The Impact of Overestimations of Surgical Control Times Across Multiple Specialties on Medical Systems

Variability and relative contribution of surgeon- and anesthesia-specific time components to total procedural time in cardiac surgery

BACKGROUND

Decreasing variability in time-intensive tasks during cardiac surgery may reduce total procedural time, lower costs, reduce clinician burnout, and improve patient access. The relative contribution and variability of surgeon control time (SCT) and anesthesia control time (ACT) to total procedural time is unknown.

METHODS

A total of 669 patients undergoing coronary artery bypass graft (CABG) surgery were enrolled. Using linear regression, we estimated adjusted SCTs and ACTs, controlling for patient and procedural covariates. The primary endpoint compared overall SCTs and ACTs. The secondary endpoint compared the variability in adjusted SCTs and ACTs. Sensitivity analyses quantified the relative importance of the specific surgeon and anesthesiologist in the adjusted linear models.

RESULTS

The median SCT was 4.1 hours (interquartile range [IQR], 3.4-4.9 hours) compared to a median ACT of 1.0 hours (IQR, 0.8-1.2 hours; P < .001). Using linear regression, the variability in adjusted SCT among surgeons (range, 1.8 hours) was 3.5-fold greater than the variability in adjusted ACT among anesthesiologists (range, 0.5 hour; P < .001). The specific surgeon and anesthesiologist accounted for 50% of the explanatory power of the predictive model (P < .001).

CONCLUSIONS

SCT variability is significantly greater than ACT variability and is strongly associated with the surgeon performing the procedure. Although these results suggest that SCT variability is an attractive operational target, further studies are needed to determine practitioner specific and modifiable attributes to reduce variability and improve efficiency.

Predictors of Duration of Transjugular Intrahepatic Portosystemic Shunt (TIPS) Procedure: A Retrospective Single-Center Study

PURPOSE

To determine the relationship between clinical, procedural, hospital, and physician characteristics with the duration of the transjugular intrahepatic portosystemic shunt (TIPS) procedure.

METHODS

This retrospective study included patients over 18 years of age who underwent an initial TIPS procedure between January 2005 and August 2020. Exclusion criteria were TIPS performed outside the institution and failed TIPS placement. A total of 154 records were included. Regression analyses were used to identify predictors of procedural duration.

RESULTS

The mean age at TIPS placement was 57 years. Seventy percent of patients were male and non-Hispanic whites (80.5%). The mean duration of the TIPS procedure was 169 minutes (SD: 78). Procedural duration was shorter when the etiology of cirrhosis was viral (mean: 144 min, SD: 84, p=0.008); the reason for TIPS was ascites (152, SD: 66, p=0.01); and the procedure did not require additional access (153 min, SD: 67, p=<.0001). The main clinical predictor of procedural duration was baseline bilirubin (Beta coefficient (β): 5.6 min, p=0.0007). In multivariable linear models, in those patients that did not require additional access, bilirubin (β: 4.9 min, p=0.005), etiology of cirrhosis, and physician experience were the main predictors of TIPS procedure duration. The effect of baseline bilirubin on procedural duration increased in the ascites group (β: 19.5 minutes, p=0.006), especially when additional access was not required.

CONCLUSIONS

 The study demonstrates an association between baseline bilirubin, etiology of cirrhosis, and physician experience with the duration of the TIPS procedure. The mechanism underlying the positive association between baseline bilirubin and procedural time is possibly related to the degree of liver fibrosis.

Accurate Operative Time Prediction in Thyroid Surgery: A Rural Tertiary Care Facility Experience

OBJECTIVE

To determine whether surgeons can estimate thyroid operative time more accurately than a system-generated average time estimate.

METHODS

Four otolaryngologists at a single institution with extensive endocrine surgery experience were asked to predict their operative times for all eligible thyroid surgeries. These estimates were compared to system-generated operative time predications based on averaging the surgeon's previous 10 cases with the same Current Procedural Terminology code. The surgeon-generated estimations and system-generated estimations were then compared to each other and the actual operative time.

RESULTS

A final sample of 73 cases was used for all analyses. Average age was 51 years old and the majority of patients were female. Surgeon-generated operative time estimates were significantly more accurate than system-generated estimates based on time averaging (P < .001). These findings were consistent across each surgeon individually and within each procedure type (hemithyroidectomy and total thyroidectomy). These findings had a power of over 99% based on mean differences.

CONCLUSION

As the financial center of modern hospitals, an efficient operating room is integral to economic success. Improving the precision of operative time estimation reduces costly unplanned staff overtime, canceled cases, and underutilization. Our research at a rural tertiary care center shows that experienced thyroid surgeons can substantially reduce the error of estimating thyroid operative times by considering individual patient characteristics. Although no objective variables have so far been identified to correlate with thyroid operative time, surgeon-generated operative time estimation is significantly more accurate than a generic system approach of averaging previous operative times.

Operating Room Usage Time Estimation with Machine Learning Models

Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better scheduling would rely on accurate estimation of surgery duration. Our model can achieve a good prediction result on surgery duration with a dozen of features. We have found the result of our best performing department-specific XGBoost model with the values 31.6 min, 18.71 min, 0.71, 28% and 27% for the metrics of root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), mean absolute percentage error (MAPE) and proportion of estimated result within 10% variation, respectively. We have presented each department-specific result with our estimated results between 5 and 10 min deviation would be more informative to the users in the real application. Our study shows comparable performance with previous studies, and the machine learning methods use fewer features that are better suited for universal usability.

Benchmarking of Anesthesia and Surgical Control Times by Current Procedural Terminology (CPT®) Codes

Over half of hospital revenue results from perioperative patient care, thus emphasizing the importance of efficient resource utilization within a hospital's suite of operating rooms (ORs). Predicting surgical case duration, including Anesthesia-controlled time (ACT) and Surgical-controlled time (SCT) has been significantly detailed throughout the literature as a means to help manage and predict OR scheduling. However, this information has previously been divided by surgical specialty, and only limited benchmarking data regarding ACT and SCT exists. We hypothesized that advancing the granularity of the ACT and SCT from surgical specialty to specific Current Procedural Terminology (CPT^®) codes will produce data that is more accurate, less variable, and therefore more useful for OR schedule modeling and management. This single center study was conducted using times from surgeries performed at the University of Colorado Hospital (UCH) between September 2018 - September 2019. Individual cases were categorized by surgical specialty based on the specialty of the primary attending surgeon and CPT codes were compiled from billing data. Times were calculated as defined by the American Association of Clinical Directors. I² values were calculated to assess heterogeneity of mean ACT and SCT times while Levene's test was utilized to assess heterogeneity of ACT and SCT variances. Statistical analyses for both ACT and SCT were calculated using JMP Statistical Discovery Software from SAS (Cary, NC) and R v3.6.3 (Vienna, Austria). All surgical cases (n = 87,537) performed at UCH from September 2018 to September 2019 were evaluated and 30,091 cases were included in the final analysis. All surgical subspecialties, with the exception of Podiatry, showed significant variability in ACT and SCT values between CPT codes within each surgical specialty. Furthermore, the variances of ACT and SCT values were also highly variable between CPT codes within each surgical specialty. Finally, benchmarking values of mean ACT and SCT with corresponding standard deviations are provided. Because each mean ACT and SCT value varies significantly between different CPT codes within a surgical specialty, using this granularity of data will likely enable improved accuracy in surgical schedule modeling compared to using mean ACT and SCT values for each surgical specialty as a whole. Furthermore, because there was significant variability of ACT and SCT variances between CPT codes, incorporating variance into surgical schedule modeling may also improve accuracy. Future investigations should include real-time simulations, logistical modeling, and labor utilization analyses as well as validation of benchmarking times in private practice settings.

Should we abandon the patient-specific instrumentation ship in total knee arthroplasty? Not quite yet!

Patient-specific Instrumentation (PSI) is an innovative technique aiding the precise implementation of the preoperative plan during total knee arthroplasty (TKA) by using patient-specific guides and cutting blocks. Despite of the theoretical advantages, studies have reported contradictory results, thus there is no consensus regarding the overall effectiveness of PSI. Through the critical assessment of a meta-analysis published lately, this correspondence aims to highlight the complexity of comparing the efficacy of PSI to standard instrumentation (SI). The accuracy of component alignment, patient-reported outcome measures (PROMs), surgery time, blood loss, transfusion rate, and postoperative complications are commonly used outcomes for investigating the efficacy of PSI-aided TKA. By assessing component alignment, the expertise of the surgeon(s) should be taken into consideration, since PSI may not provide benefits for expert surgeons but might improve accuracy and patient safety during the learning curve of novice surgeons. With respect to PROMs and postoperative complications, PSI may not improve short-term results; however, long-term follow up data is missing. Regarding transfusion rates, favorable trends can be observed, but further studies utilizing recent data are needed for a clear conclusion. When assessing surgery time, we suggest focusing on operating room turnover instead of procedure time.

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.

A Retrospective Analysis of the Variability in Case Duration for Aortic Valve Replacement and Association With Hospital Facility Types

OBJECTIVE

Currently, there are no large-scale studies that compare differences in case duration of aortic valve replacements (AVRs). The primary objective of this study was to determine associations of hospital facility type, geographic location, case volume per year, and time of day with duration of valve replacement surgery.

DESIGN

Retrospective.

SETTING

Data from the National Anesthesia Clinical Outcomes Registry.

PARTICIPANTS

National data from university and non-university hospitals.

INTERVENTIONS

No interventions.

MEASUREMENTS AND MAIN RESULTS

All AVRs from the National Anesthesia Clinical Outcomes Registry were identified from 2010 to 2014. Mean case duration for all AVRs was 360.8 ± 95.8 minutes and was presented based on facility type (university hospital, large community hospital, medium-sized community hospital, and other); US geographic region; time of day (cases performed after 5 pm and before 7 am v day shift); and case volume per year. A multivariable linear regression model was built to determine the association of various patient, procedural, and facility characteristics with case duration. University hospitals were associated with increased case duration for AVRs (p < 0.0001).

CONCLUSIONS

With this large national database, the authors demonstrated that academic hospitals, time of day of the surgery, US region, and case volume per year for a facility are related to the case duration of AVRs.

Perioperative Information Systems: Opportunities to Improve Delivery of Care and Clinical Outcomes in Cardiac and Vascular Surgery

A variety of existing perioperative informatics tools offer clinicians and researchers the opportunity to improve the delivery of care and clinical outcomes for patients undergoing cardiac and vascular surgery. Many of these tools can be used to improve the reliability of the care delivery process through the application of clinical decision support tools and/or quality improvement methodologies at a number of junctures. In this review, the authors will offer a concise overview of the existing perioperative informatics literature, with a focus on tools considered to be of utility in confronting the unique challenges inherent to cardiac and vascular surgery. The authors also highlight areas that they believe are of interest for future targeted inquiry.

Assessment of operative times of multiple surgical specialties in a public university hospital

Objective

To evaluate the indicators duration of anesthesia, operative time and time patients stay in the operating rooms of different surgical specialties at a public university hospital.

Methods

It was done by a descriptive cross-sectional study based on the operating room database. The following stages were measured: duration of anesthesia, procedure time and patient length of stay in the room of the various specialties. We included surgeries carried out in sequence in the same room, between 7:00 a.m. and 5 p.m., either elective or emergency. We calculated the 80th percentile of the stages, where 80% of procedures were below this value.

Results

The study measured 8,337 operations of 12 surgical specialties performed within one year. The overall mean duration of anesthesia of all specialties was 178.12±110.46 minutes, and the 80th percentile was 252 minutes. The mean operative time was 130.45±97.23 minutes, and the 80th percentile was 195 minutes. The mean total time of the patient in the operating room was 197.30±113.71 minutes, and the 80th percentile was 285 minutes. Thus, the variation of the overall mean compared to the 80th percentile was 41% for anesthesia, 49% for surgeries and 44% for operating room time. In average, anesthesia took up 88% of the operating room period, and surgery, 61%.

Conclusion

This study identified patterns in the duration of surgery stages. The mean values of the specialties can assist with operating room planning and reduce delays.