Hours Of Operating Room Time Research Articles

Analysis of Operating Room Allocations to Optimize Scheduling of Specialty Rotations for Anesthesia Trainees

Because specialty workloads and corresponding operating room (OR) allocations vary among days of the week, anesthesia residents and student nurse anesthetists are sometimes assigned to cases off rotation (e.g., scheduled for cardiac surgery but assigned to urology for the day). We describe a method to create hybrid rotations of two specialties (e.g., cardiac and vascular surgery), thereby reducing the numbers of days that trainees are "pulled" from their scheduled rotations. Raw data were the number of hours of OR time used by each surgical specialty on each workday for the preceding 9 months. These OR workloads were converted to the number of ORs to be allocated to each specialty for each day of the week on the basis of maximization of the efficiency of use of OR time. We considered all potential hybrid rotations of pairwise combinations of specialties to which trainees could be assigned. Integer linear programming was used to calculate the maximum number of trainees who could be scheduled to hybrid rotations and receive daily assignments matching those rotations. Validity of the results was shown by using data from a small facility for which the optimal solution could be discerned by inspection. Validity (appropriateness) of the constraints was demonstrated by the exclusion of each constraint, resulting in answers that are obviously incorrect. Novelty and usefulness of the method was evidenced by its choosing from among hundreds of thousands of potential combinations of specialties and its identifying appropriate assignments that were substantively different from current rotations. We developed a methodology to determine rotations consisting of combinations of specialties to be paired for purposes of trainee scheduling to reduce the incidence of daily assignments off rotation. Practically, with this method, anesthesia residents and student nurse anesthetists can be assigned cases within their scheduled rotations as often as possible.

Review of Behavioral Operations Experimental Studies of Newsvendor Problems for Operating Room Management

Operating room (OR) managers must plan staffing in the face of uncertain demand for OR time. Planning too much staffing results in underutilized OR time. Planning too little staffing causes overutilized time, which is approximately twice as expensive as underutilized time. Deciding how much staffing to plan for an OR is analogous to the classic newsvendor problem in operations research. A newsvendor must decide how much product to order based on its cost c and sales price p, plus estimates of the uncertain future demand for the product. The newsvendor problem has a simple mathematical solution. The correct amount of product to order is the (p - c)/p quantile of the demand for the product. This optimal order quantity is analogous mathematically to the number of hours of OR time for which staffing should be planned. We performed a systematic review of the behavioral operations experimental literature on newsvendor problems relevant to OR management. Student volunteers participating in experimental studies have great difficulty knowing how much product to order, given c, p, and the demand distribution. Decision making is only modestly improved by more frequent feedback. Even scores of rounds of ordering are insufficient for much learning to occur. Suboptimal decisions result from innate psychological biases. Students anchor on mean demand, make insufficient adjustments, and rely disproportionately on the most recent demand values. The behavior of OR managers who plan staffing for the OR is analogous to that of students participating in a newsvendor experiment. Month after month, an OR manager will plan too little staffing for the surgeon who consistently ends the day late and too much staffing for the surgeon who consistently does not fill an OR. Experimental studies of the newsvendor problem provide mechanistic insights into the reasons that OR managers make poor decisions when planning OR staffing. The students face no organizational factors or personality issues that potentially influence OR managers, strongly suggesting that observed psychological biases are innate and play an important role in influencing the decisions of OR managers. Results suggest that only the use of computerized decision-support systems can effectively circumvent these biases.

Lack of Sensitivity of Staffing for 8-Hour Sessions to Standard Deviation in Daily Actual Hours of Operating Room Time Used for Surgeons with Long Queues

At multiple facilities including some in the United Kingdom's National Health Service, the following are features of many surgical-anesthetic teams: i) there is sufficient workload for each operating room (OR) list to almost always be fully scheduled; ii) the workdays are organized such that a single surgeon is assigned to each block of time (usually 8 h); iii) one team is assigned per block; and iv) hardly ever would a team "split" to do cases in more than one OR simultaneously. We used Monte-Carlo simulation using normal and Weibull distributions to estimate the times to complete lists of cases scheduled into such 8 h sessions. For each combination of mean and standard deviation, inefficiencies of use of OR time were determined for 10 h versus 8 h of staffing. When the mean actual hours of OR time used averages < or = 8 h 25 min, 8 h of staffing has higher OR efficiency than 10 h for all combinations of standard deviation and relative cost of over-run to under-run. When mean > or = 8 h 50 min, 10 h staffing has higher OR efficiency. For 8 h 25 min < mean < 8 h 50 min, the economic break-even point depends on conditions. For example, break-even is: (a) 8 h 27 min for Weibull, standard deviation of 60 min and relative cost of over-run to under-run of 2.0 versus (b) 8 h 48 min for normal, standard deviation of 0 min and relative cost ratio of 1.50. Although the simplest decision rule would be to staff for 8 h if the mean workload is < or = 8 h 40 min and to staff for 10 h otherwise, performance was poor. For example, for the Weibull distribution with mean 8 h 40 min, standard deviation 60 min, and relative cost ratio of 2.00, the inefficiency of use of OR time would be 34% larger if staffing were planned for 8 h instead of 10 h. For surgical teams with 8 h sessions, use the following decision rule for anesthesiology and OR nurse staffing. If actual hours of OR time used averages < or = 8 h 25 min, plan 8 h staffing. If average > or = 8 h 50 min, plan 10 h staffing. For averages in between, perform the full analysis of McIntosh et al. (Anesth Analg 2006;103:1499-516).

Long-Term Forecasting of Anesthesia Workload in Operating Rooms from Changes in a Hospital’s Local Population Can Be Inaccurate

Anesthesia department planning depends on forecasting future demand for perioperative services. Little is known about long-range forecasting of anesthesia workload. We studied operating room (OR) times at Hospital A over 16 yr (1991-2006), anesthesia times at Hospital B over 26 yr (1981-2006), and cases at Hospital C over 13 yr (1994-2006). Each hospital is >100 yr old and is located in a US city with other hospitals that are >50 yr old. Hospitals A and B are the sole University hospitals in their metropolitan statistical areas (and many counties beyond). Hospital C is the sole tertiary hospital for >375 km. Each hospital's choice of a measure of anesthesia work to be analyzed was likely unimportant, as the annual hours of anesthesia correlated highly both with annual numbers of cases (r = 0.98) and with American Society of Anesthesiologist's Relative Value Guide units of work (r = 0.99). Despite a 2% decline in the local population, the hours of OR time at Hospital A increased overall (Pearson r = -0.87, P < 0.001) and for children (r = -0.84). At Hospital B, there was a strong positive correlation between population and hours of anesthesia (r = 0.97, P < 0.001), but not between annual increases in population and workload (r = -0.18). At Hospital C, despite a linear increase in population, the annual numbers of cases increased, declined with opening of two outpatient surgery facilities, and then stabilized. The predictive value of local personal income was low. In contrast, the annual increases in the hours of OR time and anesthesia could be modeled using simple time series methods. Although growth of the elderly population is a simple justification for building more ORs, managers should be cautious in arguing for strategic changes in capacity at individual hospitals based on future changes in the national age-adjusted population. Local population can provide little value in forecasting future anesthesia workloads at individual hospitals. In addition, anesthesia groups and hospital administrators should not focus on quarterly changes in workload, because workload can vary widely, despite consistent patterns over decades. To facilitate long-range planning, anesthesia groups and hospitals should save their billing and OR time data, display it graphically over years, and supplement with corresponding forecasting methods (e.g., staff an additional OR when an upper prediction bound of workload per OR exceeds a threshold).

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.

How to schedule elective surgical cases into specific operating rooms to maximize the efficiency of use of operating room time.

We considered elective case scheduling at hospitals and surgical centers at which surgeons and patients choose the day of surgery, cases are not turned away, and anesthesia and nursing staffing are adjusted to maximize the efficiency of use of operating room (OR) time. We investigated scheduling a new case into an OR by using two patient-scheduling rules: Earliest Start Time or Latest Start Time. By using several scenarios, we showed that the use of Earliest Start Time is rational economically at such facilities. Specifically, it maximizes OR efficiency when a service has nearly filled its regularly scheduled hours of OR time. However, Latest Start Time will perform better at balancing workload among services' OR time. We then used historical case duration data from two facilities in computer simulations to investigate the effect of errors in predicting case durations on the performance of these two heuristics. The achievable incremental reduction in overtime by having perfect information on case duration versus using historical case durations was only a few minutes per OR. The differences between Earliest Start Time and Latest Start Time were also only a few minutes per OR. We conclude that for facilities at which the goals are, in order of importance, safety, patient and surgeon access to OR time, and then efficiency, few restrictions need to be placed on patient scheduling to achieve an efficient use of OR time. We showed how elective cases should be scheduled to maximize the efficiency of use of operating room time. The analysis applies to surgical suites at which surgeons and patients have access to operating room time every workday.

Use of linear programming to estimate impact of changes in a hospital's operating room time allocation on perioperative variable costs.

Administrators at hospitals with a fixed annual budget may want to focus surgical services on priority areas to ensure its community receives the best health services possible. However, many hospitals lack the detailed managerial accounting data needed to ensure that such a change does not increase operating costs. The authors used a detailed hospital cost database to investigate by how much a change in allocations of operating room (OR) time among surgeons can increase perioperative variable costs. The authors obtained financial data for all patients who underwent outpatient or same-day admit surgery during a year. Linear programming was used to determine by how much changing the mix of surgeons can increase total variable costs while maintaining the same total hours of OR time for elective cases. Changing OR allocations among surgeons without changing total OR hours allocated will likely increase perioperative variable costs by less than 34%. If, in addition, intensive care unit hours for elective surgical cases are not increased, hospital ward occupancy is capped, and implant use is tracked and capped, perioperative costs will likely increase by less than 10%. These four variables predict 97% of the variance in total variable costs. The authors showed that changing OR allocations among surgeons without changing total OR hours allocated can increase hospital perioperative variable costs by up to approximately one third. Thus, at hospitals with fixed or nearly fixed annual budgets, allocating OR time based on an OR-based statistic such as utilization can adversely affect the hospital financially. The OR manager can reduce the potential increase in costs by considering not just OR time, but also the resulting use of hospital beds and implants.

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.

Administrators routinely seek to increase contribution margin (revenue minus variable costs) to better cover fixed costs, provide indigent care, and meet other community service responsibilities. Hospitals with high operating room (OR) utilizations can allocate OR time for elective surgery to surgeons based partly on their contribution margins per hour of OR time. This applies particularly when OR caseload is limited by nursing recruitment. From a hospital's annual accounting data for elective cases, we calculated the following for each surgeon's patients: variable costs for the entire hospitalization or outpatient visit, revenues, hours of OR time, hours of regular ward time, and hours of intensive care unit (ICU) time. The contribution margin per hour of OR time varied more than 1000% among surgeons. Linear programming showed that reallocating OR time among surgeons could increase the overall hospital contribution margin for elective surgery by 7.1%. This was not achieved simply by taking OR time from surgeons with the smallest contribution margins per OR hour and giving it to the surgeons with the largest contribution margins per OR hour because different surgeons used differing amounts of hospital ward and ICU time. We conclude that to achieve substantive improvement in a hospital's perioperative financial performance despite restrictions on available OR, hospital ward, or ICU time, contribution margin per OR hour should be considered (perhaps along with OR utilization) when OR time is allocated. For hospitals where elective surgery caseload is limited by nursing recruitment, to increase one surgeon's operating room time either another surgeon's time must be decreased, nurses need to be paid a premium for working longer hours, or higher-priced "traveling" nurses can be contracted. Linear programming was performed using Microsoft Excel to estimate the effect of each of these interventions on hospital contribution margin.