Forecasting surgical groups' total hours of elective cases for allocation of block time: application of time series analysis to operating room management

A Perspective on Theatre Efficiency in Terms of Theatre Utilisation and Theatre Costs and the Effects of Infection Control Protocols on These During the COVID-19 Pandemic

Background and aim The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on healthcare systems. Several local infection control methods were put in place, which have now evolved and continued in some form or the other. According to various research, as the time duration for distinct phases in the pathway rose, trauma theatre efficiency reduced. However, there is no literature, to our knowledge, that has explicitly looked at theatre utilisation and cost efficiency compared them and expressed theatre efficiency in these terms. The aim of this article is to study theatre efficiency in terms of utilisation and costs before and during the pandemic and understand the influence of infection control protocols on these. Materials and methods The data were collected retrospectively from the ORMIS theatre management software (iPath Softwares, Ohio), from December 2019 (pre-COVID) and December 2020 (COVID). Turnaround time, utilisation time and combined operative time were defined and compared. Costs incurred due to over-running, under-running and turnaround time were compared. Results Theatre utilization was 101% during COVID and 86.63% pre-COVID. The average cost of over-running as well as under-running a theatre list during the pandemic was significantly higher. Conclusion Optimal theatre utilisation and reduced time between cases improve theatre efficiency. Turnaround time, if reduced, can not only decrease costs but also increase efficiency.Theatre utilisation and efficiency can be maintained even with new infection control protocols, but these are not cost-efficient.

More surgery in December among US patients with commercial insurance is offset by unrelated but lesser surgery among patients with Medicare insurance

Study Objective

Evaluate whether there is more surgery (in the US State of Florida) at the end of the year, specifically among patients with commercial insurance.

Design

Observational cohort study.

Setting

The 712 facilities in Florida that performed inpatient or outpatient elective surgery from January 2010 through December 2019.

Results

Among patients with commercial insurance, December had more cases than November (1.108 [1.092–1.125]) or January (1.257 [1.229–1.286]). In contrast, among patients with Medicare insurance (traditional or managed care), December had fewer cases than November (ratio 0.917 [99% confidence interval 0.904–0.930]) or January (0.823 [0.807–0.839]) of the same year. Summing among all cases, December did not have more cases than November (ratio 1.003 [0.992–1.014]) or January (0.998 [0.984–1.013]). Comparing December versus November (January) ratios for cases among patients with commercial insurance to the corresponding ratios for cases among patients with Medicare, years with more commercial insurance cases had more Medicare cases (Spearman rank correlation +0.36 [+0.25], both p < 0.0001).

Conclusions

In the US State of Florida, although some surgeons' procedural workloads may have seasonal variation if they care mostly for patients with one category of insurance, surgical facilities with patients undergoing many procedures will have less variability. Importantly, more commercial insurance cases were not causing Medicare cases to be postponed or vice‐versa, providing mechanistic explanation for why forecasts of surgical demand can reasonably be treated as the sum of the independent workloads among many surgeons.

In US State of Florida, patients with commercial insurance had more surgery in December

Patients with US Medicare insurance had less surgery in December than other months

Years with more commercial insurance cases in December had more US Medicare cases too

Implication for surgical suites: busier months for some patient groups balanced by less busy for others

December Is Coming: A Time Trend Analysis of Monthly Variation in Adult Elective Anesthesia Caseload across Florida and Texas Locations of a Large Multistate Practice

BACKGROUND

Anesthesia staffing models rely on predictable surgical case volumes. Previous studies have found no relationship between month of the year and surgical volume. However, seasonal events and greater use of high-deductible health insurance plans may cause U.S. patients to schedule elective surgery later in the calendar year. The hypothesis was that elective anesthesia caseloads would be higher in December than in other months.

METHODS

This review analyzed yearly adult case data in Florida and Texas locations of a multistate anesthesia practice from 2017 to 2019. To focus on elective caseload, the study excluded obstetric, weekend, and holiday cases. Time trend decomposition analysis was used with seasonal variation to assess differences between December and other months in daily caseload and their relationship to age and insurance subgroups.

RESULTS

A total of 3,504,394 adult cases were included in the analyses. Overall, daily caseloads increased by 2.5 ± 0.1 cases per day across the 3-yr data set. After adjusting for time trends, the average daily December caseload in 2017 was 5,039 cases (95% CI, 4,900 to 5,177), a 20% increase over the January-to-November baseline (4,196 cases; 95% CI, 4,158 to 4,235; P < 0.0001). This increase was replicated in 2018: 5,567 cases in December (95% CI, 5,434 to 5,700) versus 4,589 cases at baseline (95% CI, 4,538 to 4,641), a 21.3% increase; and in 2019: 6,103 cases in December (95% CI, 5,871 to 6,334) versus 5,045 cases at baseline (95% CI, 4,984 to 5,107), a 21% increase (both P < 0.001). The proportion of commercially insured patients and those aged 18 to 64 yr was also higher in December than in other months.

CONCLUSIONS

In this 3-yr retrospective analysis, it was observed that, after accounting for time trends, elective anesthesia caseloads were higher in December than in other months of the year. Proportions of commercially insured and younger patients were also higher in December. When compared to previous studies finding no increase, this pattern suggests a recent shift in elective surgical scheduling behavior.

EDITOR’S PERSPECTIVE

Benefit of systematic selection of pairs of cases matched by surgical specialty for surveillance of bacterial transmission in operating rooms

BACKGROUND

Bacterial transmission within and between successive surgical cases occurs in operating rooms (ORs), often includes anesthesia equipment as a reservoir, and can be monitored by collecting samples and identifying bacteria by genetic testing. We evaluated how to choose cases for active surveillance to quantify the effectiveness of interventions in 2 groups of ORs (eg, rooms with germicidal lighting vs those without).

METHODS

Data were from a 7 OR single-specialty gastrointestinal endoscopy suite and from a typical 8 OR multispecialty surgical suite.

RESULTS

At the multispecialty hospital, 40.3% (SE 1.2%) of the total number of cases could be used for surveillance (ie, followed by another case of the same specialty and matched with a corresponding pair of cases from the other OR group). Random selection obtained fewer matched pairs than deliberate selection: mean ratio of random/deliberate = 0.64 (0.01) for the single-specialty and 0.51 (0.02) for the multispecialty suite (P <.001).

CONCLUSIONS

The efficiency of sampling to obtain pairs of successive surgical cases of the same specialty is impaired markedly by randomly selecting pairs of cases (or using convenience sampling) as compared to choosing pairs deliberately. This is important because the number of cases that can be suitably used for surveillance of bacterial transmission will typically be less than one-half the total case number.

US National Anesthesia Workload on Saturday and Sunday Mornings

BACKGROUND

In order to provide guidance to organizations considering elective weekend surgical case scheduling, we analyzed data from the American Society of Anesthesiologist's Anesthesia Quality Institute. We determined the US anesthesia workload on Saturdays and Sundays.

METHODS

The American Society of Anesthesiologist's Anesthesia Quality Institute data were from all US anesthesia groups that submitted cases to the National Anesthesia Clinical Outcomes Registry for 2013. For each of the N = 2,075,188 cases, we identified the local date and time of the start of anesthesia care and the duration of anesthesia care. Anesthesia workload was measured as the time from the start to the end of continuous anesthesia care. Because elective cases are rarely scheduled on Sundays, we considered the difference in workload between Saturday and Sunday to estimate elective case scheduling. This difference would be an overestimate if some patients' scheduled cases were postponed from Friday to Saturday. Data are reported as mean ± standard error; N = 13 four-week periods.

RESULTS

The difference in the anesthesia minutes between Saturdays versus Sundays 7:00 AM to 2:59 PM (ie, elective caseload) represented just 0.38% ± 0.02% of the total minutes nationwide; Saturday 1.57% ± 0.03% versus Sunday 1.19% ± 0.02%. The P < .00001 comparing the 0.38% with 1.0% and, also, with 0.5% (upper 99% confidence interval = 0.42%).

CONCLUSIONS

The imputed Saturday elective schedule represents a tiny percentage of overall anesthetic workload nationwide. Saturday elective surgery is currently an uncommon practice in the United States. Based on this prior knowledge, organizations considering changes to their current scheduling strategies should perform a thorough statistical analysis of their local workload prior to implementation and apply evidence-based criteria to guide their decision-making process.

Increasing operating room efficiency through electronic medical record analysis

We used electronic medical record (EMR) analysis to determine errors in operating room (OR) time utilisation. Over a two year period EMR data of 44,503 surgical procedures was analysed for OR duration, on-time, first case, and add-on time performance, within 19 surgical specialties. Maximal OR time utilisation at our institution could have saved over 302,620 min or 5,044 hours of OR efficiency over a two year period. Most specialties (78.95%) had inaccurately scheduled procedure times and therefore used the OR more than their scheduled allotment time. Significant differences occurred between the mean scheduled surgical durations (101.38 ± 87.11 min) and actual durations (108.18 ± 102.27 min; P < 0.001). Significant differences also occurred between the mean scheduled add-on durations (111.4 ± 75.5 min) and the actual add-on scheduled durations (118.6 ± 90.1 minutes; P < 0.001). EMR quality improvement analysis can be used to determine scheduling error and bias, in order to improve efficiency and increase OR time utilisation.

The cost of trauma operating theatre inefficiency

The National Health Service (NHS) is currently facing a financial crisis with a projected deficit of £2billion by the end of financial year 2015/16. As operating rooms (OR) are one of the costliest components in secondary care, improving theatre efficiency should be at the forefront of efforts to improve health service efficiency. The objectives of this study were to characterize the causes of trauma OR delays and to estimate the cost of this inefficiency.

A 1-month prospective single-centre study in St. Mary's Hospital. Turnaround time (TT) was used as the surrogate parameter to measure theatre efficiency. Factors including patient age, ASA score and presence of surgical and anaesthetic consultant were evaluated to identify positive or negative associations with theatre delays. Inefficiency cost was calculated by multiplying the time wasted with staff capacity costs and opportunity costs, found to be £24.77/minute.

The commonest causes for increased TT were delays in sending for patients (50%) and problems with patient transport to the OR (31%). 461 min of delay was observed in 12 days, equivalent to loss of £951.58/theatre/day. Non-statistically significant trends were seen between length of delays and advancing patient age, ASA score and absence of either a senior clinician or an anaesthetic consultant. Interestingly, the trend was not as strong for absence of an anaesthetic consultant.

This study found delays in operating TT to represent a sizable cost, with potential efficiency savings based on TT of £347,327/theatre/year. Further study of a larger sample is warranted to better evaluate the identified trends.

•

Delays in operating turnaround time result in substantial financial waste.

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Causes of delays are reported in this study.

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Trends between age, ASA score and senior clinician presence with delays were found.

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Resolving this issue could potentially save an estimated £350,000/theatre/year.

Anesthesia Workload Nationally During Regular Workdays and Weekends

BACKGROUND

We analyze data from the American Society of Anesthesiologist's (ASA) Anesthesia Quality Institute (AQI) to report the U.S. anesthesia workload by time of day and day of the week. We consider the extent to which first case starts, rather than durations of workdays and weekend cases, influence the number of anesthesia providers nationally.

METHODS

The ASA AQI data were from all the U.S. anesthesia groups that submitted cases to the National Anesthesia Clinical Outcomes Registry (NACOR) for all 12 months of 2013. For each of the n = 2,075,188 cases, we identified the local date and time of the start of anesthesia care, duration of anesthesia care, and the local time zone. Anesthesia workload was measured as the time from the start to the end of continuous anesthesia care. Data are reported as mean ± SEM with 95% confidence intervals (CIs).

RESULTS

Half (53.0% ± 0.6%) of the ASA AQI-reported weekly anesthesia workload was completed by 1:00 PM, local time, on regular workdays. The busiest 8-hour interval was from 7:30 AM to 3:30 PM and accounted for 70.3% ± 0.7% of anesthetic minutes. Although most facilities completed the majority of their weekly anesthesia workload in the mornings of regular workdays (P < 0.0001; 62.3%; CI, 58.6%-66.1%), just 24.4% of the University and large community hospitals did so (P = 0.0008 relative to half; CI, 13.8%-38.4%).

CONCLUSIONS

The results are inconsistent with widespread use of surgical facilities (i.e., anesthesia providers) in mornings only, especially at University and large community hospitals. The observed national work hours match with what would be expected if most anesthesiologists work at least 8 hours on regular workdays. Opportunity for greater use of the capital (building and equipment) probably would involve the use of additional anesthesia providers representing a second shift or use of weekends.

The accuracy of surgeons' provided estimates for the duration of hysterectomies: a pilot study

STUDY OBJECTIVE

To determine the accuracy of gynecologic surgeons' estimate of operative times for hysterectomies and to compare these with the existing computer-generated estimate at our institution.

DESIGN

Pilot prospective cohort study (Canadian Task Force classification II-2).

SETTING

Academic tertiary women's hospital in the Northeast United States.

PARTICIPANTS

Thirty gynecologic surgeons including 23 general gynecologists, 4 gynecologic oncologists, and 3 urogynecologists.

INTERVENTION

Via a 6-question survey, surgeons were asked to predict the operative time for a hysterectomy they were about to perform. The surgeons' predictions were then compared with the time predicted by the scheduling system at our institution and with the actual operative time, to determine accuracy and differences between actual and predicted times. Patient and surgery data were collected to perform a secondary analysis to determine factors that may have significantly affected the prediction.

MEASUREMENTS AND MAIN RESULTS

Of 75 hysterectomies analyzed, 36 were performed abdominally, 18 vaginally, and 21 laparoscopically. Accuracy was established if the actual procedure time was within the 15-minute increment predicted by either the surgeons or the scheduling system. The surgeons accurately predicted the duration of 20 hysterectomies (26.7%), whereas the accuracy of the scheduling system was only 9.3%. The scheduling system accuracy was significantly less precise than the surgeons, primarily due to overestimation (p = .01); operative time was overestimated on average 34 minutes. The scheduling system overestimated the time required to a greater extent than the surgeons for nearly all data examined, including patient body mass index, surgical approach, indication for surgery, surgeon experience, uterine size, and previous abdominal surgery.

CONCLUSION

Although surgeons' accuracy in predicting operative time was poor, it was significantly better than that of the computerized scheduling system, which was more likely to overestimate operative time.

Predicting Case Volume from the Accumulating Elective Operating Room Schedule Facilitates Staffing Improvements

Background:

Precise estimates of final operating room demand can only be made 1 or 2 days before the day of surgery, when it is harder to adjust staffing to match demand. The authors hypothesized that the accumulating elective schedule contains useful information for predicting final case demand sufficiently in advance to readily adjust staffing.

Methods:

The accumulated number of cases booked was recorded daily, from which a usable dataset comprising 146 consecutive surgical days (October 10, 2011 to May 7, 2012, after removing weekends and holidays), and each with 30 prior calendar days of booking history, was extracted. Case volume prediction was developed by extrapolation from estimates of the fraction of total cases booked each of the 30 preceding days, and averaging these with linear regression models, one for each of the 30 preceding days. Predictions were verified by comparison with actual volume.

Results:

The elective surgery schedule accumulated approximately three cases per day, settling at a mean ± SD final daily volume of 117 ± 12 cases. The model predicted final case counts within 8.27 cases as far in advance as 14 days before the day of surgery. In the last 7 days before the day of surgery, the model predicted the case count within seven cases 80% of the time. The model was replicated at another smaller hospital, with similar results.

Conclusions:

The developing elective schedule predicts final case volume weeks in advance. After implementation, overly high- or low-volume days are revealed in advance, allowing nursing, ancillary service, and anesthesia managers to proactively fine-tune staffing up or down to match demand.

Allocating operating room block time using historical caseload variability

Operating room (OR) allocation and planning is one of the most important strategic decisions that OR managers face. The number of ORs that a hospital opens depends on the number of blocks that are allocated to the surgical groups, services, or individual surgeons, combined with the amount of open posting time (i.e., first come, first serve posting) that the hospital wants to provide. By allocating too few ORs, a hospital may turn away surgery demand whereas opening too many ORs could prove to be a costly decision. The traditional method of determining block frequency and size considers the average historical surgery demand for each group. However, given that there are penalties to the system for having too much or too little OR time allocated to a group, demand variability should play a role in determining the real OR requirement. In this paper we present an algorithm that allocates block time based on this demand variability, specifically accounting for both over-utilized time (time used beyond the block) and under-utilized time (time unused within the block). This algorithm provides a solution to the situation in which total caseload demand can be accommodated by the total OR resource set, in other words not in a capacity-constrained situation. We have found this scenario to be common among several regional healthcare providers with large OR suites and excess capacity. This algorithm could be used to adjust existing blocks or to assign new blocks to surgeons that did not previously have a block. We also have studied the effect of turnover time on the number of ORs that needs to be allocated. Numerical experiments based on real data from a large health-care provider indicate the opportunity to achieve over 2,900 hours of OR time savings through improved block allocations.

Reasons for cancellation of elective cardiac surgery at Prince Sultan Cardiac Centre, Saudi Arabia

The cancellation of surgery is a significant drain on health resources. However, a persistent problem in most hospitals is short notice cancellation of scheduled operations, even upto the day of surgery. In some cases, patients have been prepared for surgery, and the staff is assembled and expecting to operate. In UK 8% of scheduled elective operations are cancelled within 24 hours of surgery. The reasons include cancellation by the patient, cancellation for poorly optimized medical conditions, or cancellations due to poor organization. Many of these are difficult to quantify. However, one relatively easily measured factor is the possibility that some operating lists were predictably over-booked. An operating list may over-run because of delayed starts, slow turnover, unanticipated surgical/anaesthetic problems or staff shortages. Many of these are difficult to quantify.

BACKGROUND AND OBJECTIVE

Prince Sultan Cardiac center is one of the largest referral center in the Middle East and there is no published data on the reasons for cancellation of specifically cardiac procedures. However, an audit was performed to assess the reasons for the cancellation of the cases on the day of surgery in cardiac theatres. According to one of the studies published in an Australian journal the percentage of cancelled cardiothoracic cases was determined to be 15.8%.

RESULTS

Total number of cardiac surgical patients including pediatric and adult during a period from June 2008 to May 2009 were 2191. Out of those, 1681 cases were done during the study period, 510 (23.27%) cases were cancelled during the study period. The operation theatre was functional for 331 days during the study period. Cancellations done by the surgeons were 34% while the patient's related cancellations were 32%. The administrative issues contributed to 34% in overall cancellation and anaesthetist-related cancellation were 0%.

CONCLUSION

We estimated 22% of the elective operations which were cancelled on the day of surgery were potentially avoidable. There is still a need to do further research to look for the identifiable reasons and strategic measures to eliminate the reasons for cancellation on the day of surgery.

Using mean duration and variation of procedure times to plan a list of surgical operations to fit into the scheduled list time

BACKGROUND AND OBJECTIVE

It is important that a surgical list is planned to utilise as much of the scheduled time as possible while not over-running, because this can lead to cancellation of operations. We wished to assess whether, theoretically, the known duration of individual operations could be used quantitatively to predict the likely duration of the operating list.

METHODS

In a university hospital setting, we first assessed the extent to which the current ad-hoc method of operating list planning was able to match the scheduled operating list times for 153 consecutive historical lists. Using receiver operating curve analysis, we assessed the ability of an alternative method to predict operating list duration for the same operating lists. This method uses a simple formula: the sum of individual operation times and a pooled standard deviation of these times. We used the operating list duration estimated from this formula to generate a probability that the operating list would finish within its scheduled time. Finally, we applied the simple formula prospectively to 150 operating lists, 'shadowing' the current ad-hoc method, to confirm the predictive ability of the formula.

RESULTS

The ad-hoc method was very poor at planning: 50% of historical operating lists were under-booked and 37% over-booked. In contrast, the simple formula predicted the correct outcome (under-run or over-run) for 76% of these operating lists. The calculated probability that a planned series of operations will over-run or under-run was found useful in developing an algorithm to adjust the planned cases optimally. In the prospective series, 65% of operating lists were over-booked and 10% were under-booked. The formula predicted the correct outcome for 84% of operating lists.

CONCLUSION

A simple quantitative method of estimating operating list duration for a series of operations leads to an algorithm (readily created on an Excel spreadsheet, http://links.lww.com/EJA/A19) that can potentially improve operating list planning.

[Anesthesia for surgical specialties in Catalonia, Spain, during 2003]

BACKGROUND AND OBJECTIVE

The aim of this arm of the ANESCAT study was to analyze the characteristics of patients who underwent anesthesia for surgical procedures in Catalonia, Spain, in 2003.

PATIENTS AND METHOD

Based on data obtained from the survey undertaken in 131 hospitals, we describe the anesthesia practice associated with surgical specialties, excluding anesthesia performed for obstetric and nonsurgical procedures. Data are provided for all surgical procedures and for each surgical specialty separately. The results are expressed as medians (10th-90th percentile).

RESULTS

Surgical anesthesia represented 78.4% of anesthesia practice, corresponding to an estimated 472,857 anesthetic procedures per year. The most common surgical specialties associated with anesthetic procedures were orthopedics and traumatology (23.8%), ophthalmology (20.2%), general and digestive surgery (18.9%), and gynecology (9.7%). The median duration of anesthesia was 60 (25-165) minutes. The physical status of the patients according to the American Society of Anesthesiologists (ASA) classification was as follows: ASA 1, 37.9%; ASA 2, 32.5%; ASA 3, 23.9%; and ASA 4 or higher, 5.7%. The median age of the patients was 52 (21-78) years. Ambulatory procedures accounted for 33.6% of the total, and 9.2% of patients were admitted to postoperative critical care units. The most common procedure was cataract extraction (estimated 76,963 cases) followed by inguinal herniorrhaphy (23,315). Public hospitals performed 74.4% of all anesthetic procedures. Ophthalmology and plastic surgery were the most common procedures requiring anesthesia in private hospitals.

CONCLUSIONS

Three out of 4 anesthetic procedures performed in Catalonia in 2003 was for surgery, 1 in 3 of those procedures was ambulatory, and almost 1 in 10 patients required postoperative critical care.

Understanding waiting lists as the matching of surgical capacity to demand: are we wasting enough surgical time?

If surgical 'capacity' always matched or exceeded 'demand' then there should be no waiting lists for surgery. However, understanding what is meant by 'demand', 'capacity' and 'matched' requires some mathematical concepts that we outline in this paper. 'Time' is the relevant measure: 'demand' for a surgical team is best understood as the total min required for the surgery booked from outpatient clinics every week; and 'capacity' is the weekly operating time available. We explain how the variation in demand (not just the mean demand) influences the analysis of optimum capacity. However, any capacity chosen in this way is associated with only a likelihood (that is, a probability rather than certainty) of absorbing the prevailing demand. A capacity that suitably absorbs the demand most of the time (for example, > 80% of weeks) will inevitably also involve considerable waste (that is, many weeks in which there is spare, unused capacity). Conversely, a level of capacity chosen to minimise wasted time will inevitably cause an increase in size of the waiting list. Thus the question of how to balance demand and capacity is intimately related to the question of how to balance utilisation and waste. These mathematical considerations enable us to consider objectively how to manage the waiting list. They also enable us critically to analyse the extent to which philosophies adopted by the National Health Service (such as 'Lean' or 'Six Sigma') will be successful in matching surgical capacity to demand.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

The concept of surgical operating list ‘efficiency’: a formula to describe the term

While numerous reports have sought ways of improving the efficiency of surgical operating lists, none has defined 'efficiency'. We describe a formula that defines efficiency as incorporating three elements: maximising utilisation, minimising over-running and minimising cancellations on a list. We applied this formula to hypothetical (but realistic) scenarios, and our formula yielded plausible descriptions of these. We also applied the formula to 16 consecutive elective surgical lists from three gynaecology teams (two at a university hospital and one at a non-university hospital). Again, the formula gave useful insights into problems faced by the teams in improving their performance, and it also guided possible solutions. The formula confirmed that a team that schedules cases according to the predicted durations of the operations listed (i.e. the non-university hospital team) suffered fewer cancellations (median 5% vs 8% and 13%) and fewer list over-runs (6% vs 38% and 50%), and performed considerably more efficiently (90% vs 79% and 72%; p = 0.038) than teams that did not do so (i.e. those from the university hospital). We suggest that surgical list performance is more completely described by our formula for efficiency than it is by other conventional measures such as list utilisation or cancellation rate alone.