–9), are given in column 3 of Table 3.
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c, while leaving unchanged turnovers with c 0. From Equations (2), (3), (4), and (5), the ratio of the mean turnover times before and after intervention equals
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From Equations (2) and (3), the ratio of the mean under-estimates of turnover time before and after intervention equals
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Rearranging Equation (7) and substituting it into the denominator of Equation (8), the ratio of the mean under-estimates of turnover time before and after intervention equals
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Values of 
c were 0.621 for Hospital A, 0.594 for Hospital B, 0.572 for Hospital C, and 0.562 for Hospital D. Reductions inpatient delays are given in column 4 of Table 3.
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Acknowledgments |
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Footnotes |
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Medical Data Applications (MDA), Ltd., developed and distributes CalculatORTM software that performs the analysis described in this article.
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References |
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This article has been cited by other articles:
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  C. McIntosh, F. Dexter, and R. H. Epstein 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., December 1, 2006; 103(6): 1499 - 1516. [Abstract] [Full Text] [PDF]
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F. Dexter and R. E. Wachtel Economic, Educational, and Policy Perspectives on the Preincision Operating Room Period Anesth. Analg., October 1, 2006; 103(4): 919 - 921. [Full Text] [PDF]
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A. Escobar, E. A. Davis, J. Ehrenwerth, G. A. Watrous, G. S. Fisch, Z. N. Kain, and P. G. Barash Task Analysis of the Preincision Surgical Period: An Independent Observer-Based Study of 1558 Cases Anesth. Analg., October 1, 2006; 103(4): 922 - 927. [Abstract] [Full Text] [PDF]
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H. Saadat, A. Escobar, E. A. Davis, J. Ehrenwerth, G. Watrous, G. S. Fisch, Z. N. Kain, and P. G. Barash Task Analysis of the Preincision Period in a Pediatric Operating Suite: An Independent Observer-Based Study of 656 Cases Anesth. Analg., October 1, 2006; 103(4): 928 - 931. [Abstract] [Full Text] [PDF]
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F. Dexter, J. C. Yue, and A. J. Dow Predicting anesthesia times for diagnostic and interventional radiological procedures. Anesth. Analg., May 1, 2006; 102(5): 1491 - 1500. [Abstract] [Full Text] [PDF]
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 J. C. Cendan and M. Good Interdisciplinary Work Flow Assessment and Redesign Decreases Operating Room Turnover Time and Allows for Additional Caseload Arch Surg, January 1, 2006; 141(1): 65 - 69. [Abstract] [Full Text] [PDF]
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 F. Dexter, A. Macario, R. H. Epstein, and J. Ledolter Validity and usefulness of a method to monitor surgical services' average bias in scheduled case durations: [Validite et utilite d'un monitorage du biais moyen des services chirurgicaux dans la duree prevue des cas regles] Can J Anesth, November 1, 2005; 52(9): 935 - 939. [Abstract] [Full Text] [PDF]
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F. Dexter, E. Marcon, R. H. Epstein, and J. Ledolter Validation of Statistical Methods to Compare Cancellation Rates on the Day of Surgery Anesth. Analg., August 1, 2005; 101(2): 465 - 473. [Abstract] [Full Text] [PDF]
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M. Schuster, T. Standl, H. Reissmann, L. Kuntz, and J. S. am Esch Reduction of Anesthesia Process Times After the Introduction of an Internal Transfer Pricing System for Anesthesia Services Anesth. Analg., July 1, 2005; 101(1): 187 - 194. [Abstract] [Full Text] [PDF]
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 A. Macario, F. Dexter, and D. Lubarsky Meta-analysis of trials comparing postoperative recovery after anesthesia with sevoflurane or desflurane Am. J. Health Syst. Pharm., January 1, 2005; 62(1): 63 - 68. [Abstract] [Full Text] [PDF]
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S. Z. Yoon and F. Dexter The Necessity of Guidelines for Any Workday or Four Weeks Systems for Allocating OR Times * Response Anesth. Analg., October 1, 2004; 99(4): 1263 - 1263. [Full Text] [PDF]
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F. Dexter and D. L. Brown Financial Disclosure Anesth. Analg., June 1, 2004; 98(6): 1811 - 1811. [Full Text] [PDF]
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F. Dexter, R. H. Epstein, A. E. Abouleish, C. W. Whitten, and D. A. Lubarsky Impact of Reducing Turnover Times on Staffing Costs Anesth. Analg., March 1, 2004; 98(3): 872 - 872. [Full Text] [PDF]
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R. Udelsman The Operating Room: War Results in Casualties Anesth. Analg., October 1, 2003; 97(4): 936 - 937. [Full Text] [PDF]
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