BACKGROUND: 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.

The improvement of surgical quality and the corresponding early detection of its changes is of increasing importance. To this end, sequential monitoring procedures such as the risk-adjusted CUmulative SUM chart are frequently applied. The patient risk score population (patient mix), which considers the patients\' perioperative risk, is a core component for this type of quality control chart. Consequently, it is important to be able to adapt different shapes of patient mixes and determine their impact on the monitoring scheme. This article proposes a framework for modeling the patient mix by a discrete beta-binomial and a continuous beta distribution for risk-adjusted CUSUM charts. Since the model-based approach is not limited by data availability, any patient mix can be analyzed. We examine the effects on the control chart\'s false alarm behavior for more than 100,000 different scenarios for a cardiac surgery data set. Our study finds a negative relationship between the average risk score and the number of false alarms. The results indicate that a changing patient mix has a considerable impact and, in some cases, almost doubles the number of expected false alarms.

BACKGROUND: In an advanced nuclear power plant (NPP), the operators are responsible for monitoring a massive number of alarm parameters.
OBJECTIVE: To assist the operators, a monitoring-aid system (MAS), that applies four quality control chart methods, was proposed and evaluated.
METHODS: Two types of MAS, namely, text and graph marks, were proposed and compared with the original display. To validate the proposed MAS, 17 professional engineers and operators were invited to join an experiment. Two different system states, normal and abnormal, were simulated. The operators were asked to manipulate the system, monitor the critical parameters, search for operational procedures, and deal with other secondary tasks. The primary and secondary task performance and heart rate were measured. After each task was conducted, three subjective rating questionnaires, namely, mental workload, situation awareness, and preference ratings, were implemented for the proposed MAS and the original system.
RESULTS: With the assistance of the MAS, the alarm detection rate, secondary task performance, and subjective mental workload demonstrate significant improvements. The proposed MAS helps the operators monitor critical parameters.
CONCLUSIONS: Therefore, the MAS should be considered for implementation with the control panel to increase the safety of NPPs. Furthermore, the MAS could reduce the mental workload might decrease the health hazard of the operators.

BACKGROUND: Previous studies suggest that many systematic reviews contain meta-analyses that display temporal trends, such as the first study\'s result being more extreme than later studies\' or a drift in the pooled estimate. We assessed the extent and characteristics of temporal trends using all Cochrane intervention reports published 2008-2012.
METHODS: We selected the largest meta-analysis within each report and analysed trends using methods including a Z-test (first versus subsequent estimates); generalised least squares; and cumulative sum charts. Predictors considered include meta-analysis size and review group.
RESULTS: Of 1288 meta-analyses containing at least 4 studies, the point estimate from the first study was more extreme and in the same direction as the pooled estimate in 738 (57%), with a statistically significant difference (first versus subsequent) in 165 (13%). Generalised least squares indicated trends in 717 (56%); 18% of fixed effects analyses had at least one violation of cumulative sum limits. For some methods, meta-analysis size was associated with temporal patterns and use of a random effects model, but there was no consistent association with review group.
CONCLUSIONS: All results suggest that more meta-analyses demonstrate temporal patterns than would be expected by chance. Hence, assuming the standard meta-analysis model without temporal trend is sometimes inappropriate. Factors associated with trends are likely to be context specific.

Cumulative meta-analysis (CMA) aims to aggregate accumulating evidence. Essentially a visual tool, CMA should be supplemented by formal statistical methods for assessment of the significance of the accumulating evidence, and for detection of temporal trends in effect sizes. These methods should also take into account multiple testing inherent in CMA. We review the existing methods for detection of temporal trends in effect sizes and suggest a new approach, namely the use of standard quality control (QC) charts, in particular X charts and CUSUM charts, to detect possible outliers and trends over time. We discuss the application of the QC charts to four popular measures of effect size: the odds ratios, the relative risks, the correlation coefficients and the standardized mean differences. Applications of QC charts are illustrated by three meta-analysis examples from medicine, ecology and evolutionary biology. Copyright © 2011 John Wiley & Sons, Ltd.