Getting the right case in the right room at the right time is the goal for every

OR throughput Are your operating rooms efficient? Getting the right case in the right room at the right time is the goal for every OR director. Often, though, defining how well the OR suite runs depends on whom you ask. The question, Are my ORs efficient? could be could be answered with a qualitative approach by administering a written survey to OR personnel. A more quantitative approach has been published (Macario, 2006) (see table). This OR efficiency scoring system could be used as a management tool. For example, statistical process control techniques could be used to analyze a dashboard of these 8 performance indicators to evaluate baseline performance, identify areas needing improvement, and conduct prospective monitoring. Poorly managed OR suites may score 0 to 5 points (on the 0 to 16 scale), while high scores of 13 to 16 are achievable with state-of-the-art management systems in place. The 8 metrics were chosen based on a review of more than 100 OR management articles published in the literature in the past decade. These performance indicators should be able to be computed from data already available in OR information systems. Surgeon satisfaction is also critical, but no valid and reliable instrument to measure this has been developed. Excess staffing costs due to OR allocation not being based on maximizing OR efficiency Nothing is more important than to first allocate the right amount of OR time to each service on each day of the week for its case scheduling. This is not the same as the block time! To illustrate, imagine that 2 cases each lasting 2 hours are scheduled into OR 1 with OR nurses and an anesthesiologist scheduled to work an 8-hour day. The matching of workload to staffing has been so poor that little can be done the day of surgery to increase the efficiency of use of the staff. Neither awakening patients more quickly nor reducing the turnover time, for example, will compensate for the poor initial choice of staffing for OR 1 and/or how the cases were scheduled into OR 1. Optimal allocation of OR time should be based on historical use by a particular service (ie, unit of OR allocation such as surgeon, group, department, or specialty) and then using computer software to minimize the amount of underutilized time and the more expensive overutilized time (Strum, et al, 1999). Underutilized hours reflect how early the room finishes. In the example above, if staff were scheduled to work from 7 am to 3 pm, but instead the room finished at 11 am, there would be 4 hours of underutilized time. The excess staffing cost (Strum, et al, 1999) would be 50% (4 hrs/8 hrs). On the other hand, if 9 hours of cases are performed in an OR with staff scheduled to work 8 hours, then the excess staffing cost is 25%. Overutilized hours are the hours that ORs run longer than the regularly scheduled OR hours, or 1 hour in this example. The calculation is as follows: 1 hr/8 hr=12.5%, which is then multiplied by the additional cost of staying late, which often is assumed to be a factor of 2 (related to monetary overtime cost paid to staff, as well as recruitment and retention costs related to unhappy staff because they have to stay late unpredictably). OR suites can reasonably aim to achieve a staffing cost that is within 10% of optimal (ie, workload is perfectly matched to staffing). If the key is to allocate appropriate time to each service based on historical OR 1

A scoring system for OR efficiency with 8 performance indicators Metric Points 0 1 2 Excess staffing costs >10% 5% -10% < 5% Start-time tardiness > 60 mins 45-60 mins < 45 mins (Mean tardiness of start times for elective cases per OR per day) Case cancellation rate > 10% 5% -10% < 5% PACU admission delays > 20% 10%-20% < 10% (% of workdays with at least one delay of 10 mins or greater in PACU admission because PACU is full) Contribution margin (mean) per OR hr < $1,000/hr $1,000/hr-$2,000/hr > $2,000/hr Turnover times > 40 mins 25-40 mins < 25 mins (Mean setup and cleanup turnover times for all cases) Prediction bias > 15 mins 5-15 mins < 5 mins (Bias in case duration estimates per 8 hr of OR time) Prolonged turnovers > 25% 10%-25% < 10% (% of turnovers that are more than 60 mins) Source: Reprinted with permission from Macario A. Anesthesiology. 2006;1005(2):237-240. use, how do you deal with rooms consistently running late on the day of surgery? The answer: Make the allocated time into which cases are being scheduled longer. For example, if a surgeon does 12 hours worth of cases every day he is in the OR, don t plan 8 hours of staffing (7 am to 3 pm) and have everyone frustrated by having to stay late (overtime). Rather, schedule his cases into 12 hours of allocated time (7 am to 7 pm). That way, anesthesia and nursing staff know they will be there for 12 hours when they arrive at work, and overtime costs (financial and morale) will be reduced. The common response to this approach is, No one wants to be there until 7 pm. The answer is, You are there now until 7 pm, so why not make the scheduled OR time 12 hours long and have a more predictable work day duration? Thus, optimizing staffing costs is finding a balance between overtime and finishing early. There may be concern about the ability to flex staffing enough to avoid excess staffing costs. It can be difficult to match scheduled cases with staffing perfectly so the staff still get the hours and shifts they need. For example, if Dr Smith needs a 12- hour block, the manager needs to find staff who want to work a 12-hour shift (or part-timers in some combination). Staffing is not only an OR efficiency issue but also a staff satisfaction issue. Start-time tardiness Start-time tardiness is defined as the mean tardiness of start times for elective cases per OR per day. Reducing the time patients have to wait for their surgery once they arrive at the hospital (especially if the preceding case runs late) is another important goal. If a case is supposed to start at 10 am (patient enters OR), but the case starts at 10:30 am, there are 30 minutes of tardiness. In computing this metric, no credit is given if the 10 am case starts early (for example at 9:45 am). The tardiness in starting scheduled cases should total less than 45 minutes per 8- hour OR day in well-functioning OR suites. Facilities with long work days will have greater tardiness because the longer the day, the more uncertainty about case start 2

times. Having patients medical records ready to go with all needed documents is essential for on-time starts. Case cancellation rate on day of surgery Cancellation rates vary among facilities, depending partly on the types of patients receiving care, ranging from 4.6% for outpatients (van Klei, et al, 2002) to 13% (Pollard, et al, 1999) to 18% (Basson, et al) at VA medical centers. Many cancellations are due to nonmedical problems such as a full ICU, surgeon unavailability, or bad weather. OR cancellation rates can be monitored statistically (Dexter, Marcon, et al, 2005), and well-functioning OR suites should have cancellation rates less than 5%. Monitoring cancellations correctly is not taking the ratio of the number of cancellations to the number of scheduled cases (Dexter, Marcon, et al, 2005). Postanesthesia care unit admission PACU admission delays are defined as the percentage of work days with at least one delay of 10 minutes or greater in PACU admission because the PACU is full. It is important to adjust PACU nurse staffing around the times of OR admissions. Algorithms exist that use the number of available nursing hours to find the staffing solution with the fewest number of understaffed days (Dexter, Epstein, 2005; Marcon, Dexter, 2006). Contribution margin per OR hr An OR suite that puts up with excessive surgical times can schedule itself efficiently but still lose its financial shirt if many surgeons are slow, use too many instruments or expensive implants, etc. These are all measured by the contribution margin per OR hour. The contribution margin per hour of OR time is the hospital revenue generated by a surgical case, less all the hospitalization variable labor and supply costs. Variable costs, such as implants, vary directly with the volume of cases performed. This is because fee-for-service hospitals have a positive contribution margin for almost all elective cases mostly due to a large percentage of OR costs being fixed. For US hospitals not on a fixed annual budget, contribution margin per OR hour averages $1,000 to $2,000 US per OR hour (Dexter, Ledolter et al, 2005; Dexter, Blake, et al, 2002; Macario, Dexter, et al, 2001). Turnover times Turnover time is the time from when one patient exits an OR until the next patient enters the same OR (Donham, et al, 1999). Turnover times include cleanup times and setup times but not delays between cases. Based on data collected at 31 US hospitals, turnover times at the best performing OR suites average less than 25 minutes (Dexter, Epstein, et al, 2005). Cost reduction from reducing turnover times (because OR workload is less) can only be achieved if OR allocations and staffing are reduced (Dexter, Abouleish, et al, 2003). Despite this, turnover time receives lots of attention from OR managers because it is a key satisfier for surgeons. Sometimes an OR suite reduces turnover times (by providing more staff to clean the room, for example), but new problems arise (such as not enough time for sterilizing instruments for the new case or not being able to take the patient to the PACU because there are no beds) that were hidden by long turnover times. Times between cases that are longer than a defined interval (eg, 1 hour because the to-follow surgeon is unavailable) should be considered delays, not turnovers (Dexter, Macario, et al, 1999). Prediction bias Prediction bias is defined as bias in case duration estimates per 8 hours of OR time. Prediction error equals the actual duration of the new case minus the estimated duration of the new case. Bias indicates whether the estimate is consistently too high or consistently too low, and precision reflects the magnitudes of the errors of the estimates. Efficient OR suites should aim to have a prediction bias that is less than 15 minutes (Dexter, Macario, et al, 2005). A reason for bias can be surgeons con- 3

sistently shortening their case duration estimates because they have too little OR time allocated and need to fit their list of cases into the OR time they do have. In contrast, surgeons may purposely overestimate case durations to keep control of or access to their allocated OR time so if a new case appears, their OR time is not given away. Remember that lack of historical case duration data for scheduled procedures is an important cause of inaccuracy in predicting case durations. In general, half of the cases scheduled in your OR suite tomorrow will have less than 5 previous cases of the same procedure type and same surgeon during the preceding year (Zhou, et al, 1999). It would be nice to have no uncertainty in case duration prediction. But it is present. The problem is looking for a single number that is correct most of the time. You won t get accurate estimates by using historical case duration data. Rather, from the historical data, you ll get an assessment of the uncertainty. With proper management weeks to months ahead of time, the groundwork for an efficient (well-functioning) OR suite should be in place. Statistical process control could be used to prospectively monitor a dashboard of items, such as the ones discussed above. Alex Macario, MD, MBA Department of Anesthesia Stanford University School of Medicine Summarized with permission from Macario, A. Are your hospital operating rooms efficient? Anesthesiology. 2006;105:257-260. References Abouleish A E, Dexter F, Epstein R H, et al. Labor costs incurred by anesthesiology groups because of operating rooms not being allocated and cases not being scheduled to maximize operating room efficiency. Anesth Analg. 2003;96:1109-1113. Basson M D, Butler T W, Verma H. Predicting patient nonappearance for surgery as a scheduling strategy to optimize operating room utilization in a veterans administration hospital. Anesthesiology. 2006;104(4):826-834. Dexter F, Abouleish A E, Epstein R H, et al. Use of operating room information system data to predict the impact of reducing turnover times on staffing costs. Anesth Analg. 2003;97:1119-1126. Dexter F, Blake J T, Penning D H, et al. Calculating a potential increase in hospital margin for elective surgery by changing operating room time allocations or increasing nursing staffing to permit completion of more cases: A case study. Anesth Analg. 2002;94: 138 142. Dexter F, Epstein R H, de Matta R, et al. Strategies to reduce delays in admission into a postanesthesia care unit from operating rooms. J PeriAnesth Nurs. 2005;20:92-102. Dexter F, Epstein R H, Marcon E, et al. Estimating the incidence of prolonged turnover times and delays by time of day. Anesthesiology. 2005;102:1242-1248. Dexter F, Ledolter J, Wachtel R E. Tactical decision making for selective expansion of operating room resources incorporating financial criteria and uncertainty in subspecialties future workloads. Anesth Analg. 2005;100: 1425-1432. Dexter F, Macario A, Epstein R H, et al. Validity and usefulness of a method to monitor surgical services average bias in scheduled case durations. Can J Anesth. 2005;52:935-939. Dexter F, Macario A, Qian F, et al. Forecasting surgical groups total hours of elective cases for allocation of block time. Anesthesiology. 1999;91: 1501-1508. Dexter F, Marcon E, Epstein R H, et al. Validation of statistical methods to compare cancellation rates on the day of surgery. Anesth Analg. 2005;101(2): 465-473. 4

Donham R T, Mazzei W J, Jones R L, et al. Procedural times glossary. Am J Anesthesiology. 1999;23,5 Suppl:4. Macario A. Are your hospital operating rooms efficient? A scoring system with eight performance indicators. Anesthesiology. 2006;105(2):237-240. Macario A, Dexter F, Traub R D. Hospital profitability per hour of operating room time can vary among surgeons. Anesth Analg. 2001;93:669 675. Marcon E, Dexter F. Impact of surgical sequencing on post anesthesia care unit staffing. Health Care Manag Sci. 2006; 9:81-92. Pollard J B, Olson L. Early outpatient preoperative anesthesia assessment: Does it help to reduce operating room cancellations? Anesth Analg. 1999;89: 502 505. Strum D P, Vargas L G, May J H. Surgical subspecialty block utilization and capacity planning: A minimal cost analysis model. Anesthesiology. 1999;90:1176-1185. van Klei W A, Moons K G, Rutten C L, et al. The effect of outpatient preoperative evaluation of hospital inpatients on cancellation of surgery and length of hospital stay. Anesth Analg. 2002;94: 644 649. Zhou J, Dexter F, Macario A, et al. Relying solely on historical surgical times to estimate accurately future surgical times is unlikely to reduce the average length of time cases finish late. J Clin Anesth. 1999;11:601-605. Copyright 2007. OR Manager, Inc. All rights reserved. 800/442-9918. www.ormanager.com 5