Late first-case of the day starts do not cause greater minutes of over-utilized time at an endoscopy suite with 8-hour workdays and late running rooms. A historical cohort study

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

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

Boosting efficiency in the endoscopy suite: integrating team workflows improves productivity and minimizes cost

BACKGROUND AND AIMS

Inefficiencies in the endoscopy suite cause frustration for physicians, hospital administrators, staff, and patients. Turnover time (TOT), the time between one case ending and another beginning, is subjectively disproportionate between various team members. We aimed to define perceptions of TOT and target steps within the process to improve efficiency.

METHODS

This was a prospective cohort study at a tertiary center outpatient endoscopy unit. Phase I aimed to identify the TOT process components based on time stamps in the electronic medical record (N = 686). We defined gastroenterologist perceived TOT (pTOT), anesthesia pTOT, and standard TOT (sTOT). TOT length was calculated for each subgroup. Patient transport was identified as an intervenable target. In Phase II, the task of patient transport moved from the anesthesiology team to endoscopy nurses. Mean TOT and proportion of cases with sTOT <15 minutes' preintervention (n = 2192) and postintervention (n = 292) were compared.

RESULTS

Seven key TOT components were identified that explain variations in pTOT. Average anesthesia pTOT was 15 minutes, whereas average gastroenterologist pTOT was 34 minutes (25.9% vs 57.2% of case length; P = .0007). In Phase II, mean sTOT improved from 18.51 to 14.25 minutes (P < .0001), and proportion of sTOT within 15 minutes improved from 41.79% to 58.90% (P < .0001). This intervention saved 45 minutes per room per day, allowing for a revenue potential of more than $300,000 per year per procedure room.

CONCLUSIONS

This study defines variations in TOT and shows that finding imbalances and sharing the workload significantly cuts costs and improves the overall efficiency of the different subgroups in the turnover process.

Getting going on time: reducing neurophysiology set-up times in order to contribute to improving surgery start and finish times

At the Walton Centre we conduct a relatively large number of complex and lengthy elective (booked) spinal operations. Recently, we have had a particular problem with half or more of these sessions finishing late, resulting in staff discontent and greater use of on-call staff.These operations require patient monitoring by neurophysiology clinical scientists. Before the surgeon can start the operation, in-theatre neurophysiological measurements are required to establish a baseline. We reasoned that reducing this set-up time would reduce the risk of surgery starting late, and so the whole session finishing later than expected.In this project we redesigned the neurophysiology parts of in-theatre patient preparation. We conducted five Plan-Do-Study-Act cycles over 3 months, reducing the duration of pre-surgery preparation from a mean of 70 min to around 50 min. We saw improvements in surgical start times and session finish times (both earlier by roughly comparable amounts). The ultimately impact is that we saw on-time session finishes improve from around 50% to 100%. Following this project, we have managed to sustain the changes and the improved performance.The most impactful change was to conduct in-theatre neurophysiology patient preparation simultaneously with anaesthesia, rather than waiting for this to finish; when we performed this with a pair of clinical scientists, we were able to complete neurophysiology patient preparation by the time the anaesthetist was finished, therefore not introducing delays to the start of surgery. A final change was to remove a superfluous preparatory patient-baseline measurement.This is a very challenging and complex environment, with powerful stakeholders and many factors and unpredictable events affecting sessions. Nevertheless, we have shown that we can make improvements within our span of influence that improve the wider process. While using pairs of staff requires greater resource, we found the benefit to be worthwhile.

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.

Benefit of systematic selection of pairs of cases matched by surgical specialty for surveillance of bacterial transmission in operating rooms

BACKGROUND

Bacterial transmission within and between successive surgical cases occurs in operating rooms (ORs), often includes anesthesia equipment as a reservoir, and can be monitored by collecting samples and identifying bacteria by genetic testing. We evaluated how to choose cases for active surveillance to quantify the effectiveness of interventions in 2 groups of ORs (eg, rooms with germicidal lighting vs those without).

METHODS

Data were from a 7 OR single-specialty gastrointestinal endoscopy suite and from a typical 8 OR multispecialty surgical suite.

RESULTS

At the multispecialty hospital, 40.3% (SE 1.2%) of the total number of cases could be used for surveillance (ie, followed by another case of the same specialty and matched with a corresponding pair of cases from the other OR group). Random selection obtained fewer matched pairs than deliberate selection: mean ratio of random/deliberate = 0.64 (0.01) for the single-specialty and 0.51 (0.02) for the multispecialty suite (P <.001).

CONCLUSIONS

The efficiency of sampling to obtain pairs of successive surgical cases of the same specialty is impaired markedly by randomly selecting pairs of cases (or using convenience sampling) as compared to choosing pairs deliberately. This is important because the number of cases that can be suitably used for surveillance of bacterial transmission will typically be less than one-half the total case number.

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

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