Modeling daily veterinary anesthetist patient care hours and probabilities of exceeding critical thresholds

OBJECTIVE

Use a referral dental clinic model to study how to calculate accurate 95% upper confidence limits for probabilities of workloads (total case duration, including turnover time) exceeding allocated times.

ANIMALS

Dogs and cats undergoing dental treatments.

METHODS

Managerial data (procedure date and duration) collected over 44 consecutive operative workdays were used to calculate the daily anesthetist workload. Workloads were compared with a normal distribution using the Shapiro-Wilk test, serial correlation was examined by runs test, and comparisons among weekdays were made using the Kruskal-Wallis test. The 95% confidence limits for normally distributed workloads exceeding allocated times were estimated with a generalized pivotal quantity. The impact of a number of procedures was assessed with scatterplots, Pearson linear correlation coefficients, and multivariable linear regression.

RESULTS

Mean anesthetist's workload was normally distributed (Shapiro-Wilk P = .25), without serial correlation (P = .45), and without significant differences among weekdays (P = .52). Daily workload, mean 9.39 hours and SD 3.06 hours, had 95% upper confidence limit of 4.47% for the probability that exceeding 16 hours (ie, 8 hours per each of 2 tables). There was a strong positive correlation between daily workload and the end of the workday (r = .85), significantly larger than the correlation between the end of the workday and the number of procedures (r = .64, P < .0001).

CLINICAL RELEVANCE

There are multiple managerial applications in veterinary anesthesia wherein the problem is to estimate risks of exceeding thresholds of workload, including the costs of hiring a locum, scheduling unplanned add-on cases, planning for late discharge of surgical patients to owners, and coordinating anesthetist breaks.

Pattern of Perioperative Surgical Patient Care, Equipment Handling and Operating Room Management During COVID-19 Pandemic at Jimma Medical Center

BACKGROUND

The nature of COVID-19 transmission creates significant risks in surgical departments owing to the close contact of medical staff with patients, the limited physical environment of the operating room and recovery room, the possibility of shared surgical equipment and challenges in the delivery of surgical care in all surgical departments. Globally, studies have reported that the effects of the pandemic on surgical departments are profound, potentially long-lasting and extensive. To manage these effects, different local guidelines and recommendations have been developed, with potential differences in their effectiveness and implementation. Therefore, harmonized and effective national/international guidelines for specific surgical departments during perioperative periods are pertinent to curtail the infection, and will inevitably need to be adapted for consistent and sustainable implementation by all medical staff. The pattern of surgical patient care during the COVID-19 pandemic at Jimma Medical Center (JMC), Ethiopia, has not been explored yet. The present study aimed to describe the pattern of perioperative surgical patient care, equipment handling and operating room management during the COVID-19 pandemic at JMC.

METHODS

A cross-sectional study was conducted to describe the pattern of perioperative surgical patient care, equipment handling and operating room management during the COVID-19 pandemic at JMC, using five-point Likert scales (0, not at all; 1, rarely; 2, sometimes; 3, most of the time; 4, frequently). A total of 90 respondents [35 patients (five patients from each of seven surgical departments) and 55 healthcare providers (six professionals from each of nine units, including the center of sterility room and anesthesia)] who were available during the study period, selected by a convenience sampling technique with multistage clustering, participated in the study. Data were collected using a structured questionnaire via direct observation and face-to-face interviews with patients undergoing surgery, healthcare providers and hospital administrators, against the standard surgical patient care guidelines. The collected data were manually checked for missing values and outliers, cleared, entered into EpiData (v4.3.1) and exported to SPSS (v22) for analysis. The mean score of practice was compared among different disciplines by applying the unpaired t-test. The findings of the study were reported using tables and narration. A p-value of less than 0.05 was declared as statistically significant.

RESULTS

Despite the surgical care practice having changed during the COVID-19 pandemic in all service domains, it is not implemented consistently among different surgical departments owing to different barriers (lack of training on the updated guidelines and financial constraints). The majority of surgical staff were implementing the use of preventive measures against COVID-19, while they were practiced less among patients. The guidelines for surgical practice during the preoperative phase were well applied, especially screening patients by different methods and the application of telemedicine to reduce physical contacts. But, against guidelines, elective patients were planned and underwent surgery, especially in the general surgery department. The implementation of recommended guidelines in the center of sterility room in handling surgical equipment was not very different before and during the pandemic. The extent of practice for anesthesia care, operating room management and postoperative care in the recovery room also changed, and the guidelines were sometimes applied.

CONCLUSION AND RECOMMENDATIONS

Although perioperative surgical care practice differed before and during the pandemic, the standard guidelines were inconsistently implemented among surgical departments. The implementation of recommended guidelines in the center of sterility room in handling surgical equipment was not very different before and during the pandemic. Thus, the authors developed safe surgical care guidelines throughout the different domains (infection prevention and PPE use; preoperative care, intraoperative care, operating room management, anesthesia care, equipment handling process and postoperative care) for all disciplines and shared them with all staff. We recommend that all surgical staff should access these guidelines and strictly adhere to them for surgical service during the pandemic.

More surgery in December among US patients with commercial insurance is offset by unrelated but lesser surgery among patients with Medicare insurance

Study Objective

Evaluate whether there is more surgery (in the US State of Florida) at the end of the year, specifically among patients with commercial insurance.

Design

Observational cohort study.

Setting

The 712 facilities in Florida that performed inpatient or outpatient elective surgery from January 2010 through December 2019.

Results

Among patients with commercial insurance, December had more cases than November (1.108 [1.092–1.125]) or January (1.257 [1.229–1.286]). In contrast, among patients with Medicare insurance (traditional or managed care), December had fewer cases than November (ratio 0.917 [99% confidence interval 0.904–0.930]) or January (0.823 [0.807–0.839]) of the same year. Summing among all cases, December did not have more cases than November (ratio 1.003 [0.992–1.014]) or January (0.998 [0.984–1.013]). Comparing December versus November (January) ratios for cases among patients with commercial insurance to the corresponding ratios for cases among patients with Medicare, years with more commercial insurance cases had more Medicare cases (Spearman rank correlation +0.36 [+0.25], both p < 0.0001).

Conclusions

In the US State of Florida, although some surgeons' procedural workloads may have seasonal variation if they care mostly for patients with one category of insurance, surgical facilities with patients undergoing many procedures will have less variability. Importantly, more commercial insurance cases were not causing Medicare cases to be postponed or vice‐versa, providing mechanistic explanation for why forecasts of surgical demand can reasonably be treated as the sum of the independent workloads among many surgeons.

In US State of Florida, patients with commercial insurance had more surgery in December

Patients with US Medicare insurance had less surgery in December than other months

Years with more commercial insurance cases in December had more US Medicare cases too

Implication for surgical suites: busier months for some patient groups balanced by less busy for others

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.

Managing capacity for urgent surgery: staffing, staff scheduling in-house or on-call from home, and work assignments

Parmar and colleagues developed and validated a graphical method for choosing the number of operating theatres to set aside for urgent surgical cases. We address appropriate usage of their new method for calculating anaesthesia staffing, including comparison with previously published techniques. Parmar and colleagues' method is based on all staff scheduled in-house, rather than some on-call from home. We review that this is not nearly as large a limitation as it may seem because of behavioural factors of staff assignment.

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.

Quantifying and interpreting inequality of surgical site infections among operating rooms

PURPOSE

The incidence of surgical site infection differs among operating rooms (ORs). However, cost effectiveness of interventions targeting ORs depends on infection counts. The purpose of this study was to quantify the inequality of infection counts among ORs.

METHODS

We performed a single-centre historical cohort study of elective surgical cases spanning a 160-week period from May 2017 to May 2020, identifying cases of infection within 90 days using International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes. We used the Gini index to measure inequality of infections among ORs. As a reference, the Gini index for inequality of household disposable income in the US in 2017 was 0.39, and 0.31 for Canada.

RESULTS

There were 3,148 (3.67%) infections among the 85,744 cases studied. The 20% of 57 ORs with the most and least infections accounted for 44% (99% confidence interval [CI], 36 to 52) and 5% (99% CI, 2 to 8), respectively. The Gini index was 0.40 (99% CI, 0.31 to 0.50), which is comparable to income inequality in the US. There were more infections in ORs with more minutes of cases (Spearman correlation ρ = 0.68; P < 0.001), but generally not in ORs with more total cases (ρ = 0.11; P = 0.43). Moderately long (3.3 to 4.8 hr) cases had a large effect, having greater incidences of infection, while not being so long as to have just one case per day per OR. There was substantially greater inequality in infection counts among the 557 observed combinations of OR specialty (Gini index 0.85; 99% CI, 0.81 to 0.88).

CONCLUSIONS

Inequality of infections among ORs is substantial and caused by both inequality in the incidence of infections and inequality in the total minutes of cases. Inequality in infections among OR and specialty combinations is due principally to inequality in total minutes of cases.

Proportions of Surgical Patients Discharged Home the Same or the Next Day Are Sufficient Data to Assess Cases' Contributions to Hospital Occupancy

Introduction When the hospital census is high, perioperative medical directors or operating room (OR) managers may need to consider postponing some surgical cases scheduled to be performed within the next three workdays. This scenario has arisen at hospitals in regions with large increases in admissions due to coronavirus disease 2019 (COVID-19). We compare summary measures for hospital length of stay (LOS) to guide the OR manager having to decide which cases may need to be postponed to ensure a sufficient reserve of available inpatient beds. Methods We studied the 1,201,815 ambulatory and 649,962 inpatient elective cases with a major therapeutic procedure performed during 2018 at all 412 non-federal hospitals in Florida. The data were sorted by the hospital, and then by procedure category. Statistical comparisons of LOS were made pairwise among all procedure categories with at least 100 cases at (the) each hospital, using the chi-square test (LOS ≤ 1 day versus LOS > 1 day), Student's t-test with unequal variances, and the Wilcoxon-Mann-Whitney test. The comparisons among the three tests then were repeated having sorted the data by procedure category and making statistical comparisons among all hospitals with at least 100 cases for the procedure category. Results Whether using a criterion for statistical significance of P < 0.05 or P < 0.01, and whether compared with Student's t-test with unequal variances or Wilcoxon-Mann-Whitney test, the chi-square test had greater odds (i.e., greater statistical power) to detect differences in LOS (all four with P < 0.0001 and all 95% lower confidence limits for odds ratios ≥ 3.00). The findings were consistent when the data, first sorted by procedure category and then by probability distributions of LOS, were compared between hospitals (all P < 0.0001 and the 95% lower confidence limits for odds ratio ≥ 3.72). Conclusions For purposes of comparing procedure categories pairwise at the same hospital, there was no loss of information by summarizing the probability distributions using single numbers, the percentages of cases among patients staying longer than overnight. This finding substantially simplifies the mathematics for constructing dashboards or summaries of OR information system data to help the perioperative OR manager or medical director decide which cases may need to be postponed, when the hospital census is high, to provide a sufficient reserve of inpatient hospital beds.

Predictive Model of Surgical Time for Revision Total Hip Arthroplasty

BACKGROUND

Maximizing operating room utilization in orthopedic and other surgeries relies on accurate estimates of surgical control time (SCT). A variety of case and patient-specific variables can influence the duration of surgical time during revision total hip arthroplasty (THA). We hypothesized that these variables are better predictors of actual SCT (aSCT) than a surgeon's own prediction (pSCT).

METHODS

All revision THAs from October 2008 to September 2014 from one institution were accessed. Variables for each case included aSCT, pSCT, patient age, gender, body mass index, American Society of Anesthesiologists Physical Status class, active infection, periprosthetic fracture, bone loss, heterotopic ossification, and implantation/explantation of a well-fixed acetabular/femoral component. These were incorporated in a stepwise fashion into a multivariate regression model for aSCT with a significant cutoff of 0.15. This was compared to a univariate regression model of aSCT that only used pSCT.

RESULTS

In total, 516 revision THAs were analyzed. After stepwise selection, patient age and American Society of Anesthesiologists Physical Status were excluded from the model. The most significant increase in aSCT was seen with implantation of a new femoral component (24.0 min), followed by explantation of a well-fixed femoral component (18.7 min) and significant bone loss (15.0 min). Overall, the multivariate model had an improved r² of 0.49, compared to 0.16 from only using pSCT.

CONCLUSION

A multivariate regression model can assist surgeons in more accurately predicting the duration of revision THAs. The strongest predictors of increased aSCT are explantation of a well-fixed femoral component, placement of an entirely new femoral component, and presence of significant bone loss.

Surgery scheduling optimization considering real life constraints and comprehensive operation cost of operating room

BACKGROUND

Operating Room (OR) is the core sector in hospital expenditure, the operation management of which involves a complete three-stage surgery flow, multiple resources, prioritization of the various surgeries, and several real-life OR constraints. As such reasonable surgery scheduling is crucial to OR management.

OBJECTIVE

To optimize OR management and reduce operation cost, a short-term surgery scheduling problem is proposed and defined based on the survey of the OR operation in a typical hospital in China.

METHOD

The comprehensive operation cost is clearly defined considering both under-utilization and overutilization. A nested Ant Colony Optimization (nested-ACO) incorporated with several real-life OR constraints is proposed to solve such a combinatorial optimization problem.

RESULTS

The 10-day manual surgery schedules from a hospital in China are compared with the optimized schedules solved by the nested-ACO. Comparison results show the advantage using the nested-ACO in several measurements: OR-related time, nurse-related time, variation in resources' working time, and the end time.

CONCLUSIONS

The nested-ACO considering real-life operation constraints such as the difference between first and following case, surgeries priority, and fixed nurses in pre/post-operative stage is proposed to solve the surgery scheduling optimization problem. The results clearly show the benefit of using the nested-ACO in enhancing the OR management efficiency and minimizing the comprehensive overall operation cost.

Factors determining the smooth flow and the non-operative time in a one-induction room to one-operating room setting

RATIONALE, AIMS AND OBJECTIVES

What factors determine the use of an anaesthesia preparation room and shorten non-operative time?

METHODS

A logistic regression is applied to 18 751 surgery records from AZ Sint-Jan Brugge AV, Belgium, where each operating room has its own anaesthesia preparation room. Surgeries, in which the patient's induction has already started when the preceding patient's surgery has ended, belong to a first group where the preparation room is used as an induction room. Surgeries not fulfilling this property belong to a second group. A logistic regression model tries to predict the probability that a surgery will be classified into a specific group. Non-operative time is calculated as the time between end of the previous surgery and incision of the next surgery. A log-linear regression of this non-operative time is performed.

RESULTS

It was found that switches in surgeons, being a non-elective surgery as well as the previous surgery being non-elective, increase the probability of being classified into the second group. Only a few surgery types, anaesthesiologists and operating rooms can be found exclusively in one of the two groups. Analysis of variance demonstrates that the first group has significantly lower non-operative times. Switches in surgeons, anaesthesiologists and longer scheduled durations of the previous surgery increases the non-operative time. A switch in both surgeon and anaesthesiologist strengthens this negative effect. Only a few operating rooms and surgery types influence the non-operative time.

CONCLUSION

The use of the anaesthesia preparation room shortens the non-operative time and is determined by several human and structural factors.

Presence of anesthesia resident trainees in day surgery unit has mixed effects on operating room efficiency measures

BACKGROUND

Three competing goals at academic medical centers are to increase efficiency, to optimize clinical care, and to train residents. The goal of this project was to compare day surgery operating room (OR) efficiency measures for anesthesiologists working alone, working with residents, and working with certified nurse anesthetists in a tertiary multisubspecialty teaching hospital to determine if trainees significantly impact OR efficiency.

METHODS

We retrospectively evaluated operating room times data for 2,427 day surgery cases, comparing first case on-time starts, anesthesia-controlled times, induction times, emergence times, and turnover times for the 3 anesthesiologist groups.

RESULTS

Compared to the solo anesthesiologist group, anesthesiologists working with residents had significantly longer induction, emergence, and total anesthesia-controlled times (20.2 ± 8.0 vs 18.4 ± 7.0 minutes). However, the anesthesiologists working with residents had more on-time starts (65% vs 53%) and lower turnover times 47.3 ± 13.6 vs 50.8 ± 14.5 minutes) than the solo anesthesiologist group.

CONCLUSION

The pairing of anesthesiology residents with anesthesia staff has mixed effects on OR efficiency measures in a day surgery unit.