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

Lack of Validity of Absolute Percentage Errors in Estimated Operating Room Case Durations as a Measure of Operating Room Performance: A Focused Narrative Review

Commonly reported end points for operating room (OR) and surgical scheduling performance are the percentages of estimated OR times whose absolute values differ from the actual OR times by ≥15%, or by various intervals from ≥5 to ≥60 minutes. We show that these metrics are invalid assessments of OR performance. Specifically, from 19 relevant articles, multiple OR management decisions that would increase OR efficiency or productivity would also increase the absolute percentage error of the estimated case durations. Instead, OR managers should check the mean bias of estimated OR times (ie, systematic underestimation or overestimation), a valid and reliable metric.

Operative Time Accuracy in the Era of Electronic Health Records: Addressing the Elephant in the Room

GOAL

Accurate prediction of operating room (OR) time is critical for effective utilization of resources, optimal staffing, and reduced costs. Currently, electronic health record (EHR) systems aid OR scheduling by predicting OR time for a specific surgeon and operation. On many occasions, the predicted OR time is subject to manipulation by surgeons during scheduling. We aimed to address the use of the EHR for OR scheduling and the impact of manipulations on OR time accuracy.

METHODS

Between April and August 2022, a pilot study was performed in our tertiary center where surgeons in multiple surgical specialties were encouraged toward nonmanipulation for predicted OR time during scheduling. The OR time accuracy within 5 months before trial (Group 1) and within the trial period (Group 2) were compared. Accurate cases were defined as cases with total length (wheels-in to wheels-out) within ±30 min or ±20% of the scheduled duration if the scheduled time is ≥ or <150 min, respectively. The study included single and multiple Current Procedural Terminology code procedures, while procedures involving multiple surgical specialties (combo cases) were excluded.

PRINCIPAL FINDINGS

The study included a total of 8,821 operations, 4,243 (Group 1) and 4,578 (Group 2), (p < .001). The percentage of manipulation dropped from 19.8% (Group 1) to 7.6% (Group 2), (p < .001), while scheduling accuracy rose from 41.7% (Group 1) to 47.9% (Group 2), (p = .0001) with a significant reduction of underscheduling percentage (38.7% vs. 31.7%, p = .0001) and without a significant difference in the percentage of overscheduled cases (15% vs. 17%, p = .22). Inaccurate OR hours were reduced by 18% during the trial period (2,383 hr vs. 1,954 hr).

PRACTICAL APPLICATIONS

The utilization of EHR systems for predicting OR time and reducing manipulation by surgeons helps improve OR scheduling accuracy and utilization of OR resources.

Machine learning models to predict surgical case duration compared to current industry standards: scoping review

BACKGROUND

Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration predictions. Current standards for predicting the duration of surgery are inaccurate. Artificial intelligence (AI) offers the potential for greater accuracy in predicting surgical case duration. This study aimed to investigate whether there is evidence to support that AI is more accurate than current industry standards at predicting surgical case duration, with a secondary aim of analysing whether the implementation of the models used produced efficiency savings.

METHOD

PubMed, Embase, and MEDLINE libraries were searched through to July 2023 to identify appropriate articles. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Study quality was assessed using a modified version of the reporting guidelines for surgical AI papers by Farrow et al. Algorithm performance was reported using evaluation metrics.

RESULTS

The search identified 2593 articles: 14 were suitable for inclusion and 13 reported on the accuracy of AI algorithms against industry standards, with seven demonstrating a statistically significant improvement in prediction accuracy (P < 0.05). The larger studies demonstrated the superiority of neural networks over other machine learning techniques. Efficiency savings were identified in a RCT. Significant methodological limitations were identified across most studies.

CONCLUSION

The studies suggest that machine learning and deep learning models are more accurate at predicting the duration of surgery; however, further research is required to determine the best way to implement this technology.

Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study

BACKGROUND

Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may provide opportunities for hospitals to improve upon current estimates of procedure duration.

OBJECTIVE

The aim of this study was to determine whether a machine learning algorithm scalable across multiple centers could make estimations of case duration within a tolerance limit because there are substantial resources required for operating room functioning that relate to case duration.

METHODS

Deep learning, gradient boosting, and ensemble machine learning models were generated using perioperative data available at 3 distinct time points: the time of scheduling, the time of patient arrival to the operating or procedure room (primary model), and the time of surgical incision or procedure start. The primary outcome was procedure duration, defined by the time between the arrival and the departure of the patient from the procedure room. Model performance was assessed by mean absolute error (MAE), the proportion of predictions falling within 20% of the actual duration, and other standard metrics. Performance was compared with a baseline method of historical means within a linear regression model. Model features driving predictions were assessed using Shapley additive explanations values and permutation feature importance.

RESULTS

A total of 1,177,893 procedures from 13 academic and private hospitals between 2016 and 2019 were used. Across all procedures, the median procedure duration was 94 (IQR 50-167) minutes. In estimating the procedure duration, the gradient boosting machine was the best-performing model, demonstrating an MAE of 34 (SD 47) minutes, with 46% of the predictions falling within 20% of the actual duration in the test data set. This represented a statistically and clinically significant improvement in predictions compared with a baseline linear regression model (MAE 43 min; P<.001; 39% of the predictions falling within 20% of the actual duration). The most important features in model training were historical procedure duration by surgeon, the word "free" within the procedure text, and the time of day.

CONCLUSIONS

Nonlinear models using machine learning techniques may be used to generate high-performing, automatable, explainable, and scalable prediction models for procedure duration.

Assessment of Operative Time for Lip and Oral Cancers: A Tool to Improve Operative Room Efficiency

Operation theatre (OT) time utilisation rates can be improved with an assessment of the procedure time that will result in effective scheduling of cases. Our study is the first of its kind to audit the amount of OT time required for a particular surgery in lip and oral cavity cancers, depending on the various components of this complex procedure. This prospective cross-sectional study, based on an operative room database of 323 OT sessions, was conducted in the Department of Surgical Oncology at a tertiary care centre on lip and oral cancer patients from January 1st, 2019 to December 31st, 2020. Various components of the surgery, like the primary site, operating surgeon, type of neck dissection, bone resection, and reconstructive procedure, were noted. The time of entry and exit of the patient from the OT was noted. Operative time and OT time utilisation rates were calculated. SPSS 21.0 statistical tool; Students 'T', ANOVA and Games-Howell tests were applied. In 323 OT sessions, while 303 surgeries were done for primary cases (93.8%), the remaining 20 cases were for recurrent cases (6.2%). Buccal mucosa and the floor of the mouth were the most and least common sites, respectively. The mean OT time was 212.42 ± 73.83 min, the maximum being the primary at alveolus. The mean OT late start time was 70.03 ± 23.41 min and the mean OT runover time was 37.62 ± 43.53 min. The mean time varied significantly with the type of neck dissection, bone resection, and reconstructive surgery done and the operating surgeon. The mean OT time was highest for free flap reconstructive surgery (328.71 ± 62.02 min), but it didn't vary with its type. Considering only the lip and oral cancer surgeries, the OT time utilisation rate was 57.1%. Assessment and quantification of the operative duration of lip and oral cancer surgeries will help in accurate prediction of surgical duration, better OT list planning, and thus improved OT time utilisation rate. Our research not only provides data on the historical mean of procedures, but it may also encourage other centres to adopt our quantitative approach to OT scheduling.

Setting a quality indicator for actual surgery time relative to scheduled surgery time in the context of increasing robotic-assisted thoracic surgery cases

OBJECTIVE

This study aimed to demonstrate to the involved departments the goal of increasing the number of robotic-assisted thoracic surgery (RATS) cases/surgeons and acceptable surgery times.

METHODS

This retrospective study included 1572 patients who underwent thoracic surgery from fiscal year (FY) 2018 to FY 2021. The factors evaluated included the number of surgery cases and actual and scheduled surgery times.

RESULTS

The total number of RATS and total surgery cases increased after the quality indicator (QI) setting (n = 363, 360, 417, and 432 in FY 2018, 2019, 2020, and 2021, respectively). In FY 2020, 93.3% of the QI target was achieved, while in FY 2021, 88% was achieved. The number of RATS lobectomy/segmentectomy increased as the FY progressed (n = 31, 47, 58, and 116 in FY 2018, 2019, 2020, and 2021, respectively). The mean surgical time by RATS starters decreased in FY 2020 and 2021 (171.4 min.; 74 cases; seven RATS starters) compared with those in FY 2018 and 2019 (198.0 min.; 57 cases; six RATS starters) (P = 0.002).

CONCLUSIONS

The goal of increasing the number of surgery cases and RATS cases/surgeons within the given framework was achieved by setting the QI.

Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

One of the critical issues in healthcare management is the operating room (OR) scheduling problem. Solutions to this problem consider surgery durations and allocate elective surgeries to OR sessions in order to create surgical lists of high quality. Determining the quality of a surgical list is a key undertaking within OR scheduling and is the focus of this research. Currently, probability- and/or expectation-based measures of surgical lists are used instead of statistical distributions of surgery lists to measure quality. The use of multiple measures, e.g., a combination of expectation and probability to assess a surgical list, complicates OR scheduling, so we introduce a new single measure – the OR scheduling metric – for evaluating surgical lists before their realisations, i.e., for use within OR scheduling. We apply the OR scheduling metric to an actual elective dataset and use simulation to demonstrate its use, including customised scheduling rules. We recommend the adoption of a benchmarked OR scheduling metric by the elective surgical services in hospitals with expected practical benefits in the long run, i.e., simpler OR scheduling and more desirable room utilisation, to be similar to that observed in our simulations.

The effect of overlapping surgical scheduling on operating theatre productivity: a narrative review

This article reviews the background to overlapping surgery, in which a single senior surgeon operates across two parallel operating theatres; anaesthesia is induced and surgery commenced by junior surgeons in the second operating theatre while the lead surgeon completes the operation in the first. We assess whether there is any theoretical basis to expect increased productivity in terms of number of operations completed. A review of observational studies found that while there is a perception of increased surgical output for one surgeon, there is no evidence of increased productivity compared with two surgeons working in parallel. There is potential for overlapping surgery to have some positive impact in situations where turnover times between cases are long, operations are short (<2 h) and where 'critical portions' of surgery constitute about half of the total operation time. However, any advantages must be balanced against safety, ethical and training concerns.

Machine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center

Days before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression.

Getting going on time: reducing neurophysiology set-up times in order to contribute to improving surgery start and finish times

At the Walton Centre we conduct a relatively large number of complex and lengthy elective (booked) spinal operations. Recently, we have had a particular problem with half or more of these sessions finishing late, resulting in staff discontent and greater use of on-call staff.These operations require patient monitoring by neurophysiology clinical scientists. Before the surgeon can start the operation, in-theatre neurophysiological measurements are required to establish a baseline. We reasoned that reducing this set-up time would reduce the risk of surgery starting late, and so the whole session finishing later than expected.In this project we redesigned the neurophysiology parts of in-theatre patient preparation. We conducted five Plan-Do-Study-Act cycles over 3 months, reducing the duration of pre-surgery preparation from a mean of 70 min to around 50 min. We saw improvements in surgical start times and session finish times (both earlier by roughly comparable amounts). The ultimately impact is that we saw on-time session finishes improve from around 50% to 100%. Following this project, we have managed to sustain the changes and the improved performance.The most impactful change was to conduct in-theatre neurophysiology patient preparation simultaneously with anaesthesia, rather than waiting for this to finish; when we performed this with a pair of clinical scientists, we were able to complete neurophysiology patient preparation by the time the anaesthetist was finished, therefore not introducing delays to the start of surgery. A final change was to remove a superfluous preparatory patient-baseline measurement.This is a very challenging and complex environment, with powerful stakeholders and many factors and unpredictable events affecting sessions. Nevertheless, we have shown that we can make improvements within our span of influence that improve the wider process. While using pairs of staff requires greater resource, we found the benefit to be worthwhile.

Audit of operation theater time utilization with perspective to optimize turnaround times and theater output

Background and Aims

Operation theater (OT) complex is an important area for a hospital as it needs expensive infrastructure, disposable, and reusable resources and a multidisciplinary highly qualified and efficient team, the metrics of which are key in generating revenue, and improved productivity. The efficient utilization of OT ensures maximum output in view of the investment of highly qualified doctors, equipment, and outcomes. Our study aimed to evaluate the utilization of OT functioning stepwise, reasons for delays, case cancellations, and areas of improvement if any.

Material and Methods

This prospective observational study was planned in three phases; in phase 1 audit of OT functioning was carried out for 1 month and based on data analysis recommendations were given for improvement. In phase 2, the recommendations would be implemented over 3 months and in phase 3 re-audit will be carried out for 1 month. Data analysis was done on IBM SPSS version 26 software. Descriptive statistics measures were calculated by the mean and standard deviation.

Results

The total available resource time was 52920 min and the total time utilized was 37740 min. Overall, raw utilization was 71.31%. OT was started late 63.50% times. Case cancellation occurred on 8.99% occasions.

Conclusion

We conclude that utilization of operating room time can be maximized by proper planning and realistic scheduling of elective lists, communication among team members, and resource management. Audit of OT utilization is an important tool to identify problem areas and formulate protocols accordingly.

Efficiency Metrics at an Academic Freestanding Ambulatory Surgery Center: Analysis of the Impact on Scheduled End-Times

BACKGROUND

Understanding the impact of key metrics on operating room (OR) efficiency is important to optimize utilization and reduce costs, particularly in freestanding ambulatory surgery centers. The aim of this study was to assess the association between commonly used efficiency metrics and scheduled end-time accuracy.

METHODS

Data from patients who underwent surgery from May 2018 to June 2019 at an academic freestanding ambulatory surgery center was extracted from the medical record. Unique operating room days (ORDs) were analyzed to determine (1) duration of first case delays, (2) turnover times (TOT), and (3) scheduled case duration accuracies. Spearman's correlation coefficients and mixed-effects multivariable linear regression were used to assess the association of each metric with scheduled end-time accuracy.

RESULTS

There were 1378 cases performed over 300 unique ORDs. There were 86 (28.7%) ORDs with a first case delay, mean (standard deviation [SD]) 11.2 minutes (15.1 minutes), range of 2-101 minutes; the overall mean (SD) TOT was 28.1 minutes (19.9 minutes), range of 6-83 minutes; there were 640 (46.4%) TOT >20 minutes; the overall mean (SD) case duration accuracy was -6.6 minutes (30.3 minutes), range of -114 to 176; and there were 389 (28.2%) case duration accuracies ≥30 minutes. The mean (SD) scheduled end-time accuracy was 6.9 minutes (68.3 minutes), range of -173 to 229 minutes; 48 (15.9%) ORDs ended ≥1 hour before scheduled end-time and 56 (18.6%) ORDs ended ≥1 hour after scheduled end-time. The total case duration accuracy was strongly correlated with the scheduled end-time accuracy (r = 0.87, 95% confidence interval [CI], 0.84-0.89, P < .0001), while the total first case delay minutes (r = 0.12, 95% CI, 0.01-0.21, P = .04) and total turnover time (r = -0.16, 95% CI, 0.21-0.05, P = .005) were less relevant. Case duration accuracy had the highest association with the dependent variable (0.95 minutes changed in the difference between actual and schedule end time per minute increase in case duration accuracy, 95% CI, 0.90-0.99, P < .0001), compared to turnover time (estimate = 0.87, 95% CI, 0.75-0.99, P < .0001) and first case delay time (estimate = 0.83, 95% CI, 0.56-1.11, P < .0001).

CONCLUSIONS

Standard efficiency metrics are similarly associated with scheduled end-time accuracy, and addressing problems in each is requisite to having an efficient ambulatory surgery center. Pursuing methods to narrow the gap between scheduled and actual case duration may result in a more productive enterprise.

Ability of a Complexity Scoring System to Predict Veterinary Student Surgical Procedure and Clinic Duration

The Midwestern University College of Veterinary Medicine hosts student-run free clinics that offer surgical sterilization of male and female dogs and cats, with the goal of 20 surgical cases per clinic. Surgical complexity varies significantly between the surgical procedures for males (castration) and females (ovariohysterectomy) and is also influenced by weight and age for dogs. A surgical complexity scoring system was implemented to ensure the minimum number of patients while providing a diverse mix of cases. The aim of this study was to determine whether the surgical complexity scoring system accurately predicted procedure duration. Surgical records were collected between August 2016 and October 2019. Points (1-5) were assigned to each patient at the time of appointment based on species, sex, additional procedure, age and weight, and the schedule was targeted for 50 points. Each point was predicted to account for 15 minutes of surgical time. The duration for each point category was assessed via rank-sum against the predicted median. Sixteen clinics occurred during the study period, having a mean of 40.4 points and 17 patients, 29.5 (74%) of which were allocated to students. There were 264 surgeries, with 241 (91%) having complete start and end times. Surgical duration for student surgeries was not different from the estimate for each point category, with the exception of 2-points, which had a median 5.0 minutes longer than anticipated (p = .0004). The surgical complexity scoring system is an effective tool to optimize scheduling of educational spay/neuter mobile clinics.

Rational performance metrics for operating theatres, principles of efficiency, and how to achieve it

BACKGROUND

Several performance metrics are commonly used by National Health Service (NHS) organizations to measure the efficiency and productivity of operating lists. These include: start time, utilization, cancellations, number of operations and gap time between operations. The authors describe reasons why these metrics are flawed, and use clinical evidence and mathematics to define a rational, balanced efficiency metric.

METHODS

A narrative review of literature on the efficiency and productivity of elective NHS operating lists was undertaken. The aim was to rationalize how best to define and measure the efficiency of an operating list, and describe strategies to achieve it.

RESULTS

There is now a wealth of literature on how optimally to measure the performance of elective surgical lists. Efficiency may be defined as the completion of all scheduled operations within the allocated time with no over- or under-runs.

CONCLUSION

Achieving efficiency requires appropriate scheduling using specific procedure mean (or median) times and their associated variance (standard deviation or interquartile range) to calculate the probability they can be completed on time. The case mix may be adjusted to yield better time management. This review outlines common misconceptions applied to managing scheduled operating theatre lists and the challenges of measuring unscheduled operations in emergency settings.

Rational planning of operating lists: a prospective comparison of 'booking to the mean' vs. 'probabilistic case scheduling' in urology

The efficient use of operating theatres requires accurate case scheduling. One common method is 'booking to the mean'. Here, the mean times for individual operations are summed to approximate the time allocated to the list. An alternative approach is 'probabilistic scheduling'. Here, the means and standard deviation of the individual case times are combined to estimate the probability that the planned list will finish on time. This study assessed how probabilistic booking would have changed list utilisation, over-running and case cancellations in 60 urology lists during eight months that had been 'booked to the mean'. Booking to the mean resulted in 53/60 (88%) lists over-running and correctly predicted the finish times in just 13% of lists. Out of 264 patients, 36 (14%) were cancelled on the day due to over-runs in 24/60 (40%) lists. In contrast, probabilistic scheduling correctly predicted an over-run or under-run in 77% of lists, which would have allowed the case mix to be adjusted to prevent cancellation and optimise utilisation.

Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration

BACKGROUND

Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set to improve estimation of case-time duration relative to current standards.

STUDY DESIGN

We developed models to predict case-time duration using linear regression and supervised machine learning. For each of these models, we generated an all-inclusive model, service-specific models, and surgeon-specific models. In the latter 2 approaches, individual models were created for each surgical service and surgeon, respectively. Our data set included 46,986 scheduled operations performed at a large academic medical center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared with our institutional standard of using average historic procedure times and surgeon estimates. Models were evaluated based on accuracy, overage (case duration > predicted + 10%), underage (case duration < predicted - 10%), and the predictive capability of being within a 10% tolerance threshold.

RESULTS

The machine learning algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracy, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the machine learning surgeon-specific model.

CONCLUSIONS

Our study is a notable advancement toward statistical modeling of case-time duration across all surgical departments in a large tertiary medical center. Machine learning approaches can improve case duration estimations, enabling improved operating room scheduling, efficiency, and reduced costs.

Timeliness of Operating Room Case Planning and Time Utilization: Influence of First and To-Follow Cases

Resource and cost constraints in hospitals demand thorough planning of operating room schedules. Ideally, exact start times and durations are known in advance for each case. However, aside from the first case’s start, most factors are hard to predict. While the role of the start of the first case for optimal room utilization has been shown before, data for to-follow cases are lacking. The present study therefore aimed to analyze all elective surgery cases of a university hospital within 1 year in search of visible patterns. A total of 14,014 cases scheduled on 254 regular working days at a university hospital between September 2015 and August 2016 underwent screening. After eliminating 112 emergencies during regular working hours, 13,547 elective daytime cases were analyzed, out of which 4,346 ranked first, 3,723 second, and 5,478 third or higher in the daily schedule. Also, 36% of cases changed start times from the day before to 7:00 a.m., with half of these (52%) resulting in a delay of more than 15 min. After 7:00 a.m., 87% of cases started more than 10 min off schedule, with 26% being early and 74% late. Timeliness was 15 ± 72 min (mean ± SD) for first, 21 ± 84 min for second, and 25 ± 93 min for all to-follow cases, compared to preoperative day planning, and 21 ± 45, 23 ± 61, and 19 ± 74 min compared to 7:00 a.m. status. Start time deviations were also related to procedure duration, with cases of 61–90 min duration being most reliable (deviation 9.8 ± 67 min compared to 7:00 a.m.), regardless of order. In consequence, cases following after 61–90 min long cases had the shortest deviations of incision time from schedule (16 ± 66 min). Taken together, start times for elective surgery cases deviate substantially from schedule, with first and second cases falling into the highest mean deviation category. Second cases had the largest deviations from scheduled times compared to first and all to-follow cases. While planned vs. actual start times differ among specialties, cases of 61–90 min duration had the most reliable start times, with neither shorter nor longer cases seeming to improve timeliness of start times.

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.

Enhancement opportunities in operating room utilization; with a statistical appendix

BACKGROUND

The purpose of this study was to assess the direct and indirect relationships between first-case tardiness (or "late start"), turnover time, underused operating room (OR) time, and raw utilization, as well as to determine 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.

MATERIALS AND METHODS

(Multiple) linear regression analysis and mediation effect analysis were applied to a data set from all eight University Medical Centers in the Netherlands. This data set consisted of 190,071 OR days (on which 623,871 surgical cases were performed).

RESULTS

Underused OR time at the end of the day had the strongest influence on raw utilization, followed by late start and turnover time. The relationships between the three "nonoperative" time indicators were negligible. The impact of the partial indirect effects of "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.

Continuous interprofessional coordination in perioperative work: an exploratory study

Coordination of perioperative work is challenging. Advancements in diagnostic and therapeutic possibilities have not been followed by similar advancements in the ability to coordinate care. In this paper, we report on a study that explored the nature of continuous coordination as practiced by perioperative staff in order to coordinate their own activities with respect to those of their colleagues. We conducted in-depth interviews (n = 14), and combined observations and focused interviews (n = 31) with perioperative staff (physicians, nurses, technicians, and cleaners) at a major university hospital in Norway. Data were analysed qualitatively with systematic text condensation. The results indicated that a surgical schedule was important for informing staff members about the cases and tasks they had been assigned. Staff also depended on ad hoc, explicit communication to ensure timeliness of particular perioperative activities. This, however, left little room for adjustments of other activities. Hence, to be able to proactively coordinate their own work some staff tried to predict future perioperative activities by observing the workplace, monitoring the surgical scheduling software for changes, and sharing their colleagues' progress updates and predictions. These findings could be important for those developing support for perioperative coordination.

An audit of operating room time utilization in a teaching hospital: is there a place for improvement?

Aim. To perform a thorough and step-by-step assessment of operating room (OR) time utilization, with a view to assess the efficacy of our practice and to identify areas of further improvement. Materials and Methods. We retrospectively analyzed the most ordinary general surgery procedures, in terms of five intervals of OR time utilization: anaesthesia induction, surgery preparation, duration of operation, recovery from anaesthesia, and transfer to postanaesthesia care unit (PACU) or intensive care unit (ICU). According to their surgical impact, the procedures were defined as minor, moderate, and major. Results. A total of 548 operations were analyzed. The mean (SD) time in minutes for anaesthesia induction was 19 (9), for surgery preparation 13 (8), for surgery 115 (64), for recovery from anaesthesia 12 (8), and for transfer to PACU/ICU 12 (9). The time spent in each step presented an ascending escalation pattern proportional to the surgical impact (P = 0.000), which was less pronounced in the transfer to PACU/ICU (P = 0.006). Conclusions. Albeit, our study was conducted in a teaching hospital, the recorded time estimates ranged within acceptable limits. Efficient OR time usage and outliers elimination could be accomplished by a better organized transfer personnel service, greater availability of anaesthesia providers, and interdisciplinary collaboration.

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

BACKGROUND

Predicting total procedure time (TPT) entails several elements subject to variability, including the two main components: surgeon-controlled time (SCT) and anesthesia-controlled time (ACT). This study explores the effect of ACT on TPT as a proportion of TPT as opposed to a fixed number of minutes. The goal is to enhance the prediction of TPT and improve operating room scheduling.

METHODS

Data from six university medical centres (UMCs) over seven consecutive years (2005-2011) were included, comprising 330,258 inpatient elective surgical cases. Based on the actual ACT and SCT, the revised prediction of TPT was determined as SCT × 1.33. Differences between actual and predicted total procedure times were calculated for the two methods of prediction.

RESULTS

The predictability of TPT improved when the scheduling of procedures was based on predicting ACT as a proportion of SCT.

CONCLUSIONS

Efficient operating room (OR) management demands the accurate prediction of the times needed for all components of care, including SCT and ACT, for each surgical procedure. Supported by an extensive dataset from six UMCs, we advise grossing up the SCT by 33% to account for ACT (revised prediction of TPT = SCT × 1.33), rather than employing a methodology for predicting ACT based on a fixed number of minutes. This recommendation will improve OR scheduling, which could result in reducing overutilized OR time and the number of case cancellations and could lead to more efficient use of limited OR resources.

Priority setting in neurosurgery as exemplified by an everyday challenge

BACKGROUND

The allocation of limited healthcare resources poses a constant challenge for clinicians. One everyday example is the prioritization of elective neurosurgical operating room (OR) time in circumstances where cancellations may be encountered. The bioethical framework, Accountability for Reasonableness (A4R) may inform such decisions by establishing conditions that should be met for ethically-justifiable priority setting.

OBJECTIVE

Here, we describe our experience in implementing A4R to guide decisions regarding elective OR prioritization.

METHODS

The four primary expectations of the A4R process are: (1) relevance, namely achieved by support for the process and criteria for decisions amongst all stakeholders; (2) publicity, satisfied by the effective communication of the results of the deliberation; (3) challengeability through a fair appeals process; and (4) Oversight of the process to ensure that opportunities for its improvement are available.

RESULTS

A4R may be applied to inform OR time prioritization, with benefits to patients, surgeons and the institution itself. We discuss various case-, patient-, and surgeon-related factors that may be incorporated into the decision-making process. Furthermore, we explore challenges encountered in the implementation of this process, including the need for timely neurosurgical decision-making and the presence of hospital-based power imbalances.

CONCLUSION

The authors recommend the implementation of a fair, deliberative process to inform priority setting in neurosurgery, as demonstrated by the application of the A4R framework to allocate limited OR time.

Patient experiences with interventions to reduce surgery cancellations: a qualitative study

BACKGROUND

The cancellation of planned surgery harms patients, increases waiting times and wastes scarce health resources. Previous studies have evaluated interventions to reduce cancellations from medical and management perspectives; these have focused on cost, length of stay, improved efficiency, and reduced post-operative complications. In our case a hospital had experienced high cancellation rates and therefore redesigned their pathway for elective surgery to reduce cancelations. We studied how patients experienced interventions to reduce cancellations.

METHODS

We conducted a comparative, qualitative case study by interviewing 8 patients who had experienced the redesigned pathway, and 8 patients who had experienced the original pathway. We performed a content analysis of the interviews using a theory-based coding scheme. Through a process of coding and condensing, we identified themes of patient experience.

RESULTS

We identified three common themes summarizing patients' positive experiences with the effects of the interventions: the importance of being involved in scheduling time for surgery, individualized preparation before the hospital admission, and relationships with few clinicians during their hospital stay.

CONCLUSIONS

Patients appreciated the effects of interventions to reduce cancellations, because they increased their autonomy. Unanticipated consequences were that the telephone reminder created a personalized dialogue and centralization of surgical preparation and discharge processes improved continuity of care. Thus apart from improving surgical logistics, the pathway became more patient-centered.

A new pathway for elective surgery to reduce cancellation rates

Background

The cancellation of planned surgeries causes prolonged wait times, harm to patients, and is a waste of scarce resources. To reduce high cancellation rates in a Norwegian general hospital, the pathway for elective surgery was redesigned. The changes included earlier clinical assessment of patients, better planning and documentation systems, and increased involvement of patients in the scheduling of surgeries. This study evaluated the outcomes of this new pathway for elective surgery and explored which factors affected the outcomes.

Methods

We collected the number of planned operations, performed operations, and cancellations per month from the hospital’s patient administrative system. We then used Student's t-test to analyze differences in cancellation rates (CRs) before and after interventions and a u-chart to analyze whether the improvements were sustained. We also conducted semi-structured interviews with employees of the hospital to explore the changes in the surgical pathway and the factors that facilitated these changes.

Results

The mean CR was reduced from 8.5% to 4.9% (95% CI for mean reduction 2.6-4.5, p < 0.001). The reduction in the CR was sustained over a period of 26 months after the interventions. The median number of operations performed per month increased by 17% (p = 0.04). A clear improvement strategy, involvement of frontline clinicians, introduction of an electronic scheduling system, and engagement of middle managers were important factors for the success of the interventions.

Conclusion

The redesign of the old clinical pathway contributed to a sustained reduction in cancellations and an increased number of performed operations.