Mixed effects logistic regression modeling of daily evaluations of nurse anesthetists’ work habits adjusting for leniency of the rating anesthesiologists

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.

Patient and Operational Factors Do Not Substantively Affect the Annual Departmental Quality of Anesthesiologists' Clinical Supervision and Nurse Anesthetists' Work Habits

INTRODUCTION

Although safety climate, teamwork, and other non-technical skills in operating rooms probably influence clinical outcomes, direct associations have not been shown, at least partially due to sample size considerations. We report data from a retrospective cohort of anesthesia evaluations that can simplify the design of prospective observational studies in this area. Associations between non-technical skills in anesthesia, specifically anesthesiologists' quality of clinical supervision and nurse anesthetists' work habits, and patient and operational factors were examined.

METHODS

Eight fiscal years of evaluations and surgical cases from one hospital were included. Clinical supervision by anesthesiologists was evaluated daily using a nine-item scale. Work habits of nurse anesthetists were evaluated daily using a six-item scale. The dependent variables for both groups of staff were binary, whether all items were given the maximum score or not. Associations were tested with patient and operational variables for the entire day.

RESULTS

There were 40,718 evaluations of faculty anesthesiologists by trainees, 53,772 evaluations of nurse anesthetists by anesthesiologists, and 296,449 cases that raters and ratees started together. Cohen's d values were small (≤0.10) for all independent variables, suggesting a lack of any clinically meaningful association between patient and operational factors and evaluations given the maximum scores. For supervision quality, the day's count of orthopedic cases was a significant predictor of scores (P = 0.0011). However, the resulting absolute marginal change in the percentage of supervision scores equal to the maximum was only 0.8% (99% confidence interval: 0.2% to 1.4%), i.e., too small to be of clinical or managerial importance. Neurosurgical cases may have been a significant predictor of work habits (P = 0.0054). However, the resulting marginal change in the percentage of work habits scores equal to the maximum, an increase of 0.8% (99% confidence interval: 0.1% to 1.6%), which was again too small to be important.

CONCLUSIONS

When evaluating the effect of assigning anesthesiologists and nurse anesthetists with different clinical performance quality on clinical outcomes, supervision quality and work habits scores may be included as independent variables without concern that their effects are confounded by association with the patient or case characteristics. Clinical supervision and work habits are measures of non-technical skills. Hence, these findings suggest that non-technical performance can be judged by observing the typical small sample size of cases. Then, associations can be tested with administrative data for a far greater number of patients because there is unlikely to be a confounding association between patient and case characteristics and the clinicians' non-technical performance.

Lack of Benefit of Adjusting Adaptively Daily Invitations for the Evaluation of the Quality of Anesthesiologists' Supervision and Nurse Anesthetists' Work Habits

Introduction Whenever a department implements the evaluation of professionals, a reasonable operational goal is to request as few evaluations as possible. In anesthesiology, evaluations of anesthesiologists (by trainees) and nurse anesthetists (by anesthesiologists) with valid and psychometrically reliable scales have been made by requesting daily evaluations of the ratee's performance on the immediately preceding day. However, some trainees or nurse anesthetists are paired with the same anesthesiologist for multiple days of the same week. Multiple evaluations from the same rater during a given week may contribute little incremental information versus one evaluation from that rater for the week. We address whether daily evaluation requests could be adjusted adaptively to be made once per week, hopefully substantively reducing the number of evaluation requests. Methods Every day since 1 July 2013 at the studied department, anesthesia residents and fellows have been requested by email to evaluate anesthesiologists' quality of supervision provided during the preceding day using the De Oliveira Filho supervision scale. Every day since 29 March 2015, the anesthesiologists have been requested by email to evaluate the work habits of the nurse anesthetists during the preceding day. Both types of evaluations were made for interactions throughout the workday together, not for individual cases. The criterion for an electronic request to be sent is that the pair worked together for at least one hour that day. The current study was performed using evaluations of anesthesiologists' supervision and nurse anesthetists' work habits through 30 June 2023. Results If every evaluation request were completed by trainees on the same day it was requested, trainees would have received 13.5% fewer requests to evaluate anesthesiologists (9367/69,420), the maximum possible reduction. If anesthesiologists were to do the same for their evaluations of nurse anesthetists, the maximum possible reduction would be 7.1% fewer requests (4794/67,274). However, because most evaluations were completed after the day of the request (71%, 96,451/136,694), there would be fewer requests only if the evaluation were completed before or on the day of the next pairing. Consequently, in actual practice, there would have been only 2.4% fewer evaluation requests to trainees and 1.5% fewer to anesthesiologists, both decreases being significantly less than 5% (both adjusted P <0.0001). Among the trainees' evaluations of faculty anesthesiologists, there were 1.4% with very low scores, specifically, a mean score of less than three out of four (708/41,778). Using Bernoulli cumulative sum (CUSUM) among successive evaluations, 72 flags were raised over the 10 years. Among those, there were 36% with more than one rater giving an exceptionally low score during the same week (26/72). There were 97% (70/72) with at least one rater contributing more than one score to the recent cumulative sum. Conclusion Conceptually, evaluation requests could be skipped if a rater has already evaluated the ratee that week during an earlier day working together. Our results show that the opportunity for reductions in evaluation requests is significantly less than 5%. There may also be impaired monitoring for the detection of sudden major decreases in ratee performance. Thus, the simpler strategy of requesting evaluations daily after working together is warranted.

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.

Effect of Insufficient Interaction on the Evaluation of Anesthesiologists’ Quality of Clinical Supervision by Anesthesiology Residents and Fellows

Introduction

In this study, we tested whether raters’ (residents and fellows) decisions to evaluate (or not) critical care anesthesiologists were significantly associated with clinical interactions documented from electronic health record progress notes and whether that influenced the reliability of supervision scores. We used the de Oliveira Filho clinical supervision scale for the evaluation of faculty anesthesiologists. Email requests were sent to raters who worked one hour or longer with the anesthesiologist the preceding day in an operating room. In contrast, potential raters were requested to evaluate all critical care anesthesiologists scheduled in intensive care units during the preceding week.

Methods

Over 7.6 years, raters (N=172) received a total of 7764 requests to evaluate 21 critical care anesthesiologists. Each rater received a median/mode of three evaluation requests, one per anesthesiologist on service that week. In this retrospective cohort study, we related responses (2970 selections of “insufficient interaction” to evaluate the faculty, and 3127 completed supervision scores) to progress notes (N=25,469) electronically co-signed by the rater and anesthesiologist combination during that week.

Results

Raters with few jointly signed notes were more likely to select insufficient interaction for evaluation (P < 0.0001): 62% when no joint notes versus 1% with at least 20 joint notes during the week. Still, rater-anesthesiologist combinations with no co-authored notes accounted not only for most (78%) of the evaluation requests but also most (56%) of the completed evaluations (both P < 0.0001). Among rater and anesthesiologist combinations with each anesthesiologist receiving evaluations from multiple (at least nine) raters and each rater evaluating multiple anesthesiologists, most (72%) rater-anesthesiologist combinations were among raters who had no co-authored notes with the anesthesiologist (P < 0.0001).

Conclusions

Regular use of the supervision scale should be practiced with raters who were selected not only from their scheduled clinical site but also using electronic health record data verifying joint workload with the anesthesiologist.