Nurse anesthetists' preferences for anesthesiologists' participation in patient care at a large teaching hospital

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.

Overall anesthesia department quality of clinical supervision of trainees over a year evaluated using mixed effects models

BACKGROUND

Earlier studies of supervision in anesthesiology focused on how to evaluate the quality of individual anesthesiologist's clinical supervision of trainees. What is unknown is how to evaluate clinical supervision collectively, as provided by the department's faculty anesthesiologists. This information can be a metric that departments report annually or use to evaluate the effect of programs on the quality of clinical supervision over time.

METHODS

This retrospective cohort study used all 48,788 evaluations of the 115 faculty anesthesiologists using the De Oliveira Filho supervision scale completed by 202 residents and fellows over nine academic years at one department.

RESULTS

The distributions of mean scores among raters had marked negative skewness and were inconsistent with normal distributions. Consequently, accurate confidence intervals were impracticably wide, and their interpretation suggested lack of validity. In contrast, the logits of the proportions of scores equaling the maximum possible value, calculated for each rater, followed distributions sufficiently close to normal for statistically reliable use in random effects modeling. Parameters and confidence intervals were estimated using the intercept only random effects models, and then inverses computed to convert results from the logit scale to proportions. That approach is analogous to random effect meta-analysis of proportional incidence (or prevalence). Departments that chose to use semi-annual or annual surveys of trainees regarding supervision quality, and report those raw counts, will have far lower estimates of supervision quality versus when calculated accurately using daily evaluations of individual anesthesiologists.

CONCLUSIONS

Random effects meta-analysis of percentage incidences of maximum scores is a suitable statistical approach to analyze the daily supervision scores of individual anesthesiologists to evaluate the overall quality of clinical supervision provided to the trainees by the department over a year.

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.

The anesthetizing sites supervised to anesthesiologist ratio is an invalid surrogate for group productivity in academic anesthesia departments when used without consideration of the corresponding managerial decisions

When the anesthesiologist does not individually perform the anesthesia care, then to make valid comparisons among US anesthesia departments, one must consider the staffing ratio (i.e., how many cases each anesthesiologist supervises when working with Certified Registered Nurse Anesthetists [CRNAs] or Certified Anesthesiologist Assistants [CAA]). The staffing ratio also must be considered when accurately measuring group productivity. In this narrative review, we consider anesthesia departments with non-physician anesthesia providers and anesthesiology residents. We investigate the validity of such departments assessing the overall ratio of anesthetizing sites supervised per anesthesiologist as a surrogate for group clinical productivity. The sites/anesthesiologist ratio can be estimated accurately using the arithmetic mean calculated by anesthesiologist, the harmonic mean calculated by case, or the harmonic mean calculated by CRNA or CAA, but not by the arithmetic mean ratio by case. However, there is lack of validity to benchmarking the percentage time that anesthesiologists are supervising the maximum possible number of CRNAs or CAAs when some of the anesthesiologists also are supervising resident physicians. Assignments can differ in the total number anesthesiologists needed while every anesthesiologist is supervising as many sites as possible. Similarly, there is lack of validity to limiting assessment to the anesthesiologists supervising only CRNAs or CAAs. There also is lack of validity to limiting assessment only to cases performed by supervised CRNAs or CAAs. When cases can be assigned to anesthesiology residents or CRNAs or CAAs, increasing sites/anesthesiologist while limiting consideration to the CRNAs or CAAs creates incentive for the CRNAs or CAAs to be assigned cases, even when lesser productivity is the outcome. Decisions also can increase sites/anesthesiologist without increasing productivity (e.g., when one anesthesiologist relieves another before the end of the regular workday). A suitable alternative approach to fallaciously treating the sites/anesthesiologist ratio as a surrogate for productivity is that, when a teaching hospital supplies financial support, a responsibility of the anesthesia department is to explain annually the principal factors affecting productivity at each facility it manages and to show annually that decisions were made that maximized productivity, subject to the facilities' constraints.

Sustained management of the variability in work hours among anesthesiologists providing patient care in operating rooms and not on call to work late if necessary

STUDY OBJECTIVE

We evaluated a department's long-term (6.5-year) success of achieving an overall and individual incidence of anesthesiologists working late of approximately 20% of days when not on call to work late, if necessary, and providing care in operating rooms.

DESIGN

Historical cohort study, January 2014 through September 2020.

SETTING

Inpatient surgical suite of large teaching hospital.

MAIN RESULTS

The percentage of days worked past 5:00 PM was mean (standard deviation) 17.7% (5.0%) of days, 99% confidence interval (CI) 15.0% to 20.4%. There was considerable variability among quarters, the coefficient of variation being 28% (99% CI 20% to 45%). This was caused, in part, by anesthesiologists less often working late during January-March versus July-September (14.0% [4.5%] versus 21.6% [3.2%]; P = 0.0031; N = 7 years each). The N = 67 anesthesiologists not on call differed in their percentages of workdays finishing after 5:00 PM (P < 0.0001). While the mean was 18% (6%), the coefficient of variation was 37% (29% to 49%). There were no significant outliers. In contrast, not only were there differences among anesthesiologists in the relative risks of working late when receiving relief versus when not handing off a case (P < 0.0001), there were outliers.

CONCLUSIONS

An anesthesia department aiming for a 20% incidence of anesthesiologists having to work late when not on call can achieve this objective, long-term, within a few percent (e.g., 2%). Seasonal variation can contribute to variability among quarters in the overall departmental incidence. Individual anesthesiologists can have variability among themselves, though, and that is caused by large heterogeneity in their relative risks of working late when receiving relief versus when not handing off a case. For departments choosing to provide information to anesthesiologists to increase predictability, factors to consider should include season of the year and the individual anesthesiologist.