Estimating Surgical Case Durations and Making Comparisons Among Facilities

A Robust ORMS Framework for Taiwanese Healthcare: Taguchi's Dynamic Method in Action

The study focused on the design of an ORMS in a medical center in central Taiwan, which also functions as a teaching hospital. Background/Objectives: The research objectives were to design an ORMS simulation system based on the status quo of the operating room planning and scheduling in the medical center, obtain the optimal parameter setting in the ORMS, and find improvement strategies according to the sensitivity analysis based on the optimal parameter setting for total performance. Methods: Taguchi's dynamic method was adopted to design the ORMS under human and material resource constraints. The scope of the study was internal medicine patients of the ORMS. A neural network was used to construct a relationship between parameters and performances. A genetic algorithm was used to obtain the optimal parameter setting for optimal performance. Results: This study successfully established a robust operating room management system (ORMS) to help hospital manager to plan and schedule operating rooms and take the ORMS into account to meet patient needs. Decision-makers can use the insights from the sensitivity analysis to refine their strategies effectively. The sensitivity analysis showed that the impact power (the percentage change in d) of the "number of circulating nurses (-0.15 to -1.25; -0.25 to -1.85)" factor was less than (<) that of the "number of holding nurses (-0.85 to -2.04; -0.91 to -2.07)" factor < that of the "number of preoperative beds (-2.57 to -4.53; -2.23 to -4.10)" factor < that of the "number of anesthetists (-3.13 to -7.50)" factor. Conclusions: In the optimal parameter setting obtained, the number of holding nurses was 18, the number of circulating nurses was 20, the number of anesthetists was 15, and the number of preoperative beds was 12. The optimal performance was 0.91.

Incidence of prolonged time to tracheal extubation and its associated factors among adult patients undergoing elective surgery at Jimma Medical Center, Jimma, Oromia, Ethiopia, 2024

PURPOSE

Extubation refers to removing the breathing tube from the patient's airway after surgery under general anesthesia with tracheal intubation. Extubation procedures typically take less than 15 min, and if they take more, they are prolonged. Whether or not to extubate a patient depends on several factors, including the patient's preoperative status, the type of surgery, anesthetic methods, and expected recovery after the procedure. Thus, the study's objective was to determine the incidence of prolonged extubation and its associated factors among adult patients undergoing surgery at Jimma Medical Center.

METHODS

A prospective observational study through a consecutive sampling technique was conducted. Ethical clearance and approval were obtained from the institutional review board of Jimma University. Data on the extubation time and possible associated factors for a prolonged extubation time were collected using a data collection checklist. After being entered into EpiData 4.6 and exported into SPSS 25, descriptive analyses and logistic regression were carried out. In multivariate variables, p ≤ 0.05 was declared as statistical significance.

RESULT

Three-hundred eight adult patients were enrolled in the current study. Of these, the incidence of prolonged extubation was 24.7% (95% CI [20.0-29.9]). The identified associated factors were age ≥ 55 years (AOR = 5.7, 95% CI [2.62, 12.69], p ≤ 001); ASAPS > II (AOR = 4.27, 95% CI [1.59, 11.45], p = 004); BMI ≥ 30 kg/m2 (AOR = 6.6, 95% CI [2.37, 18.36], p ≤ 001); the use of benzodiazepine (AOR = 3.43, 95% CI [1.42, 8.25], p = 0.006); using of isoflurane (AOR = 0.35, 95% CI [0.15, 0.78], p = 0.011); prone position (AOR = 4.68, 95% CI [1.56, 14.07], p = 0.006); extubation in afternoon (AOR = 2.69, 95% CI [1.26, 5.74]; p = 0.011); and duration of surgery ≥ 210 min (AOR = 5.2, 95% CI [2.32, 11.72], p ≤ 0.001).

CONCLUSIONS

The study found that prolonged time to extubation occurred in one-fourth of the patients. The independent factors statistically associated with prolonged extubation were older ages, higher ASA class, obesity (≥ 30 kg/m2), the use of benzodiazepine, halothane for maintenance, prone position, extubation in the afternoon, and longer procedures (≥ 210 min).

Estimating sample means and standard deviations from the log-normal distribution using medians and quartiles: evaluating reporting requirements for primary and secondary endpoints of meta-analyses in anesthesiology

PURPOSE

Clinical trials often report medians and quartiles due to skewed data distributions. We sought to evaluate the methods currently used in meta-analyses in anesthesiology to estimate means and standard deviations (SDs) from medians and quartiles.

METHODS

We simulated sample sizes (n = 15, 27, 51) and coefficients of variation (CV = 0.15, 0.3, 0.5), representative scenarios in anesthesiology studies, generating data that have a log-normal distribution with zero log-scale means. We calculated generalized confidence intervals for the ratios of means and ratios of SDs using means and SDs estimated from three quartiles in time scale, using Luo et al.'s and Wan et al.'s methods, McGrath et al.'s quantile estimation and Box-Cox transformation, and Cai et al.'s maximum likelihood estimation method.

RESULTS

The method by Luo et al. and Wan et al. produced 95% confidence intervals for the ratio of means with coverage ranging from 92.4% to 93.6%, and for SDs from 79.2 to 89.6. McGrath et al.'s quantile estimation method yielded coverage for mean ratios between 88.5% and 91.5% and SDs between 78.0 and 82.7. McGrath et al.'s Box-Cox transformation method showed coverage for mean ratios from 86.6% to 94.4% and SDs from 67.1 to 83.1. The maximum likelihood estimation method by Cai et al. for nonnormal distributions showed coverage for mean ratios from 78.9% to 86.4% and SDs from 67.6 to 78.0.

CONCLUSIONS

All evaluated methods of estimating means and standard deviations from quartiles of log-normal distributed data result in confidence interval coverages below the expected 95%. Because these methods are widely used in meta-analyses of anesthesiology data, P values reported as < 0.05 cannot be trusted. Anesthesiology journals and investigators should revise reporting requirements for continuous skewed variables. We advise reporting the quartiles, mean, and SD, or the quartiles and including the raw data for the relevant variables as supplemental content. This holistic approach could improve the reliability of statistical inferences in meta-analyses of anesthesiology research, particularly when skewed distributions are involved.

Predictive Validity of Anesthesiologists' Quality of Clinical Supervision and Nurse Anesthetists' Work Habits Assessed by Their Associations With Operating Room Times

BACKGROUND

At all Joint Commission-accredited hospitals, the anesthesia department chair must report quantitative assessments of anesthesiologists' and nurse anesthetists' (CRNAs') clinical performance at least annually. Most metrics lack evidence of usefulness, cost-effectiveness, reliability, or validity. Earlier studies showed that anesthesiologists' clinical supervision quality and CRNAs' work habits have content, convergent, discriminant, and construct validity. We evaluated predictive validity by testing for (expected) small but statistically significant associations between higher quality of supervision (work habits) and reduced probabilities of cases taking longer than estimated.

METHODS

Supervision quality of each anesthesiologist was evaluated daily by assigned trainees using the 9-item de Oliveira Filho scale. The work habits of each CRNA were evaluated daily by assigned anesthesiologists using a 6-item scale. Both are scored binary, 1 if all items are rated the maximum, 0 otherwise. From 40,718 supervision evaluations and 53,722 work habit evaluations over 8 fiscal years, 16 mixed-effects logistic regression models were estimated, with raters as fixed effects and ratees (anesthesiologists or CRNAs) as random effects. Empirical Bayes means in the logit scale were obtained for 561 anesthesiologist-years and 605 CRNA-years. The binary-dependent variable was whether the case took longer than estimated from the historical mean time for combinations of scheduled procedures and surgeons. From 264,060 cases, 8 mixed-effects logistic regression models were fitted, 1 per fiscal year, using ratees as random effects. Predictive validity was tested by pairing the 8 one-year analyses of clinical supervision, and the 8 one-year analyses of work habits, by ratee, with the 8 one-year analyses of whether OR time was longer than estimated. Bivariate errors in variable linear least squares linear regressions minimized total variances.

RESULTS

Among anesthesiologists, 8.2% (46/561) had below-average supervision quality, and 17.7% (99/561), above-average. Among CRNAs, 6.3% (38/605) had below-average work habits, and 10.9% (66/605) above-average. Increases in the logits of the quality of clinical supervision were associated with decreases in the logits of the probabilities of cases taking longer than estimated, unitless slope = -0.0361 (SE, 0.0053), P < .00001. Increases in the logits of CRNAs' work habits were associated with decreases in the logits of probabilities of cases taking longer than estimated, slope = -0.0238 (SE, 0.0054), P < .00001.

CONCLUSIONS

Predictive validity was confirmed, providing further evidence for using supervision and work habits scales for ongoing professional practice evaluations. Specifically, OR times were briefer when anesthesiologists supervised residents more closely, and when CRNAs had better work habits.

Taylor Series Approximation for Accurate Generalized Confidence Intervals of Ratios of Log-Normal Standard Deviations for Meta-Analysis Using Means and Standard Deviations in Time Scale

With contemporary anesthetic drugs, the efficacy of general anesthesia is assured. Health-economic and clinical objectives are related to reductions in the variability in dosing, variability in recovery, etc. Consequently, meta-analyses for anesthesiology research would benefit from quantification of ratios of standard deviations of log-normally distributed variables (e.g., surgical duration). Generalized confidence intervals can be used, once sample means and standard deviations in the raw, time, scale, for each study and group have been used to estimate the mean and standard deviation of the logarithms of the times (i.e., "log-scale"). We examine the matching of the first two moments versus also using higher-order terms, following Higgins et al. 2008 and Friedrich et al. 2012. Monte Carlo simulations revealed that using the first two moments 95% confidence intervals had coverage 92%-95%, with small bias. Use of higher-order moments worsened confidence interval coverage for the log ratios, especially for coefficients of variation in the time scale of 50% and for largern = 50 $$ \left(n=50\right) $$ sample sizes per group, resulting in 88% coverage. We recommend that for calculating confidence intervals for ratios of standard deviations based on generalized pivotal quantities and log-normal distributions, when relying on transformation of sample statistics from time to log scale, use the first two moments, not the higher order terms.

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.

Generalized Confidence Intervals for Ratios of Standard Deviations Based on Log-Normal Distribution when Times Follow Weibull Distributions

Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, n ≈ 25 and coefficients of variation ≈ 0.30 . The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to 0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences in the confidence limits were small from a clinical or managerial perspective, with a maximum absolute difference in ratios of 0.016. Thus, P < 0.05 is reliable, but investigators should plan for Type II errors at greater than nominal rates.

Lateness of Acetaminophen and Non-steroidal Anti-inflammatory Drug Administrations in a Retrospective Cohort of Medication Administration Records Among Patients After Cesarean Delivery

INTRODUCTION

In an earlier study of patients after cesarean delivery, the concurrent versus alternating administration of acetaminophen and non-steroidal anti-inflammatory drugs was associated with a substantial reduction in total postoperative opioid use. This likely pharmacodynamic effect may differ if the times when nurses administer acetaminophen and non-steroidal anti-inflammatory drugs often differ substantively from when they are due. We examined the "lateness" of analgesic dose administration times, the positive difference if administered late, and the negative value if early.

METHODS

The retrospective cohort study used all 67,900 medication administration records for scheduled (i.e., not "as needed") acetaminophen, ibuprofen, and ketorolac among all 3,163 cesarean delivery cases at the University of Iowa between January 2021 and December 2023. Barcode scanning at the patient's bedside was used right before each medication administration.

RESULTS

There were 95% of doses administered over a 4.8-hour window, from 108 minutes early (97.5% one-sided upper confidence limit 105 minutes early) to 181 minutes late (97.5% one-sided lower limit 179 minutes late). Fewer than half of doses (46%, P <0.0001) were administered ±30 minutes of the due time. The intraclass correlation coefficient was approximately 0.11, showing that there were small systematic differences among patients. There likewise were small to no systematic differences in lateness based on concurrent administrations of acetaminophen and ibuprofen or ketorolac, time of the day that medications were due, weekday, year, or number of medications to be administered among all such patients within 15 minutes.

DISCUSSION

Other hospitals should check the lateness of medication administration when that would change their ability to perform or apply the results of analgesic clinical trials (e.g., simultaneous versus alternating administration).

Survey of Lactating Anesthesiologists Using Wearable Breast Milk Pumps While Working in Operating Rooms and Other Clinical Settings

We performed a prospective Internet survey study of anesthesiologists lactating in 2022 or 2023. Approximately half (48%, 75 of 156) lacked convenient dedicated lactation space and approximately half (55%, 86 of 155) used a wearable breast pump. The vast majority using a wearable pump did so in clinical settings, including operating rooms (88%, 76 of 86). When using during cases, approximately half reported that milk production was sufficient to substitute for lactation pumping sessions (52%, 39 of 75). Based on probability distributions of surgical times, future research can evaluate the usefulness of wearable pumps based on the objective of reducing anesthesiologists' durations of lactation sessions to <15 minutes.

Surgical procedure prediction using medical ontological information

BACKGROUND AND OBJECTIVE

Predicting the duration of surgical procedures is an important step in scheduling operating rooms. Many factors have been shown to influence the duration of a procedure, in this research we aim to use medical ontological information to improve the predictions.

METHODS

This paper presents two methods for incorporating the medical information about a surgical procedure into the prediction of the duration of the procedure. The first method uses the Systematised Nomenclature of Medicine Clinical Terms to relate different procedures to each other. The second uses simple text fragments. The relationships between types of procedures are included in a regression model for the procedure duration. These methods are applied to data from New Zealand healthcare facilities and the accuracy of the estimations of the durations is compared. In addition a simulation of scheduling the procedures in an operating room is performed.

RESULTS

It is shown that both of the methods provide an improvement in the prediction of procedure durations. When compared to a traditional categorical encoding, the ontological information provides an improvement in the continuous ranked probability scores of the prediction of procedure durations from 18.4 min to 17.1 min, and from 25.3 to 21.3 min for types of procedures that are not performed very often.

CONCLUSIONS

Different methods for encoding medical ontological information in surgery procedure duration predictions are presented, and show an improvement over traditional models. The improvement in duration prediction is shown to improve the efficiency of scheduling in a simple simulation.

Quantile regression forests for individualized surgery scheduling

Determining the optimal surgical case start times is a challenging stochastic optimization problem that shares a key feature with many other healthcare operations problems. Namely, successful problem solutions require using a vast array of available historical data to create distributions that accurately capture a case duration's uncertainty for integration into an optimization model. Distribution fitting is the conventional approach to generate these distributions, but it can only employ a limited, aggregate portion of the detailed patient features available in Electronic Medical Records systems today. If all the available information can be taken advantage of, then distributions individualized to every case can be constructed whose precision would support higher quality solutions in the presence of uncertainty. Our individualized stochastic optimization framework shows how the quantile regression forest (QRF) method predicts individualized distributions that are integrable into sample-average approximation, robust optimization, and distributionally robust optimization models for problems like surgery scheduling. In this paper, we present some related theoretical performance guarantees for each formulation. Numerically, we also study our approach's benefits relative to three other traditional models using data from Memorial Sloan Kettering Cancer Center in New York, NY, USA.

Feasibility of Anesthesiologists Giving Nurse Anesthetists 30-Minute Lunch Breaks and 15-Minute Morning Breaks at a University’s Facilities

Background

Managers of an anesthesia department sought an estimation of how often each anesthesiologist can give lunch breaks and morning breaks to nurse anesthetists to plan staff scheduling. When an anesthesiologist supervising the nurse anesthetists can give a break, it would be preferred because fewer extra nurse anesthetists would be scheduled to facilitate breaks.

Methodology

Our methodological development used retrospective cohort data from the three surgical suites of a single anesthesia department. Surgical times were estimated using three years of data from October 2016 through September 2019, with 95,146 cases. Comparison was made with the next year from October 2019 through September 2020, with 30,987 cases. The 5% lower prediction bounds for surgical time were estimated based on two-parameter, log-normal distributions. The times when two and three sequential rooms had overlapping lower prediction limits were calculated. Sequential rooms were used because that was how anesthesiologists’ assignments were made at the studied department, when feasible given constraints. Percentages of cases were reported with 15 minutes available starting sometime between 9:00 and 10:30 and 30 minutes starting sometime between 11:15 and 12:45, times characteristic for the studied department. At the studied university’s facilities, the nurse anesthetists were independent practitioners (e.g., an anesthesiologist supervising two nurse anesthetists each with a long case could give a break to one of the two rooms).

Results

The percentage of days for which an anesthesiologist could give a lunch break (11:15-12:45) was close to the percentage of cases when an anesthesiologist could give the same-length break anytime throughout the workday. In other words, the length of the break was important, not the time of the day of the break. The absolute percentages also depended on how many rooms the anesthesiologist supervised, the duration of cases, and facility. For example, among anesthesiologists at the adult surgical suite supervising three nurse anesthetists, a lunch break could be given by the anesthesiologist on at most one-third of the days without affecting workflow.

Conclusions

Our results show that the feasibility of an anesthesiologist clinically supervising one, two, or three rooms to give lunch breaks to the nurse anesthetists in the rooms depends principally on how many rooms are supervised, the duration of the break, and the facility’s percentage of cases with surgical times longer than that duration. The specific numerical results will differ among departments. Our methodology would be useful to other departments where anesthesiologists are clinically supervising independent practitioners, sometimes during cases long enough for a break, and there is anesthesiologist backup help. Such departments can use our methodology to plan their staff scheduling for additional nurse anesthetists to give the remaining breaks.

Case duration prediction and estimating time remaining in ongoing cases

In this issue of the British Journal of Anaesthesia, Jiao and colleagues applied a neural network model for surgical case durations to predict the operating room times remaining for ongoing anaesthetics. We review estimation of case durations before each case starts, showing why their scientific focus is useful. We also describe managerial epidemiology studies of historical data by the scheduled procedure or distinct combinations of scheduled procedures included in each surgical case. Most cases have few or no historical data for the scheduled procedures. Generalizability of observational results such as theirs, and automatic computer assisted clinical and managerial decision-making, are both facilitated by using structured vocabularies when analysing surgical procedures.

Average and longest expected treatment times for ultraviolet light disinfection of rooms

BACKGROUND

Planning Ultraviolet-C (UV-C) disinfection of operating rooms (ORs) is equivalent to scheduling brief OR cases. The study purpose was evaluation of methods for predicting surgical case duration applied to treatment times for ORs and hospital rooms.

METHODS

Data used were disinfection times with a 3-tower UV-C disinfection system in N=700 rooms each with ≥100 completed treatments.

RESULTS

The coefficient of variation of mean treatment duration among rooms was 19.6% (99% confidence interval [CI] 18.2%-21.0%); pooled mean 18.3 minutes among the 133,927 treatments. The 50^th percentile of coefficients of variation among treatments of the same room was 27.3% (CI 26.3%-28.4%), comparable to variabilities in durations of surgical procedures. The ratios of the 90^th percentile to mean differed among rooms. Log-normal distributions had poor fits for 33% of rooms. Combining results, we calculated 90% upper prediction limits for treatment times by room using a distribution-free method (e.g., third longest of preceding 29 durations). This approach was suitable because, once UV-C disinfection started, the median difference between the duration estimated by the system and actual time was 1 second.

CONCLUSIONS

Times for disinfection should be listed as treatment of a specific room (e.g., "UV-C main OR16"), not generically (e.g., "UV-C"). For estimating disinfection time after single surgical cases, use distribution-free upper prediction limits, because of considerable proportional variabilities in duration.

Statistical Design of Overnight Trials for the Evaluation of the Number of Operating Rooms That Can Be Disinfected by an Ultraviolet Light Disinfection Robotic System

Background and objective

The number of ultraviolet light disinfection robot systems that are needed for a facility’s surgical suite(s) and/or procedure suite(s) depends in part on how many rooms need to be disinfected overnight by each robot and how long this will take. The answer needs to be determined separately for each surgical and procedure suite because those variables vary both among facilities and among operating rooms or procedure rooms within facilities. In this study, we consider statistical designs to assess how many rooms a facility can reliably (≥90% chance) disinfect overnight using an ultraviolet light disinfection robot system.

Methods

We used 133,927 observed disinfection times from 700 rooms as a population from which repeated samples were drawn with replacement in Monte-Carlo simulations. We used eight-hour and 10-hour shift lengths being multiples of 40 hours for full-time hourly employees.

Results

One possible strategy that we examined was to estimate total disinfection times by estimating the mean for each room and then summing up the means. However, that did not correctly answer the question of how many rooms can reliably be available for the next day’s first case. Summing up a percentile (e.g., 90%) instead also was inaccurate, because the proper percentile depended on the number of rooms.

A suitable strategy is a brief trial (e.g., nine nights or 19 nights) with the endpoint being the daily number of rooms disinfected. Empirically, the smallest count of rooms disinfected among nine nights or the second smallest count among 19 nights are 10^th percentiles (i.e., ≈90% probability that at least that number of rooms can be disinfected in the future). The drawback is that while this approach gives the probability of a night with fewer rooms disinfected, it does not give information as to how many fewer rooms may either skip ultraviolet decontamination or start late the next workday because disinfection was not completed. Our simulations showed that there is a substantial probability (≥95%) of at most two rooms fewer or one room greater than the 10^th percentile with a nine-night trial and one room fewer or greater with a 19-night trial.

Conclusions

Because probability distributions of disinfection times are heterogeneous both among rooms and among treatments for the same room, each facility should plan to perform its own trial of nine nights or 19 nights. This will provide results that are within two rooms or one room of the correct answer in the long term. This information can be used when planning purchasing decisions, leasing, and technician staffing decisions.

Briefest Time to Perform a Series of Preoperative Nerve Blocks in Multiple Patients: A Simulation Study

Introduction

To mitigate first-case delays in operating rooms, sufficient additional time must be allotted when anesthesiologists perform preoperative nerve blocks in multiple patients who are scheduled as the initial cases of the day. We used spinal anesthetics performed in dedicated block rooms located just outside the operating room suite to estimate the briefest times needed to complete a series of spinal, epidural, peripheral, or other regional nerve blocks. We followed this approach because even though the studied hospital had a busy regional anesthesia service, sample sizes were insufficient and electronic data were not available to directly study the time to perform the many other nerve blocks they perform.

Methods

We studied a historical cohort of 8,462 adult patients undergoing spinal anesthesia between 2005 and 2017. Preoperative evaluation, consent, and holding area tasks were completed before entering the block room; the times to complete these tasks were not available for study. Upon block room entry, the electronic anesthetic record was started, a timeout conducted with patient participation, vital signs taken, and the spinal performed. The interval from entry until intrathecal injection was the spinal block time. Because fits of these times to probability distributions previously used for anesthesia times were poor (p < 0.001), percentiles of times to perform one or more spinal anesthetics were calculated using Monte-Carlo simulation (100,000 samples with replacement) from the empirical distributions.

Results

The mean spinal block time was 8.8 minutes. The 90% upper prediction limit for one block was 14 minutes, with progressively decreasing times for each subsequent block for a 90% chance of finishing on time. For example, for three first-case regional or neuraxial blocks performed outside the operating room by one anesthesiologist, the first patient needs to arrive at least 38 minutes earlier than non-block patients to mitigate operating room start delays.

Conclusions

These minimum time estimates can help nursing leadership ensure that sufficient time will be available after patients are ready for anesthesia to avoid first-case delays when preoperative regional anesthesia is performed outside the operating room. Given that inadequate sample size and documentation issues likely exist universally for the various non-neuraxial preoperative nerve blocks, we recommend that hospitals use our estimates as a minimum starting point rather than try to calculate times using their own data. Then, as a systems-based metric to assess all steps in the process, track the percentages of days for which all blocks were completed in sufficient time to avoid a first-case delay for those patients. Adjustments to the arrival times would then be implemented, if needed, to meet hospital objectives for on-time starts.

Prolonged time to extubation after general anaesthesia is associated with early escalation of care: A retrospective observational study

BACKGROUND

Prolonged time to extubation after general anaesthesia has been defined as a time from the end of surgery to airway extubation of at least 15 min. This occurrence can result in ineffective utilisation of operating rooms and delays in patient care. It is unknown if unanticipated delayed extubation is associated with escalation of care.

OBJECTIVES

To assess the frequency of 'prolonged extubation' after general anaesthesia and its association with 'escalation of care before discharge from the postanaesthesia care unit', defined as administration of reversal agents for opioids and benzodiazepines, airway re-intubation and need for ventilatory support. In addition, we tried to identify independent factors associated with 'prolonged extubation'.

DESIGN

Single-centre retrospective study of cases performed from 1 January 2010 to 31 December 2014.

SETTING

A large US tertiary academic medical centre.

PATIENTS

Adult general anaesthesia cases excluding cardiothoracic, otolaryngology and neurosurgery procedures, classified as: Group 1 - regular extubation (≤15 min); Group 2 - prolonged extubation (≥16 and ≤60 min); Group 3 - very prolonged extubation (≥61 min).

MAIN OUTCOME MEASURES

First, cases with prolonged time to extubation; second, instances of escalation of care per extubation group; third, independent factors associated with prolonged time to extubation.

RESULTS

A total of 86 123 cases were analysed. Prolonged extubation occurred in 8138 cases (9.5%) and very prolonged extubation in 357 cases (0.4%). In Groups 1, 2 and 3 respectively, naloxone was used in 0.4, 4.1 and 3.9% of cases, flumazenil in 0.03, 0.6 and 2% and respiratory support in 0.2, 0.7 and 2%, and immediate re-intubation occurred in 0.1, 0.3 and 2.8% of cases. Several patient-related, anaesthesia-related and procedure-related factors were independently associated with prolonged time to extubation.

CONCLUSION

Prolonged time to extubation occurred in nearly 10% of cases and was associated with an increased incidence of escalation of care. Many independent factors associated with 'prolonged extubation' were nonmodifiable by anaesthetic management.

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.

Case study of the prediction of elective surgery durations in a New Zealand teaching hospital

We present an elective surgery redesign project involving several New Zealand hospitals that is primarily data-driven. One of the project objectives is to improve the predictions of surgery durations. We address this task by considering two approaches: (a) linear regression modelling, and (b) improvement of the data quality. For (a) we evaluate the accuracy of predictions using two performance measures. These predictions are compared to the surgeons' estimates that may subsequently be adjusted. We demonstrate using the historical surgical lists that the estimates from our prediction techniques improve the scheduling of elective surgeries by minimising the occurrences of list under- and over-runs. For (b), we discuss how the surgical data motivates a review of the surgery procedure classification which takes into account the design of the electronic booking form. The proposed hierarchical classification streamlines the specification of surgery types and therefore retains the potential for improved predictions.

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.

Importance of operating room case scheduling on analyses of observed reductions in surgical site infections from the purchase and installation of capital equipment in operating rooms

BACKGROUND

We review the impact of the consequences of operating room (OR) management decision making on power analyses for observational studies of surgical site infections (SSIs) among patients receiving care in ORs with interventions versus without interventions involving physical changes to ORs. Examples include ventilation systems, bactericidal lighting, and physical alterations to ORs.

METHODS

We performed a narrative review of operating room management and surgical site infection articles. We used 10-years of operating room data to estimate parameters for use in statistical power analyses.

RESULTS

Creating pivot tables or monthly control charts of SSI per case by OR and comparing among ORs with or without intervention is not recommended. This approach has low power to detect a difference in SSI rates among the ORs with or without the intervention. The reason is that appropriate OR case scheduling decision making causes risk factors for SSI to differ among ORs, even when stratifying by surgical specialty. Such risk factors include case duration, urgency, and American Society of Anesthesiologists' Physical Status. Instead, analyze SSI controlling for the OR, where the patient had surgery, and matching patients using these variables is preferable. With α = 0.05, 600 cases per OR, 5 intervention ORs, and 5 or 1 control patients for each intervention patient, reasonable power (≅94% or 78%, respectively) can be achieved to detect reductions (3.6% to 2.4%) in the incidence of SSI between ORs with or without the intervention.

CONCLUSIONS

By using this matched cohort design, the effect of the purchase and installation of capital equipment in ORs on SSI can be evaluated meaningfully.

Futility of Cluster Designs at Individual Hospitals to Study Surgical Site Infections and Interventions Involving the Installation of Capital Equipment in Operating Rooms

Anesthesia workspaces are integral components in the chains of many intraoperative bacterial transmission events resulting in surgical site infections (SSI). Matched cohort designs can be used to compare SSI rates among operating rooms (ORs) with or without capital equipment purchases (e.g., new anesthesia machines). Patients receiving care in intervention ORs (i.e., with installed capital equipment) are matched with similar patients receiving care in ORs lacking the intervention. We evaluate statistical power of an alternative design for clinical trials in which, instead, SSI incidences are compared directly among ORs (i.e., the ORs form the clusters) at single hospitals (e.g., the 5 ORs with bactericidal lights vs. the 5 other ORs). Data used for parameter estimates were SSI for 24 categories of procedures among 338 hospitals in the State of California, 2015. Estimated statistical power was ≅8.4% for detecting a reduction in the incidence of SSI from 3.6% to 2.4% over 1 year with 5 intervention ORs and 5 control ORs. For ≅80% statistical power, >20 such hospitals would be needed to complete a study in 1 year. Matched paired cluster designs pair similar ORs (e.g., 2 cardiac ORs, 1 to intervention and 1 to control). With 5 pairs, statistical power would be even less than the estimated 8.4%. Cluster designs (i.e., analyses by OR) are not suitable for comparing SSI among ORs at single hospitals. Even though matched cohort designs are non-randomized and thus have lesser validity, matching patients by their risk factors for SSI is more practical.

An Evaluation of the Hybrid Model for Predicting Surgery Duration

The degree of accuracy in surgery duration estimation directly impacts on the quality of planned surgical lists. Model selection for the prediction of surgery duration requires technical expertise and significant time and effort. The result is often a collection of viable models, the performance of which varies across different strata of the surgical population. This paper proposes a prediction framework to be used after a comprehensive model selection process has been completed for surgery duration prediction. The framework produces a partition of the surgical cases and a "hybrid model" that allocates different predictors from the collection of viable models to different parts of the surgical population. The intention is a flexible prediction process that can reassign models and adapt as surgical processes change. The framework is tested via a simulation study, and its utility is demonstrated by predicting surgery durations for Ear, Nose and Throat surgeries in a New Zealand hospital. The results indicate that the hybrid model is effective, performing better than standard model selection in two of the three simulation studies, and marginally worse when the selected model was the true underlying process.

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.

Discharges with surgical procedures performed less often than once per month per hospital account for two-thirds of hospital costs of inpatient surgery

STUDY OBJECTIVE

Most surgical discharges (54%) at the average hospital are for procedures performed no more often than once per month at that hospital. We hypothesized that such uncommon procedures would be associated with an even greater percentage of the total cost of performing all surgical procedures at that hospital.

DESIGN

Observational study.

SETTING

State of Texas hospital discharge abstract data: 4th quarter of 2015 and 1st quarter of 2016.

PATIENTS

Inpatients discharged with a major therapeutic ("operative") procedure.

MEASUREMENTS

For each of N=343 hospitals, counts of discharges, sums of lengths of stay (LOS), sums of diagnosis related group (DRG) case-mix weights, and sums of charges were obtained for each procedure or combination of procedures, classified by International Classification of Diseases version 10 Procedure Coding System (ICD-10-PCS). Each discharge was classified into 2 categories, uncommon versus not, defined as a procedure performed at most once per month versus those performed more often than once per month.

MAIN RESULTS

Major procedures performed at most once per month per hospital accounted for an average among hospitals of 68% of the total inpatient costs associated with all major therapeutic procedures. On average, the percentage of total costs associated with uncommon procedures was 26% greater than expected based on their share of total discharges (P<0.00001). Average percentage differences were insensitive to the endpoint, with similar results for the percentage of patient days and percentage of DRG case-mix weights.

CONCLUSIONS

Approximately 2/3rd (mean 68%) of inpatient costs among surgical patients can be attributed to procedures performed at most once per month per hospital. The finding that such uncommon procedures account for a large percentage of costs is important because methods of cost accounting by procedure are generally unsuitable for them.

Narrative Review of Statistical Reporting Checklists, Mandatory Statistical Editing, and Rectifying Common Problems in the Reporting of Scientific Articles

Considerable attention has been drawn to poor reproducibility in the biomedical literature. One explanation is inadequate reporting of statistical methods by authors and inadequate assessment of statistical reporting and methods during peer review. In this narrative review, we examine scientific studies of several well-publicized efforts to improve statistical reporting. We also review several retrospective assessments of the impact of these efforts. These studies show that instructions to authors and statistical checklists are not sufficient; no findings suggested that either improves the quality of statistical methods and reporting. Second, even basic statistics, such as power analyses, are frequently missing or incorrectly performed. Third, statistical review is needed for all papers that involve data analysis. A consistent finding in the studies was that nonstatistical reviewers (eg, "scientific reviewers") and journal editors generally poorly assess statistical quality. We finish by discussing our experience with statistical review at Anesthesia & Analgesia from 2006 to 2016.

The Risks to Patient Privacy from Publishing Data from Clinical Anesthesia Studies

In this article, we consider the privacy implications of posting data from small, randomized trials, observational studies, or case series in anesthesia from a few (e.g., 1-3) hospitals. Prior to publishing such data as supplemental digital content, the authors remove attributes that could be used to re-identify individuals, a process known as "anonymization." Posting health information that has been properly "de-identified" is assumed to pose no risks to patient privacy. Yet, computer scientists have demonstrated that this assumption is flawed. We consider various realistic scenarios of how the publication of such data could lead to breaches of patient privacy. Several examples of successful privacy attacks are reviewed, as well as the methods used. We survey the latest models and methods from computer science for protecting health information and their application to posting data from small anesthesia studies. To illustrate the vulnerability of such published data, we calculate the "population uniqueness" for patients undergoing one or more surgical procedures using data from the State of Texas. For a patient selected uniformly at random, the probability that an adversary could match this patient's record to a unique record in the state external database was 42.8% (SE < 0.1%). Despite the 42.8% being an unacceptably high level of risk, it underestimates the risk for patients from smaller states or provinces. We propose an editorial policy that greatly reduces the likelihood of a privacy breach, while supporting the goal of transparency of the research process.

Uncommon combinations of ICD10-PCS or ICD-9-CM operative procedure codes account for most inpatient surgery at half of Texas hospitals

STUDY OBJECTIVE

Recently, there has been interest in activity-based cost accounting for inpatient surgical procedures to facilitate "value based" analyses. Research 10-20years ago, performed using data from 3 large teaching hospitals, found that activity-based cost accounting was practical and useful for modeling surgeons and subspecialties, but inaccurate for individual procedures. We hypothesized that these older results would apply to hundreds of hospitals, currently evaluable using administrative databases.

DESIGN

Observational study.

SETTING

State of Texas hospital discharge abstract data for 1st quarter of 2016, 4th quarter of 2015, 1st quarter of 2015, and 4th quarter of 2014.

PATIENTS

Discharged from an acute care hospital in Texas with at least 1 major therapeutic ("operative") procedure.

MEASUREMENTS

Counts of discharges for each procedure or combination of procedures, classified by ICD-10-PCS or ICD-9-CM.

MAIN RESULTS

At the average hospital, most surgical discharges were for procedures performed at most once a month at the hospital (54%, 95% confidence interval [CI] 51% to 55%). At the average hospital, approximately 90% of procedures were performed at most once a month at the hospital (93%, CI 93% to 94%). The percentages were insensitive to the quarter of the year. The percentages were 3% to 6% greater with ICD-10-PCS than for the superseded ICD 9 CM.

CONCLUSIONS

There are many different procedure codes, and many different combinations of codes, relative to the number of different hospital discharges. Since most procedures at most hospitals are performed no more than once a month, activity-based cost accounting with a sample size sufficient to be useful is impractical for the vast majority of procedures, in contrast to analysis by surgeon and/or subspecialty.

Quantitative Assessment of Statistical Reviews of Patient Safety Research Articles

OBJECTIVES

For 8.5 consecutive years, all patient safety articles of a journal underwent statistical review before publication. We sought to establish the prevalence of statistical themes in the statistical reviews, consideration of contemporary statistical methods, and their associations with time to journal receipt of authors' revision.

METHODS

An initial set of statistical themes was created using the statistical editor's notes. For example, for the statistical theme of "CONSORT checklist," the search term needed was "CONSORT." A complete (exhaustive) list of additional themes was obtained inductively.

RESULTS

Among the 273 subsequent reviews for manuscripts that were ultimately accepted, the number of paragraphs that included a theme of a statistical method was only weakly associated with longer revision times (Kendall τ = 0.139 ± 0.039, P = 0.0004). Among the total 3274 paragraphs of statistical reviews, 72.2% did not include a theme of a statistical method (e.g., the editor instead asked the authors to clarify what statistical method had been used) (95% confidence interval [CI] = 70.6%-73.7%, P < 0.0001 versus 50%).Among the 207 manuscripts with a review that included a statistical method, 47.3% included a contemporary topic (e.g., generalized pivotal methods) (95% CI = 40.4%-54.4%). However, among the 911 corresponding paragraphs of statistical review comments, only 16.0% included a contemporary theme (95% CI = 13.7%-18.6%).

CONCLUSIONS

The revised versions of patient safety articles, which are eventually to be accepted for publication, have many statistical limitations especially in the reporting (writing) of basic statistical methods and results. The results suggest a need for education of patient safety investigators to include statistical writing.

First Job Search of Residents in the United States: A Survey of Anesthesiology Trainees' Interest in Academic Positions in Cities Distant from Previous Residences

We surveyed anesthesiology residents to evaluate the predictive effect of prior residence on desired location for future practice opportunities. One thousand five hundred United States anesthesiology residents were invited to participate. One question asked whether they intend to enter academic practice when they graduate from their residency/fellowship training. The analysis categorized the responses into "surely yes" and "probably" versus "even," "probably not," and "surely no." "After finishing your residency/fellowship training, are you planning to look seriously (e.g., interview) at jobs located more than a 2-hour drive from a location where you or your family (e.g., spouse or partner/significant other) have lived previously?" Responses were categorized into "very probably" and "somewhat probably" versus "somewhat improbably" and "not probable." Other questions explored predictors of the relationships quantified using the area under the receiver operating characteristic curve (area under the curve) ± its standard error. Among the 696 respondents, 36.9% (N = 256) would "probably" consider an academic practice. Fewer than half of those (P < 0.0001) would "very probably" consider a distant location (31.6%, 99% CI 24.4%-39.6%). Respondents with prior formal research training (e.g., PhD or Master's) had greater interest in academic practice at a distant location (AUC 0.63 ± 0.03, P = 0.0002). Except among respondents with formal research training, a good question to ask a job applicant is whether the applicant or the applicant's family has previously lived in the area.