Influence of procedure classification on process variability and parameter uncertainty of surgical case durations

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital's level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.

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.

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.

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.

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

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

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.

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.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.

Predicting the duration of surgery and procedures in a veterinary referral center

OBJECTIVE

To evaluate the ability of veterinary personnel to predict the duration of surgery and associated procedures in a referral center.

STUDY DESIGN

Prospective observational study.

SAMPLE POPULATION

Experienced surgeons (ES; n = 2, board certified for 10+ years), inexperienced surgeons (IS; n = 2, residency completed, not board certified), anesthesia animal health technicians (AAHT; n = 3) and surgery animal health technicians (SAHT; n = 2).

METHODS

Surgeons and technicians predicted surgery duration (skin incision to final stitch/staple) and total procedure duration (TPD; from induction of anesthesia to extubation). Predictions were compared to actual durations with Bland-Altman plots to assess agreement (accuracy) as indicated by bias (mean of observed differences) and limits of agreement (LOA; bias ±1.96 SD).

RESULTS

All groups underestimated TPD. Experienced surgeons predicted their own TPD more accurately (bias -20.1 ± 30.4 minutes [±SD]) and more consistently (narrower LOA) than IS for their own TPD (-40.1 ± 41.0 minutes). Experienced surgeon TPD predictions by AAHT were more accurate than those by ES (bias -16.0 ± 28.9 minutes, LOA 5% narrower). Inexperienced surgeon TPD predictions by AAHT were less consistent (wider LOA) than those by IS. Own surgery duration predictions by surgeons were similar in magnitude (ES surgery duration [ESSD] 8.3 ± 18.3, IS surgery duration [ISSD] surgery duration -7.9 ± 27.2 minutes), with greater consistency by ES (LOA 30% narrower). Anesthesia animal health technician predictions were similar to those of surgeons (ESSD 3.0 ± 19.3, ISSD -9.0 ± 28.7 minutes). Surgery animal health technician predictions were similar to those of AAHT for ESSD but were less accurate for ISSD.

CONCLUSION

Surgery duration was more accurately predicted than TPD, which was most accurately predicted by anesthesia technicians.

CLINICAL SIGNIFICANCE

Surgical procedure planning should involve personnel best able to predict total procedure durations; in this case, anesthesia technicians. Accurate planning will promote efficient operating room and personnel use.

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.

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

OBJECTIVE

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

DESIGN

Retrospective.

SETTING

Data from the National Anesthesia Clinical Outcomes Registry.

PARTICIPANTS

National data from university and non-university hospitals.

INTERVENTIONS

No interventions.

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Surgical Workflow Management Schemata for Cataract Procedures

Objective: Workflow guidance of surgical activities is a challenging task. Because of variations in patient properties and applied surgical techniques, surgical processes have a high variability. The objective of this study was the design and implementation of a surgical workflow management system (SWFMS) that can provide a robust guidance for surgical activities. We investigated how many surgical process models are needed to develop a SWFMS that can guide cataract surgeries robustly.

Methods: We used 100 cases of cataract surgeries and acquired patient-individual surgical process models (iSPMs) from them. Of these, randomized subsets iSPMs were selected as learning sets to create a generic surgical process model (gSPM). These gSPMs were mapped onto workflow nets as work-flow schemata to define the behavior of the SWFMS. Finally, 10 iSPMs from the disjoint set were simulated to validate the workflow schema for the surgical processes. The measurement was the successful guidance of an iSPM.

Results: We demonstrated that a SWFMS with a workflow schema that was generated from a subset of 10 iSPMs is sufficient to guide approximately 65% of all surgical processes in the total set, and that a subset of 50 iSPMs is sufficient to guide approx. 80% of all processes.

Conclusion: We designed a SWFMS that is able to guide surgical activities on a detailed level. The study demonstrated that the high inter-patient variability of surgical processes can be considered by our approach.

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.

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.

Effect of Individual Surgeons and Anesthesiologists on Operating Room Time

BACKGROUND

Variability in operating room (OR) time causes overutilization and underutilization of the available ORs. There is evidence that for a given type of procedure, the surgeon is the major source of variability in OR time. The primary aim was to quantify the variability between surgeons and anesthesiologists. As illustration, the value of modeling the individual surgeons and anesthesiologist for OR time prediction was estimated.

METHODS

OR data containing 16,480 cases were obtained from a general surgery department. The total amount of variability in OR time accounted for by the type of procedure, first and second surgeon, and the anesthesiologist was determined with the use of linear mixed models. The effect on OR time prediction was evaluated as reduction in overtime and idle time per case.

RESULTS

Differences between first surgeons can account for only 2.9% (2.0%-4.2%) of the variability in OR time. Differences between anesthesiologists can account for 0.1% (0.0%-0.3%) of the variability in OR time. Incorporating the individual surgeons and anesthesiologists led to an average reduction of overtime and idle time of 1.8 (95% confidence interval, 1.7-2.0, 10.5% reduction) minutes and 3.0 (95% confidence interval, 2.8%-3.2, 17.0% reduction) minutes, respectively.

CONCLUSIONS

In comparison with the type of procedure, differences between surgeons account for a small part of OR time variability. The impact of differences between anesthesiologists on OR time is negligible. A prediction model incorporating the individual surgeons and anesthesiologists has an increased precision, but improvements are likely too marginal to have practical consequences for OR scheduling.

Predictive / Reactive Planning and Scheduling of a Surgical Suite with Emergency Patient Arrival

This paper surveys the problem of predictive / reactive scheduling of an integrated operating theatre with two types of demand for surgery: 1) elective or known demand; 2) emergency or uncertain demand. The stochastic arrival of emergency patients with uncertain surgery time enforces the scheduler to react to disruption and modify scheduling plan of elective patients. We focus on this predictive / reactive scheduling problem which has not been investigated in such way before. As in hospitals, at the time of occurrence a disruption in a surgical suite, the scheduler has not enough time to make the best decision; we propose a new approach based on two-stage stochastic programming model with recourse which determines the best recourse strategy in advance of any disruption occurrence. Using the proposed approach, the primary schedule is generated in such a way that it can absorb disruption with minimum effect on planned elective surgeries. For the first time in operating theatre planning, two new significant sets of performance measures comprising "robustness" and "stability" measures are considered in generation of primary schedule which will be shown to be of great importance in efficiency of surgical suite planning. Computational experiments performed on sets of generated problem based on the data obtained from a non-profit hospital. In order to demonstrate efficiency of the proposed method, computational results of the proposed approach are compared with classic approach.

Use of simulation to assess a statistically driven surgical scheduling system

BACKGROUND

To maximize operating room (OR) utilization, better estimates of case duration lengths are needed. We used computerized simulation to determine whether scheduling OR cases using a statistically driven system that incorporates patient and surgery-specific factors in the process of case duration prediction improves OR throughput and utilization.

METHODS

We modeled surgical and anesthetic length of vascular surgical procedures as a function of patient and operative characteristics using a multivariate linear regression approach (Predictive Modeling System [PMS]). Mean historical operative time per surgeon (HMS) and mean anesthetic time were also calculated for each procedure type. A computerized simulation of scheduling in a single OR performing vascular operations was then created using either the PMS or the HMS.

RESULTS

Compared to HMS, scheduling the operating room using the PMS increased throughput by a minimum of 15% (99.8% cumulative probability, P < 0.001). The PMS was slightly more likely to lead to overtime (mean 13% versus 11% of operative days during a calendar year, P < 0.001). However, the overtime lasted longer in the HMS group (mean 140 versus 95 min per day of overtime, P < 0.001). PMS was associated with lower OR underutilization rate (mean 23% versus 34% of operative days, P < 0.001) and less lengthy OR underutilization (mean 120 versus 193 min per day of underutilization, P < 0.001).

CONCLUSIONS

This computerized simulation demonstrates that using the PMS for scheduling in a single operating room increases throughput and other measures of surgical efficiency.

Overutilization and underutilization of operating rooms - insights from behavioral health care operations management

The planning of surgery durations is crucial for efficient usage of operating theaters. Both planning too long and too short durations for surgeries lead to undesirable consequences, e.g. idle time, overtime, or rescheduling of surgeries. We define these consequences as operating room inefficiency. The overall objective of planning surgery durations is to minimize expected operating room inefficiency, since surgery durations are stochastic. While most health care studies assume economically rational behavior of decision makers, experimental studies have shown that decision makers often do not act according to economic incentives. Based on insights from health care operations management, medical decision making, behavioral operations management, as well as empirical observations, we derive hypotheses that surgeons' behavior deviates from economically rational behavior. To investigate this, we undertake an experimental study where experienced surgeons are asked to plan surgeries with uncertain durations. We discover systematic deviations from optimal decision making and offer behavioral explanations for the observed biases. Our research provides new insights to tackle a major problem in hospitals, i.e. low operating room utilization going along with staff overtime.

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.

Accuracy of patient's turnover time prediction using RFID technology in an academic ambulatory surgery center

Patients flow in outpatient surgical unit is a major issue with regards to resource utilization, overall case load and patient satisfaction. An electronic Radio Frequency Identification Device (RFID) was used to document the overall time spent by the patients between their admission and discharge from the unit. The objective of this study was to evaluate how a RFID-based data collection system could provide an accurate prediction of the actual time for the patient to be discharged from the ambulatory surgical unit after surgery. This is an observational prospective evaluation carried out in an academic ambulatory surgery center (ASC). Data on length of stay at each step of the patient care, from admission to discharge, were recorded by a RFID device and analyzed according to the type of surgical procedure, the surgeon and the anesthetic technique. Based on these initial data (n = 1520), patients were scheduled in a sequential manner according to the expected duration of the previous case. The primary endpoint was the difference between actual and predicted time of discharge from the unit. A total of 414 consecutive patients were prospectively evaluated. One hundred seventy four patients (42%) were discharged at the predicted time ± 30 min. Only 24% were discharged behind predicted schedule. Using an automatic record of patient's length of stay would allow an accurate prediction of the discharge time according to the type of surgery, the surgeon and the anesthetic procedure.

Enhancement opportunities in operating room utilization; with a statistical appendix

BACKGROUND

The purpose of this study was to assess the direct and indirect relationships between first-case tardiness (or "late start"), turnover time, underused operating room (OR) time, and raw utilization, as well as to determine which indicator had the most negative impact on OR utilization to identify improvement potential. Furthermore, we studied the indirect relationships of the three indicators of "nonoperative" time on OR utilization, to recognize possible "trickle down" effects during the day.

MATERIALS AND METHODS

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

RESULTS

Underused OR time at the end of the day had the strongest influence on raw utilization, followed by late start and turnover time. The relationships between the three "nonoperative" time indicators were negligible. The impact of the partial indirect effects of "nonoperative" time indicators on raw utilization were statistically significant, but relatively small. The "trickle down" effect that late start can cause resulting in an increased delay as the day progresses, was not supported by our results.

CONCLUSIONS

The study findings clearly suggest that OR utilization can be improved by focusing on the reduction of underused OR time at the end of the day. Improving the prediction of total procedure time, improving OR scheduling by, for example, altering the sequencing of operations, changing patient cancellation policies, and flexible staffing of ORs adjusted to patient needs, are means to reduce "nonoperative" time.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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