For surgical cases with general anesthesia, components of nonsurgical operating room (OR) time include induction (e.g., arrival of patient in the OR until the start of positioning), extubation (e.g., dressing on patient to extubation), and OR exit. The interval between the end of surgery and extubation (i.e., extubation time) is of special interest to anesthesia care providers and companies because it is affected by anesthetic agents.1–3
The impact of delays in extubation on the steps taken for the patient to progress out of the OR (i.e., workflow) are challenging to assess because such delays may or may not be a bottleneck to the patient leaving the OR. There is a difference between an extubation that takes 10 minutes, with everyone in the OR doing clinical work during the interval, versus an extubation that takes 15 minutes, with most people in the OR idle for some part of the interval. The relationship between extubation time and workflow depends partly on the parallel tasks occurring in the OR (e.g., removal of drapes and cleaning of the patient). Because these parallel tasks typically take longer than 3 minutes to perform, a reduction in the extubation time from 6 minutes to 3 minutes probably cannot reduce the OR time by 3 minutes. In contrast, a reduction in extubation time from 15 minutes to 12 minutes probably can reduce OR time by 3 minutes. Such a reduction of 3.0 minutes can reduce costs by around 3.5 minutes' worth.2,4–7 We developed a measure for observational studies and for lean engineering projects to assess the influence of extubation times on OR workflow.
The study was approved by the State University of New York Upstate Medical University IRB without requirement for written patient or staff consent. The cohort of patients was adults (≥18 years) undergoing elective (scheduled) general anesthesia at an academic hospital. A convenience sample of 100 tracheal extubations were observed May to July 2011. Every one of these patients was extubated in the OR.
During the first month's 36 cases, observation was used to develop the measure (see Appendix). The primary observer (D.M.) discussed with each anesthesiologist assigned to the ORs during the study period that she was performing an “efficiency” project involving observation of and timeliness of extubations. In addition, the primary observer was frequently asked by OR staff (e.g., when idle during extubations) what she was doing, and she explained that she was doing an efficiency project involving observation of and timeliness of extubations. She noted some initial self-consciousness on the part of some staff about being observed, but over the initial month of observation, the staff became accustomed to her presence. At the hospital, observers unrelated to our study were present daily in the ORs (e.g., medical and nursing students).
A functional definition was created for when the dressing was on the patient. No dressing was applied for some procedures (e.g., surgery in the nasal cavity). The time calculation was considered to begin when the surgeon appeared to finish, generally when the surgeon performed the final wipe of the surgical area. For other procedures, the dressing was elaborate, with multiple layers (e.g., gauze, padding, and elastic bandage). The time calculation began when the first layer of dressing was completed.
The endpoint was whether at least one person was idle in the OR for at least 1 minute between when the dressing was applied (or equivalent) and the patient was extubated. The assessment potentially included, but was not limited to, OR staff members (e.g., surgeon, surgical resident, anesthesiologist, anesthesiology resident, nursing staff, or medical student). Before closure, others routinely were present (e.g., radiology technicians, neurological monitoring technicians, device manufacturer representatives). However, rarely (<5%) were they still present when the dressing was placed. By extubation, the people commonly present were the 5 required: a member of the surgical team, an OR nurse, the surgical technologist, the anesthesia provider, and the anesthesiologist. A person was not considered to be “in the OR” if part of their body was outside of the OR (e.g., the count excluded a supervisor in the hallway who slightly opened the door to ask a question), although this definition rarely (<5%) influenced the count of people. A person was considered idle when doing no visible physical activity potentially related to patient care.8,9 For example, a person was considered idle when doing nothing other than looking around. A person was not considered to be idle if they were discussing care of the patient (e.g., debriefinga), holding any patient care equipment, using a computer or phone, cleaning up, removing their surgical gown, tidying the patient's gown or linen, etc.
Measuring idleness was straightforward, because each person present typically worked diligently doing things either until extubation or until they stopped and just stood (or sat) idle waiting for the patient to wake up. People rarely changed location in the OR from the time when idle until the patient woke up. Consequently, the observers used no worksheet, tool (artifact), or systematic notes to record peoples' locations. The assessment of idle people was, effectively, simply the process of looking for stationary (idle) people for 1 to 2 minutes before the time of extubation. Video 1 (see Supplemental Digital Content 1, http://links.lww.com/AA/A396) shows animation of a typical observation period, and online supplement 1 (see Supplemental Digital Content 2, http://links.lww.com/AA/A397) shows the animation using slides.
During the first month, the primary observer was inside each OR for at least part of the extubation time. However, most of the ORs have windows into corridors on 3 sides. The windows are so large that the people in the OR can fully see the observer watching them and vice versa (online supplement 2, see Supplemental Digital Content 3, http://links.lww.com/AA/A398). For the subsequent quantitative study, the observer(s) rarely entered the ORs.
The measure was tested for 64 extubations over the next 5 weeks. Every OR has a large analog clock with a second hand on it that is visible from (most) windows. The predictive validity of the measure was quantified using the Cochran-Armitage trend test between the binary presence/absence of at least 1 idle person and extubation time. The correlation would be reduced by poor functional definitions (e.g., of “idle”), presence of a medical student just observing for some cases, and/or staff knowing the purpose of the study and deliberately acting idle or active.
For the final 30 of 64 extubations, D.M. and M.O.K. both observed for idle people. If the measure were practically hard to apply, then the reliability would be impaired by M.O.K.'s lack of prior use of the measure. The sample size of 30 was chosen to detect an inter-rater reliability exceeding 90%. If there were perfect inter-rater reliability for n = 30/30, the lower 95% confidence limit for the incidence of perfect reliability would be >90% by the method of Blyth-Still-Casella.
The time from end of surgery to tracheal extubation was not part of the measure. Nonetheless, its interrater reliability was assessed because the reliability and validity of the count of people during the interval is sensitive to the reliability of the interval itself (e.g., definition of “dressing on patient”). Interrater reliability was quantified using Spearman's rank correlation coefficient and Krippendroff's α.10
P values and 95% confidence intervals were estimated using Monte-Carlo simulation to an accuracy of better than 0.0001 (StatXact-9, Cytel Software Corporation, Cambridge, MA).
There was significant positive dose response between the probability of at least 1 person idle and the extubation time (P < 0.0001, Table 1).
The 2 raters' listings of cases with no versus 1 or more people idle were identical (30/30). The 95% lower confidence limit is >90%.
The minutes from end of surgery to tracheal extubation were identical for 25/30 extubations, differed by 1 minute for 3 extubations, and differed by 2 minutes for 2 extubations. The rank correlation equaled 0.993 (P < 0.0001, 95% lower confidence limit 0.986). Krippendroff's α10 equaled 0.989.
The association of OR times with extubation times depends on intraoperative workflow.11,12 We developed a simple, valid, and reliable observational measure of the influence of extubation time on OR workflow. Longer time to extubation increases the chance of at least 1 person being idle in the OR. The endpoint needs to be used with stratification (e.g., by surgeon, as done previously),2 because typical extubation times and numbers of people present differ among surgeons, facilities, etc.
From previous studies,2,3,13–17 it is economically rational for people to be idle briefly while waiting for extubation, which supports the construct validity of the measure. Economic analyses are performed from a societal perspective. OR nurses, surgical technologists, housekeepers, etc., should be idle briefly, waiting for anesthesia providers.13–15 This is just as generally anesthesia providers should be waiting, briefly, for surgeons to arrive,18,19 because the typical surgeon has more years of costly education than the typical anesthesia provider. Times for tracheal extubation are highly variable in duration.2,3 Thus, a surgical suite focused on throughput should occasionally have staff idle, waiting for extubation. Outside of ORs, there typically are additional idle personnel (e.g., postanesthesia care unit nurses) waiting for the end of cases, because surgical suites appropriately staff for multiple ORs in which cases end simultaneously.14,15,17
Development and testing was done for adult cases, not pediatrics. Surgeon presence in the OR during and soon after extubation may be different for pediatrics and, if so, the appropriate measure may be different. No casts were observed; the definition of “dressing on” the patient being the time of the first layer would not be suitable for a patient with a spica cast. Furthermore, extubations were done awake, not deep.
Measuring the total idle time of all personnel in the OR may seem preferable to our measure. However, this was impractical for a single observer to do without error. We considered the use of only 1 observer to be practical economically for routine use of the measure. Furthermore, not only is the direct cost of OR time not proportional to the total idle time,2,4–7 but neither is the intangible cost of impaired OR workflow.2 The intangible cost is statistically significant and managerially important.2,20 Surgeons' perceptions of nonoperative times are not proportional to the times.21,22 The binary endpoint captures this relationship.
In a previous study at an outpatient surgery center, there was at least 1 person idle for at least 1 minute during extubation for n = 7/7 cases with propofol general anesthesia.3 This finding highlights that for use of the endpoint, the threshold may need to be adjusted for local practice. For example, if at the facility a housekeeper always enters the OR once the dressing has been placed on the patient then the binary threshold would be at least 2 people idle. Our instrument is suitable for lean engineering projects, not benchmarking studies.
There are 2 other major limitations to the generalizability of the measure to other facilities.
First, a trained observer needs to have an unobstructed view of all corners of the OR at the end of surgery. This matches the conclusions that investigators have reached for assessing anesthesia workload during induction.23–25 Neither cameras (Video 1, see Supplemental Digital Content 1, http://links.lww.com/AA/A396)26 nor self-reporting27 were considered23–25 to be practical. In our experience, personnel standing at the bedside frequently obscured the endotracheal tube from view from one orientation (e.g., a single camera). Development of the measure was facilitated by the presence of multiple large windows into the studied hospital's ORs (Online Supplement 2, see Supplemental Digital Content 3, http://links.lww.com/AA/A398). However, many facilities do not have this characteristic.
Second, the relationship between whether there is at least 1 person idle and OR workflow assumes that extubation is performed in the OR routinely, as compared to the postanesthesia care unit or an immediately adjacent room.28 That workflow differs among facilities.
In summary, we developed a measure to quantify the consequences on OR workflow of the time from end of surgery to tracheal extubation. The measure is simple, being solely the binary value: presence of at least 1 person doing no visible physical activity potentially related to patient care for at least 1 minute during the period. The measure had perfect reliability when administered by observers in or adjacent to the OR. The measure supplements previous work for quantifying the influence of induction time on OR workflow.23–25
Franklin Dexter is the Statistical Editor and Section Editor for Economics, Education, and Policy for Anesthesia & Analgesia. This manuscript was handled by Steve Shafer, Editor-in-Chief, and Dr. Dexter was not involved in any way with the editorial process or decision.
Name: Danielle Masursky, PhD.
Contribution: This author helped design the study, conduct the study, and write the manuscript.
Attestation: Danielle Masursky has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.
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.
Name: Michael O. Kwakye, Medical Student.
Contribution: This author helped conduct the study.
Attestation: Michael O. Kwakye has seen the original study data and approved the final manuscript.
Name: Bettina Smallman, MD, FRCPC.
Contribution: This author helped conduct the study.
Attestation: Bettina Smallman approved the final manuscript.
a Patterson P. Hardwiring the right-site process. OR Manager 2011;27(8):9–11.
APPENDIX: QUALITATIVE OBSERVATIONS NOT INCLUDED IN THE MEASURE
The number of healthcare providers who left the OR and returned was not a reliable measure of the consequences on OR workflow of longer extubation times. Excluding brief routine activities like going to get a warm blanket or the transport bed, such leaving and reentering occurred rarely, only once or twice over the 100 observed extubations (i.e., “<5%”). As reported previously,8,9 the postoperative surgical debriefings occurred after the circulating nurse completed counting (see footnote a in Methods). Although the surgeon frequently left before the dressing was on the patient, letting the resident finish the case, the surgeon rarely (<5%) returned to the OR until the next case (see Discussion). The anesthesiologist almost never (<5%) left the OR (e.g., to check their other OR) and returned between the period from dressing on the patient to extubation.
A prolonged extubation1,2 (≥15 minutes) would be especially consequential if there were >8 hours of cases scheduled in the OR and another case scheduled to follow in the OR on the same day.6,7,29,30 However, including this information on subsequent cases in the OR was impractical for the measure for two reasons.
First, as we reported previously,31,32 managers (appropriately) increased staffing when there were >8 hours of cases in the OR and a case to-follow the extubation. Thus, the presence of a to-follow case was correlated to the number of people idle in the OR.
Next, “whether there was another case scheduled,” was too static (i.e., “whether” or not) because the decision appropriately changed over time. The decision can appropriately change whenever an add-on is scheduled.33,34 The decision can also change whenever times remaining in other ongoing cases in the suite are updated,35 as that affects in which ORs add-on cases are performed and whether cases are moved.29,31,33,34 Inquiring whether the anesthesia provider knew the future plan for the OR would have made the measure no longer observational and would have been a distraction36,37 from clinical care, because the anesthesia provider would need to be asked.
Contemporary studies of team activity in ORs would consider a comment by a surgeon, surgical resident, and/or nurse about the extubation time to be a distracting communication not contributing to the care of the patient.36,37 Among the first 36 extubations, no such comments were heard. Thus, it was evident that the presence of such comments would not be included in our measure.
1. Apfelbaum JL, Grasela TH, Hug CC Jr, McLeskey CH, Nahrwold ML, Roizen MF, Stanley TH, Thisted RA, Walawander CA, White PF. The initial clinical experience of 1819 physicians in maintaining anesthesia with propofol: characteristics associated with prolonged time to awakening. Anesth Analg 1993;77:S10–4
2. 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
3. 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
4. Dexter F, Macario A, Manberg PJ, Lubarsky DA. Computer simulation to determine how rapid anesthetic recovery protocols to decrease the time for emergence or increase the phase I post anesthesia care unit bypass rate affect staffing of an ambulatory surgery center. Anesth Analg 1999;88:1053–63
5. Abouleish AE, Dexter F, Whitten CW, Zavaleta JR, Prough DS. Quantifying net staffing costs due to longer-than-average surgical case durations. Anesthesiology 2004;100:403–12
6. McIntosh C, Dexter F, Epstein RH. Impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: tutorial using data from an Australian hospital. Anesth Analg 2006;103:1499–516
7. Dexter F, Epstein RH. Typical savings from each minute reduction in tardy first case of the day starts. Anesth Analg 2009;108:1262–7
8. Berenholz SM, Schumacher K, Hayanga AJ, Simon M, Goeschel C, Pronovost PJ, Shanley CJ, Welsh RJ. Implementing standardized operating room briefings and debriefings at a large regional medical center. Jt Comm J Qual patient Saf 2009;35:391–7
9. Wolf FA, Way LW, Stewart L. The efficacy of medical team training: improved team performance and decreased operating room delays: a detailed analysis of 4863 cases. Ann Surg 2010;252:477–83
10. Freelon DG. ReCal: intercoder reliability calculation as a web service. International J of Internet Science 2010;5:20–33
11. Sandberg WS, Daily B, Egan M, Stahl JE, Goldman JM, Wiklund RA, Rattner D. Deliberate perioperative systems design improves operating room throughput. Anesthesiology 2005;103:406–18
12. Cendan JC, Good M. Interdisciplinary work flow assessment and redesign decreases operating room turnover time and allows for additional caseload. Archiv Surg 2006;141:65–9
13. Dexter F, Wachtel RE, Epstein RH. Impact of average patient acuity on staffing of the phase I PACU. J PeriAnesth Nurs 2006;21:303–10
14. Marcon E, Dexter F. Observational study of surgeons' sequencing of cases and its impact on post-anesthesia care unit and holding area staffing requirements at hospitals. Anesth Analg 2007;105:119–26
15. 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
16. O'Neill L, Dexter F, Wachtel RE. Should anesthesia groups advocate funding of clinics and scheduling systems to increase operating room workload? Anesthesiology 2009;111:1016–24
17. Baumgart A, Denz C, Bender HJ, Schleppers A. Show work context affects operating room processes: using data mining and computer simulation to analyze facility and process design. Qual Manag Health Care 2009;18:305–14
18. Koenig T, Neumann C, Ocker T, Kramer S, Spies C, Schuster M. Estimating the time needed for induction of anaesthesia and its importance in balancing anaesthetists' and surgeons' waiting times around the start of surgery. Anaesthesia 2011;66:556–62
19. Epstein RH, Dexter F. Influence of supervision ratios by anesthesiologists on first-case starts and critical portions of anesthetics. Anesthesiology 2012;116:683–91
20. Masursky D, Dexter F, Isaacson SA, Nussmeier NA. Surgeons' and anesthesiologists' perceptions of turnover times. Anesth Analg 2011;112:440–4
21. Eappen S, Flanagan H, Lithman R, Bhattacharyya N. The addition of a regional block team to the orthopedic operating rooms does not improve anesthesia-controlled times and turnover time in the setting of long turnover times. J Clin Anesth 2007;19:85–91
22. 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
23. Davis EA, Escobar A, Ehrenwerth J, Watrous GA, Fisch GS, Kain ZN, Barash PG. Resident teaching versus the operating room schedule: an independent observer-based study of 1558 cases. Anesth Analg 2006;103:932–7
24. Saadat H, Escobar A, Davis EA, Ehrenwerth J, Watrous G, Fisch GS, Kain ZN, Barash PG. Task analysis of the preincision period in a pediatric operating suite: an independent observer-based study of 656 cases. Anesth Analg 2006;103:928–31
25. Escobar A, Davis EA, Ehrenwerth J, Watrous GA, Fisch GS, Kain ZN, Barash PG. Task analysis of the preincision surgical period: an independent observer-based study of 1558 cases. Anesth Analg 2006;103:922–7
26. Xiao Y, Dexter F, Hu P, Dutton RP. Usage of distributed displays of operating room video when real-time occupancy status was available. Anesth Analg 2008;106:554–60
27. Overdyk FJ, Harvey SC, Fishman RL, Shippey F. Successful strategies for improving operating room efficiency at academic institutions. Anesth Analg 1998;86:896–906
28. Sandberg WS, Daily B, Egan M, Stahl JE, Goldman JM, Wiklund RA, Rattner D. Deliberate perioperative systems design improves operating room throughput. Anesthesiology 2005;103:406–18
29. Dexter F, Epstein RD, 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
30. 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
31. Wachtel RE, Dexter F. Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg 2009;108:1889–901
32. Dexter EU, Dexter F, Masursky D, Kasprowicz KA. Prospective trial of thoracic and spine surgeons' updating of their estimated case durations at the start of cases. Anesth Analg 2010;110:1164–8
33. 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
34. 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
35. 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
36. Sevdalis N, Healey AN, Vincent CA. Distracting communications in the operating theatre. J Eval Clin Pract 2007;13:390–4
37. Arora S, Hull L, Sevdalis N, Tierney T, Nestel D, Woloshynowych M, Darzi A, Kneebone R. Factors compromising safety in surgery: stressful events in the operating room. Am J Surg 2010;199:60–5