A Simple Method for Deciding When Patients Should Be Ready on the Day of Surgery Without Procedure-Specific Data

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).

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.

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

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

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.

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.

Slated versus actual operating room entry time in a British Columbia health authority

PURPOSE

To determine how frequently the published operating room (OR) schedule of case start times correlated with the actual OR entry time for elective cases in the Fraser Health Authority (FHA) in British Columbia, Canada. Society guidelines recommend periods of fasting of two hours prior to the induction of general anesthesia, but patients frequently end up fasting much longer. This review aimed to determine when patients arrive in the OR-either earlier than their scheduled time or later. The premise of some is that patients often arrive earlier, and advising short fasting times on the basis of the OR slate time is unreliable. I wished to determine whether this fear is justified.

METHODS

The computerized OR management database was queried for slated time of entry and actual time of entry for elective surgical cases in 11 hospitals in the FHA. The difference in slated vs actual entry time of patients (in 30 min blocks) was reviewed to examine the proportion of patients entering the OR earlier than 90 min from their slated time. Additionally, anesthesiologists from the Royal Columbian/Eagle Ridge Hospitals were surveyed for their recall of case delays that were related to inappropriate consumption of fluids.

RESULTS

One hundred and twenty-three thousand eight hundred and sixty-five cases from 11 hospitals over a 32-month period were analyzed. A very small proportion of cases (753 of 123,865 cases, 0.6%) entered the OR earlier than 90 min from their slated time. Relatively few cases were actually cancelled because of inappropriate fluid consumption in the recall of anesthesiologists in two institutions.

CONCLUSION

In the FHA, the OR schedule is a reliable guide to providing instructions on timing of preoperative fluid consumption in appropriately selected elective surgical patients. Quality of care and patient satisfaction will safely be enhanced by limiting the period of fasting for elective surgical patients.

Elective change of surgeon during the OR day has an operationally negligible impact on turnover time

STUDY OBJECTIVE

To compare turnover times for a series of elective cases with surgeons following themselves with turnover times for a series of previously scheduled elective procedures for which the succeeding surgeon differed from the preceding surgeon.

DESIGN

Retrospective cohort study.

SETTING

University-affiliated teaching hospital.

MEASUREMENTS

The operating room (OR) statistical database was accessed to gather 32 months of turnover data from a large academic institution. Turnover time data for the same-surgeon and surgeon-swap groups were batched by month to minimize autocorrelation and achieve data normalization. Two-way analysis of variance (ANOVA) using the monthly batched data was performed with surgeon swapping and changes in procedure category as variables of turnover time. Similar analyses were performed using individual surgical services, hourly time intervals during the surgical day, and turnover frequency per OR as additional covariates to surgeon swapping.

MAIN RESULTS

The mean (95% confidence interval [CI]) same-surgeon turnover time was 43.6 (43.2 - 44.0) minutes versus 51.0 (50.5 - 51.6) minutes for a planned surgeon swap (P < 0.0001). This resulted in a difference (95% CI) of 7.4 (6.8 - 8.1) minutes. The exact increase in turnover time was dependent on surgical service, change in subsequent procedure type, time of day when the turnover occurred, and turnover frequency.

CONCLUSIONS

The investigated institution averages 2.5 cases per OR per day. The cumulative additional turnover time (far less than one hour per OR per day) for switching surgeons definitely does not allow the addition of another elective procedure if the difference could be eliminated. A flexible scheduling policy allowing surgeon swapping rather than requiring full blocks incurs minimal additional staffed time during the OR day while allowing the schedule to be filled with available elective cases.

"Nil per oral after midnight": Is it necessary for clear fluids?

Fasting before general anaesthesia aims to reduce the volume and acidity of stomach contents, thus reducing the risk of regurgitation and aspiration. Recent guidelines have recommended a shift in fasting policies from the standard ‘nil per oral from midnight’ to a more relaxed policy of clear fluid intake a few hours before surgery. The effect of preoperative oral administration of 150 ml of water 2 h prior to surgery was studied prospectively in 100 ASA I and II patients, for elective surgery. Patients were randomly assigned to two groups. Group I (n = 50) was fasting overnight while Group II (n = 50) was given 150 ml of water 2 h prior to surgery. A nasogastric tube was inserted after intubation and gastric aspirate was collected for volume and pH. The gastric fluid volume was found to be lesser in Group II (5.5 ± 3.70 ml) than Group I (17.1 ± 8.2 ml) which was statistically significant. The mean pH values for both groups were similar. Hence, we conclude that patients not at risk for aspiration can be allowed to ingest 150 ml water 2 h prior to surgery.

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

BACKGROUND

Predictive variability of operating room (OR) times influences decision making on the day of surgery including when to start add-on cases, whether to move a case from one OR to another, and where to assign relief staff. One contributor to predictive variability is process variability, which arises among cases of the same procedure(s). Another contributor is parameter uncertainty, which is caused by small sample sizes of historical data.

METHODS

Process variability was quantified using absolute percentage errors of surgeons' bias-corrected estimates of OR time. The influence of procedure classification on process variability was studied using a dataset of 61,353 cases, each with 1 to 5 scheduled and actual Current Procedural Terminology (CPT) codes (i.e., a standardized vocabulary). Parameter uncertainty's sensitivity to sample size was quantified by studying ratios of 90% prediction bounds to medians. That studied dataset of 65,661 cases was used previously to validate a Bayesian method to calculate 90% prediction bounds using combinations of surgeons' scheduled estimates and historical OR times.

RESULTS

(1) Process variability differed significantly among 11 groups of surgical specialty and case urgency (P < 0.0001). For example, absolute percentage errors exceeded the overall median of 22% for 57% of urgent spine surgery cases versus 42% of elective spine surgery cases. (2) Process variability was not increased when scheduled and actual CPTs differed (P = 0.23 without and P = 0.47 with stratification based on the 11 groups), because most differences represented known (planned) options inherent to procedures. (3) Process variability was not associated with incidence of procedures (P = 0.79), after excluding cataract surgery, a procedure with high relative variability. (4) Parameter uncertainty from uncommon procedures (0-2 historical cases) accounted for essentially all of the uncertainty in decisions dependent on estimates of OR times. The Bayesian method moderated the effect of small sample sizes on uncertainty in estimates of OR times. In contrast, from prior work, the use of broad categories of procedures reduces parameter uncertainty but at the expense of increased process variability.

CONCLUSIONS

For procedures with few historic data, the Bayesian method allows for effective case duration prediction, permitting use of detailed procedure descriptions. Although fine resolution of scheduling procedures increases the chance of performed procedure(s) differing from scheduled procedure(s), this does not increase process variability. Future studies need both to address differences in process variability among specialties and accept the limitation that findings from one may not apply to others.

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

BACKGROUND

Operating room (OR) whiteboards (status displays) communicate times remaining for ongoing cases to perioperative stakeholders (e.g., postanesthesia care unit, anesthesiologists, holding area, and control desks). Usually, scheduled end times are shown for each OR. However, these displays are inaccurate for predicting the time that remains in a case. Once a case scheduled for 2 h has been on-going for 1.5 h, the median time remaining is not 0.5 h but longer, and the amount longer differs among procedures.

METHODS

We derived the conditional Bayesian lower prediction bound of a case's duration, conditional on the minutes of elapsed OR time. Our derivations make use of the posterior predictive distribution of OR times following an exponential of a scaled Student t distribution that depends on the scheduled OR time and several parameters calculated from historical case duration data. The statistical method was implemented using Structured Query Language (SQL) running on the anesthesia information management system (AIMS) database server. In addition, AIMS workstations were sent instant messages displaying a pop-up dialog box asking for anesthesia providers' estimates for remaining times. The dialogs caused negotiated interruptions (i.e., the anesthesia provider could reply immediately, keep the dialog displayed, or defer response). There were no announcements, education, or efforts to promote buy-in.

RESULTS

After a case had been in the OR longer than scheduled, the median remaining OR time for the case changes little over time (e.g., 35 min left at 2:30 pm and also at 3:00 pm while the case was still on-going). However, the remaining time differs substantially among surgeons and scheduled procedure(s) (16 min longer [10th percentile], 35 min [50th], and 86 min [90th]). We therefore implemented an automatic method to estimate the times remaining in cases. The system was operational for >119 of each day's 120 5-min intervals. When instant message dialogs appearing on AIMS workstations were used to elicit estimates of times remaining from anesthesia providers, acknowledgment was on average within 1.2 min (95% confidence interval [CI] 1.1-1.3 min). The 90th percentile of latencies was 6.5 min (CI: 4.4-7.0 min).

CONCLUSIONS

For cases taking nearly as long as or longer than scheduled, each 1 min progression of OR time reduces the median time remaining in a case by <1 min. We implemented automated calculation of times remaining for every case at a 29 OR hospital.

Operative Time and Other Outcomes of the Electrothermal Bipolar Vessel Sealing System (LigaSure™) Versus Other Methods for Surgical Hemostasis: A Meta-Analysis

A meta-analysis was performed of 29 prospective, randomized trials (published January 1, 2000, to August 14, 2007) comparing an electrothermal bipolar vessel sealing system (EBVS-LigaSure™, Covidien) (total n = 1107 patients) with either clamping with suture ligation/ electrocauterization (n = 1079 patients) or ultrasonic energy (eg, Harmonic Scalpel®, Johnson & Johnson). Hemorrhoidectomy (12 articles), hysterectomy (4 articles), and thyroidectomy (3 articles) were the most common procedures. For 15 of 26 studies reporting standard deviations, the normalized mean operative time reduction for EBVS equaled 28% (95% confidence interval [CI] 18%-39%, P < .0001) compared with conventional surgical hemostasis. Operative time was reduced with EBVS in 24 of 26 studies ( P < .0001). EBVS was associated with 43 mL (95% CI 14-73 mL, P = .0021) less blood loss, fewer complications (odds ratio 0.66, 95% CI 0.47-0.92, P = .02), and mean reduction in postoperative pain of 2.8 units (95% CI 1.5-4.1, P < .0001). Five studies used ultrasonic energy as the comparator, but none reported standard deviation so data could not be pooled.