The American Society of Anesthesiologists has embraced the concept of the Perioperative Surgical Home as a means through which anesthesiologists can add value to the health systems in which they practice.a One key listed element of the Perioperative Surgical Home is to support “scheduling initiatives to reduce cancellations and increase efficiency.”b This is a conceptually sensible objective because cancellations (even those that result from moving a case to a subsequent day) disrupt the operating room (OR) schedule, create unnecessary work related to pulling supplies for case carts that are not used, and add to the workload of the scheduling office. Cases added on the day of surgery are another source of disruption to the planned OR schedule. This element of the Perioperative Surgical Home, scheduling and managing inpatient case cancellations and add-on cases, is the one with the greatest opportunity for cost reduction among hospital patients.1,2 In this article, we address just this one element of the Perioperative Surgical Home and not other components, such as standardization of care, preoperative risk stratification/optimization, and postoperative and postdischarge integration and coordination of care.
Cancellations of cases scheduled for the current or the following day and the booking of add-on cases require OR management decisions. For cases cancelled on the day of surgery, decisions relate to moving cases, calling earlier for “to-follow” cases, arranging for staff to stay late to finish the day’s workload, and/or using gaps in the OR schedule for breaks.3–5 For cancellations of cases scheduled for the next day, decisions involve changes to case sequence, alterations in instructions to outpatients as to when to arrive and/or the time when eating and drinking should cease, and, for inpatients, communication with patients, their families, and other care providers as to the scheduled start time. Even an assessment that nothing needs to be changed is a management decision.
For some procedures, such as repair of fractured hips, observational studies show that an interval to surgery >24 hours6 or 48 hours7 after admission increases the risk of patient morbidity and mortality. An interval to cholecystectomy >48 hours after the onset of symptoms for acute cholecystitis increases the risk of a failed laparoscopic approach.8 For both procedures, there are also studies on different managerial decisions influencing patient waiting times (i.e., evidence-based management matters).9,10 However, for many procedures, there are no data to determine whether delaying surgery results in patient harm and/or how best to reduce waiting times. Furthermore, from a management perspective, rescheduling of cancelled cases leads to slightly less variability in daily workload, provided that cases are scheduled based on maximizing the efficiency of use of OR time.11–13 Achieving a cancellation rate approaching 0% would not be desirable because this would likely mean that some patients would be undergoing procedures that should have been rescheduled because of the acute onset of medical conditions (e.g., new-onset atrial fibrillation, overnight development of a fever with a productive cough) or severe exacerbations of chronic medical conditions noted on arrival on the day of surgery (e.g., blood glucose of 600 mg/dL or arterial blood pressure of 220/130).
When anesthesiologists adeptly manage the preoperative processes (e.g., via clinics and/or call centers), it is possible to achieve outpatient cancellation rates on the day of surgery ≤2% at many facilities, including tertiary hospitals.1 Cancellation rates are much greater among perioperative inpatients (e.g., 8.1%1 among nonacademic hospitals, and 18.1%14 to 21%15 among academic hospitals).
In the current study, we took advantage of a hospital in which nearly all patients who are inpatient preoperatively (i.e., “inpatients”) are seen on the day before the scheduled surgical date by anesthesia residents. The residents use a mobile electronic preoperative evaluation system (JeffSprint™, Thomas Jefferson University, Philadelphia, PA) that requires the encounter to be signed electronically for the evaluation to be saved to the database.c The timestamps of these signature events were combined with timestamps of when cases were scheduled and cancelled from the OR scheduling system.16 This allowed us to evaluate issues related to the timing of the preoperative anesthesia evaluation and when inpatient cases were scheduled and cancelled on the OR management process as related to potential benefits from the Perioperative Surgical Home.
Using these unique data, we tested the following hypotheses and their associated implications:
- Hypothesis: Most (>50%) inpatient cases that were performed were evaluated by an anesthesia resident on the day before surgery using the electronic preoperative system. This hypothesis was tested for cases booked in the OR scheduling system as of 7:00 PM on the day before surgery (hypothesis 1a). As a sensitivity analysis, we also studied cases booked as of 11:00 PM (hypothesis 1b) because our residents do not typically wake up patients after 11:00 PM.
- Implication: If true, then expending effort to see a greater percentage of inpatient consultations on the day before surgery would be unlikely to reduce inpatient cancellation rates substantively, and this should not be an objective of the Perioperative Surgical Home.
- Hypothesis: Most (>50%) inpatient cases that were cancelled on the day of surgery were evaluated by an anesthesia resident on the day before surgery using the electronic preoperative system. This hypothesis was tested for cases booked in the OR scheduling system as of 7:00 PM on the day before surgery (hypothesis 2a). As a sensitivity analysis, we also studied cases booked as of 11:00 PM (hypothesis 2b), as for hypothesis 1.
- Implication: If true, then expending effort to see a greater percentage of inpatient consultations on the day before surgery would be unlikely to reduce inpatient cancellation rates substantively and should not be an objective of the Perioperative Surgical Home.
- Hypothesis: Most (>50%) inpatients evaluated by residents using the electronic preoperative system on the day before surgery had their evaluation conducted before 6:00 PM.
- Implication: If true, then evaluating patients earlier would be unlikely to reduce the number of cancellations, and this too should not be an objective.
- Hypothesis: There was considerable heterogeneity among cancelled surgical procedures, as reflected by a Herfindahl index17,d of <0.15.e A low value for the Herfindahl index indicates that many procedures account for the cancellations, not just a few dominant (i.e., very common) ones.f As a sensitivity analysis, we also considered a threshold of <0.05.
- Implication: If true, then concentrating on specific procedures to reduce cancellations (e.g., via clinical pathways) would not have an important overall impact on the cancellation rate and would not be a worthwhile activity within the Perioperative Surgical Home.
- Hypothesis: Of inpatients whose cases were cancelled after 7:00 AM on the day before surgery, most (>50%) had another procedure performed within 7 days of the cancellation. The basis for this hypothesis was our previous study of regular workday elective inpatient and outpatient surgical center and main OR suite case cancellations, where the same or a similar case was scheduled within 1 year of the cancellation in 86% of cases.12
- Implication: If false, then for at least half the patients, the indication for the cancelled procedure no longer existed and/or the cancellation was appropriate medically or in accordance with patient or family wishes. Attempting to prevent such cancellations might be medically counterproductive.
- Hypothesis: There were ≥1 inpatient cancellations and add-on cases scheduled per hour (between 6 AM and 3:59 PM) at the study hospital.
- Implication: If true, then the anesthesiologist(s) running the ORs should be engaged with the scheduling office throughout the regular workday.
The overall implication of these hypotheses, if accepted or rejected as noted above, would be that the strategy of the Perioperative Surgical Home for inpatient cancellations should not principally be to try to prevent them but rather to manage the disruptions to the OR schedule that they cause. The latter involves engaging with the scheduling office from the start of the workday and applying evidence-based management strategies to reduce the financial impact of the cancellations on the inefficiency of use of OR time, after first considering issues related to patient safety and surgeon’s access to the OR schedule.18–30 Such decisions cannot be made without education,31,32 because they require a detailed understanding of the quantitative aspects of OR management science not intuition or heuristics.21,22
The Thomas Jefferson University IRB determined that this study was not human subjects research.
Data related to the scheduling, rescheduling, and cancelling of cases were retrieved from the primary and audit tables in the OR scheduling system database (ORSOS®, McKesson, Kansas City, MO). The cases studied were those of inpatients at Thomas Jefferson University Hospital scheduled to be performed during 26 consecutive 4-week periods between July 2012 and June 2014. Data from 7 additional days also were retrieved so that we could evaluate cases that might have been performed within 7 days of cancellation during the final week of the study data set. Data pertaining to the timing of electronic preoperative patient evaluations were retrieved from the preoperative evaluation system database (SQL Server 2008 R2; Microsoft, Redmond, WA), which is stored on the anesthesia information management system server.
Specific data extracted included the internal case identifier, scheduled surgical dates, dates and times when cases initially were entered into the scheduling system, dates and times when cases were either cancelled or the surgical dates were changed, dates and times when the patients’ electronic preoperative consults for the scheduled day of surgery were signed, internal codes of the primary scheduled procedures, and whether cases were scheduled for an inpatient or outpatient. If the preoperative evaluation was electronically signed and saved multiple times on the day before surgery (e.g., the evaluation was interrupted, saved, and completed later), the signature timestamp was used from when the patient was seen initially. If the date of surgery was changed (i.e., the case was rescheduled but the a new case number was not created), this was coded as a cancellation for the original date of surgery and a new scheduling event for the new date of surgery, with the time of cancellation and rescheduling set to when the rescheduled case was saved in the system.g
Changes to the OR schedule that only involved moving the scheduled start time of the case on the same date were not counted as events requiring a management decision. This was a conservative approach, because including such resequencing events would have increased the number of decisions that anesthesiologists would need to consider on the day of surgery. Excluding such events reduced the likelihood that hypothesis 6 would be satisfied.
Case Exclusions from the Data Set
Figure 1 provides numerical details of case exclusions. After all scheduled case data were extracted, outpatient cases were excluded because we studied inpatient cancellations. Cases performed on hospital holidays and weekends were excluded because the scheduling office is closed on those dates and the OR caseload is low. Cancellations occurring >1 day before the day of surgery were excluded because the OR schedule is not finalized until the afternoon before the day of surgery, and such patients would not have been on the list of consults to be seen by residents. When a case was scheduled, cancelled, and rescheduled for a new date of surgery within 15 minutes, the initial scheduling and cancellation events were excluded because the cancellation likely was a clerical error.h Every one of these exclusions had the effect of our underestimating the true number of decisions made (see Discussion).
Case Inclusion/Exclusion Criteria for the Hypothesis-Driven Analyses
Table 1 describes the cases included in the analyses related to each hypothesis, along with explanations why some days were excluded. To summarize the criteria, days excluded had workflow characteristics that were substantively different from the included days. For example, we excluded Sundays and Fridays from the analysis of when patients were seen preoperatively (hypothesis 3) because there were very few scheduled cases on Saturdays that needed to be seen and the time course of consultations on Sundays was different from Mondays through Thursdays.
For our primary analyses of cancellations, we considered the total minutes of cancelled cases on each day of surgery as a fraction of the total scheduled minutes of cases for that day that had been entered in the OR scheduling system before the day of surgery (i.e., we excluded add-on cases entered on the day of surgery). We considered the minutes of cancelled cases to be primary, rather than the number of cancelled cases, because there is greater impact on the OR schedule resulting from cancellation of a long procedure compared with a short procedure.33 When calculated by service within hospitals, there is a wide range of differences among services for cancellation rates calculated using case counts versus total scheduled minutes.1 There are also considerable differences for the same services among hospitals.1 However, to facilitate comparison of our findings with cancellation rates in published studies (most of which are based on case counts rather than cancelled minutes), we also calculated, as secondary analyses, the cancellation rates using numerical counts of cancelled cases as a fraction of the number of scheduled cases.
Scheduled minutes were calculated using an unbiased Bayesian approach that incorporated historical information on the combination of surgeon and main procedure and the requested time to create an unbiased estimate.24,34–38
Our primary analyses included cases entered into the OR scheduling system as of 7:00 PM on the day before the day of surgery (hypotheses 1a and 2a), as we have done previously.1,12,16 As a sensitivity analysis, we repeated the analyses for cases entered as of 11:00 PM on the day before the day of surgery (hypotheses 1b and 2b). We classified cases added on the day of surgery to be performed that day as add-on cases. For convenience, when we refer to “the day before surgery” for cases that were cancelled, this means “the day before the scheduled day of surgery.”
We considered a patient as seen in advance if the patient was evaluated in our electronic preoperative system on the calendar day before the scheduled day of surgery. For technical reasons,i at the study hospital, preoperative evaluations for cases added on the day of surgery usually were completed on paper, so these manual encounters could not be analyzed. Again, we consequently designed our hypotheses so that such necessary exclusions have the effect of reducing the likelihood for hypotheses being satisfied (see Discussion).
For quantification of the scheduling office workload on the day of surgery, we determined the hourly number of add-on cases and all inpatient cancellations according to when the transactions were entered into the OR scheduling system. Counts of cases rather than scheduled minutes were used for these analyses because the number of cases not their scheduled duration influences the number of potential OR management decisions.
Hypothesis 1 (Most Performed Cases Were Evaluated on the Day Before Surgery)
For inpatient cases performed on Tuesdays through Fridays, we determined the percentage of cases in each of the 26 four-week batches that had been seen on the calendar day before surgery. This analysis was conducted for those cases which had been booked in the OR scheduling system as of 7:00 PM (hypothesis 1a) and as of 11:00 PM (hypothesis 1b) on the day before the day of surgery.
Hypothesis 2 (Most Cancelled Cases Were Evaluated on the Day Before Surgery)
For inpatient cases cancelled on the day of surgery on Tuesdays through Fridays, we determined the percentage of scheduled minutes of cases in each of the 26 four-week batches among patients who had been seen on the day before surgery. This analysis was conducted for those cases booked in the OR scheduling system as of 7:00 PM (hypothesis 2a) and as of 11:00 PM (hypothesis 2b) on the day before surgery. Analyses were repeated using case counts.
Hypothesis 3 (Most Evaluated Patients Were Seen Before 6 PM)
For inpatient cases scheduled to be performed the next day, and that were evaluated by an anesthesia resident on Mondays through Thursdays using the electronic preoperative system, we determined the hour during which the consult was initially seen. For each of the 26 four-week batches, we determined the percentage of patients who had been seen before 6:00 PM among all evaluations conducted on the days between 7:00 AM and 11:59 PM.
Hypothesis 4 (the Herfindahl Index Among Cancelled Procedures Was <0.15, Reflecting No Dominant Procedures)
As a measure of the diversity among cancelled procedures, the Herfindahl (Simpson’s) index was calculated within each of the 26 four-week batches using both case counts and scheduled durations of the cancelled proceduresj (footnotes d and e in the Introduction). This index is at most 1 and can be very small approaching but not reaching zero (e.g., for 1000 equally common procedures, the index would be 0.001, where 0.001 = 1000 × (1/1000)2).k Smaller fractions indicate greater diversity (i.e., lack of few dominant procedures accounting for most cancellations). These calculations were computed for the primary scheduled surgical procedure of the cancelled cases. This approach had the deliberate effect of our overestimating the actual Herfindahl index (i.e., reducing the chance of hypothesis 4 being satisfied). Cases were included if cancelled after 7:00 AM on the day before surgery.
Hypothesis 5 (Most Inpatient Cases That Were Cancelled Were Subsequently Performed Within 7 Days of the Cancellation)
For each inpatient whose case was cancelled, we determined whether any case on that patient was performed as an inpatient within 7 days after the cancellation. We determined the fraction of such cases of the total cases cancelled within each of the 26 four-week batches and determined whether most (>50%) of the cases were performed subsequently. This overestimated the fraction, because some of these cases were likely for procedures unrelated to the original cancelled procedure. This approach biased the results in favor of satisfying hypothesis 5 (see the Results for this hypothesis, below). Cases were included if cancelled after 7:00 AM on the day before surgery (Table 1).
Hypothesis 6 (Scheduling Office Activity Creates the Need for ≥1 Decision per Hour at the Study Hospital)
For each hour of the day on Mondays through Fridays, the number of inpatient cancellations for cases scheduled on the current day or the following day and the number of add-on cases scheduled were determined. Then, the averages were calculated for each of the 26 four-week batches. We normalized our numbers for formal testing purposes based on the median number of rooms with a first case start each day within the batch because the number of such ORs varied from batch to batch (range, 33–38). For each 1-hour interval between 6:00 AM and 3:59 PM, the fractions were normalized by dividing the median number of first case starts in each batch and then multiplying by 10 to provide the average number of events in each hour per 10 ORs with first case starts. This approach is generalizable to other hospitals. The 26 fractions among the 4-week batches were compared with >0.28 using the 1-sided Student t test for means for each hour (see next section). The value of 0.28 corresponded at the study hospital to at least 1 inpatient cancellation or add-on decision per hour based on the median numbers of ORs opened each day (n = 36) for first case starts among all 28 four-week batches. Activity outside regular working hours (i.e., before 6:00 AM and after 4:00 PM) was not tested versus ≥1 decision per hour because the anesthesiologist on call would already be involved in decisions related to such cancellation and add-on decisions. For this workload analysis, minutes of cancelled cases were not considered because the number of decisions is proportional to the number of cases cancelled or added on not their duration.
All calculations for which a mean ± SE are reported were performed using batch mean methods with batching by 4-week intervals to eliminate the effects of autocorrelation, as previously described and appropriate for OR management analyses.13,24,34,39–43
For comparisons of proportions to most (>50%, hypotheses 1, 2, 3), fractions in the 26 four-week batches for the indicated comparison were compared with >0.5 using the 1 group, 1-sided Student t test (Systat 12; Systat Software, San Jose, CA). “Most” was selected as the population mean comparator because the managerial implication is that if an event happens most of the time, then one should prepare for it. Finding that an event occurs in at least 50% of cases necessarily includes all values <50% (e.g., >25%, >10%).l The 95% confidence intervals (95% CI) for the means of the 26 four-week intervals were calculated by multiplying the SE by the inverse of the t statistic for n = 25 degrees of freedom. Comparisons of the Herfindahl indices with <0.15 and <0.05 (hypothesis 4) and the normalized number of scheduling office events with ≥1 (hypothesis 6) were each evaluated similarly. P < 0.05 was required to claim statistical significance. For the multiple comparisons of the normalized decisions per hour (hypothesis 4), each of the 10 reported P values was Bonferroni corrected by multiplying by 10.
Characteristics of the Population Studied
There were 103,135 scheduled and rescheduled cases in the data set, of which 24,735 (24%) were for inpatients (Fig. 1). Among inpatient cases, there were 22,985 with unique case identification numbers. Of all inpatient cases, 21,472 (86.8%) were scheduled exactly 1 time, 3263 (13.2%) were scheduled ≥2 times, 641 (2.6%) were scheduled ≥3 times, and 122 (0.6%) were scheduled 4 or 5 times (Fig. 1). No case was scheduled >5 times. After exclusion of holidays, weekends, cases cancelled >1 day before the scheduled day of surgery, and cases in which 2 changes to the scheduled date of surgery were entered ≤15 minutes apart, there were 22,154 cases that were analyzed (Fig. 1).
Inpatient cancellations after 7:00 AM on the day before the scheduled day of surgery (Tuesdays through Fridays) occurred in 22.6% ± 0.5% of scheduled inpatient minutes and 26.8% ± 0.4% of scheduled inpatient cases. Inpatient cancellations on the scheduled day of surgery occurred in 14.0% ± 0.3% of scheduled inpatient minutes and 11.8% ± 0.2% of scheduled inpatient cases.
Hypothesis 1: (Most Performed Cases Were Evaluated on the Day Before Surgery)
Among the 7101 performed cases entered in the OR scheduling system as of 7:00 PM on the previous calendar day, an anesthesia resident performed a preoperative anesthesia evaluation on the previous calendar day in 86.2% ± 0.6% of cases (95% CI, 85.0%–87.5%, P <10–6) compared with >50% of cases. Hypothesis 1a was accepted.
Among the 7913 cases performed on the scheduled day of surgery (Tuesdays through Fridays) that had been entered in the OR scheduling system as of 11:00 PM on the previous calendar day, an anesthesia resident performed a preoperative anesthesia evaluation on the previous calendar day in 83.1% ± 0.6% of cases (95% CI, 81.8%–84.4%, P <10–6) compared with >50% of cases. Hypothesis 1b was accepted.
Hypothesis 2: (Most Cancelled Cases Were Evaluated on the Day Before Surgery)
Among the 2162 cases cancelled on the day of surgery that had been scheduled as of 7:00 PM on the prior calendar date, a preoperative evaluation had been completed for 67.6% ± 1.6% of the total cancelled minutes (95% CI, 64.4%–70.8%) and 71.0% ± 1.4% of the cases (95% CI, 68.2%–73.8%; Fig. 2). Both were P < 10–6 compared with >50%. Hypothesis 2a was accepted.
Among the 2562 cases cancelled on the day of surgery that had been scheduled as of 11:00 PM, a preoperative evaluation had been completed for 58.0% ± 1.7% of the total cancelled minutes (95% CI, 54.0%–61.9%) and for 60.9% ± 1.7% of the cases (95% CI, 57.4%–64.4%). Both were P < 10–6 compared with >50%. Hypothesis 2b was accepted.
Hypothesis 3: (Most Preoperative Consults Were Seen as of 6:00 PM)
Of the 9532 consults seen between 7:00 AM and 11:59 PM Monday through Thursday on the day before the scheduled date of surgery (Fig. 3), 62.3% ± 1.5% were seen as of 6:00 PM (95% CI, 59.1%–65.1%, P < 10–6) compared with >50%. Hypothesis 3 was accepted.
Hypothesis 4: (the Herfindahl Index Among Cancelled Procedures Was <0.15, Reflecting No Dominant Procedures)
Previous studies of cancellation among patients who were inpatient preoperatively were analyzed (appropriately) by procedure (e.g., repair of fractured hip6,7 or cholecystectomy).8 However, among the different primary procedures in the current study that were cancelled, 0.0% accounted for >5% of the total minutes of cancelled cases (95% binomial CI, 0.0%–2.1%; n = 0/172 procedures). Because the sample included many procedures that were observed only once, and not all procedures available in the system for scheduling were present in the data set, the upper confidence limit is an overestimate of the actual value (see footnote k in Methods).44 Among the procedures observed, 82.0% each accounted for <1.0% of total minutes of cancelled cases (95% binomial CI, 75.4%–87.4%, n = 141/172 procedures) and 83.1% each accounted for <1.0% of total number of cancelled cases (95% binomial CI, 76.7%–88.4%, n = 143/172 procedures).
Among the 8108 cases cancelled after 7:00 AM on the day before the scheduled day of surgery, the Herfindahl index (footnote d in Methods) based on cancelled minutes was 0.021 ± 0.001 (95% CI, 0.020–0.023, P < 10–6) compared with <0.15). The Herfindahl index based on the number of cancelled cases was 0.035 ± 0.002 (95% CI, 0.032–0.039, P < 10–6) compared with < 0.15.
As sensitivity analysis, given that the threshold choice of <0.15 was from a different application (footnote e in Introduction), the Herfindahl index was recalculated using a threshold value of <0.05 (i.e., testing for much wider diversity among procedures). Both were also P < 10–6.
These results show that there is considerable diversity among the procedures with respect to cancellations (i.e., no specific procedures to target; footnote d in Introduction and footnote j in the Methods). Hypothesis 4 was accepted.
Hypothesis 5: (Most Inpatient Cancelled Cases Were Performed Subsequently Within 7 Days of the Cancellation)
Among the 8710 cases cancelled after 7:00 AM on the day before the scheduled date of surgery (including all dates), 50.6% ± 0.9% had any procedure rescheduled and performed within 7 days after the cancellation (95% CI, 48.9%–52.4%; P = 0.122 compared with >0.50; Table 2). This percentage overstates the actual percentage of rescheduled cases because it includes any procedure that the patient underwent, even if the procedure were unrelated to the cancelled procedure. If such cases had been excluded, the result would have been a smaller percentage of cases subsequently performed and a larger P value. Hypothesis 5 was rejected.
Hypothesis 6: (Scheduling Office Activity Creates the Need for ≥1 Decision per Hour)
On Mondays through Thursdays, between 6:00 AM and 3:59 PM, there were 0.59 ± 0.02 cancellation or add-on scheduling events that resulted in potential decisions per 10 OR first case start locations (95% CI, 0.54–0.64; P < 10–6 for ≥1 decision per hour at the 36 OR study hospital; Fig. 4). During each of the 10 one-hour intervals between 6:00 AM and 3:59 PM, the scheduling office activity exceeded the threshold (Fig. 4). Hypothesis 6 was accepted.
There are 4 primary findings in this study that relate to the Perioperative Surgical Home’s potential to provide value to health systems for issues related to inpatient case cancellations and add-on case scheduling.
First, most cases were evaluated by an anesthesia resident 1 day before surgery, regardless of whether the surgery was performed or cancelled. This result indicates that the decision to cancel a case was not likely to have been affected substantively by the timing of the anesthesia preoperative evaluation (hypotheses 1a, 1b, 2a, 2b, 3).
The standard of care in anesthesia is that patients are evaluated before surgery. Our findings do not change that. Our findings simply suggest that incremental efforts to see more patients, or to see them earlier on the day before surgery, would not substantively reduce the inpatient cancellation rate.
Our 11.8% inpatient cancellation incidence on the day of surgery may be modestly less than that reported previously by 2 academic hospitals (18.1% and 21%),14,15 and more than that of several private hospitals (8.1%).1 To try to gain an international perspective on inpatient cancellations, we searched extensively online (i.e., PubMed, Google Scholar) but were unable to find studies with relevant data from health care systems outside the United States, as of October 2014.
Our 27% inpatient cancellation incidence including the day before surgery may seem large, but our values include all inpatient cancellation activity, including cases that never appeared on the final OR schedule. For anesthesiologists working in the OR and hospital administrators who are not directly involved with the scheduling office, their perception of the cancellation rate likely is based on the final OR schedule. Without performing a detailed evaluation of the OR scheduling system audit logs and applying validated methods,42 a hospital would not know its actual magnitude of inpatient cancellations and resulting disruptions to planned schedules.
The substantial inpatient cancellation rate we observed at the study hospital was despite the large percentage of patients who were seen on the day before the scheduled surgery date (Fig. 2). At the hospital, most cancelled inpatient cases are scheduled within 1 working day of surgery.1 The pair of findings suggests a tenuous relationship between the preoperative anesthesia evaluation and the chance of preventing cancellations from changes in the preoperative evaluation process on the day before surgery. An element of the Perioperative Surgical Home with net cost savings potential is to reduce unnecessary interventions that do not have potential to benefit patients.2 Our results suggest value to hospitals by not spending money in an attempt to reduce inpatient cancellations by having patients seen earlier by anesthesia on the day before the scheduled date of surgery.
Second, there was substantial heterogeneity among inpatient procedures that were cancelled (hypothesis 4). No 1 type of procedure accounted for a substantive portion of cancelled time. Consequently, targeting a few specific procedures (e.g., through establishment of clinical pathways) will not result in a substantive reduction in cancellation rates, increase OR productivity, or change the need for frequent OR management decision making on the day of surgery. For example, consider the procedures “Open Reduction, Internal Fixation Hip Fracture” and “Trochanteric Fixation Nail,” both of which are performed for hip fractures, a condition for which there is evidence that timely surgery reduces morbidity7,8 and has appropriate cost utility.9 These 2 procedures combined accounted for only 1% of the total minutes of cancelled procedures that were observed and even less with the entire population of possible procedures in the OR scheduling system included.44
The absence of benefit of focusing on specific procedures is especially important because the cost and utility of reducing cancellation rates both depend on the procedure (i.e., these are not elective cases planned months to weeks in advance). One cannot necessarily group various procedures (e.g., upper and lower endoscopy procedures) because the patient populations and indications are often quite different. Grouping procedures together, for example, using the Agency for Healthcare Research and Quality Clinical Classifications Software, would also not be useful because the goal, from a scheduling perspective, is to have unbiased estimates of the durations of cancelled cases.35–38 This increase in variance generally is greater than the utility of increasing the sample size.36–38 There is too much heterogeneity in case duration among collapsed procedures to do this. The Perioperative Surgical Home can bring greater value to hospitals by focusing its efforts on following evidence-based management on the day of surgery when cancellations or add-on cases disrupt the planned OR schedule1 rather than on trying to establish clinical pathways to reduce inpatient cancellations.
Third, a subsequent procedure was not performed on most inpatients whose cases were cancelled (rejection of hypothesis 5). This result suggests that attempts to reduce cancellations might be counterproductive. For cases where surgery was not subsequently performed, the indication for the procedure must no longer have been present or the patient and/or family elected not to proceed with surgery. Regardless, it would appear that the cancellation was in the best interest of the patient. For many procedures, it is unclear whether a patient would benefit when his or her case is rescheduled (Table 2). However, even for procedures where evidence-based clinical pathways favor rapid transfer to the OR, the overall impact of such procedures will be small because they are a small fraction of the total minutes of cancelled OR time. Our results suggest lack of value in attempting to reduce inpatient cancellation rates if the result is to increase unnecessary or ill-advised surgery.
Finally, our results show that disruptions to the OR schedule throughout the entire working day due to inpatient cancellations and scheduling of add-on cases should be expected. The peak of case cancellations and add-on case scheduling at the start of the day (7:00 AM to 8:00 AM), and the continued presence of such events throughout the regular workday, imply that anesthesiologists need to be involved in managing the OR schedule throughout the day, moving case times, room assignments, and accommodating additional cases, as well as possible to improve patient care, decrease waiting times, and reduce the number of expected hours of overused time.1,11,16,18,20,22,25,29,30 As emphasized throughout the Methods, Figure 4 (deliberately) greatly underestimates the true numbers of decisions. Thus, the time course of inpatient cancellations reinforces our previous recommendations that anesthesiologists need to be involved in constructing the final OR schedule on the day before the day of surgery and throughout the day of surgery.16 This is a direct area of potential financial return for Perioperative Surgical Home: “scheduling initiatives to reduce cancellations and increase efficiency” (footnote b in Introduction). Anesthesiologists can uniquely play this role,20 both on the day of surgery and the previous workday, through application of management knowledge related to maximizing the efficiency of use of OR time.45
Our study has several limitations. First, this is a single-center study at a large academic tertiary care adult hospital. Our findings may not be directly applicable to small- and medium-sized community hospitals or to pediatric hospitals.1,16,46 It is likely that each hospital will have different cancellation rates, number of add-on cases, etc.1,16,46 However, it is also likely that there will be wide heterogeneity among cancelled inpatient procedures elsewhere. Tertiary referral hospitals are, by definition, hospitals for which their physiologically complex procedures (i.e., inpatients) are highly diverse.17,47,48
Second, we do not know the reasons why the inpatient cancellations occurred. Tung et al.28 showed that there are often multiple reasons, and they cannot be distinguished into unique categories.1
Finally, although anesthesia residents completed an in-person evaluation of nearly all scheduled inpatients on the evening before surgery, we do not know the extent to which they communicated medical issues to the surgical team or whether better communication might have reduced or increased the cancellation rate. All preoperative evaluations for patients who undergo anesthesia are reviewed by the anesthesia attending before surgery. This is documented and electronically signed by the anesthesiologist in the anesthesia record. Although the anesthesiologist is called the night before surgery for many inpatient cases, we do not have documentation of this event. Furthermore, inpatient cases often are added on the schedule to be performed in the first available room, so there is no attending assigned at the time the patient is evaluated. However, the relatively low incidence of cases performed after cancellation suggests that communication issues were not a root cause of cancellations. Furthermore, our estimate of cases subsequently performed was conservative (i.e., likely higher) because we included any procedure scheduled not just the procedure that was cancelled or something related.
We conclude that for inpatient cancellations, management decisions on the day before surgery and on the day of surgery matter. There is little benefit from performing anesthesia evaluations sooner or in a greater percentage of cases. Inpatient cancellations appear to be an inevitable consequence of taking care of sick, hospitalized patients in the context of diagnostic uncertainty and changing medical conditions. Consequently, as we have reported earlier,18,19,21,22,24,25,29,30 there is value to the process of managing disruptions on the day of surgery by the application of evidence-based decision making related to maximizing the efficiency of use of OR time, one element of the Perioperative Surgical Home.
Name: Richard H. Epstein, MD.
Contribution: This author helped design the study, conduct the study, analyze the data, write the manuscript, and is also the archival author.
Attestation: Richard H. Epstein approved the final manuscript.
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 approved the final manuscript.
RECUSE NOTEDr. Franklin Dexter is the Statistical Editor and Section Editor for Economics, Education, and Policy for Anesthesia & Analgesia. This manuscript was handled by Dr. Steven L. Shafer, Editor-in-Chief, and Dr. Dexter was not involved in any way with the editorial process or decision.
a American Society of Anesthesiologists. Perioperative Surgical Home. Available at: http://www.asahq.org/psh. Accessed March 8, 2015.
b Kash B, Cline K, Menser T, Zhang Y. The Perioperative surgical home: a comprehensive review for the American Society of Anesthesiologists. College Station, Texas, Texas A&M University. June 12, 2014. Available at: http://FDshort.com/KashPSH2014. Accessed March 8, 2015.
c Saccocci M, Greshner A, Epstein RH. Electronic Pre-Op Anesthesia Consult Management (Scientific Exhibit) International Anesthesia Research Society Annual Meeting, 2008.
d The Herfindahl index (see http://en.wikipedia.org/wiki/Herfindahl_index#Formula; accessed March 8, 2005) is used in a variety of domains as an assessment of diversity. In ecology, it is referred to as the Simpson index (see http://www.countrysideinfo.co.uk/simpsons.htm; accessed March 8, 2005). For making comparisons of the diversity of procedures within hospitals, it is sometimes referred to as the internal Herfindahl.17 For example, we previously studied surgery in infants and toddlers in Iowa in 2001.17 At the large pediatric hospital in the State of Iowa, the Herfindahl index for the procedures was 0.07 ± 0.01. Thus, any 2 randomly selected procedures performed in these patients, on different or same days, had just a 7% chance of being the same. In contrast, at the small community hospital, which was performing more pediatric surgery than any other in Iowa, the Herfindahl index was 0.66 ± 0.02. The latter represents a highly concentrated market of procedures, principally composed of myringotomy tube placement and adenoidectomy.
e For use in analysis of competition within businesses in a given sector (typically as a part of the assessment of potential effects of a proposed merger), the Department of Justice 2010 guidelines considers a Herfindahl index <0.15 (or 1500 if percentages rather than fractions are used) to be an unconcentrated market. See: U.S. Department of Justice and the Federal Trade Commission. Merger guidelines. Available at http://FDshort.com/DOJHerfindahl. Accessed March 8, 2015.
f Considering the combination of procedures for a given case, rather than only the primary scheduled procedure, would increase the number of procedures substantially, thereby lowering the Herfindahl index. For example, consider 2 cases with procedure right hemicolectomy and right hemicolectomy with ureteral stents. In our calculation, only 1 procedure would be considered (i.e., hemicolectomy). If we were to concatenate the procedures for each case, there would be 2 procedures. The greater the number of procedures, the greater the heterogeneity, which results in a smaller Herfindahl index. Our approach is thus conservative (i.e., we overestimate the value of the Herfindahl index).
g For example, if a case was initially scheduled on August 1 at 9:00 AM to be performed on August 2 at 4:00 PM and was rescheduled on August 2 at 11:00 AM to be performed on August 3 at 5:00 PM, there would be 2 rows in the data set. The first row would have a creation date of August 1 at 9:00 AM, a date of surgery of August 2, and a cancellation date of August 2 at 11:00 AM. The second row would have a creation date of August 2 at 11:00 AM, a date of surgery of August 3, and a performed date of August 3.
h For example, if the date of surgery for a unique case was changed at 9:00 AM on November 30, 2011, to December 1, 2011, and then changed again at 9:05 AM on November 30, 2011, to December 2, 2011, the initial change of the surgery date to December 1, 2011, was ignored.
i The reason for this dichotomy is that during the study period, we were unable to print from the mobile tablets we use (iPad mini; Apple, Cupertino, CA) to network printers. This is a limitation of the Apple iOS operating system, security requirements of our network, and the Chrome browser. The preoperative evaluations for the next day were batch printed the evening before the day of surgery using a separate desktop application.
j The internal Herfindahl index is calculated as the sum of the squares of the fractions of each proportion. For example, if there were 4 procedures each contributing to 25% of the cancelled cases, the index would be 0.252 + 0.252 + 0.252 + 0.252 = 0.25. If 1 procedure contributed to 70% and the others contributed to 10%, the index would be 0.72 + 0.12 + 0.12 + 0.12 = 0.52. The smaller the value of the index, the greater the heterogeneity.
k Figure 3 from Reference (45) shows a log-normal distribution for the number of cases of each procedure among outpatient surgical cases in the United States. The data are from the National Survey of Ambulatory Surgery. We generated 30,000 log-normally distributed random numbers. The Herfindahl index equaled 0.026. Sorting the 30,000 “procedures” in descending sequence, the 1570th procedure equaled 0.027. Thus, to be accurate to 3 digits, the least common 95% of procedures could be missed. Among the most common 5, 10, 50, and 500 procedures, the Herfindahl indices were 0.242, 0.152, 0.062, and 0.032, respectively.
l For example, suppose a television meteorologist correctly predicted rain for the next day on >50% of evening weather forecasts during 13 consecutive 4-week intervals (P = 0.01).Then, it would be wise to carry an umbrella following such predictions. Conservative individuals might choose to do so if the predictions were correct on only 40% or even 25% of the days, but testing for >50% implies that the P values for these alternative thresholds are also ≤0.01.
1. 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
2. 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
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17. Dexter F, Wachtel RE, Yue JC. Use of discharge abstract databases to differentiate among pediatric hospitals based on operative procedures: surgery in infants and young children in the state of Iowa. Anesthesiology. 2003;99:480–7
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21. 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
22. 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
23. Xiao Y, Dexter F, Hu P, Dutton RP. The use of distributed displays of operating room video when real-time occupancy status was available. Anesth Analg. 2008;106:554–60
24. 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
25. 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
26. 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
27. 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
28. Tung A, Dexter F, Jakubczyk S, Glick DB. The limited value of sequencing cases based on their probability of cancellation. Anesth Analg. 2010;111:749–56
29. 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
30. 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
31. 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
32. Wachtel RE, Dexter F. Difficulties and challenges associated with literature searches in operating room management, complete with recommendations. Anesth Analg. 2013;117:1460–79
33. Ehrenfeld JM, Dexter F, Rothman BS, Johnson AM, Epstein RH. Case cancellation rates measured by services differ if based on the number of cases or the number of minutes cancelled. Anesth Analg. 2013;117:711–6
34. 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
35. 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
36. 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
37. 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
38. 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;117:204–9
39. 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
40. 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
41. 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
42. 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
43. Dexter F. High-quality operating room management research. J Clin Anesth. 2014;26:341–2
44. Dexter F, Traub RD, Fleisher LA, Rock P. What sample sizes are required for pooling surgical case durations among facilities to decrease the incidence of procedures with little historical data? Anesthesiology. 2002;96:1230–6
45. Masursky D, Dexter F, Nussmeier NA. Operating room nursing directors’ influence on anesthesia group operating room productivity. Anesth Analg. 2008;107:1989–96
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47. Wachtel RE, Dexter F. Differentiating among hospitals performing physiologically complex operative procedures in the elderly. Anesthesiology. 2004;100:1552–61
48. Kanter RK, Dexter F. Criteria for identification of comprehensive pediatric hospitals and referral regions. J Pediatr. 2005;146:26–9