Overutilized Operating Room Time Research Articles

Implications of Patient Age and ASA Physical Status for Operating Room Management Decisions.

In elderly, high-risk patients, operating room (OR) turnaround times are especially difficult to estimate, and the managerial implications of patient age and ASA physical status for OR management decisions remain unclear. We hypothesized that evaluating patient age and ASA physical status in the right model would improve accuracy of turnaround time estimates and, thus, would have decisive implications for OR management. By using various multivariate techniques, we modeled turnaround times of 13,632 OR procedures with respect to multiple variables including surgical list, age, ASA physical status, duration of the procedure, and duration of the preceding procedure. We first assessed correlations and general descriptive features of the data. Then, we constructed decision tables for OR management consisting of 50th and 95th percentiles of age/ASA-dependent estimates of turnaround times. In addition, we applied linear and generalized linear multivariate models to predict turnaround times. The forecasting power of the models was assessed in view of single cases but also in view of critical managerial key figures (50th and 95th percentile turnaround times). The models were calibrated on 80% of the data, and their predictive value was tested on the remaining 20%. We considered our data in a Monte Carlo simulation to deduce actual reductions of overutilized OR time when applying the results as presented in this work. Using the best models, we achieved an increase in predictive accuracy of 7.7% (all lists), ranging from 2.5% (general surgery) to 21.0% (trauma surgery) relative to age/ASA-independent medians of turnaround times. All models decreased the forecasting error, signifying a relevant increase in planning accuracy. We constructed a management decision table to estimate age/ASA-dependent turnaround time for OR scheduling at our hospital. The decision tables allow OR managers at our hospital to schedule procedures more accurately. Evaluation of patient age and ASA physical status as variables can help to better predict turnaround times, which can facilitate scheduling, for example, to schedule overlapping induction rooms, to reduce overutilized OR time by optimizing allocation of patients to several ORs, and to improve logistics of prioritizing transportation of advanced age/high ASA physical status patients to the OR.

Decreasing the Hours That Anesthesiologists and Nurse Anesthetists Work Late by Making Decisions to Reduce the Hours of Over-Utilized Operating Room Time.

In this special article, we evaluate how to reduce the number of hours that anesthesiologists and nurse anesthetists work beyond the end of their scheduled shifts. We limit consideration to surgical suites where the hours of cases in each operating room (OR) average 8 hours or more per day. Let "allocated hours" refer to the hours into which cases are scheduled, calculated months in advance for each combination of service and day of the week. Over-Utilized time is the OR workload exceeding allocated time. Reducing Over-Utilized time is the key to reducing the hours that anesthesia providers work late. Certain decisions that reduce Over-Utilized time and reduce the hours that anesthesiologists and nurse anesthetists work late are made by the surgical committee or perioperative medical director months in advance. Such decisions include increasing the number of first case starts and planning staffing for turnovers and lunch breaks during the busiest times of the day. However, most decisions substantively influencing Over-Utilized OR time are made within 1 workday before the day of surgery and on the day of surgery, because only then are ORs sufficiently full that changes can be made to minimize Over-Utilized time. Decisions to reduce Over-Utilized time on the day of surgery include targeting ORs with expected Over-Utilized time and taking steps to reduce it, including making effective staff assignments and appropriately scheduling add-on cases.

Comparing Policies for Case Scheduling Within 1 Day of Surgery by Markov Chain Models

In previous studies, hospitals' operating room (OR) schedules were influenced markedly by decisions made within a few days of surgery. At an academic hospital, 46% of ORs had their last case scheduled or changed within 1 working day of surgery, and a private hospital had 64%. Many of these changes were for patients who were admitted before surgery (i.e., inpatient cases). In this study, we investigate the impact on OR productivity of how cases are scheduled within 1 working day before the day of surgery. We consider the case-scheduling choice between 2 ORs. We compare 3 scheduling policies: Best Fit Descending, Worst Fit Descending, and Worst Fit Ascending. "Descending" strategies consider new cases from longest to shortest, whereas "Ascending" considers new cases from shortest to longest. Best Fit schedules each new case into the OR with sufficient but the least remaining underutilized OR time for the case. Worst Fit does the same but with the most remaining time. For our application, Best Fit chooses a later start time, whereas Worst Fit chooses an earlier start time. In our computational model, cases are of 2 possible durations, brief or long. Case cancellation is incorporated explicitly, and the number of new cases to schedule depends on the current number of scheduled cases in each OR, both new from previous studies. The number of cases in each OR is modeled as a Markov chain, evolving between 2 periods, corresponding to 1 day and 0 days before the day of surgery. For each scheduling policy, we evaluate the mean overutilized OR time and productivity. Our sensitivity analyses cover many cancellation rates, arrival settings, case durations, and initial conditions (i.e., how cases are scheduled into the 2 ORs preceding 1 workday before the day of surgery). Best Fit Descending and Worst Fit Descending achieved almost the same overutilized time and productivity. Worst Fit Ascending caused greater overutilized time (as much as 6.6 minutes more per OR) and thus lesser productivity (as much as 1.6% less) compared with Best Fit Descending or Worst Fit Descending. When the staff were scheduled for less time than the optimal allocated OR time, there were nearly the same differences between the staff productivity resulting from the use of Worst Fit Ascending rather than Worst Fit Descending or Best Fit Descending. Scheduling office decision making within 1 day before surgery should be based on statistical forecasts of expected total OR workload (i.e., forecasts that include the addition of non-elective cases and the subtraction of cases that cancel). As long as a case is not scheduled into overutilized time when less overutilized time could be achieved in another OR, and cases are considered in descending sequence of scheduled durations, the differences in overutilized time and productivity among the scheduling policies are small. Cognitive bias in staff scheduling causes a significant reduction in productivity, but the differences among scheduling policies are nearly the same as when there is no bias.

Associated Roles of Perioperative Medical Directors and Anesthesia: Hospital Agreements for Operating Room Management.

As reviewed previously, decision making can be made systematically shortly before the day of surgery based on reducing the hours of overutilized operating room (OR) time and tardiness of case starts (i.e., patient waiting). We subsequently considered in 2008 that such decision making depends on rational anesthesia-hospital agreements specifying anesthesia staffing. Since that prior study, there has been a substantial increase in understanding of the timing of decision making to reduce overutilized OR time. Most decisions substantively influencing overutilized OR time are those made within 1 workday before the day of surgery and on the day of surgery, because only then are ORs sufficiently full that case scheduling and staff assignment decisions affect overutilized OR time. Consequently, anesthesiologists can easily be engaged in such decisions, because generally they must be involved to ensure that the corresponding anesthesia staff assignments are appropriate. Despite this, at hospitals with >8 hours of OR time used daily in each OR, computerized recommendations are superior to intuition because of cognitive biases. Decisions need to be made by a Perioperative Medical Director who has knowledge of the principles of perioperative managerial decision making published in the scientific literature rather than by a committee lacking this competency. Education in the scientific literature, and when different analytical methods should be used, is important. The addition that we make in this article is to show that an agreement between an anesthesia group and a hospital can both reduce overutilized OR time and patient waiting: The anesthesia group and hospital will ensure, hourly, that, when there are case(s) waiting to start, the number of ORs in use for each service will be at least the number that maximizes the efficiency of use of OR time. Neither the anesthesia group nor the hospital will be expected to run more than that number of ORs without mutual agreement. Agreements assure that processes mutually beneficial to organizations, but not necessarily to individuals at each point in time, are performed as designed, especially in the setting of cognitive biases.

The influence of anesthesia-controlled time on operating room scheduling in Dutch university medical centres

Predicting total procedure time (TPT) entails several elements subject to variability, including the two main components: surgeon-controlled time (SCT) and anesthesia-controlled time (ACT). This study explores the effect of ACT on TPT as a proportion of TPT as opposed to a fixed number of minutes. The goal is to enhance the prediction of TPT and improve operating room scheduling. Data from six university medical centres (UMCs) over seven consecutive years (2005-2011) were included, comprising 330,258 inpatient elective surgical cases. Based on the actual ACT and SCT, the revised prediction of TPT was determined as SCT × 1.33. Differences between actual and predicted total procedure times were calculated for the two methods of prediction. The predictability of TPT improved when the scheduling of procedures was based on predicting ACT as a proportion of SCT. Efficient operating room (OR) management demands the accurate prediction of the times needed for all components of care, including SCT and ACT, for each surgical procedure. Supported by an extensive dataset from six UMCs, we advise grossing up the SCT by 33% to account for ACT (revised prediction of TPT = SCT × 1.33), rather than employing a methodology for predicting ACT based on a fixed number of minutes. This recommendation will improve OR scheduling, which could result in reducing overutilized OR time and the number of case cancellations and could lead to more efficient use of limited OR resources.

Lack of Utility of a Decision Support System to Mitigate Delays in Admission from the Operating Room to the Postanesthesia Care Unit

When the phase I postanesthesia care unit (PACU) is at capacity, completed cases need to be held in the operating room (OR), causing a "PACU delay." Statistical methods based on historical data can optimize PACU staffing to achieve the least possible labor cost at a given service level. A decision support process to alert PACU charge nurses that the PACU is at or near maximum census might be effective in lessening the incidence of delays and reducing over-utilized OR time, but only if alerts are timely (i.e., neither too late nor too early to act upon) and the PACU slot can be cleared quickly. We evaluated the maximum potential benefit of such a system, using assumptions deliberately biased toward showing utility. We extracted 3 years of electronic PACU data from a tertiary care medical center. At this hospital, PACU admissions were limited by neither inadequate PACU staffing nor insufficient PACU beds. We developed a model decision support system that simulated alerts to the PACU charge nurse. PACU census levels were reconstructed from the data at a 1-minute level of resolution and used to evaluate if subsequent delays would have been prevented by such alerts. The model assumed there was always a patient ready for discharge and an available hospital bed. The time from each alert until the maximum census was exceeded ("alert lead time") was determined. Alerts were judged to have utility if the alert lead time fell between various intervals from 15 or 30 minutes to 60, 75, or 90 minutes after triggering. In addition, utility for reducing over-utilized OR time was assessed using the model by determining if 2 patients arrived from 5 to 15 minutes of each other when the PACU census was at 1 patient less than the maximum census. At most, 23% of alerts arrived 30 to 60 minutes prior to the admission that resulted in the PACU exceeding the specified maximum capacity. When the notification window was extended to 15 to 90 minutes, the maximum utility was <50%. At most, 45% of alerts potentially would have resulted in reassigning the last available PACU slot to 1 OR versus another within 15 minutes of the original assignment. Despite multiple biases that favored effectiveness, the maximum potential benefit of a decision support system to mitigate PACU delays on the day on the surgery was below the 70% minimum threshold for utility of automated decision support messages, previously established via meta-analysis. Neither reduction in PACU delays nor reassigning promised PACU slots based on reducing over-utilized OR time were realized sufficiently to warrant further development of the system. Based on these results, the only evidence-based method of reducing PACU delays is to adjust PACU staffing and staff scheduling using computational algorithms to match the historical workload (e.g., as developed in 2001).

A Behavioral Study of Daily Mean Turnover Times and First Case of the Day Start Tardiness

Previous research has identified 2 psychological biases in operating room (OR) decisions on the day of surgery: risk attitude of the decision-maker at the OR control desk and decisions made by OR staff to increase clinical work per unit time during the hours they are assigned. Resulting decisions are worse than random chance at reducing overutilized time. To isolate the second bias from decisions at the OR control desk, previous studies of the second bias have analyzed decisions made in non-OR locations and on nights/weekends. Another way to isolate the second bias from decisions at the OR control desk is to study facilities with negligible overutilized OR time. We examined the second bias using data from such a facility. One year of data was collected from a 5-OR hospital. Allocated OR time that minimized the inefficiency of use of OR time was determined first to confirm there was virtually no overutilized OR time. A structural equation model was then built to evaluate the relations among variables while controlling for other correlations. We tested the hypothesis that nonoperative times were no longer on days with little versus relatively large workload. The extra ORs were not cost efficient (i.e., the mean potential improvement varied among days from 21.1% ± 0.2% [SE] to 38.9% ± 0.2%), resulting in very little overutilized OR time. However, conditioned on the preceding tactical decision of running extra ORs, the allocated OR time during the studied period was that which minimized the inefficiency of use of OR time. As the preceding results showed that the facility was suitable for the behavioral study, the behavioral study was performed, and the hypothesized relation confirmed. Each 1-hour decrease in the daily estimated (total) duration of elective cases resulted in a managerially unimportant decrease in the mean turnover times (0.41 ± 0.21 minutes, P = 0.053). Excluding turnovers when there were >2 turnovers occurring simultaneously, there was no significant decrease (0.17 ± 0.24 minutes, P = 0.464) in the mean turnover times per each 1-hour decrease in the daily estimated (total) duration. Similarly, after excluding prolonged turnovers (>60 minutes), there was no significant decrease (0.16 ± 0.16 minutes, P = 0.315) in the mean turnover times per each 1-hour decrease in the daily estimated (total) duration. Previous experimental and observational studies found many clinicians maintained high clinical work per unit time during the hours to which they were assigned. We tested and confirmed a prediction of this bias as was applied during regularly scheduled OR hours among an entire surgical team. Overall, the staff worked just as quickly on days with few or many hours of cases. The OR staff did not slow down, thus filling the time. These results have important implications for the cost utility of information technologies to facilitate managerial decision-making on the day of surgery.

Descriptive Study of Case Scheduling and Cancellations Within 1 Week of the Day of Surgery

We performed a descriptive study of operating room (OR) case scheduling within 1 week of the day of surgery. The data used were from the case scheduling and transaction audit tables of a hospital's anesthesia and OR information management systems. Each change to a scheduled case in the OR information system was captured in an audit table, including the date and time when the change was made. The timestamps allowed reconstruction of the elective OR schedule for each date of surgery at preceding dates (e.g., 2 workdays ahead). The sample size was n = 17 consecutive 4-week periods. The allocated OR time, for each combination of service and day of the week, was the number of hours that minimized the inefficiency of use of OR time, a weighted combination of the hours of underutilized OR time and the more expensive hours of overutilized OR time. Data are reported as mean ±SE. (1) The percentage of OR date combinations with at least 1 add-on case was 24.1% ± 0.3%. The most recent addition of a case to an OR occurred 1 working day before surgery for 22.3% ± 0.4% of OR date combinations. At least half (51.5% ± 0.5%) of ORs had its last case scheduled or changed within 2 working days of surgery. In addition, when allocated OR time was filled and the service scheduled additional case(s), the median time ahead when each such case was scheduled was 2.2 ± 0.2 workdays. Thus, managers can productively focus on the day of surgery starting 2 working days before surgery. (2) Once allocated time was full, the ratio of the net additional cases scheduled to the total number performed was 1.2% ± 0.6%. However, 11.1% ± 1.7% of the total were additional cases. Thus, schedulers should rely on the allocated time to be predictive of the actual (net) workload that will occur in the future, on the day of surgery. (3) For service and day combinations for which 2 working days ahead the scheduled hours exceeded the allocated hours, there was no significant net increase in minutes of cases scheduled (P = 0.79), unlike when the scheduled hours were less than allocated (P < 0.0001). Thus, additional hours of cases scheduled within the same number of workdays are heterogeneous both within and among services based on the prior hours of cases scheduled. Planning anesthesia assignments, ORs to target, etc., can be done productively starting 2 working days ahead of surgery. There are so many changes to the OR schedule those last 2 workdays that anesthesia groups should be engaged with the scheduling office during that period. The primary predictor of additional net hours of cases to be scheduled is the difference between the allocated (i.e., forecasted) OR time and the hours scheduled so far.

Success of Commonly Used Operating Room Management Tools in Reducing Tardiness of First Case of the Day Starts

One of the declared objectives of surgical suite management in Germany is to increase operating room (OR) efficiency by reducing tardiness of first case of the day starts. We analyzed whether the introduction of OR management tools by German hospitals in response to increasing economic pressure was successful in achieving this objective. The OR management tools we considered were the appointment of an OR manager and the development and adoption of a surgical suite governance document (OR charter). We hypothesized that tardiness of first case starts was less in ORs that have adopted one or both of these tools. Using representative 2005 survey data from 107 German anesthesiology departments, we used a Tobit model to estimate the effect of the introduction of an OR manager or OR charter on tardiness of first case starts, while controlling for hospital size and surgical suite complexity. Adoption reduced tardiness of first case starts by at least 7 minutes (mean reduction 15 minutes, 95% confidence interval (CI): 7-22 minutes, P < 0.001). Reductions in tardiness of first case starts figure prominently the objectives of surgical suite management in Germany. Our results suggest that the appointment of an OR manager or the adoption of an OR charter support this objective. For short-term decision making on the day of surgery, this reduction in tardiness may have economic implications, because it reduced overutilized OR time.

Modeling Procedure and Surgical Times for Current Procedural Terminology-Anesthesia-Surgeon Combinations and Evaluation in Terms of Case-Duration Prediction and Operating Room Efficiency: A Multicenter Study

Gains in operating room (OR) scheduling may be obtained by using accurate statistical models to predict surgical and procedure times. The 3 main contributions of this article are the following: (i) the validation of Strum's results on the statistical distribution of case durations, including surgeon effects, using OR databases of 2 European hospitals, (ii) the use of expert prior expectations to predict durations of rarely observed cases, and (iii) the application of the proposed methods to predict case durations, with an analysis of the resulting increase in OR efficiency. We retrospectively reviewed all recorded surgical cases of 2 large European teaching hospitals from 2005 to 2008, involving 85,312 cases and 92,099 h in total. Surgical times tended to be skewed and bounded by some minimally required time. We compared the fit of the normal distribution with that of 2- and 3-parameter lognormal distributions for case durations of a range of Current Procedural Terminology (CPT)-anesthesia combinations, including possible surgeon effects. For cases with very few observations, we investigated whether supplementing the data information with surgeons' prior guesses helps to obtain better duration estimates. Finally, we used best fitting duration distributions to simulate the potential efficiency gains in OR scheduling. The 3-parameter lognormal distribution provides the best results for the case durations of CPT-anesthesia (surgeon) combinations, with an acceptable fit for almost 90% of the CPTs when segmented by the factor surgeon. The fit is best for surgical times and somewhat less for total procedure times. Surgeons' prior guesses are helpful for OR management to improve duration estimates of CPTs with very few (<10) observations. Compared with the standard way of case scheduling using the mean of the 3-parameter lognormal distribution for case scheduling reduces the mean overreserved OR time per case up to 11.9 (11.8-12.0) min (55.6%) and the mean underreserved OR time per case up to 16.7 (16.5-16.8) min (53.1%). When scheduling cases using the 4-parameter lognormal model the mean overutilized OR time is up to 20.0 (19.7-20.3) min per OR per day lower than for the standard method and 11.6 (11.3-12.0) min per OR per day lower as compared with the biased corrected mean. OR case scheduling can be improved by using the 3-parameter lognormal model with surgeon effects and by using surgeons' prior guesses for rarely observed CPTs. Using the 3-parameter lognormal model for case-duration prediction and scheduling significantly reduces both the prediction error and OR inefficiency.

Calculating Institutional Support That Benefits Both the Anesthesia Group and Hospital

Institutional support to anesthesia groups for clinical care is very common, particularly when compensation for certified registered nurse anesthetists and anesthesiology residents is considered. Poor contracts can reduce incentives for good operating room (OR) management. We show that two types of agreements for institutional support are rational, and that alternatives to those models increase profit for either the hospital or anesthesia group at the expense of the other. For both agreements, costs are based on survey data, not actual costs. Terms in equations are not recalculated regularly, thereby preventing undesirable incentives such as the anesthesia group profiting from reduced OR workload. Support is not based on hours worked late, because such an agreement would ignore the underutilized OR time sustained by the group. The support would create a disincentive to decision-making that would reduce overutilized OR time such as decreasing turnovers and starting add-on cases expeditiously. For groups with uncommonly low net collections, group profit is higher if the hospital provides support expected to assure a reasonable (fair) income for the group to recruit and retain members. For what is likely the majority of groups, with average net collections per anesthesia hour exceeding the hospital's compensation per scheduled hour, expected profit is higher if institutional support is payment at a reasonable rate (fair market value) for the expected incremental hours of underutilized OR time (i.e., nonbillable idle time) caused by the specialty-specific staffing (i.e., OR allocations). Such an agreement creates incentives whereby the hospital and anesthesia group both profit from increased OR workload and from more accurate specialty-specific staffing.

Operating Room Managerial Decision-Making on the Day of Surgery With and Without Computer Recommendations and Status Displays

There are three basic types of decision aids to facilitate operating room (OR) management decision-making on the day of surgery. Decision makers can rely on passive status displays (e.g., big screens or whiteboards), active status displays (e.g., text pager notification), and/or command displays (e.g., text recommendations about what to do). Anesthesiologists, OR nurses, and housekeepers were given nine simulated scenarios (vignettes) involving multiple ORs to study their decision-making. Participants were randomized to one of four groups, all with an updated paper OR schedule: with/without command display and with/without passive status display. Participants making decisions without command displays performed no better than random chance in terms of increasing the predictability of work hours, reducing over-utilized OR time, and increasing OR efficiency. Status displays had no effect on these end-points, whereas command displays improved the quality of decisions. In the scenarios for which the command displays provided recommendations that adversely affected safety, participants appropriately ignored advice. Anesthesia providers and nursing staff made decisions that increased clinical work per unit time in each OR, even when doing so resulted in an increase in over-utilized OR time, higher staffing costs, unpredictable work hours, and/or mandatory overtime. Organizational culture and socialization during clinical training may be a cause. Command displays showed promise in mitigating this tendency. Additional investigations are in our companion paper.

Identification of systematic underestimation (bias) of case durations during case scheduling would not markedly reduce overutilized operating room time

Study objective If a case has a high probability of taking longer than scheduled, then increasing the case's scheduled duration could reduce over-utilized operating room (OR) time. We studied surgeons' and schedulers' case scheduling behavior to evaluate whether such a strategy would be useful. Design Observational study. Setting University hospital. Measurements The probability of each of 66,561 cases taking longer than scheduled was estimated with an accuracy to within 1-2%. Main results Overall underestimation by surgeons and schedulers was 22 minutes for each 8 hours of used operating room (OR) time. If a 90% or 95% chance of taking longer than scheduled were required to conclude that a case's duration was deliberately underestimated, and if such cases' scheduled durations were changed, overall underestimation would be reduced by only 0.2 or 0.9 minutes per 8 hours of used OR time because only 0.1% or 0.6% of used OR time met that criterion. In contrast, underestimation would be reduced by 20 minutes if the cases identified were those with only a 50% to 60% chance of taking longer than scheduled because they accounted for more than 40% of OR time. Persistent underestimation of cases' durations was caused not by poor decisions for a few outlier cases, but instead by slight underestimation for many cases. Surgeons' and schedulers' behavior that fit cases into staffed (allocated) OR time was to underestimate slightly the duration of many cases. Conclusion The impact of inaccurate, scheduled case duration on staffing costs and unpredictable work hours can be reduced by allocating appropriate total hours of OR time (ie, staffing) for the cases that will get done, regardless of the inaccuracy of the scheduled durations of those cases.

Holiday and Weekend Operating Room On-Call Staffing Requirements

Every facility that performs cases on holidays has in some way decided on its operating room (OR) and anesthesia staffing for holidays. Previous studies have not examined how best to calculate appropriate holiday staffing. We analyzed weekend and holiday data from a university hospital. There were high rank correlations between the number of cases starting during each 12-h period of a holiday, the total hours of OR time used, and the patients and surgeons waiting for cases to start. Weekend and holiday 12-h periods were divided into 8 categories (e.g., Saturday 7:00 am to 7:00 pm). There was perfect rank correlation between the mean number of cases starting during each 12-h period and appropriate staffing during the 12-h period, whether quantified by total hours of cases or by the under-utilized and over-utilized OR time resulting from staffing decisions. The number of cases starting during each period of a holiday is a statistically valid end point for OR managers to use to evaluate how busy holidays are relative to weekend days. To be useful, the statistic must be combined with mathematically valid assessments of appropriate weekend staffing on-call, whether in-house, or from home.

Impact of surgical sequencing on post anesthesia care unit staffing

This paper analyzes the impact of sequencing rules on the phase I post anesthesia care unit (PACU) staffing and over-utilized operating room (OR) time resulting from delays in PACU admission. The sequencing rules are applied to each surgeon's list of cases independently. Discrete event simulation shows the importance of having a sufficient number of PACU nurses. Sequencing rules have a large impact on the maximum number of patients receiving care in the PACU (i.e., peak of activity). Seven sequencing rules are tested, over a wide range of scenarios. The largest effect of sequencing was on the percentage of days with at least one delay in PACU admission. The best rules are those that smooth the flow of patients entering in the PACU (HIHD (Half Increase in OR time and Half Decrease in OR time) and MIX (MIX OR time)). We advise against using the LCF (Longest Cases First) and equivalent sequencing methods. They generate more over-utilized OR time, require more PACU nurses during the workday, and result in more days with at least one delay in PACU admission.

Operating room efficiency and scheduling

The review focuses on six papers published in 2004 that pertain to operating room (OR) efficiency. When to release OR time was much less important than was having the correct OR allocations in the first place. If OR time must be released, then this decision should be based on the historical scheduled workload. Several interventions combined can reduce average turnover times by 7 min per case, probably reducing labor costs by approximately 1.5%. Use of a block room for patients undergoing brachial plexus anesthesia for upper extremity surgery reduces overall OR time by 4 min per upper extremity case. Improving prediction of case duration can reduce over-utilized OR time by just 5.4 min per OR per workday. Interventions studied included changing when OR time was released, reducing surgical times, reducing turnover times, reducing OR times with block rooms, and improving prediction of case duration. The incremental improvements in OR efficiency and reductions in labor costs were small as compared with allocating OR time and scheduling cases appropriately.

When to release allocated operating room time to increase operating room efficiency.

We studied when allocated, but unfilled, operating room (OR) time of surgical services should be released to maximize OR efficiency. OR time was allocated for two surgical suites based on OR efficiency. Then, we analyzed real OR schedules. We added new hypothetical cases lasting 1, 2, or 3 h into OR time of the service that had the largest difference between allocated and scheduled cases (i.e., the most unfilled OR time) 5 days before the day of surgery. The process was repeated using the updated OR schedule available the day before surgery. The pair-wise difference in resulting overutilized OR time was calculated for n = 754 days of data from each of the two surgical suites. We found that postponing the decision of which service gets the new case until early the day before surgery reduces overutilized OR time by <15 min per OR per day as compared to releasing the allocated OR time 5 days before surgery. These results show that when OR time is released has a negligible effect on OR efficiency. This is especially true for ambulatory surgery centers with brief cases or large surgical suites with specialty-specific OR teams. What matters much more is having the correct OR allocations and, if OR time needs to be released, making that decision based on the scheduled workload. Provided operating room (OR) time is allocated and cases are scheduled based on maximizing OR efficiency, then whether OR time is released five days or one day before the day of surgery has a negligible effect on OR efficiency.