Journal of Clinical Anesthesia (2014) 26, 341–342
Editorial
High-quality operating room management research ☆ In this month’s issue of the Journal of Clinical Anesthesia, Austin and his colleagues quantify the extra turnover time sustained when there are different surgeons versus the same surgeon between two successive cases in an operating room (OR) [1]. The difference averages 7.4 minutes, but may potentially be as brief as 6.8 minutes or as long as 8.1 minutes [1]. This result is useful, because whether turnovers between two surgeons would be greater than turnovers between the same surgeon’s cases is not an issue. Had there been no difference, the results might reasonably be discounted, because when a different surgeon follows, the second surgeon may be delayed in arriving at the facility (ie, the turnover increased) [2]. The question of importance (for years) has been how large a mean increase should be expected. As is nicely summarized in the authors’ Discussion, the magnitude of difference is not large enough to warrant limiting surgeons’ flexibility in scheduling [1]. Furthermore, the difference in the mean turnover time from the overall average is sufficiently small that the yield in incorporating the difference in OR information system software likely is insufficient to warrant the expense [1]. Readers can rely on these findings. They are a good, incremental advance in the science of OR management. Relying on the findings means that, in the absence of repeating the entire study at one’s own institution, including the analysis, one may make decisions appropriately based on the assumption that their own findings would be the same. Make management decisions based on the assumption that the readers' own institution’s turnover times would be the same. The authors systematically reviewed the extensive previous literature of turnover times and nicely summarized that this difference had not previously been estimated. The authors’ review provides the context with which to understand and apply their findings [1]. One frustrating feature of many other OR management science articles is the lack of reliance on prior relevant work. A consequence is that findings are not reproducible at even
☆
The author has no disclosures or conflicts of interest to report.
http://dx.doi.org/10.1016/j.jclinane.2014.05.005 0952-8180/© 2014 Elsevier Inc. All rights reserved.
the authors’ own organization, nonetheless at other facilities [3]. This is very much not the situation of Austin and colleagues’ study. Readers can rely on there being strong a priori evidence that, for their organization, the findings would be the same. Recently, I worked with Dr. Wachtel to understand how it could be that, with the ease of use of PubMed, previous relevant studies were not identified [3]. The motivation for our study was a review article on turnover times that, itself, missed most of the articles.1 The reason, in part, was that entering “operating room turnovers” into the PubMed search box without quotation marks did (and does) not result in appropriate articles [3]. This situation occurs because PubMed’s search tools are designed for clinical questions [3]. The implication for readers is that unless one is routinely reading scientific articles in OR management (eg, more than one per wk), to find relevant articles one should rely on experts [1,3]. If not obtaining advice by email, rely on lectures online1 that include the specific vocabulary and corresponding references [3]. From such sources, find a related article and then rely on its references. The article by Austin and colleagues is sufficient and provides an excellent review of much of what is known about turnover times [1]. The authors appropriately analyze the data using batches [1,4]. There can be batches by four-week periods [5-7]. There can be batches by month or several (eg, 5) individual surgeon’s “blocks” [8,9]. It does not matter; that is, the results will be the same. What is important is that turnover times are correlated to one another, and this correlation must be taken into account in analyses [1,6,10]. The mechanism of the correlation is, in part, the personnel who facilitate turnovers [6,11]. These personnel are waiting for the extubation to occur, and then will start cleaning [12,13]. If the numbers of simultaneous turnovers exceed the numbers of such personnel, then there is waiting [6,10]. There also are 1
Lessons on Turnover Time. Available at www.FranklinDexter.net/ bibliography_TurnoverTimes.htm, www.FranklinDexter.net/education.htm, www.FranklinDexter.net/Lectures/TurnoverTime.pdf, and www. FranklinDexter.net/Lectures/PatientStreamTurnoverReducingOR.pdf. Accessed May 13, 2014.
342
Editorial
behavioral effects that occur from surgeon behavior after a prolonged extubation (eg, leaving the surgical suite when there is an unusually long anesthesia and/or turnover time) [14]. Once batched, the data conveniently also follow normal distributions facilitating analysis [1,6,9]. For readers, the implication is that when evaluating turnover times (and, frankly, most every other endpoint in OR management), unless a hospital report contains a confidence interval, do not trust it, and when provided the confidence interval ask whoever made the calculation how they know that it is an accurate confidence interval. If mention is not made by that individual of “batching” or “control chart” (they are equivalent terms) [8,9], or the individual who performed the analyses does not know of a (the) scientific paper showing that their method is accurate, discount the information. Likely, it is not meaningful. Instead, rely on high quality scientific evidence from other organizations. Rest assured, the Austin et al paper in this month’s issue [1] is one that you may rely upon. Franklin Dexter, PhD, MD (Professor of Anesthesia; Director, Division of Management Consulting) Division of Management Consulting Department of Anesthesia, University of Iowa Iowa City, IA 52242, USA E-mail address: [email protected]
References [1] Austin TM, Lam HV, Shin NS, Daily BJ, Dunn PF, Sandberg WS. Elective change of surgeon during the OR day has an operationally negligible impact on turnover time. J Clin Anesth 2014;26:XXX-XXX.
[2] Wachtel RE, Dexter F. Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg 2009;108:1889-901. [3] Wachtel RE, Dexter F. Difficulties and challenges associated with literature searches in operating room management, complete with recommendations. Anesth Analg 2013;117:1460-79. [4] 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. [5] 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 [erratum: Anesth Analg 2012;114:693]. [6] 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. [7] Dexter F, Epstein RH. Increased mean time from end of surgery to operating room exit in a historical cohort of cases with prolonged time to extubation. Anesth Analg 2013;117:1453-9. [8] Seim A, Andersen B, Sandberg WS. Statistical process control as a tool for monitoring nonoperative time. Anesthesiology 2006;105:370-80. [9] Smith MP, Sandberg WS, Foss J, et al. High-throughput operating room system for joint arthroplasties durably outperforms routine processes. Anesthesiology 2008;109:25-35. [10] 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. [11] 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. [12] Wachtel RE, Dexter F, Epstein RH, Ledolter J. Meta-analysis of desflurane and propofol average times and variability in times to extubation and following commands. Can J Anaesth 2011;58:714-24. [13] Masursky D, Dexter F, Kwakye MO, Smallman B. Measure to quantify the influence of time from end of surgery to tracheal extubation on operating room workflow. Anesth Analg 2012;115:402-6. [14] Dexter F, Bayman EO, Epstein RH. Statistical modeling of average and variability of time to extubation for meta-analysis comparing desflurane to sevoflurane. Anesth Analg 2010;110:570-80.
Editorial
High-quality operating room management research ☆ In this month’s issue of the Journal of Clinical Anesthesia, Austin and his colleagues quantify the extra turnover time sustained when there are different surgeons versus the same surgeon between two successive cases in an operating room (OR) [1]. The difference averages 7.4 minutes, but may potentially be as brief as 6.8 minutes or as long as 8.1 minutes [1]. This result is useful, because whether turnovers between two surgeons would be greater than turnovers between the same surgeon’s cases is not an issue. Had there been no difference, the results might reasonably be discounted, because when a different surgeon follows, the second surgeon may be delayed in arriving at the facility (ie, the turnover increased) [2]. The question of importance (for years) has been how large a mean increase should be expected. As is nicely summarized in the authors’ Discussion, the magnitude of difference is not large enough to warrant limiting surgeons’ flexibility in scheduling [1]. Furthermore, the difference in the mean turnover time from the overall average is sufficiently small that the yield in incorporating the difference in OR information system software likely is insufficient to warrant the expense [1]. Readers can rely on these findings. They are a good, incremental advance in the science of OR management. Relying on the findings means that, in the absence of repeating the entire study at one’s own institution, including the analysis, one may make decisions appropriately based on the assumption that their own findings would be the same. Make management decisions based on the assumption that the readers' own institution’s turnover times would be the same. The authors systematically reviewed the extensive previous literature of turnover times and nicely summarized that this difference had not previously been estimated. The authors’ review provides the context with which to understand and apply their findings [1]. One frustrating feature of many other OR management science articles is the lack of reliance on prior relevant work. A consequence is that findings are not reproducible at even
☆
The author has no disclosures or conflicts of interest to report.
http://dx.doi.org/10.1016/j.jclinane.2014.05.005 0952-8180/© 2014 Elsevier Inc. All rights reserved.
the authors’ own organization, nonetheless at other facilities [3]. This is very much not the situation of Austin and colleagues’ study. Readers can rely on there being strong a priori evidence that, for their organization, the findings would be the same. Recently, I worked with Dr. Wachtel to understand how it could be that, with the ease of use of PubMed, previous relevant studies were not identified [3]. The motivation for our study was a review article on turnover times that, itself, missed most of the articles.1 The reason, in part, was that entering “operating room turnovers” into the PubMed search box without quotation marks did (and does) not result in appropriate articles [3]. This situation occurs because PubMed’s search tools are designed for clinical questions [3]. The implication for readers is that unless one is routinely reading scientific articles in OR management (eg, more than one per wk), to find relevant articles one should rely on experts [1,3]. If not obtaining advice by email, rely on lectures online1 that include the specific vocabulary and corresponding references [3]. From such sources, find a related article and then rely on its references. The article by Austin and colleagues is sufficient and provides an excellent review of much of what is known about turnover times [1]. The authors appropriately analyze the data using batches [1,4]. There can be batches by four-week periods [5-7]. There can be batches by month or several (eg, 5) individual surgeon’s “blocks” [8,9]. It does not matter; that is, the results will be the same. What is important is that turnover times are correlated to one another, and this correlation must be taken into account in analyses [1,6,10]. The mechanism of the correlation is, in part, the personnel who facilitate turnovers [6,11]. These personnel are waiting for the extubation to occur, and then will start cleaning [12,13]. If the numbers of simultaneous turnovers exceed the numbers of such personnel, then there is waiting [6,10]. There also are 1
Lessons on Turnover Time. Available at www.FranklinDexter.net/ bibliography_TurnoverTimes.htm, www.FranklinDexter.net/education.htm, www.FranklinDexter.net/Lectures/TurnoverTime.pdf, and www. FranklinDexter.net/Lectures/PatientStreamTurnoverReducingOR.pdf. Accessed May 13, 2014.
342
Editorial
behavioral effects that occur from surgeon behavior after a prolonged extubation (eg, leaving the surgical suite when there is an unusually long anesthesia and/or turnover time) [14]. Once batched, the data conveniently also follow normal distributions facilitating analysis [1,6,9]. For readers, the implication is that when evaluating turnover times (and, frankly, most every other endpoint in OR management), unless a hospital report contains a confidence interval, do not trust it, and when provided the confidence interval ask whoever made the calculation how they know that it is an accurate confidence interval. If mention is not made by that individual of “batching” or “control chart” (they are equivalent terms) [8,9], or the individual who performed the analyses does not know of a (the) scientific paper showing that their method is accurate, discount the information. Likely, it is not meaningful. Instead, rely on high quality scientific evidence from other organizations. Rest assured, the Austin et al paper in this month’s issue [1] is one that you may rely upon. Franklin Dexter, PhD, MD (Professor of Anesthesia; Director, Division of Management Consulting) Division of Management Consulting Department of Anesthesia, University of Iowa Iowa City, IA 52242, USA E-mail address: [email protected]
References [1] Austin TM, Lam HV, Shin NS, Daily BJ, Dunn PF, Sandberg WS. Elective change of surgeon during the OR day has an operationally negligible impact on turnover time. J Clin Anesth 2014;26:XXX-XXX.
[2] Wachtel RE, Dexter F. Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg 2009;108:1889-901. [3] Wachtel RE, Dexter F. Difficulties and challenges associated with literature searches in operating room management, complete with recommendations. Anesth Analg 2013;117:1460-79. [4] 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. [5] 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 [erratum: Anesth Analg 2012;114:693]. [6] 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. [7] Dexter F, Epstein RH. Increased mean time from end of surgery to operating room exit in a historical cohort of cases with prolonged time to extubation. Anesth Analg 2013;117:1453-9. [8] Seim A, Andersen B, Sandberg WS. Statistical process control as a tool for monitoring nonoperative time. Anesthesiology 2006;105:370-80. [9] Smith MP, Sandberg WS, Foss J, et al. High-throughput operating room system for joint arthroplasties durably outperforms routine processes. Anesthesiology 2008;109:25-35. [10] 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. [11] 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. [12] Wachtel RE, Dexter F, Epstein RH, Ledolter J. Meta-analysis of desflurane and propofol average times and variability in times to extubation and following commands. Can J Anaesth 2011;58:714-24. [13] Masursky D, Dexter F, Kwakye MO, Smallman B. Measure to quantify the influence of time from end of surgery to tracheal extubation on operating room workflow. Anesth Analg 2012;115:402-6. [14] Dexter F, Bayman EO, Epstein RH. Statistical modeling of average and variability of time to extubation for meta-analysis comparing desflurane to sevoflurane. Anesth Analg 2010;110:570-80.
