David M. C. Powell

Fatigue risk management: Organizational factors at the regulatory and industry/company level

This paper focuses on the development of fatigue risk management systems (FRMS) in the transport sector. The evolution of regulatory frameworks is traced, from uni-dimensional hours of service regulations through to frameworks that enable multi-dimensional FRMS. These regulatory changes reflect advances in understanding of human error in the aetiology of accidents, and in fatigue and safety science. Implementation of FRMS shifts the locus of responsibility for safety away from the regulator towards companies and individuals, and requires changes in traditional roles. Organizational, ethnic, and national culture need to be considered. Recent trends in the work environment have potential to adversely affect FRMS, including precarious employment and shortages of skilled labour. Essential components of an FRMS, and examples of FRMS in different transport modes, are described. It is vital that regulators, employer, and employees have an understanding of the causes and consequences of fatigue that is sufficient for them to meet their responsibilities in relation to FRMS. While there is a strong evidence base supporting the principles of FRMS, experience with implementation is more limited. The evidence base for effective implementation will expand, since FRMS is data-driven, and ongoing evaluation is integral. We strongly advocate that experience be shared wherever possible.

The occupational health and safety of flight attendants.

In order to perform safety-critical roles in emergency situations, flight attendants should meet minimum health standards and not be impaired by factors such as fatigue. In addition, the unique occupational and environmental characteristics of flight attendant employment may have consequential occupational health and safety implications, including radiation exposure, cancer, mental ill-health, musculoskeletal injury, reproductive disorders, and symptoms from cabin air contamination. The respective roles of governments and employers in managing these are controversial. A structured literature review was undertaken to identify key themes for promoting a future agenda for flight attendant health and safety. Recommendations include breast cancer health promotion, implementation of Fatigue Risk Management Systems, standardization of data collection on radiation exposure and health outcomes, and more coordinated approaches to occupational health and safety risk management. Research is ongoing into cabin air contamination incidents, cancer, and fatigue as health and safety concerns. Concerns are raised that statutory medical certification for flight attendants will not benefit either flight safety or occupational health.

Fatigue in Airline Pilots After an Additional Day's Layover Period

INTRODUCTION We investigated the effect of an additional days layover on reducing fatigue in two different duties: a two-pilot crew flying between Auckland and Brisbane, and a three-pilot crew flying between Auckland and Los Angeles. METHODS Pilots completed a reaction time task, the Samn-Perelli fatigue scale, and the Karolinska Sleepiness Scale on both outward and return flights. The flights were conducted with and without a 1-d layover (Brisbane) and with a 1- or 2-d layover (Los Angeles). RESULTS On the Brisbane route, the addition of a layover resulted in a significant reduction of fatigue, sleepiness, and reaction time. At top of descent, Samn-Perelli fatigue was reduced from over 5.0 to under 4.5. In contrast, the addition of an extra day layover in Los Angeles had no significant effect on the same measures during the return flight; on both flights Samn-Perelli fatigue was over 5.0 at top of descent. DISCUSSION The results suggest that the addition of an extra nights layover has different effects depending on the type of operation. Layover periods need to ensure adequate opportunity to recover from any sleep deficit arising from the outbound journey, but the benefit of increased layover time may be limited if time-zone shifts cause a mismatch between local time and the circadian rhythm of sleep.

Non-technical skills evaluation in the critical care air ambulance environment : introduction of an adapted rating instrument - an observational study

BackgroundIn the isolated and dynamic health-care setting of critical care air ambulance transport, the quality of clinical care is strongly influenced by non-technical skills such as anticipating, recognising and understanding, decision making, and teamwork. However there are no published reports identifying or applying a non-technical skills framework specific to an intensive care air ambulance setting. The objective of this study was to adapt and evaluate a non-technical skills rating framework for the air ambulance clinical environment.MethodsIn the first phase of the project the anaesthetists’ non-technical skills (ANTS) framework was adapted to the air ambulance setting, using data collected directly from clinician groups, published literature, and field observation. In the second phase experienced and inexperienced inter-hospital transport clinicians completed a simulated critical care air transport scenario, and their non-technical skills performance was independently rated by two blinded assessors. Observed and self-rated general clinical performance ratings were also collected. Rank-based statistical tests were used to examine differences in the performance of experienced and inexperienced clinicians, and relationships between different assessment approaches and assessors.ResultsThe framework developed during phase one was referred to as an aeromedical non-technical skills framework, or AeroNOTS. During phase two 16 physicians from speciality training programmes in intensive care, emergency medicine and anaesthesia took part in the clinical simulation study. Clinicians with inter-hospital transport experience performed more highly than those without experience, according to both AeroNOTS non-technical skills ratings (p = 0.001) and general performance ratings (p = 0.003). Self-ratings did not distinguish experienced from inexperienced transport clinicians (p = 0.32) and were not strongly associated with either observed general performance (rs = 0.4, p = 0.11) or observed non-technical skills performance (rs = 0.4, p = 0.1).DiscussionThis study describes a framework which characterises the non-technical skills required by critical care air ambulance clinicians, and distinguishes higher and lower levels of performance.ConclusionThe AeroNOTS framework could be used to facilitate education and training in non-technical skills for air ambulance clinicians, and further evaluation of this rating system is merited.

Fatigue in Air Medical Clinicians Undertaking High-acuity Patient Transports

Abstract Background. Fatigue is likely to be a significant issue for air medical transport clinicians due to the challenging nature of their work, but there is little published evidence for this. Objective. To prospectively assess the levels and patterns of fatigue in air medical transport teams and determine whether specific mission factors influenced clinician fatigue. Methods. Physicians and flight nurses from two intensive care interhospital transport teams routinely completed fatigue report forms before and after patient transport missions over a 4-month period. Data collected included subjective ratings of fatigue (Samn-Perelli and visual analog scale), mission difficulty and performance. Multivariate hierarchical logistic and linear models were used to evaluate the influence of various mission characteristics on post-mission fatigue. Results. Clinicians returned 403 fully complete fatigue report forms at an estimated overall return rate of 73%. Fatigue increased significantly over the course of missions, and on 1 of every 12 fatigue reports returned clinicians reported severe post-mission fatigue (that is, levels of 6 or 7 on the Samn-Perelli scale). Factors that impacted significantly on clinician fatigue were the pre-mission fatigue level of the clinician, night work, mission duration, and mission difficulty. Poorer self-rated performance was significantly associated with higher levels of fatigue (r = −0.4, 95% CI −0.5 to −0.3), and for the 6-month period leading up to the study clinicians reported a total of 22 occasions on which they should have declined a mission due to fatigue. Conclusions. These results suggest that clinicians undertaking interhospital transports of even moderate duration experience high levels of fatigue on a relatively frequent basis. In the unique and challenging environment of air medical transport, prior fatigue, long or difficult missions, and the disadvantageous effect of night work on normal circadian rhythms are a combination where there are minimal safety margins for clinicians’ performance capacity. Fatigue prevention or fatigue resistance measures could positively affect air medical clinicians in this context.

Automated Collection of Fatigue Ratings at the Top of Descent: A Practical Commercial Airline Tool

INTRODUCTION There is a need to develop an efficient and accurate way of assessing pilot fatigue in commercial airline operations. We investigated the validity of an automated system to collect pilot ratings of alertness at the top of descent, comparing the data obtained with existing results from previous studies and those predicted by the validated SAFE fatigue model. METHODS Boeing 777 pilots were prompted to enter a Samn-Perelli fatigue scale rating directly into the flight management system of the aircraft shortly prior to descent on a variety of short- and long-haul commercial flights. These data were examined to evaluate whether the patterns were in line with predicted effects of duty length, crew number, and circadian factors. We also compared the results with data from previous studies as well as SAFE model predictions for equivalent routes. RESULTS The effects of duty length, time of day, and crew complement were in line with expected trends and with data from previous studies; the correlation with predictions from the SAFE model was high (r = 0.88). Fatigue ratings were greater on longer trips (except where mitigated by adding an extra pilot) and on overnight sectors (4.68 vs 3.77). DISCUSSION The results suggest that the automated collection of subjective ratings is a valid way to collect data on fatigue in an airline setting. This method has potential benefits for the crew in assessing fatigue risk prior to approach, as part of a fatigue risk management system, with the possibility of wider safety benefits.

The impact of fatigue on the non‐technical skills performance of critical care air ambulance clinicians

The relationship between fatigue‐related risk and impaired clinical performance is not entirely clear. Non‐technical factors represent an important component of clinical performance and may be sensitive to the effects of fatigue. The hypothesis was that the sum score of overall non‐technical performance is degraded by fatigue.

Comparison of In-Flight Measures with Predictions of a Bio-Mathematical Fatigue Model

INTRODUCTION Bio-mathematical models are increasingly used for predicting fatigue in airline operations, and have been proposed as a possible component of fatigue risk management systems (FRMS). There is a need to continue to evaluate fatigue models against data collected from crews conducting commercial flight operations. METHODS A comparison was made between several in-flight studies of pilot fatigue, conducted over a 10-yr period on a variety of operations, and the predictions of a widely used bio-mathematical model, the System for Aircrew Fatigue Evaluation (SAFE). The in-flight studies collected a variety of subjective ratings as well as reaction time on a performance task. RESULTS Overall correlation between observed and predicted fatigue was stronger for subjective fatigue than reaction time. More detailed analysis on selected studies shows discrepancies between predicted and observed fatigue, which may be explained by a variety of confounders. Closer analysis of the duty time, time of day, and schedule length show discrepancies of up to 15% between observed and predicted fatigue. DISCUSSION This study provides comparison between the predictions of one bio-mathematical model, SAFE, and observed fatigue measures across a number of operations. Possible causes of discrepancies are discussed. There is potential for more comparison studies of this type with the various available models.