Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and "instant messaging" updates from anesthesia providers

Larger anesthesia practitioner per operating room ratios are needed to prevent unnecessary non-operative time than to mitigate patient risk: A narrative review

When choosing the anesthesia practitioner to operating room (OR) ratio for a hospital, objectives are applied to mitigate patient risk: 1) ensuring sufficient anesthesiologists to meet requirements for presence during critical intraoperative events (e.g., anesthesia induction) and 2) ensuring sufficient numbers to cover emergencies outside the ORs (e.g., emergent reintubation in the post-anesthesia care unit). At a 24-OR suite with each anesthesiologist supervising residents in 2 ORs, because critical events overlapped among ORs, ≥14 anesthesiologists were needed to be present for all critical events on >90% of days. The suitable anesthesia practitioner to OR ratio would be 1.58, where 1.58 = (24 + 14)/24. Our narrative review of 22 studies from 17 distinct hospitals shows that the practitioner to OR ratio needed to reduce non-operative time is reliably even larger. Activities to reduce non-operative times include performing preoperative evaluations, making prompt evidence-based decisions at the OR control desk, giving breaks during cases (e.g., lunch or lactation sessions), and using induction and block rooms in parallel to OR cases. The reviewed articles counted the frequency of these activities, finding them much more common than urgent patient-care events. Our review shows, also, that 1 anesthesiologist per OR, working without assistants, is often more expensive, from a societal perspective, than having a few more anesthesia practitioners (i.e., ratio > 1.00). These results are generalizable among hundreds of hospitals, based on managerial epidemiology studies. The implication of our narrative review is that existing studies have already shown, functionally, that artificial intelligence and monitoring technologies based on increasing the safety of intraoperative care have little to no potential to influence anesthesia or OR productivity. There are, in contrast, opportunities to use sensor data and decision-support to facilitate communication among anesthesiologists outside of ORs to choose optimal task sequences that reduce non-operative times, thereby increasing production and OR efficiency.

Inhalational Agent Dosing Behaviors of Anesthesia Practitioners Cause Variability in End-Tidal Concentrations at the End of Surgery and Prolonged Times to Tracheal Extubation

INTRODUCTION

Prolonged times to tracheal extubation are intervals from the end of surgery to extubation ≥15 minutes. We examined why there are associations with the end-tidal inhalational agent concentration as a proportion of the age‑adjusted minimum alveolar concentration (MAC fraction) at the end of surgery.

METHODS

The retrospective cohort study used 11.7 years of data from one hospital. All p‑values were adjusted for multiple comparisons.

RESULTS

There was a greater odds of prolonged time to extubation if the anesthesia practitioner was a trainee (odds ratio 1.68) or had finished fewer than five cases with the surgeon during the preceding three years (odds ratio 1.12) (both P<0.0001). There was a greater risk of prolonged time to extubation if the MAC fraction was >0.4 at the end of surgery (odds ratio 2.66, P<0.0001). Anesthesia practitioners who were trainees and all practitioners who had finished fewer than five cases with the surgeon had greater mean MAC fractions at the end of surgery and had greater relative risks of the MAC fraction >0.4 at the end of surgery (all P<0.0001). The source for greater MAC fractions at the end of surgery was not greater MAC fractions throughout the anesthetic because the means during the case did not differ among groups. Rather, there was substantial variability of MAC fractions at the end of surgery among cases of the same anesthesia practitioner, with the mean (standard deviation) among practitioners of each practitioner's standard deviation being 0.35 (0.05) and the coefficient of variation being 71% (13%).

CONCLUSION

More prolonged extubations were associated with greater MAC fractions at the end of surgery. The cause of the large MAC fractions was the substantial variability of MAC fractions among cases of each practitioner at the end of surgery. That variability matches what was expected from earlier studies, both from variability among practitioners in their goals for the MAC fraction given at the start of surgical closure and from inadequate dynamic forecasting of the timing of when surgery would end. Future studies should examine how best to reduce prolonged extubations by using anesthesia machines' display of MAC fraction and feedback control of end-tidal agent concentration.

Anesthesia Practitioners' Goals for Sevoflurane Minimum Alveolar Concentration at the End of Surgery and the Incidence of Prolonged Extubations: A Prospective and Observational Study

BACKGROUND

Prolonged times to tracheal extubation (≥15 minutes from dressing on the patient) are consequential based on their clinical and economic effect. We evaluated the variability among anesthesia practitioners in their goals for the age-adjusted end-tidal minimum alveolar concentration of sevoflurane (MAC) at surgery end and achievement of their goals.

METHODS

We prospectively studied a cohort of 56 adult patients undergoing general anesthesia with sevoflurane as the sole anesthetic agent, scheduled operating room time of at least 3 hours, and non-prone positioning. At the start of surgical closure, an observer asked the anesthesia practitioner their goal for MAC when the surgical drapes are lowered (i.e., the functional end of surgery for the studied procedures). When the drapes were lowered, the MAC achieved was recorded, and the values were compared.

RESULTS

The standard deviation of the practitioners' MAC goal was large, 0.199 (N = 56 cases, 95% confidence interval 0.17-0.24), not significantly different from the standard deviation of the MAC achieved of 0.253, P = 0.071. The MAC goal and MAC achieved were correlated pairwise, Pearson r =0.65, P < 0.0001. There was no incremental effect of operating room conversation(s) related to case progress on the association (partial correlation ‑0.01, P = 0.96). Differences among practitioners in the MAC achieved at surgery end were consequential. Specifically, for the N = 12 cases with prolonged extubation, the mean MAC was 0.60 (standard deviation 0.10) versus 0.48 (0.21) among the N = 44 cases without prolonged extubation (P = 0.0070).

CONCLUSIONS

The standard deviation of the MAC goal among practitioners was sufficiently large to contribute significantly to the variability in the MAC achieved at the end of surgery. We confirmed prospectively that the age-adjusted end-tidal MAC at the end of surgery matters clinically and economically because differences of 0.60 versus 0.48 were associated with more prolonged extubations. Our novel finding is that the MAC achieved ≥0.60 were caused in part by the anesthesia practitioners' stated MAC goals when surgical closures started.

Preventing Prolonged Times to Awakening While Mitigating the Risk of Patient Awareness: Gas Man Computer Simulations of Sevoflurane Consumption From Brief, High Fresh Gas Flow Before the End of Surgery

Prolonged times to tracheal extubation are associated with adverse patient and economic outcomes. We simulated awakening patients from sevoflurane after long-duration surgery at 2% end-tidal concentration, 1.0 minimum alveolar concentration (MAC) in a 40-year-old. Our end-of-surgery target was 0.5 MAC, the Michigan Awareness Control Study's threshold for intraoperative alerts. Consider an anesthetist who uses a 1 liter/minute gas flow until surgery ends. During surgical closure, the inspired sevoflurane concentration is reduced from 2.05% to 0.62% (i.e., MAC-awake). The estimated time to reach 0.5 MAC is 28 minutes. From a previous study, 28 minutes exceeded ≥95% of surgical closure times for all 244 distinct surgical procedures (N=23,343 cases). Alternatively, the anesthetist uses 8 liters/minute gas flow with the vaporizer at MAC-awake for 1.8 minutes, which reduces the end-tidal concentration to 0.5 MAC. The anesthetist then increases the vaporizer to keep end-tidal 0.5 MAC until the surgery ends. An additional simulation shows that, compared with simulated end-tidal agent feedback control, this approach consumed 0.45 mL extra agent. Simulation results are the same for an 80-year-old patient. The extra 0.45 mL has a global warming potential comparable to driving 26 seconds at 40 kilometers (25 miles) per hour, comparable to route modification to avoid potential roadway hazards.

Process model analysis of parenchyma sparing laparoscopic liver surgery to recognize surgical steps and predict impact of new technologies

BACKGROUND

Surgical process model (SPM) analysis is a great means to predict the surgical steps in a procedure as well as to predict the potential impact of new technologies. Especially in complicated and high-volume treatments, such as parenchyma sparing laparoscopic liver resection (LLR), profound process knowledge is essential for enabling improving surgical quality and efficiency.

METHODS

Videos of thirteen parenchyma sparing LLR were analyzed to extract the duration and sequence of surgical steps according to the process model. The videos were categorized into three groups, based on the tumor locations. Next, a detailed discrete events simulation model (DESM) of LLR was built, based on the process model and the process data obtained from the endoscopic videos. Furthermore, the impact of using a navigation platform on the total duration of the LLR was studied with the simulation model by assessing three different scenarios: (i) no navigation platform, (ii) conservative positive effect, and (iii) optimistic positive effect.

RESULTS

The possible variations of sequences of surgical steps in performing parenchyma sparing depending on the tumor locations were established. The statistically most probable chain of surgical steps was predicted, which could be used to improve parenchyma sparing surgeries. In all three categories (i-iii) the treatment phase covered the major part (~ 40%) of the total procedure duration (bottleneck). The simulation results predict that a navigation platform could decrease the total surgery duration by up to 30%.

CONCLUSION

This study showed a DESM based on the analysis of steps during surgical procedures can be used to predict the impact of new technology. SPMs can be used to detect, e.g., the most probable workflow paths which enables predicting next surgical steps, improving surgical training systems, and analyzing surgical performance. Moreover, it provides insight into the points for improvement and bottlenecks in the surgical process.

Continuous real-time prediction of surgical case duration using a modular artificial neural network

BACKGROUND

Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration.

METHODS

Preoperative (e.g. procedure name) and intraoperative (e.g. medications and vital signs) variables were retrieved from anaesthetic records of surgeries performed between March 1, 2019 and October 31, 2019. A modular artificial neural network was developed and compared with a Bayesian approach and the scheduled surgical duration. Continuous ranked probability score (CRPS) was used as a measure of time error to assess model accuracy. For evaluating clinical performance, accuracy for each approach was assessed in identifying cases that ran beyond 15:00 (commonly scheduled end of shift), thus identifying opportunities to avoid overtime labour costs.

RESULTS

The analysis included 70 826 cases performed at eight hospitals. The modular artificial neural network had the lowest time error (CRPS: mean=13.8; standard deviation=35.4 min), which was significantly better (mean difference=6.4 min [95% confidence interval: 6.3-6.5]; P<0.001) than the Bayesian approach. The modular artificial neural network also had the highest accuracy in identifying operating theatres that would overrun 15:00 (accuracy at 1 h prior=89%) compared with the Bayesian approach (80%) and a naïve approach using the scheduled duration (78%).

CONCLUSIONS

A real-time neural network model using preoperative and intraoperative data had significantly better performance than a Bayesian approach or scheduled duration, offering opportunities to avoid overtime labour costs and reduce the cost of surgery by providing superior real-time information for perioperative decision support.

Similarities Between Pediatric and General Hospitals Based on Fundamental Attributes of Surgery Including Cases Per Surgeon Per Workday

Introduction

Operating room (OR) management decision-making at both pediatric and adult hospitals is determined, in large part, by the same fundamental attributes of surgery and other considerations related to case duration prediction. These include the non-preemptive nature of surgeries, wide prediction limits for case duration, and constraints to moving or resequencing cases on the day of surgery. Another attribute fundamentally affecting OR management is the median number of cases a surgeon performs on their OR days. Most adult surgeons have short lists of cases (i.e., one or two cases per day). Similarly, at adult hospitals, growth in caseloads is mostly due to the subset of those surgeons who also operate just once or twice per week. It is unknown if these characteristics of surgery apply to pediatric surgeons and pediatric hospitals as well.

Methods

Our retrospective cohort study included all elective surgical cases performed at the six pediatric hospitals in Florida during 2018 and 2019 (n = 71,340 cases). We calculated the percentages of combinations of surgeon, date, and hospital (lists) comprising one or two cases, or just one case, and determined if the values were statistically >50% (i.e., indicative of “most”). We determined if most of the growth in caseload and intraoperative work relative value units (wRVUs) at the pediatric hospitals between 2018 and 2019 accrued from low-caseload surgeons. Results are reported as mean ± standard error of the mean.

Results

Averaging among the six pediatric hospitals, the non-holiday weekday lists of most surgeons at each facility had just one or two elective cases, inpatient and/or ambulatory (68.1%; p = 0.016 vs. 50%, n = 27,557 lists). Growth in surgical caseloads from 2018 to 2019 was mostly attributable to surgeons who in 2018 averaged ≤2.0 cases per week (76.3% ± 5.4%, p = 0.0085 vs. 50%). Similarly, growth in wRVUs was mostly attributable to these low-caseload surgeons (73.8% ± 5.4%, p = 0.017 vs. 50%).

Conclusions

Like adult hospitals, most pediatric surgeons’ lists of cases consist of only one or two cases per day, with many lists containing a single case. Similarly, growth at pediatric hospitals accrued from low-caseload surgeons who performed one or two cases per week in the preceding year. These findings indicate that hospitals desiring to increase their surgical caseload should ensure that low-caseload surgeons are provided access to the OR schedule. Additionally, since percent-adjusted utilization and raw utilization cannot be accurately measured for low-caseload surgeons, neither metric should be used to allocate OR time to individual surgeons. Since most adult and pediatric surgeons have low caseloads, this is a fundamental attribute of surgery.

Suggested Work Guidelines, Based on Operating Room Data, for Departments with a Breast Milk Pumping Supervising Anesthesiologist

Objective: Coordinating breast milk pumping sessions is challenging for lactating anesthesiologists who supervise multiple simultaneous anesthetics. We quantify the minimum percentages of adjacent operating rooms (ORs) for which there could reliably (≥95%) be at least 30 minutes during the surgical time when the anesthesiologist covering three anesthetics could have her rooms covered by another anesthesiologist. Methods: The historical cohort study was from a large U.S. teaching hospital. We calculated the 5% lower prediction bounds of surgical times from 3 years of historical data, and then applied them to surgical start times from adjacent ORs during the next 1 year. Results: For >2/3rd of cases, an anesthesiologist supervising three ORs would lack a reliable 30-minute period of overlapping surgical times, and an even smaller chance per case at the ambulatory surgery center, 10% (9-11%). For approximately 42% (41-43%) of sufficiently long individual cases, there was absence of a 30-minute period during which both of the two adjacent ORs' cases were suitable for the anesthesiologist to receive a break (p < 0.0001 compared with one-third). Conclusions: Even when making assumptions that were deliberately unrealistic (e.g., anesthesiologists' responsibilities are only for ongoing OR cases), there is no practical mechanism for an anesthesiologist supervising three ORs to start cases, be relieved for a breast milk pumping session, and then return in time for the end of the anesthetics (e.g., tracheal extubation). Departments with anesthesiologists who are breastfeeding should consider having options for temporary clinical assignments, commensurate with training and experience, that do not require supervising >2 ORs.

Bayesian hierarchical modeling of operating room times for surgeries with few or no historic data

In this work it is proposed a modeling for operating room times based on a Bayesian Hierarchical structure. Specifically, it is employed a Bayesian generalized linear mixed model with an additional hierarchical level on the random effects. This configuration allows the estimation of operating room times (ORT) with few or no historical observations, without requiring a prior surgeon's estimate. In addition to the widely used lognormal distribution, it is also studied the gamma distribution to model the operating room times. For the scale parameters related to the random effects (surgeon and surgical group), which are important quantities in this type of modeling, different kinds of prior distributions such as Half-Cauchy, Sbeta2, and uniform are studied. A Bayesian version of the classical ANOVA is implemented to identify relevant predictors for the operating room times. We find that lognormal models outperform the gamma models in estimating upper prediction bounds (UB). Especially, the best ORT predictions for cases with few or no historical data (i.e., between 0 and 3 historical cases) are obtained with the [Formula: see text], SBeta2 model. With a deviation of less than 1% with respect to the nominal coverage of the upper bound predictions UB80% and UB90% and an average absolute percentage error of 38.5% in the point estimate.

Percentages of Cases in Operating Rooms of Sufficient Duration to Accommodate a 30-Minute Breast Milk Pumping Session by Anesthesia Residents or Nurse Anesthetists

INTRODUCTION

Accommodating breast milk pumping sessions is required by US federal statute, but fulfillment is challenging for US anesthesia providers (e.g., anesthesia residents and nurse anesthetists). Considerations of good anesthesia practices (e.g., being present for critical portions of cases, including induction and emergence) create limits on which procedures are suitable for such relief. Our objective was to quantify the minimum percentages of cases for which there could reliably (≥ 95%) be at least 30 minutes during the surgical time when the anesthesia provider could receive such breaks.

METHODS

We studied all surgical cases performed at an anesthesia department over four years, including its inpatient surgical suite, pediatric hospital, and ambulatory surgery center. The 5% lower prediction bounds of surgical times (surgery or procedure start to end) were calculated from three years of historical data (October 1, 2016, to September 30, 2019) based on two-parameter lognormal distributions. The prediction bounds were compared to actual surgical start times during the next one year (October 1, 2019, to September 30, 2020). We considered the interval available for a breast milk pumping session during a case to be from 15 minutes after the start of the surgical time (to allow completion of initial documentation, other activities, and hand-off to the relieving anesthesia provider) until the end of the surgical time.

RESULTS

The lower prediction bounds were accurate, with 4.9% (4.6% - 5.2%) of future cases' surgical times being briefer, matching the nominal 5.0% rate. Applying these bounds, approximately 39% of cases (99% confidence interval 39% - 40%) were reliably of sufficient duration for the anesthesia provider delivering care in that one operating room to receive a 30-minute break for breast milk pumping session between 15 minutes after the start of surgery and procedure end. This percentage (39%) was substantially less than the 72% of the surgical times that were observed, in retrospect, to be sufficiently long because the lower 5% prediction bounds accounted correctly for the uncertainty in the duration of each case. The observed 39% prevalence was significantly fewer than half the cases (P < 0.0001 vs. 50%) suitable for such relief.

CONCLUSIONS

Individuals making operating room assignments for anesthesia providers need to consider the 5% lower prediction bounds of surgical times for cases in the room when making such assignments for women who require time for breast milk pumping sessions. Such considerations will generally result in assignments to rooms with one or more long-duration cases. Such a strategy may involve changes in preferred assignments for the anesthesia providers and alteration in the order of rotations for anesthesia residents (e.g., palliative care rotation rather than transition to practice at a pediatric ambulatory surgery center). When making room assignments for anesthesia providers who are breastfeeding, our results show that the 5% lower prediction bounds of surgical times need to be calculated; relying on the average surgical times for procedures is insufficient. Our paper also shows how to perform the mathematics using a spreadsheet program or equivalent.

Sustained management of the variability in work hours among anesthesiologists providing patient care in operating rooms and not on call to work late if necessary

STUDY OBJECTIVE

We evaluated a department's long-term (6.5-year) success of achieving an overall and individual incidence of anesthesiologists working late of approximately 20% of days when not on call to work late, if necessary, and providing care in operating rooms.

DESIGN

Historical cohort study, January 2014 through September 2020.

SETTING

Inpatient surgical suite of large teaching hospital.

MAIN RESULTS

The percentage of days worked past 5:00 PM was mean (standard deviation) 17.7% (5.0%) of days, 99% confidence interval (CI) 15.0% to 20.4%. There was considerable variability among quarters, the coefficient of variation being 28% (99% CI 20% to 45%). This was caused, in part, by anesthesiologists less often working late during January-March versus July-September (14.0% [4.5%] versus 21.6% [3.2%]; P = 0.0031; N = 7 years each). The N = 67 anesthesiologists not on call differed in their percentages of workdays finishing after 5:00 PM (P < 0.0001). While the mean was 18% (6%), the coefficient of variation was 37% (29% to 49%). There were no significant outliers. In contrast, not only were there differences among anesthesiologists in the relative risks of working late when receiving relief versus when not handing off a case (P < 0.0001), there were outliers.

CONCLUSIONS

An anesthesia department aiming for a 20% incidence of anesthesiologists having to work late when not on call can achieve this objective, long-term, within a few percent (e.g., 2%). Seasonal variation can contribute to variability among quarters in the overall departmental incidence. Individual anesthesiologists can have variability among themselves, though, and that is caused by large heterogeneity in their relative risks of working late when receiving relief versus when not handing off a case. For departments choosing to provide information to anesthesiologists to increase predictability, factors to consider should include season of the year and the individual anesthesiologist.

Caseload is increased by resequencing cases before and on the day of surgery at ambulatory surgery centers where initial patient recovery is in operating rooms and cleanup times are longer than typical

Study objective

The coronavirus disease 2019 (COVID-19) pandemic impacts operating room (OR) management in regions with high prevalence (e.g., >1.0% of asymptomatic patients testing positive). Cases with aerosol producing procedures are isolated to a few ORs, initial phase I recovery of those patients is in the ORs, and multimodal environmental decontamination applied. We quantified the potential increase in productivity from also resequencing these cases among those 2 or 3 ORs.

Design

Computer simulation provided sample sizes requiring >100 years experimentally. Resequencing was limited to changes in the start times of surgeons' lists of cases.

Setting

Ambulatory surgery center or hospital outpatient department.

Main results

With case resequencing applied before and on the day of surgery, there were 5.6% and 5.5% more cases per OR per day for the 2 ORs and 3 ORs, respectively, both standard errors (SE) < 0.1%. Resequencing cases among ORs to start cases earlier permitted increases in the hours into which cases could be scheduled from 10.5 to 11.0 h, while assuring >90% probability of each OR finishing within the prespecified 12-h shift. Thus, the additional cases were all scheduled before the day of surgery. The greater allocated time also resulted in less overutilized time, a mean of 4.2 min per OR per day for 2 ORs (SE 0.5) and 6.3 min per OR per day for 3 ORs (SE 0.4). The benefit could be achieved while limiting application of resequencing to days when the OR with the fewest estimated hours of cases has ≤8 h.

Conclusions

Some ambulatory surgery ORs have unusually long OR times and/or room cleanup times (e.g., infection control efforts because of the pandemic). Resequencing cases before and on the day of surgery should be considered, because moving 1 or 2 cases occasionally has little to no cost with substantive benefit.

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COVID-19 influences management for aerosol producing procedures.

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Simulation studied case resequencing applied before and on the day of surgery.

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>5% more queued cases can be done per OR per day with practical heuristic.

Obtaining and Modeling Variability in Travel Times From Off-Site Satellite Clinics to Hospitals and Surgery Centers for Surgeons and Proceduralists Seeing Office Patients in the Morning and Performing a To-Follow List of Cases in the Afternoon

BACKGROUND

Hospitals achieve growth in surgical caseload primarily from the additive contribution of many surgeons with low caseloads. Such surgeons often see clinic patients in the morning then travel to a facility to do 1 or 2 scheduled afternoon cases. Uncertainty in travel time is a factor that might need to be considered when scheduling the cases of to-follow surgeons. However, this has not been studied. We evaluated variability in travel times within a city with high traffic density.

METHODS

We used the Google Distance Matrix application programming interface to prospectively determine driving times incorporating current traffic conditions at 5-minute intervals between 9:00 AM and 4:55 PM during the first 4 months of 2018 between 4 pairs of clinics and hospitals in the University of Miami health system. Travel time distributions were modeled using lognormal and Burr distributions and compared using the absolute and signed differences for the median and the 0.9 quantile. Differences were evaluated using 2-sided, 1-group t tests and Wilcoxon signed-rank tests. We considered 5-minute signed differences between the distributions as managerially relevant.

RESULTS

For the 80 studied combinations of origin-to-destination pairs (N = 4), day of week (N = 5), and the hour of departure between 10:00 AM and 1:55 PM (N = 4), the maximum difference between the median and 0.9 quantile travel time was 8.1 minutes. This contrasts with the previously published corresponding difference between the median and the 0.9 quantile of 74 minutes for case duration. Travel times were well fit by Burr and lognormal distributions (all 160 differences of medians and of 0.9 quantiles <5 minutes; P < .001). For each of the 4 origin-destination pairs, travel times at 12:00 PM were a reasonable approximation to travel times between the hours of 10:00 AM and 1:55 PM during all weekdays.

CONCLUSIONS

During mid-day, when surgeons likely would travel between a clinic and an operating room facility, travel time variability is small compared to case duration prediction variability. Thus, afternoon operating room scheduling should not be restricted because of concern related to unpredictable travel times by surgeons. Providing operating room managers and surgeons with estimated travel times sufficient to allow for a timely arrival on 90% of days may facilitate the scheduling of additional afternoon cases especially at ambulatory facilities with substantial underutilized time.

What delays your case start? Exploring operating room inefficiencies

INTRODUCTION

Improving operating room (OR) inefficiencies benefits the OR team, hospital, and patients alike but the available literature is limited. Our goal was, using a novel surgical application, to identify any OR incidents that cause delays from the time the patient enters the OR till procedure start (preparatory phase).

MATERIALS AND METHODS

We conducted an IRB approved, prospective, observational study between July 2018 and January 2019. Using a novel surgical application (ExplORer Surgical) three observers recorded disrupting incidents and their duration during the preparatory phase of a variety of general surgery cases. Specifically, the number and duration of anesthesia delays, unnecessary/distracting conversations, missing items, and other delays were recorded from the moment they started until they stopped affecting the normal workflow.

RESULTS

Ninety-six OR cases were assessed. 20 incidents occurred in 18 (19%) of those cases. The average preparatory duration for all the cases was 20.7 ± 8.6 min. Cases without incidents lasted 19.5 ± 7.4 min while cases with incidents lasted 25.9 ± 11.2 min, p = 0.03. The average incident lasted 3.7 min, approximately 18% of the preparatory phase duration.

CONCLUSION

The use of the ExplORer Surgical app allowed us to accurately record the incidents happening during the preparatory phase of various general surgery operations. Such incidents significantly prolonged the preparatory duration. The identification of those inefficiencies is the first step to targeted interventions that may eventually optimize the efficiency of preoperative preparation.

A Statistical Model-driven Surgical Case Scheduling System Improves Multiple Measures of Operative Suite Efficiency: Findings From a Single-center, Randomized Controlled Trial

OBJECTIVE

We sought to determine whether a data-driven scheduling approach improves Operative Suite (OS) efficiency.

BACKGROUND

Although efficient use of the OS is a critical determinant of access to health care services, OS scheduling methodologies are simplistic and do not account for all the available characteristics of individual surgical cases.

METHODS

We randomly scheduled cases in a single OS by predicting their length using either the historical mean (HM) duration of the most recent 4 years; or a regression modeling (RM) system that accounted for operative and patient characteristics. The primary endpoint was the imprecision in prediction of the end of the operative day. Secondary endpoints included measures of OS efficiency; personnel burnout captured by the Maslach Burnout Inventory; and a composite endpoint of 30-day mortality, myocardial infarction, wound infection, bleeding, amputation, or reoperation.

RESULTS

Two hundred and seven operative days were allocated to scheduling with either the RM or the HM methodology. Mean imprecision in predicting the end of the operative day was higher with the HM approach (30.8 vs 7.2 minutes, P = 0.024). RM was associated with higher throughput (379 vs 356 cases scheduled over the course of the study, P = 0.04). The composite rate of adverse 30-day events was similar (2.2% vs 3.2%, P = 0.44). The mean depersonalization score was higher (3.2 vs 2.0, P = 0.044), and mean personal accomplishment score was lower during HM weeks (37.5 vs 40.5, P = 0.028).

CONCLUSIONS

Compared to the HM scheduling approach, the proposed data-driven RM scheduling methodology improves multiple measures of OS efficiency and OS personnel satisfaction without adversely affecting clinical outcomes.

Development and Validation of an Algorithm to Classify as Equivalent the Procedures in ICD-10-PCS That Differ Only by Laterality

BACKGROUND

The switch from International Classification of Diseases, Ninth Revision, Clinical Modification to International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS) for coding of inpatient procedures in the United States increased the number of procedural codes more than 19-fold, in large part due to the addition of laterality. We examined ICD-10-PCS codes for pairs of mirror-image procedures that are surgically equivalent.

METHODS

We developed an algorithm in structured query language (SQL) to identify ICD-10-PCS codes differing only by laterality. We quantified the impact of laterality on the number of commonly performed major therapeutic procedures (ie, surgical diversity) using 2 quarters of discharge abstracts from Texas.

RESULTS

Of the 75,789 ICD-10-PCS codes from federal fiscal year 2017, 16,839 (22.3%) pairs differed only by laterality (with each pair contributing 2 codes). With the combining of equivalent codes, diversity in the state of Texas decreased from 78.2 to 74.1 operative procedures (95% confidence interval, 5.1 to -3.1; P < .001).

CONCLUSIONS

Our algorithm identifies ICD-10-PCS codes that differ only by laterality. However, laterality had a small effect on surgical diversity among major therapeutic procedures. Our SQL code and the lookup table will be useful for all US inpatient analyses of ICD-10-PCS surgical data, because combining procedures differing only by laterality will often be desired.

Improving intraoperative handoffs for ambulatory anesthesia: challenges and solutions for the anesthesiologist

Permanent transitions of care from one anesthesia provider to another are associated with adverse events and mortality. There are currently no available data on how to mitigate these poor patient outcomes other than to reduce the occurrence of such handoffs. We used data from an ambulatory surgery center to demonstrate the steps that can be taken to achieve this goal. First, perform statistical forecasting using many months of historical data to create optimal, as opposed to arbitrary shift durations. Second, consider assigning the anesthesia providers designated to work late, if necessary, to the ORs estimated to finish the earliest, rather than latest. We performed multiple analyses showing the quantitative advantage of this strategy for the ambulatory surgery center with multiple brief cases. Third, sequence the cases in the 1 or 2 ORs with the latest scheduled end times so that the briefest cases are finished last. If a supervising anesthesiologist needs to be relieved early for administrative duties (eg, head of the group to meet with administrators or surgeons), assign the anesthesiologist to an OR that finishes with several brief cases. The rationale for these recommendations is that such strategies provide multiple opportunities for a different anesthesia provider to assume responsibility for the patients between cases, thus avoiding a handoff altogether.

RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations

Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, however, challenging to preoperatively predict surgery duration since it varies significantly depending on the patient condition, surgeon skills, and intraoperative situation. In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos. The previous state-of-the-art approaches for RSD prediction are dependent on manual annotation, whose generation requires expensive expert knowledge and is time-consuming, especially considering the numerous types of surgeries performed in a hospital and the large number of laparoscopic videos available. A crucial feature of RSDNet is that it does not depend on any manual annotation during training, making it easily scalable to many kinds of surgeries. The generalizability of our approach is demonstrated by testing the pipeline on two large datasets containing different types of surgeries: 120 cholecystectomy and 170 gastric bypass videos. The experimental results also show that the proposed network significantly outperforms a traditional method of estimating RSD without utilizing manual annotation. Further, this paper provides a deeper insight into the deep learning network through visualization and interpretation of the features that are automatically learned.

Independent Predictors of Increased Operative Time and Hospital Length of Stay Are Consistent Across Different Surgical Approaches to Pancreatoduodenectomy

BACKGROUND

While minimally invasive approaches are increasingly being utilized for pancreatoduodenectomy (PD), factors associated with prolonged operative time (OpTime) and hospital length of stay (LOS) remain poorly defined, and it is unclear whether these factors are consistent across surgical approaches.

METHODS

The ACS-NSQIP targeted pancreatectomy database from 2014 to 2016 was used to identify all patients who underwent open (OPD), laparoscopic (LPD), or robotic (RPD) pancreatoduodenectomy. Multivariable linear regression analyses were used to evaluate predictors of OpTime and LOS, as well as quantify the changes observed relative to each surgical approach.

RESULTS

Among 10,970 patients, PD procedure types varied: 9963 (92%) open, 418 (4%) laparoscopic, and 409 (4%) robotic. LOS was longer for the open and laparoscopic approaches (11 vs. 11 vs. 10 days, P = 0.0068), whereas OpTime was shortest for OPD (366 vs. 426 vs. 435 min, P < 0.0001). Independent predictors of a prolonged OpTime were ASA class ≥ 3 (P = 0.0002), preoperative XRT (P < 0.0001), pancreatic duct < 3 mm (P = 0.0001), T stage ≥ 3 (P = 0.0108), and vascular resection (P < 0.0001) for OPD; T stage ≥ 3 (P = 0.0510) and vascular resection (P = 0.0062) for LPD; and malignancy (P = 0.0460) and conversion to laparotomy (P = 0.0001) for RPD. Independent predictors of increased LOS were age ≥ 65 years (P = 0.0002), ASA class ≥ 3 (P = 0.0012), hypoalbuminemia (P < 0.0001), and preoperative blood transfusion (P < 0.0001) for OPD as well as an OpTime > 370 min (all p < 0.05) and specific postoperative complications (all p < 0.05) for all surgical approaches.

CONCLUSIONS

Perioperative risk factors for prolonged OpTime and hospital LOS are relatively consistent across open, laparoscopic, and robotic approaches to PD. Particular attention to these factors may help identify opportunities to improve perioperative quality, enhance patient satisfaction, and ensure an efficient allocation of hospital resources.

Keys to optimizing operating room efficiency

Healthcare is in constant transformation. Health systems should focus on improving efficiency to meet a growing demand for high-quality, low-cost health care. The operating room is one of the biggest sources of revenue and one of the largest areas of expense. Therefore, operating room management is a critical key to success. The aim of this article is to analyze the current principles of organization, optimization and clinical management of the operating room and its impact on the quality and safety of care.

'It is Time to Prepare the Next patient' Real-Time Prediction of Procedure Duration in Laparoscopic Cholecystectomies

Operating Room (OR) scheduling is crucial to allow efficient use of ORs. Currently, the predicted durations of surgical procedures are unreliable and the OR schedulers have to follow the progress of the procedures in order to update the daily planning accordingly. The OR schedulers often acquire the needed information through verbal communication with the OR staff, which causes undesired interruptions of the surgical process. The aim of this study was to develop a system that predicts in real-time the remaining procedure duration and to test this prediction system for reliability and usability in an OR. The prediction system was based on the activation pattern of one single piece of equipment, the electrosurgical device. The prediction system was tested during 21 laparoscopic cholecystectomies, in which the activation of the electrosurgical device was recorded and processed in real-time using pattern recognition methods. The remaining surgical procedure duration was estimated and the optimal timing to prepare the next patient for surgery was communicated to the OR staff. The mean absolute error was smaller for the prediction system (14 min) than for the OR staff (19 min). The OR staff doubted whether the prediction system could take all relevant factors into account but were positive about its potential to shorten waiting times for patients. The prediction system is a promising tool to automatically and objectively predict the remaining procedure duration, and thereby achieve optimal OR scheduling and streamline the patient flow from the nursing department to the OR.

Online time and resource management based on surgical workflow time series analysis

PURPOSE

Hospitals' effectiveness and efficiency can be enhanced by automating the resource and time management of the most cost-intensive unit in the hospital: the operating room (OR). The key elements required for the ideal organization of hospital staff and technical resources (such as instruments in the OR) are an exact online forecast of both the surgeon's resource usage and the remaining intervention time.

METHODS

This paper presents a novel online approach relying on time series analysis and the application of a linear time-variant system. We calculated the power spectral density and the spectrogram of surgical perspectives (e.g., used instrument) of interest to compare several surgical workflows.

RESULTS

Considering only the use of the surgeon's right hand during an intervention, we were able to predict the remaining intervention time online with an error of 21 min 45 s ±9 min 59 s for lumbar discectomy. Furthermore, the performance of forecasting of technical resource usage in the next 20 min was calculated for a combination of spectral analysis and the application of a linear time-variant system (sensitivity: 74 %; specificity: 75 %) focusing on just the use of surgeon's instrument in question.

CONCLUSION

The outstanding benefit of these methods is that the automated recording of surgical workflows has minimal impact during interventions since the whole set of surgical perspectives need not be recorded. The resulting predictions can help various stakeholders such as OR staff and hospital technicians. Moreover, reducing resource conflicts could well improve patient care.

DIEP Flap for Breast Reconstruction Using Epidural Anesthesia with the Patient Awake

Supplemental Digital Content is available in the text.

Background:

Many articles have been published about breast reconstruction using the deep inferior epigastric perforator (DIEP) flap; however, few articles have been published in plastic/reconstructive surgery journals describing the difference between anesthetic techniques and recovery in microsurgical patients.

Methods:

We analyzed 16 patients who underwent DIEP flap for breast reconstruction. Patients were divided into 2 groups: group 1: general anesthesia (n = 9); group 2: epidural block with the patient awake (n = 7). In group 2, the peridural block was done at 2 levels: thoracic (T2–T3) and lumbar (L2–L3).

Results:

The success rate was 100% with no partial or total loss of the flap. There was no difference between groups in regard to postoperative pain in the first 5 days (Visual Analog Scale). Analgesia used in group 1 was buprenorphine and ketorolac, and in group 2, only ketorolac without opioid derivatives. Immediate postoperative recovery was better in the peridural group than in the group administered general anesthesia (P = 0.0001).

Conclusions:

DIEP flap with peridural block and the patient awake during surgery is a feasible technique with better recovery in the immediate postoperative period, achieving good analgesia level with minimal intravenous medication.

Influencing Anesthesia Provider Behavior Using Anesthesia Information Management System Data for Near Real-Time Alerts and Post Hoc Reports

In this review article, we address issues related to using data from anesthesia information management systems (AIMS) to deliver near real-time alerts via AIMS workstation popups and/or alphanumeric pagers and post hoc reports via e-mail. We focus on reports and alerts for influencing the behavior of anesthesia providers (i.e., anesthesiologists, anesthesia residents, and nurse anesthetists). Multiple studies have shown that anesthesia clinical decision support (CDS) improves adherence to protocols and increases financial performance through facilitation of billing, regulatory, and compliance documentation; however, improved clinical outcomes have not been demonstrated. We inform developers and users of feedback systems about the multitude of concerns to consider during development and implementation of CDS to increase its effectiveness and to mitigate its potentially disruptive aspects. We discuss the timing and modalities used to deliver messages, implications of outlier-only versus individualized feedback, the need to consider possible unintended consequences of such feedback, regulations, sustainability, and portability among systems. We discuss statistical issues related to the appropriate evaluation of CDS efficacy. We provide a systematic review of the published literature (indexed in PubMed) of anesthesia CDS and offer 2 case studies of CDS interventions using AIMS data from our own institution illustrating the salient points. Because of the considerable expense and complexity of maintaining near real-time CDS systems, as compared with providing individual reports via e-mail after the fact, we suggest that if the same goal can be accomplished via delayed reporting versus immediate feedback, the former approach is preferable. Nevertheless, some processes require near real-time alerts to produce the desired improvement. Post hoc e-mail reporting from enterprise-wide electronic health record systems is straightforward and can be accomplished using system-independent pathways (e.g., via built-in e-mail support provided by the relational database management system). However, for some of these enterprise-wide systems, near real-time data access, necessary for CDS that generates concurrent alerts, has been challenging to implement.

A Narrative Review of Meaningful Use and Anesthesia Information Management Systems

The US federal government has enacted legislation for a federal incentive program for health care providers and hospitals to implement electronic health records. The primary goal of the Meaningful Use (MU) program is to drive adoption of electronic health records nationwide and set the stage to monitor and guide efforts to improve population health and outcomes. The MU program provides incentives for the adoption and use of electronic health record technology and, in some cases, penalties for hospitals or providers not using the technology. The MU program is administrated by the Department of Health and Human Services and is divided into 3 stages that include specific reporting and compliance metrics. The rationale is that increased use of electronic health records will improve the process of delivering care at the individual level by improving the communication and allow for tracking population health and quality improvement metrics at a national level in the long run. The goal of this narrative review is to describe the MU program as it applies to anesthesiologists in the United States. This narrative review will discuss how anesthesiologists can meet the eligible provider reporting criteria of MU by applying anesthesia information management systems (AIMS) in various contexts in the United States. Subsequently, AIMS will be described in the context of MU criteria. This narrative literature review also will evaluate the evidence supporting the electronic health record technology in the operating room, including AIMS, independent of certification requirements for the electronic health record technology under MU in the United States.

The nature and sources of variability in pediatric surgical case duration

BACKGROUND

Case time variability confounds surgical scheduling and decreases access to limited operating room resources. Variability arises from many sources and can differ among institutions serving different populations. A rich literature has developed around case time variability in adults, but little in pediatrics.

OBJECTIVE

We studied the effect of commonly used patient and procedure factors in driving case time variability in a large, free-standing, academic pediatric hospital.

METHODS

We analyzed over 40 000 scheduled surgeries performed over 3 years. Using bootstrapping, we computed descriptive statistics for 249 procedures and reported variability statistics. We then used conditional inference regression trees to identify procedure and patient factors associated with pediatric case time and evaluated their predictive power by comparing prediction errors against current practice. Patient and procedure factors included patient's age and weight, medical status, surgeon identity, and ICU request indicator.

RESULTS

Overall variability in pediatric case time, as reflected by standard deviation, was 30% (25.8, 34.7) of the median case time. Relative variability (coefficient of variation), was largest among short cases. For a few procedure types, the regression tree can improve prediction accuracy if extreme behavior cases are preemptively identified. However, for most procedure types, no useful predictive factors were identified and, most notably, surgeon identity was unimportant.

CONCLUSIONS

Pediatric case time variability, unlike adult cases, is poorly explained by surgeon effect or other characteristics that are commonly abstracted from electronic records. This largely relates to the 'long-tailed' distribution of pediatric cases and unpredictably long cases. Surgeon-specific scheduling is therefore unnecessary and similar cases may be pooled across surgeons. Future scheduling efforts in pediatrics should focus on prospective identification of patient and procedural specifics that are associated with and predictive of long cases. Until such predictors are identified, daily management of pediatric operating rooms will require compensatory overtime, capacity buffers, schedule flexibility, and cost.

Amalgamation of management information system into anaesthesiology practice: A boon for the modern anaesthesiologists

Over the years, traditional anaesthesia record keeping system has been the backbone of anaesthesiology ever since its introduction in the 1890s by Dr. Harvey Cushing and Dr. Ernest A. Codman. Besides providing the important information regarding patients’ vital physiologic parameters, paper records had been a reliable source for various clinical research activities. The introduction of electronic monitoring gadgets and electronic record keeping systems has revolutionised the anaesthesiology practice to a large extent. Recently, the introduction of anaesthesia information management system (AIMS), which incorporates all the features of monitoring gadgets, such as electronic storage of large accurate data, quality assurance in anaesthesia, enhancing patient safety, ensuring legal protection, improved billing services and effecting an organisational change, is almost a revolution in modern-day anaesthesiology practice. The clinical research activities that are responsible for taking anaesthesiology discipline to higher peaks have also been boosted by the amalgamation of AIMS, enabling multicenter studies and sharing of clinical data. Barring few concerns in its installation, cost factors and functional aspects, the future of AIMS seems to be bright and will definitely prove to be a boon for modern-day anaesthesiology practice.

Technology diffusion of anesthesia information management systems into academic anesthesia departments in the United States

BACKGROUND

Anesthesia information management systems (AIMS) are electronic health records that automatically import vital signs from patient monitors and allow for computer-assisted creation of the anesthesia record. When most recently surveyed in 2007, it was estimated that at least 16% of U.S. academic hospitals (i.e., with an anesthesia residency program) had installed an AIMS. At least an additional 28% reported that they were in the process of implementing, or searching for an AIMS. In this study, we updated the adoption figures as of May 2013 and examined the historical trend of AIMS deployment in U.S. anesthesia residency programs from the perspective of the theory of diffusion of technologic innovations.

METHODS

Questionnaires were sent by e-mail to program directors or their identified contact individuals at the 130 U.S. anesthesiology residency programs accredited as of June 30, 2012 by the Accreditation Council for Graduate Medical Education. The questionnaires asked whether the department had an AIMS, the year of installation, and, if not present, whether there were plans to install an AIMS within the next 12 months. Follow-up e-mails and phone calls were made until responses were obtained from all programs. Results were collected between February and May 2013. Implementation percentages were determined using the number of accredited anesthesia residency programs at the start of each academic year between 1987 and 2013 and were fit to a logistic regression curve using data through 2012.

RESULTS

Responses were received from all 130 programs. Eighty-seven (67%) reported that they currently are using an AIMS. Ten programs without a current AIMS responded that they would be installing an AIMS within 12 months of the survey. The rate of AIMS adoption by year was well fit by a logistic regression curve (P = 0.90).

CONCLUSIONS

By the end of 2014, approximately 75% of U.S. academic anesthesiology departments will be using an AIMS, with 84% adoption expected between 2018 and 2020. Historical adoption of AIMS has followed Roger's 1962 formulation of the theory of diffusion of innovation.