Enhancement opportunities in operating room utilization; with a statistical appendix

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Enhancement opportunities in operating room utilization; with a statistical appendix Elizabeth van Veen-Berkx, MSc,a,* Sylvia G. Elkhuizen, PhD,b Sanne van Logten, MSc,c Wolfgang F. Buhre, MD, PhD,d Cor J. Kalkman, MD, PhD,e Hein G. Gooszen, MD, PhD,f and Geert Kazemier, MD, PhD,g for the Dutch Operating Room Benchmarking Collaborative a

Department of Operating Rooms, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands Institute for Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands c Department of Pulmonary Services, Diaconessen Hospital Utrecht, Utrecht, The Netherlands d Division of Anesthesiology and Pain Therapy, Maastricht University Medical Center, Maastricht, The Netherlands e Department of Anesthesiology, University Medical Center Utrecht, Utrecht, The Netherlands f Department of Operating Rooms, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands g Department of Surgery, VU University Medical Center Amsterdam, Amsterdam, The Netherlands b

article info

abstract

Article history:

Background: The purpose of this study was to assess the direct and indirect relationships

Received 20 May 2014

between first-case tardiness (or “late start”), turnover time, underused operating room (OR)

Received in revised form

time, and raw utilization, as well as to determine which indicator had the most negative

14 October 2014

impact on OR utilization to identify improvement potential. Furthermore, we studied the

Accepted 24 October 2014

indirect relationships of the three indicators of “nonoperative” time on OR utilization, to

Available online 1 November 2014

recognize possible “trickle down” effects during the day. Materials and methods: (Multiple) linear regression analysis and mediation effect analysis

Keywords:

were applied to a data set from all eight University Medical Centers in the Netherlands. This

Operating rooms

data set consisted of 190,071 OR days (on which 623,871 surgical cases were performed).

Utilization

Results: Underused OR time at the end of the day had the strongest influence on raw uti-

Nonoperative time

lization, followed by late start and turnover time. The relationships between the three

Performance indicators

“nonoperative” time indicators were negligible. The impact of the partial indirect effects of

Benchmarking

“nonoperative” time indicators on raw utilization were statistically significant, but relatively small. The “trickle down” effect that late start can cause resulting in an increased delay as the day progresses, was not supported by our results. Conclusions: The study findings clearly suggest that OR utilization can be improved by focusing on the reduction of underused OR time at the end of the day. Improving the prediction of total procedure time, improving OR scheduling by, for example, altering the sequencing of operations, changing patient cancellation policies, and flexible staffing of ORs adjusted to patient needs, are means to reduce “nonoperative” time. ª 2015 Elsevier Inc. All rights reserved.

* Corresponding author. Department of Operating Rooms, Erasmus University Medical Center Rotterdam, Room number: Hs-324, PO BOX 2040, 3000 CA Rotterdam, The Netherlands. Tel.: þ31 6 2434 2635. E-mail addresses: [email protected], [email protected] (E. van Veen-Berkx). 0022-4804/$ e see front matter ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jss.2014.10.044

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1.

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Introduction

Health care today is faced with several challenges as follows: rising costs, changing demographics, aging population, technological innovations, and changing patients’ demands. Hospitals and operating room (OR) departments in particular, aim to improve quality and safety, as well as utilization and efficiency. ORs are cost-intensive, multiprofessional parts of health care organizations [1]. Generally, more than 60% of patients admitted to the hospital are treated in the OR [2]. ORs typically account for more than 40% of a hospital’s total revenues and a similarly large proportion of its total expenses [3]. Thus, efficient usage of OR capacity is pivotal. In ORs, inefficiencies can occur at several moments throughout the day, before, during, between, and after cases [4,5]. OR capacity is often evaluated by the indicator “raw utilization,” which is the percentage of allocated OR time that a patient was physically present in the room [1]. The time when there is no patient present in the OR, so-called “nonoperative” time, is the sum of three performance indicators as follows: first-case tardiness (or “late start” as it is referred to in the rest of this article), turnover time, and underused OR time. Several studies have evaluated OR utilization, mainly by analyzing one aspect of “nonoperative” time, such as late start [5e10] and turnover time [11e13] or the aspects of underused and overused time at the end of the day [14,15]. Most of these studies have focused merely on one hospital, a small number of surgical departments, or simulation of data. Multicenter studies using an extensive empirical data set in view of evaluating OR inefficiencies are scarce. Besides, previous studies have not yet evaluated the way in which all performance indicators interact. We hypothesized that the three indicators of “nonoperative” time may each negatively impact OR utilization. Therefore, we determined the relationship between late start, turnover time, underused time and OR utilization, in all eight University Medical Centers (UMCs) in the Netherlands. We assessed which indicator had the most negative impact on OR utilization to identify improvement potential. Furthermore, we studied the indirect relationships of the three indicators of “nonoperative” time on OR utilization, to recognize possible “trickle down” effects during the day.

2.

Materials and methods

2.1.

Research setting

In 2004, the OR departments of all eight UMCs in the Netherlands established a benchmarking collaborative, which has been active up to today. The objective is to improve OR performance by mutual learning from best practices. Each UMC provides data on all surgical cases performed in the individual center to a central OR Benchmark database. This extensive databasedtoday containing more than one million records of surgical casesdis used to calculate key performance indicators of the utilization of OR capacity. These

indicators are based on internationally recognized definitions [16e18].

2.2.

Performance indicators

OR time was evaluated by the indicator “raw utilization” (%), which was defined as the total amount of time patients are present in the OR divided by the total amount of allocated block time (generally from 8 AM until 4 PM) per day  100%. Block time was allocated to a specific surgical department. The definition of raw utilization excluded turnover time and overused OR time [1,5]. Raw utilization was calculated considering all cases operated on within block time, whether they were elective or emergency cases. However, emergency cases, which started after block time, were not considered for calculating any of the performance indicators. “Nonoperative” time was assessed by three performance indicators as follows: first-case tardiness (or “late start”), turnover time, and underused OR time. The indicator firstcase tardiness (a “late start” of merely the first surgical case of the day) was defined by the difference in minutes between the scheduled starting time (generally 8:00 AM) and the actual room entry time of the first patient on that day (per OR). This value was zero if the case entered the OR early or exactly on the scheduled time [5,6]. The common scheduled starting time was adjusted in case of an intentionally altered starting time [5]. The indicator turnover time represented the cumulative turnover time in minutes per OR day. Turnover time was defined as the time interval between two succeeding cases; the time between one patient leaving the OR and the next patient entering that OR [11], also known as cleaning time [19]. Underused OR time at the end of the day was quantified by the difference in minutes between the actual and scheduled (generally 4 PM) room exit time of the last patient of the day, finishing before 4 PM [20]. The common scheduled finishing time was adjusted in case of an intentionally extended finishing time. Raw utilization, late start, turnover time, and underused time are indicators measured once per OR day, meaning: once per OR per weekday per hospital (e.g., if an UMC facilitates 20 ORs, 20 OR days were recorded per weekday, if all these 20 ORs were staffed that particular day and allocated to a specific surgical department). One OR day is generally equal to 8 h of block time allocated to a specific surgical department in a specific OR. An OR day was defined as a combination of one OR and one date on which at least one surgical case was performed. Block time was not allocated during weekends or holidays, thus performance indicators were only measured during weekdays.

2.3.

Data collection

Data were prospectively collected and analyzed retrospectively for the purpose of this study. All data were registered electronically by the OR nursing staff in the Hospital Information System and validated by the surgeon and anesthesiologist in charge after completion of the operation. Since 2005,

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anonymized data records of all surgical cases performed at eight UMCs are sent to a central OR Benchmark database [5,21,22]. At the start of the collaborative, data definitions of time intervals were harmonized among all benchmarking participants [21,23]. The collaborative cooperates with an independent data management center, which administers the longitudinal data collection to the central OR benchmark database (Julius Center for Health Sciences, Utrecht, the Netherlands). This center provides professional expertise to facilitate the processing of data records and performs reliability checks before data are ready for analysis. Reliability checks consist of a check for missing values; a consistency check to determine if data are in accordance with earlier data deliveries; the correctness of data was studied to check if values are outside of a designated range; extreme values were removed from the data set according to predetermined outlier filtering rules (e.g., OR utilization 25 X 110%; late start 0> X 240 min; turnover time 0> X 120 min; and underused time 0> X 240 min). Raw utilization, late start, turnover time, and underused time were sequentially recorded for successive OR days over 7 y. The original central OR Benchmark database consisted of a total of 289,977 OR-days on which 986,649 surgical cases were performed at eight UMCs over a 7-y period from 2005e2011. To define a consistent group of data, only in-patient cases were included and all out-patient cases were excluded. In the Netherlands, in contrast to in-patient surgery, the out-patient surgery workflow varies significantly from center to center (different scheduling team, planning horizon, and planning methodology). That is why the out-patient OR process is considered as a distinct process, which should be analyzed separately. In some Dutch UMCs, large OR departments are divided into a main (the largest) OR location and different sub OR locations. Sublocations, such as a Children’s Hospital, Cancer Center or Thorax Center, were also excluded because these sublocations are separate organizational units. OR days with a missing registration of the specific OR location and labeled as “location unknown” were excluded. A total of 190,071 OR days were left for further statistical analyses. A total of 623,871 surgical cases were performed during these 190,071 OR-days.

2.4. Statistical analysis (see also enclosed statistical appendix) Data analysis was performed using SPSS Statistics 20 (IBM SPSS Statistics for Windows, version 20.0, IBM Corp Released 2011; Armonk, NY). Appendix Table A in the Statistical Appendix shows all direct and indirect (other word for “mediation”) relationships between the performance indicators that were assessed and the specific sets of statistical analyses applied. Simple linear regression analysis was used to identify direct relationships and corresponding R-squared (R2) values between performance indicators. Multiple linear regression analysis was applied to assess the direct relationship (with corresponding R2 values) between the response variable raw utilization and the three predictor variables late start, turnover time, and underused time. To justify the use of (multiple)

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linear regression analysis, additional tests were performed to test the general assumptions [24]. R2 values can be interpreted as representing the percentage of variation in the dependent variable explained by variation in the independent variables. The higher the R2, the better the regression model fits the data. To evaluate the indirect effect of these three indicators of “nonoperative” time on OR utilization, a mediation analysis was completed [25], which investigates whether the effect of a predictor variable X on a response variable Y was influenced by a third predictor variable, known as a mediator variable M. The mediational effect in which X leads to Y through M is also called the indirect effect. The mediation analysis was conducted by the Baron and Kenny method [25], Figure A. The indirect effects were translated into the following three hypotheses: a. late start initiated an increase in turnover time; b. late start initiated a decrease in underused time; c. turnover time initiated a decrease in underused time.

3.

Results

The eight centers together realized a mean standard deviation (SD) raw utilization of 82% (16%) and a median of 87%, for all in-patient OR days: a total of 190,071 OR days on which 623,871 cases were performed from 2005 up to and including 2011. Mean (SD) of late start 26 min (40) and median of 10 min; mean (SD) turnover time of 33 min (27) and median of 25 min; and mean (SD) underused time of 65 min (55) with a median of 49 min. Additional tests to check the general assumptions of regression analysis showed that linearity, as well as independence, were not violated. However, the assumptions regarding homoscedasticity and normal error distribution were violated. To correct for heteroscedasticity, several analyses were computed, of which all details are described in the Statistical Appendix. Based on these extra analyses, we conclude that the results in this study were not influenced by heteroscedasticity [26]. Concerning normality of the error distribution the assumption, linear regression is considered robust against this assumption, particularly with large sample sizes (n  1000) [27e29], which was the case in this study.

3.1. First set of analyses: direct effect of “nonoperative” time indicators on raw utilization The empirical data of all UMCs showed that the later the start, the lesser the raw utilization; correlation coefficient R ¼ 0.524 (P < 0.01). Twenty-seven percent (R2 ¼ 0.274) of raw utilization was explained by late start (P < 0.01). The longer the turnover time, the lesser the raw utilization; correlation coefficient R ¼ 0.378 (P < 0.01). Merely 14% (R2 ¼ 0.143) of raw utilization could be explained by turnover time (P < 0.01). The more underused time, the less raw utilization; R ¼ 0.639 (P < 0.01). Forty-one percent (R2 ¼ 0.409) of raw utilization can be attributed to underused time (P < 0.01). Based on multiple linear regression (P < 0.01), underused time showed the highest absolute b-value of 0.699 and thus the greatest negative influence, followed by late start (b ¼ 0.500)

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Table e Results of mediation analysis. Hypothesis

a

Analyzed mediation/ indirect effects

Test

Sobel test Indirect effect

z-value ¼ a  b/Square root (b2  SE2a þ a2  SE2b) a  b ¼ 0.012

M ¼ 34 þ 0.071X (SEa ¼ 0.003) Y ¼ 86  0.225X Y ¼ 92  0.217X  0.171 M (SEb ¼ 0.001) Sobel test ¼ 23.44

<0.001 <0.001 <0.001 <0.01

Partial mediation effect c’ ¼ 0.217

Predictor variable late start (X) Response variable raw utilization (Y) Mediator variable underused time (M) M ¼ i1 þ aX (SEa) Y ¼ i2 þ cX Y ¼ i3 þ c’X þ bM (SEb) Sobel test Indirect effect

z-value ¼ a  b/Square root (b2  SE2a þ a2  SE2b) a  b ¼ 0.029

M ¼ 62 þ 0.171X (SEa ¼ 0.006) Y ¼ 86  0.225X Y ¼ 92  0.214X  0.170 M (SEb ¼ 0.001) Sobel test ¼ 28.11

<0.001

M ¼ i1 þ aX (SEa) Y ¼ i2 þ cX Y ¼ i3 þ c’X þ bM (SEb)

Indirect effect

z-value ¼ a  b/Square root (b2  SE2a þ a2  SE2b) a  b ¼ 0.026

and finally turnover time (b ¼ 0.383). Overall, 87% (R2 ¼ 0.867) of raw utilization was explained by the three indicators of “nonoperative” time together (P < 0.01).

3.2. Second set of analyses: interaction between late start, turnover time, and underused time Data of all UMCs showed that late start and turnover time, as well as late start and underused time, had a significant positive relationship (P < 0.01). In other words, the later the start, the longer the turnover time, and the more underused time. Turnover time and underused time also showed a significant yet negative relationship, meaning that the longer the turnover time, the less underused time. These

Small, yet significant partial indirect effect: The later the start, the more underused time at the end of the day, the less raw utilization.

<0.001 <0.001 <0.01

partial mediation effect c’ ¼ 0.214

Predictor variable turnover time (X) Response variable raw utilization (Y) Mediator variable underused time (M)

Sobel test

Results (all eight centers) Small, yet significant partial indirect effect: The later the start, the longer the turnover time, the less raw utilization.

Y ¼ i2 þ cX Y ¼ i3 þ c’X þ bM (SEb)

c

P value

Predictor variable late start (X) Response variable raw utilization (Y) Mediator variable turnover time (M) M ¼ i1 þ aX (SEa)

b

Value

M ¼ 62  0.138X (SEa ¼ 0.007) Y ¼ 90  0.196X Y ¼ 96  0.210X  0.192 M (SEb ¼ 0.001) Sobel test ¼ 19.61

<0.001

Small, yet significant partial indirect effect: The longer the turnover time, the less underused time at the end of the day, resulting in a little more raw utilization.

<0.001 <0.001 <0.01

partial mediation effect c’ ¼ 0.210

relationships were not strong, but statistically significant, based on low values of determination coefficients (R2) merely 1% of turnover time was explained by late start; 2% of underused time was explained by late start and 1% by turnover time.

3.3. Third set of analyses: indirect effect of “nonoperative” time indicators on OR utilization Mediation analysis investigated the mediational effects (X leads to Y through M), which are also named “indirect effects.” Table shows the three hypotheses on indirect effects that were evaluated and the corresponding results on an overall level of all eight UMCs (complete data set).

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Figure e (A) Mediation analysis (theory). (B) Mediation analysis (one part of the results).

3.4. Hypothesis a: late start initiated an increase in turnover time This first hypothesis was confirmed based on the complete data set. Data showed a partial indirect effect of turnover time on the relationship between late start and raw utilization. In other words, the later the start, the longer the turnover time, the lessser the raw utilization. This indirect effect was small, but statistically significant, Figure B.

3.5. Hypothesis b: late start initiated a decrease in underused time This second hypothesis was rejected based on the complete data set. Mediation analysis results demonstrated that late start had a direct and indirect (through underused time)

negative influence on raw utilization. That is, the later the start, the more underused time at the end of the day, the lesser the raw utilization. This indirect effect was small, yet statistically significant.

3.6. Hypothesis c: turnover time initiated a decrease in underused time Based on the complete data set of all eight centers, this final hypothesis was confirmed. Results showed that the longer the turnover time, the lesser the raw utilization. Mediation analysis revealed that the longer the turnover time, the lesser underused time at the end of the day, resulting in a little more raw utilization. This indirect effect was small, but statistically significant. All statistical analyses were performed for all UMCs in total (the complete data set) and per hospital separately. The results

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showed no significant differences between hospitals (no interhospital variability) and therefore the results from the collaborative were consistent across each university hospital.

4.

Discussion

The findings from this Dutch, nationwide, multicenter study show that in a university hospital environment, underused OR time at the end of the day has the strongest influence on raw utilization, followed by late start, and turnover time. A direct negative relationship between all three indicators (late start, turnover time, and underused time) and raw utilization was found. Late start and underused time showed a positive relationship, whereas a negative relationship between turnover time and underused time was observed. Based on our findings, late start, turnover time, and underused time were “stand-alone” aspects with an important direct influence on raw utilization and only a minor influence on each other. We were unable to verify the reported “trickle down” effect [10], caused by late start and resulting in an increased delay as the day progresses. The interaction between the three “nonoperative” indicators and their indirect effects on raw utilization were inconsequential. The findings from this study are important for hospital management and surgical teams because they clearly suggest that improving OR utilization should be focused on reducing the amount of underused time at the end of the day. Potential solutions and interventions to address the issue of underused OR time are improving the prediction of the total procedure time of surgical cases; altering the sequencing of scheduled operations, and altering patient cancellation policies. These interventions are discussed in the following, respectively. When an operation takes longer than predicted, subsequent operations may need to be postponed or even cancelled. When the actual time of an operation is shorter than predicted, the OR remains unused at the end of the day. Both situations are unwelcome and could lead to suboptimal utilization of the OR [2]. The reduction of underused time may be possible by improving the prediction of the total procedure time of operations and thus improving OR scheduling. Scheduling surgical procedures is complex because predicting total procedure time entails several elements subject to variability, including the two main components: surgeoncontrolled time and anesthesia-controlled time, each with a considerable random chance component [22]. The efficiency of OR scheduling is greatly improved with better ability to accurately predict the time needed for all components of care for each surgical case [2,22,30e35]. An alternative method to enhance OR scheduling and generate reductions in underused time, as well as overtime, is to alter the sequencing of scheduled operations. Prior studies suggested that it is better to schedule short procedures before long operations and alternating between the two, which can limit the variability in case duration and can make predictability more accurate [36e38]. A reduction of underused time might also be possible by altering patient cancellation policies [2,39e41]. A practice applied in many Dutch (university) hospitals is a “zero

tolerance for overtime” policy because OR management presumes it is more economically profitable to finish the daily OR caseloads during “regular” hours than to create overtime [40,41]. A consequence of this policy may be that a patient scheduled in the final allocated hours (or late afternoon) will be cancelled last-minute to avoid overtime. This leads to immaterial damage concerning postponed or cancelled patients and to financial losses for the hospital concerning underutilization of scarce OR capacity. Because all OR personnel in Dutch UMCs are contracted and paid for at least 8 h on each day worked, underused time leads to economic losses for the hospital due to these fixed labor costs. Tessler [41] and Stepaniak [40], however, showed in their previous work that it is more cost-effective to proceed with an operation after regular hours than to cancel this operation. Overtime does have a financial effect owing to the payment of overtime wages beyond the regular rate for 36 h a week (in Dutch UMCs). Working overtime can also have a negative influence on job satisfaction of registered nurses and is considered a reason to change their employment status [42,43]. To better absorb the consequences of underused time and overtime, one option could be to employ OR personnel on a flexible basis adjusted to patient needs, as suggested in previous research [6,12,44,45]. The direct and indirect effects of “nonoperative” time on raw utilization is worthwhile studying because former research concluded that late start can cause a “trickle down” effect resulting in an increased delay (of e.g., turnover time) as the day progresses, potentially affecting the rest of the scheduled patients [10]. Our research, however, implicates that the indirect effects of late start through turnover time and underused time do not have a major impact on raw utilization. Therefore, these recent results reconfirm several earlier studies that resources spent solely on trying to achieve on time starts of scheduled first cases will not considerably improve OR utilization or productivity [8,11,46e48], and the “trickle down” effect has not yet been verified [6,8,20,48]. Our study suggests that a late start can be caught up throughout the rest of the OR day, either during operative time or due to a quicker turnover. Future research should investigate this specific subject to reveal its principles. The findings in this study are subject to at least two limitations. First, data for this study were gathered in tertiary referral centers only, and therefore generalization of the findings to general hospitals may be limited. Our earlier research showed that the complexity of surgical cases and their duration are generally greater than those in general hospitals [22]. This level of complexity of the patient case mix in UMCs can make it more difficult to accurately predict their duration and hamper efficient scheduling. Uncertainty, variability, and length in the duration of surgery contribute to the difficulty of scheduling [1,49], which may lead to either much underused time or unwanted overtime at the end of the day. One can imagine that in general hospitals with less complex patients, shorter case durations and the attendant reduced variability, case durations can be more accurately predicted. This, in turn, will result in more effective scheduling with efficient use of OR resources [2] (with less underused time and less overtime). One can also imagine that in general hospitals with smaller OR facilities (e.g., with a total of up to 10 ORs), turnover times can be shorter than in UMCs with large OR

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facilities (20 or more ORs). Smaller facilities deal with a shorter patient transport time from ward to the holding area and from the holding area to the OR. All statistical analyses were performed for all UMCs in total (complete data set) and per hospital separately. The results showed no significant inter-hospital variability, and the same conclusions were justified for all UMCs. In other words, the results from the collaborative were consistent across each hospital. This may argue for increased generalizability of our results in a university hospital OR environment. Second, the study does not consider all performance indicators relevant to “the end of an OR day” because the study focuses solely on “nonoperative” time and raw utilization. The end of one OR day balances between either underused OR time or overtime (also called overused OR time), along with the potential cancellations of elective surgical cases. Reasons for cancellation should also be involved in detail. Avoidable cancellations and unavoidable cancellations are relevant in this respect. Investigation of the indirect effects of late start and turnover time should evaluate the relationship between start and finish times, considering all relevant indicators. Nonetheless, earlier research has showed that the most common factor for cancellation is lack of availability of OR time [50]. Future research requires a clear and unambiguous definition of a cancellation and its reasons. Moreover, future research should involve the specific registration of which operations were overruled, elective or emergency cases. This information is generally registered on paper or Excel sheet, separate from digital clinical records or Hospital Information System, and therefore problematic to pool with databases similar to the one used in this study. Although this research concentrates on efficient utilization of scarce and expensive OR capacity, improving utilization regularly concurs with improving quality and patient safety. One Dutch UMC implemented preoperative cross-functional teams based on a socio-technical design, responsible for OR scheduling, with the aim to increase efficient utilization of OR capacity and enhance patient safety [51]. The crossfunctional team meets once a week to discuss the OR schedule of the following week and to evaluate the OR performance of the previous week, in terms of utilization, cancellations, and all relevant issues concerning optimal planning, performance, and safety. This approach led to an organizational learning effect and demonstrated that OR utilization as well as patient safety can be improved by allowing the individual health care workers to function as a team. Although their study is preliminary, it now serves as a starting point for more comprehensive studies in cooperation with the Dutch OR Benchmarking Collaborative to expand these initial findings [51].

5.

Conclusions

In summary, we suggest that hospital management and surgical teams direct scarce financial means and efforts on decreasing underused time because it has the strongest influence on OR utilization. This advice is supported by an extensive, nationwide, and diverse OR data set of eight UMCs and the relationships found. Reduction of underused time can be accomplished by engaging the challenge to enhance OR scheduling.

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Acknowledgment The authors sincerely thank all members of the steering and project committee of the Dutch Operating Room Benchmarking Collaborative for their participation and contribution. Current Members of the Dutch Operating Room Benchmarking Collaborative (the Netherlands): Academic Medical Center Amsterdam: Ron Balm, MD, PhD, Department of Vascular Surgery; Diederich C.C. Cornelisse, MSc, Department of Operating Rooms; Maastricht University Medical Center: Wolfgang F. Buhre, MD, PhD, Department of Anesthesiology and Pain Therapy; Hub J. Ackermans, Department of Operating Rooms; Erasmus University Medical Center Rotterdam: Robert Jan Stolker, MD, PhD, Department of Anesthesiology and Division of Emergency, Perioperative and Intensive Care; JeanneBezstarosti, MD, Department of Anesthesiology and Department of Operating Rooms; Leiden University Medical Center: Rob C.M. Pelger, MD, PhD, Department of Urology; Roald R. Schaad, MD, Department of Anesthesiology; University Medical Center Groningen: Irmgard KroonemanSmits, MBA, Department of Operating Rooms; Peter Meyer, MD, PhD, Department of Anesthesiology and Department of Operating Rooms; Radboud University Medical Center Nijmegen: Hein G. Gooszen, MD, PhD, Department of Operating Rooms; Mirjam van Dijk-Jager, Department of Operating Rooms; Simon A.W. Broecheler, MSc, Department of Operating Rooms and Department of Anesthesiology; University Medical Center Utrecht: A. Christiaan Kroese, MD, Department of Anesthesiology and Department of Operating Rooms; Jeffrey Kanters, Department of Operating Rooms; University of Twente: Johannes J. Krabbendam, PhD; Erwin W. Hans, PhD, Department of Operational Methods for Production and Logistics; VU University Medical Center: Derk P. Veerman, MD, PhD, Department of Anesthesiology and Operative Care; Kjeld H. Aij, MBA, Department of Anesthesiology and Operative Care. The authors also sincerely thank Ewout W. Steyerberg, PhD, Professor of Medical Decision Making and Daan Nieboer, Researcher, Department of Public Health, Erasmus University Medical Center Rotterdam for their statistical advice. Authors’ contributions: E.V.V.-B. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. E.V.V.-B., S.G.E., and G.K. contributed to the study concept and design and drafting of the article. E.V.V.-B., S.V.L., G.K., and participants Dutch Operating Room Benchmarking Collaborative did the acquisition of data. E.V.V.-B., S.V.L., S.G.E., and G.K. did the analysis and interpretation of data. G.K., W.F.B., H.G.G., and C.J.K. did the critical revision of the article for important intellectual content. E.V.V.-B., S.V.L., and S.G.E. did the statistical analysis. The participants Dutch Operating Room Benchmarking Collaborative provided the administrative, technical, or material support. G.K., W.F.B., H.G.G., C.J.K., and E.V.V.-B. did the study supervision. G.K., W.F.B., H.G.G., C.J.K., and S.G.E. approved the final article and the revised article.

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Disclosure Conflict of interest: None declared.

references

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[20] Dexter F, Macario A. When to release allocated operating room time to increase operating room efficiency. Anesth Analg 2004;98:758. table of contents. [21] Kazemier G, Van Veen-Berkx E. Comment on “identification and use of operating room efficiency indicators: the problem of definition”. Can J Surg 2013;56:E103. [22] Van Veen-Berkx E, Bitter J, Elkhuizen SG, et al. The influence of anesthesia-controlled time on operating room scheduling in Dutch university medical centers. Can J Anesth 2014;61:524. [23] Van Houdenhoven M, van Oostrum JM, Hans EW, Wullink G, Kazemier G. Improving operating room efficiency by applying bin-packing and portfolio techniques to surgical case scheduling. Anesth Analg 2007;105:707. [24] Bruce N, Pope D, Stanistreet D. Quantitative methods for health research: a practical interactive guide to epidemiology and statistics. 2nd ed. West Sussex, England: John Wiley and Sons Ltd.; 2008. [25] Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51:1173. [26] Hayes AF, Cai L. Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction and software implementation. Behav Res Methods 2007;39:709. [27] Agresti A, Finlay B. Statistical methods for the social sciences. 4th ed. Pearson Prentice Hall; 2009. [28] De Heus P, Van der Leeden R, Gazendam B. Toegepaste dataanalyse. 7th ed. Gravenhage, the Netherlands: Reed Business’s; 2008. [29] Lumley T, Diehr P, Emerson S, Chen L. The importance of the normality assumption in large public health data sets. Annu Rev Public Health 2002;23:151. [30] Dexter F, Dexter EU, Ledolter J. Influence of procedure classification on process variability and parameter uncertainty of surgical case durations. Anesth Analg 2010;110:1155. [31] Strum DP, Sampson AR, May JH, Vargas LG. Surgeon and type of anesthesia predict variability in surgical procedure times. Anesthesiology 2000;92:1454. [32] Ehrenwerth J, Escobar A, Davis EA, et al. Can the attending anesthesiologist accurately predict the duration of anesthesia induction? Anesth Analg 2006;103:938. [33] Wright IH, Kooperberg C, Bonar BA, Bashein G. Statistical modeling to predict elective surgery time. Comparison with a computer scheduling system and surgeon-provided estimates. Anesthesiology 1996;85:1235. [34] Dexter F, Macario A. Applications of information systems to operating room scheduling. Anesthesiology 1996;85:1232. [35] Pandit JJ, Tavare A. Using mean duration and variation of procedure times to plan a list of surgical operations to fit into the scheduled list time. Eur J Anaesthesiol 2011;28:493. [36] Denton B, Viapiano J, Vogl A. Optimization of surgery sequencing and scheduling decisions under uncertainty. Health Care Manag Sci 2007;10:13. [37] Iser JH, Denton BT, King RE. Heuristics for balancing operating room and post-anesthesia resources under uncertainty. Proceedings of the 2008 Winter Simulation Conference 2008. [38] Lebowitz P. Schedule the short procedure first to improve OR efficiency. AORN J 2003;78:651. 657e9. [39] Tyler DC, Pasquariello CA, Chen CH. Determining optimum operating room utilization. Anesth Analg 2003;96:1114. [40] Stepaniak PS, Mannaerts GH, de Quelerij M, de Vries G. The effect of the operating room coordinator’s risk appreciation on operating room efficiency. Anesth Analg 2009;108:1249. [41] Tessler MJ, Kleiman SJ, Huberman MM. A “zero tolerance for overtime” increases surgical per case costs. Can J Anaesth 1997;44:1036. [42] Shader K, Broome ME, Broome CD, West ME, Nash M. Factors influencing satisfaction and anticipated turnover for nurses in an academic medical center. J Nurs Adm 2001;31:210.

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Appendix. Statistical appendix Assumptions (multiple) linear regression analysis Additional tests were performed to test the four general assumptions with the aim to justify the use of (multiple) linear regression analysis [1]: 1. linearity of the relationship between dependent and independent variables; 2. assumption of homoscedasticity (the errors and/or residuals have the same variance); 3. assumption of independence (the errors are independent of each other); 4. assumption of normality (the errors are normally distributed). To prevent “over fitting” of the regression analysis, collinearity statistics consisting of tolerance and variance inflation factor values for all three independent variables were computed [2]. Furthermore, additional tests regarding influence diagnostics on regression coefficients, such as Cook Distance and DiFference of FITS, were performed. Extreme values i.e., outliers were checked to prevent them from distorting the estimates of regression coefficients [2].

Results Additional tests to check the general assumptions of regression analysis showed that linearity and independence were not violated. However, the assumptions regarding homoscedasticity and normal error distribution were violated. To correct for heteroscedasticity, bias-corrected and accelerated confidence intervals were computed. These bootstrapped confidence intervals for the regression coefficients were considered narrow. Moreover, generalized linear models were applied, and heteroscedasticity-robust standard errors (also called Huber-White standard errors) [3] were calculated. Comparing these heteroscedasticity-robust standard errors with the original regression output showed the same statistically significant results for all regression estimates, as well as minor standard errors. Therefore, we conclude that the analysis reported in this study was not influenced by heteroscedasticity [4]. Concerning normality of the error distribution the assumption, linear regression is considered robust against this assumption, particularly with large sample sizes (n  1000) [5e7], which was the case in this study.

Collinearity statistics regarding all three independent variables showed tolerance values as well as variance inflation factor values close to 1 and therefore indicate that multicollinearity is not a problem in this study [2]. Cook Distance scores had a minimum value of 0.000 and a maximum value of 0.029, and were not larger than the threshold of “1” [2]. Standardized DiFference of FITS showed a minimum value of 0.017 and a maximum value of 0.015, and were not larger than the threshold of “2” [2]. The independent data management center removed extreme values from the data set. No data points used in this study were considered influential.

Mediation analysis by Baron and Kenny To evaluate the indirect effect of these three indicators of nonoperative time on OR utilization, a mediation analysis was completed [8], which investigates whether the effect of a predictor variable X on a response variable Y is influenced by a third predictor variable, known as a mediator variable M. The mediation analysis was conducted by the Baron and Kenny method [8]. This method contained four different steps, see Appendix Table A fourth set of analyses, aed. First, the direct relationship between variables X and Y, as well as the direct relationship between variables X and M were tested with simple linear regression analysis. Second, a multiple regression analysis of variables M and X on variable Y was applied to check whether the direct relationships last when X and M both influence Y. Third, a multiple regression analysis was applied to assess the influence of M and X on Y. At last, significance was determined for all preceding steps to confirm mediation. Partial mediation means that variable X has a direct effect on variable Y, and that variable X has an indirect effect on variable Y through variable M (Figure A in article). Full mediation means that variable X does not have a direct effect on variable Y, but variable X purely has an indirect effect on variable Y through variable M (Figure A in article). To explain the “a, b, c, and c’ paths,” the Figure contains two parts (A) and (B). Figure A explicates the theory of mediation analysis and the Sobel test equation by the Baron and Kenny method [8]. Figure B explicates the actual coefficients of the recent study above the arrows linking the variables. The example concentrates on one of the total three partial mediation effects analyzed, see Appendix Table A “Overview of statistical analyses applied,” fourth set of analyses, first bullet: predictor variable late start (X) / response variable raw utilization (Y) / mediator variable turnover time (M).

Results For all results of the mediation analysis by Baron and Kenny, see article.

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references statistical appendix [5] [1] Bruce N, Pope D, Stanistreet D. Quantitative methods for health research: a practical interactive guide to epidemiology and statistics. 2nd ed. West Sussex, England: John Wiley and Sons Ltd.; 2008. [2] Field A. Discovering statistics using IBM SPSS statistics. Thousand Oaks, CA: SAGE Publications Ltd; 2013. [3] Huber PJ. The behavior of maximum likelihood estimates under nonstandard conditions. Berkeley, California: University of California Press; 1967. [4] Hayes AF, Cai L. Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction

[6]

[7]

[8]

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and software implementation. Behav Res Methods 2007;39: 709. Agresti A, Finlay B. Statistical methods for the social sciences. 4 ed. Pearson Prentice Hall; 2009. De Heus P, Van der Leeden R, Gazendam B. Toegepaste dataanalyse. 7 ed. Gravenhage, the Netherlands: Reed Business ’s; 2008. Lumley T, Diehr P, Emerson S, Chen L. The importance of the normality assumption in large public health data sets. Annu Rev Public Health 2002;23:151. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51:1173.

Appendix Table A e Overview of statistical analyses applied (in the Statistical Appendix) Sets of analyses 1.

2.

3.

4.

Relationship Direct linear relationship regarding raw utilization:  predictor (independent) variable late start / response (dependent) variable raw utilization  predictor variable turnover time / response variable raw utilization  predictor variable underused time / response variable raw utilization Direct linear relationship regarding raw utilization:  predictor variables late start, turnover time, and underused time / response variable raw utilization Direct linear relationship regarding nonoperative time:  predictor variable late start / response variable turnover time  predictor variable late start / response variable underused time  predictor variable turnover time / response variable underused time Partial mediation effect:  predictor variable late start (X) / response variable raw utilization (Y) / mediator variable turnover time (M)  predictor variable late start (X) / response variable raw utilization (Y) / mediator variable underused time (M)  predictor variable turnover time (X) / response variable raw utilization (Y) / mediator variable underused time (M)

ab ffi Sobel test equation : z ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 2

ðb  SEa Þ þ ða  SEa Þ

Statistical analysis Simple linear regression analysis

Multiple linear regression analysis including interaction effects

Simple linear regression analysis

Mediation effect analysis was calculated by the Baron and Kenny method: a. simple linear regression with X predicting M to test for path an M ¼ i1 þ aX b. simple linear regression with X predicting Y to test for path c Y ¼ i2 þ cX c. multiple regression analysis with both X and M predicting Y to test for path b (M / Y) and for path c’ (X & M / Y) Y ¼ i3 þ c’X þ bM d. significance test applied to a, b, and c: P value checked on a and b. Sobel test checked on c / mediation is confirmed when all parts of the analysis are significant i1, i2, and i3 represent the intercepts for each model