FATIGUE IN AIR MEDICAL CLINICIANS UNDERTAKING HIGH-ACUITY PATIENT TRANSPORTS Julia A. Myers, NZ DipPhys, MHealSc, Michael F. Haney, M.D, PhD, Robin F. Griffiths, MB, ChB, (Hons.), MPP, FAFOEM, Nevil F. Pierse, MSc, PhD, David M.C. Powell, MB, ChB, FAFOEM
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ABSTRACT
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. Key words: fatigue; transportation of patients; air ambulances; medical staff
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 SamnPerelli 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
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INTRODUCTION The impact of fatigue is an important consideration for the air medical clinical environment: air medical clinicians work in clinical isolation and in a role that frequently involves high pressure, overnight work, and extended durations of duty.1–3 When critically ill patients require transport via air ambulance, clinicians must plan and deliver advanced levels of care under challenging conditions,1 with the added impact of factors from the aviation environment, such as altitude, restricted space and equipment, noise, and vibration.2 Fatigue is influenced by factors such as circadian rhythm, duration of sleep, and work hours.4 There are many published reports focused on understanding and managing the risks associated with fatigue in shift workers, particularly in safety-critical fields, where the additional influence of workload has also been noted.5–7 Research specific to health care has linked extended shifts, lack of recovery time, and night work to increased rates of fatigue and medical error8–10 and lower quality patient care.11 In aviation the risks associated with fatigue have been well recognized and there are a number of studies examining factors related to long or successive duty periods, circadian disruption, and high mental workload,12–15 but research has focused almost exclusively on cockpit crew. There is some work from prehospital air transport settings, which suggests that overall shift length does not impact adversely on cognitive or technical skills performance in air medical clinicians.1,16,17 However, that research did not account for actual workload during duty, with the clinicians in those studies obtaining significant rest and sleep during extended duty periods. It is likely that fatigue is a significant issue for air medical clinicians, in light of the nature of their work and the high workload associated with planning and delivering care in such a unique clinical environment.
Received March 17, 2014 from Occupational and Aviation Medicine, University of Otago, Wellington, New Zealand (JAM, MFH, RFG, DMCP), Anesthesia and Intensive Care Medicine, Ume˚a University Medical Faculty, Ume˚a, Sweden (MFH), and Department of Public Health, University of Otago, Wellington, New Zealand (NFP). Revision received May 8, 2014; accepted for publication May 23, 2014. The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper. We thank the Research Office and Flight Service clinicians from the PICU (Auckland, NZ), and also the Wellington ICU Flight Service and clinicians for their invaluable help in undertaking this study. Address correspondence to Julie Myers, Department of Medicine, University of Otago Wellington, PO Box 7343, Wellington 6242, New Zealand. E-mail: [email protected]. doi: 10.3109/10903127.2014.936633
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However, very little has been published that documents or clarifies the impact of fatigue in an air medical clinical environment. The objective of this research was to prospectively assess the levels and patterns of fatigue in air medical transport teams, with a specific focus on workload or missions actually undertaken. We hypothesized that air medical clinicians experience fatigue during missions, and that the degree of fatigue is influenced by multiple mission factors.
In both teams it was common for clinicians to extend their duties when conducting an air medical transport whether it was through extending a routine shift or returning “on-call” (after hours or on a rostered day off) to carry out a patient transport. However, it was not common practice for hospital-based clinicians to undertake additional transport work at outside agencies, and at both facilities there are duty limits and fatigue risk-management policies in place.
Procedure
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METHOD Study Population The study protocol was approved in advance by the Human Ethics Committee of the University of Otago [Ref no. 12/233]. All participants were clinicians (flight nurses and medical registrars) who worked in one of two New Zealand-based specialist interhospital transport teams, one pediatric team, and one general team, which transported mainly adult cases. The population and geographical characteristics of New Zealand, together with centralized tertiary care services, give rise to interhospital transport teams that are primarily staffed by intensive care clinicians: nurses with advanced training in critical care flight nursing and physicians from specialist training programs for intensive care, pediatrics, anesthetics, or emergency medicine. The clinicians often leave routine clinical duties to retrieve high-dependency or critical care patients from smaller regional hospitals when tertiary care is required. The pediatric transport team for this study always included a physician and at least one flight nurse. All clinicians in the pediatric team were based in the pediatric intensive care unit (PICU) but scheduled to cover flight duty on specific shifts if the need for retrieval arose, and also to provide on-call cover. These clinicians were therefore often flying “off the floor,” leaving routine clinical duties in the PICU if a patient transport was required during a scheduled shift. All clinicians in the general transport team were based in a general intensive care unit (ICU), with physicians usually flying “off the floor” when retrievals were required, but with flight nurses generally alternating ICU clinical shifts with designated patient transport shifts. The general team did not always transport with a physician on board if patients were nonventilated and deemed to be relatively stable, in which case the clinical team might consist of two flight nurses or a single flight nurse as sole clinician. An additional crew person also provided logistical and practical support for the patient transports, but while they may have had a background in emergency services or rescue, they were not designated members of the medical team.
All clinicians who were designated as members of the PICU and general ICU transport teams were approached by the research team with initial information about the purpose and requirements of the study. They were informed that participation was voluntary and that no individual information, whether provided at initial enrollment or during the study, would be identifiable or available to clinical supervisors or managers. Participants provided informed consent and completed an initial enrollment questionnaire, which requested demographic details and information about the extent of their clinical air medical experience. There was one general question about whether their sleep at home was regularly affected by living with children or other housemates. There were also two general questions about whether they had declined to go on any patient transports in the last 6 months because they were too fatigued, and how often over the last 6 months they had completed a transport mission but thought they should have declined because they were too fatigued. They also completed the Epworth Sleepiness Scale (ESS) for which scores greater than ten are indicative of excessive daytime sleepiness.18 The study took place over a 4-month period from November 2012 to March 2013. Fatigue report forms were attached to routine paperwork so clinicians who were enrolled in the study could complete a personal fatigue report form at the beginning and end of every patient transport mission. Attempts were made to maximize objective reporting in what may have been a sensitive situation by assignment of a unique study ID number, which allowed clinicians to return coded fatigue reports into closed return boxes in ICU clinical areas, once each transport mission was fully complete. Return boxes were cleared by research staff and no individual flight or clinician data were available to flight service operations or management.
Fatigue Report Form Data The operational definition of fatigue for this study was based on the definition from the International Civil Aviation Organization (ICAO), which states that “fatigue is a physiological state of reduced mental or physical performance capability resulting from sleep
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38 loss or extended wakefulness, circadian phase, or workload (mental and/or physical activity) that can impair a crew member’s alertness and ability to safely operate an aircraft or perform safety-related duties.”19 Fatigue was rated on the 7-point Samn-Perelli (SP) fatigue scale.20 This well-established scale was originally developed for U.S. aircrew and is recommended for use in fatigue risk-management systems by the International Civil Aviation Organization.19 It has also been used extensively in aviation and transport research.6,12,14,15,21 Subjects are asked to rate their fatigue on a scale where 1 = fully alert and wide awake; 2 = very lively, responsive but not at peak; 3 = OK, somewhat fresh; 4 = a little tired, less than fresh; 5 = moderately tired, let down; 6 = extremely tired, very difficult to concentrate; 7 = completely exhausted, unable to function effectively. Ratings were also made on a 100-mm visual analog scale (VAS) with “alert” at the low end of the scale up to “drowsy” at the upper limit. Other data collected on each fatigue report form included sleep in the previous 24 hours, in-mission sleep time, whether clinicians were on call or on a rostered shift, team composition for the mission, whether they were a physician or flight nurse, and whether the transport was fixed-wing or helicopter. These specialist teams sometimes also transport by road for weather or logistical reasons. All patients required intensive care transport, but they were noted as category A acuity if they were ventilated or needing other forms of advanced life support, such as pacing, vasoactive medication, or an intra-aortic balloon pump. They were noted as category B if they were high dependency, having generally undergone an acute incident requiring intensive care or specialist treatment with the potential for deterioration to the level where advanced life support may be required. Examples included patients with acute myocardial infarction, subarachnoid hemorrhage, spinal injury, or multitrauma. Clinicians were also asked to provide a single rating for the overall difficulty of each mission taking everything into account, including weather, logistical issues, patient condition, or unexpected events. They rated this on a 100-mm VAS from “straightforward” to “very difficult.” Similarly, self-rated performance was rated on a 100-mm VAS from “poor” to “excellent.” Mission duration was measured in hours and defined as the time from when direct preparation for the transport started, including mission planning and equipment packing, right through to when the mission was fully complete. This included transport on both the outward and inward leg of the mission and full handover to the receiving ICU clinical team. Day missions were defined as those which started at 6 a.m. or later and were fully completed by midnight of the same day, with all other missions classified as night missions.
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Statistical Analysis Post-mission SP fatigue scores of 6 or 7 were categorized as severe based on previous literature.6,20 The nature of the study meant that repeated flights were undertaken by the same individuals. The baseline characteristics of study participants and a weighted reflection of the individual mission fatigue reports they returned are reported descriptively (Table 1). In the analysis of our primary outcome of interest, the post-mission fatigue level, we allowed for the repeated measures by grouping the missions by individual in hierarchical models. As outlined above, post-mission fatigue was measured by two scales, a categorized SP scale and a continuous VAS scale. We therefore used a multivariate hierarchical logistic model on the SP scale and multivariate hierarchical linear model on the VAS scale. All potential covariates (listed in Table 2) were included in the initial models and these were then reduced by backward elimination. This type of model thus gives results on the outcome of post-mission fatigue, mutually adjusted for various factors, importantly including pre-mission fatigue. To keep the models consistent across both outcomes, mission difficulty was not eliminated for the logistic model despite its borderline significance of p = 0.06. A sensitivity analysis disregarding the data from three clinicians who had previously refused missions due to fatigue was also undertaken. The statistical analysis was done in R version 15.2 using the nlme package.22
RESULTS In total 62 clinicians with a wide range of transport experience were recruited to participate in the study (Table 1). The average (SD) age of flight nurses was 40 (±7) years compared to 33 (±5) for the physicians. The average (SD) air medical transport experience of flight nurses was 74 (±69) months in contrast to the physicians who had an average of 7 (±22) months experience. At enrollment more flight nurses than physicians reported routinely disturbed sleep outside of work, and returned ESS scores greater than 10. On their pre-study enrollment questionnaires three clinicians reported declining to go on a patient transport mission in the 6 months leading up to this study due to fatigue. These data also showed that in the 6 months leading up to the study clinicians reported undertaking a total of 22 transports which they should, in retrospect, have declined. There were 403 fully complete fatigue report forms returned from 277 different transport missions. With 331 missions in total tasked to these services during the study period, this represented 84% of missions for which fatigue reports were returned (Table 2). However, the overall estimated return rate for fatigue reports was 73% based on the total number of
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TABLE 1. Baseline characteristics of participating air medical clinicians and fatigue reports returned Flight nurses (n = 29)
Characteristic
Age in years, mean (±SD) Female participants, n (%) Experience in months, mean (±SD) Regular sleep disturbance, n (%) Epworth score >10, n (%) Declined a mission in past 6 months due to fatigue, n (%) Should have declined a mission in past 6 months due to fatigue, number of occasions (%)
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a
40 (±7) 26 (90) 74 (±69) 12 (41) 5 (17) 3 17
Fatigue reportsa (n = 258)
Physicians (n = 33)
33 (±5) 15 (45) 7 (±22) 5 (15) 2 (6) 0 5
209 (87) 94 (39) 63 (26) 37 (14) 80 (32)
Fatigue reports (n = 145)
53 (44) 19 (16) 4 (3) 0 11 (7.5)
Approximately 3% of fatigue reports returned did not include an ID number from which baseline characteristics could be reported.
critical care patient transport missions undertaken over the period of the study, the number of clinicians required for each mission, and an assumption that all transport clinicians were enrolled in the study. Flight nurses returned 258 fatigue reports (64%) and physicians returned 145 fatigue reports (36%), with 61% of the reports from missions undertaken by fixed-wing aircraft, 19% by helicopter, and the remaining 20% by road. The average (SD) duration of missions was 5.7 (±2.8) hours with 32% of reports from night missions. The average (SD) amount of sleep clinicians reported having in the 24 hours preceding their mission was 7 (±1.6) hours and average (SD) in-mission sleep time (reported) was 4 (±19) minutes. On 28% of the fatigue reports returned clinicians were
working on-call, and for a further 50% the clinicians had left a PICU or ICU clinical shift to undertake the mission. On 85% of reports returned there was more than one clinician on the mission and for almost half (46%), the patients being transported were category A, requiring ventilation or advanced life support. The median (IQR) number of missions undertaken by each clinician was 4 (2–7) (Table 2). On 1 in every 12 of the fatigue reports returned (8.2%) clinicians reported severe post-mission fatigue (that is, levels of 6 or 7 on the SP scale). As a proportion of missions for which data was returned there was at least one clinician reporting severe fatigue on 11.2% of missions. Fatigue increased significantly from the start to the end
TABLE 2. Summary of Mission Fatigue and Associated Characteristics Characteristic
Part 1. Total number of missions reported on (n = 277) Missions undertaken per clinician, median (IQR) Missions where clinicians reported severe fatigue, n (%) Night missions (inclusive of a period between midnight and 6 am), n (%) Two or more clinicians on the mission, n (%)
Number
4 (2–7) 31 (11.2) 86 (31) 217 (78)
Part 2. Summary of data from fatigue report forms (n = 403) 247 (61) General 156 (39) Pediatric 244 (61) Fixed-wing Transport mode, n (%) 78 (19) Helicopter 81 (20) Road Night mission (inclusive of a period between midnight and 6 am), n (%) 130 (32) Mission duration, mean hours (±SD) 5.7 (±2.8) Sleep during mission, mean minutes (±SD) 4 (±19) Two or more clinicians on the mission, n (%) 343 (85) Sleep in the 24 hours previous to the mission, mean hours (±SD) 7 (±1.6) On-call from home, n (%) 112 (28) Transport mission undertaken “off the floor,” n (%) 204 (50) Rostered for flight duty only, n (%) 87 (22) Patient acuity category A, n (%) 186 (46) Consecutive previous shifts worked, mean (±SD) 2 (±1.3) VAS fatigue pre-mission, mean (±SD) 21 (±18) VAS fatigue post mission, mean (±SD) 37 (±24) Increase between pre- and post-mission VAS fatigue, mean (95% confidence interval) 15.8 (13.8–17.9)∗ SP fatigue score ≥6 pre-mission, n (%) 1 (0.2) SP fatigue score ≥6 post-mission, n (%) 33 (8.2) VAS self-rated performance, mean (±SD) 78 (±16) VAS self-rated mission difficulty, mean (±SD) 28 (±27)
Range
1–28
Retrieval service, n (%)
1.25–20 0–240 2–14
0–9 0–78 0–100
16–100 0–100
∗ p < 0.001. Total number of transport missions tasked to the services during the study period, n = 331; number of clinicians required for transport missions during the study period, n = 550.
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TABLE 3. Hierarchical Linear Regression with Fatigue Level as Measured on a VAS Scale (0–100) as the Outcome
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Pre-mission fatigue (VAS scale 0–100) Mission duration Night mission Mission difficulty (0–1)
Estimated effect size per unit
95% LCL
95% UCL
P-value
0.57
0.46
0.68
< 0.0001
2.11 10.49 11.7
1.24 6.55 4.8
2.98 14.42 18.6
< 0.0001 < 0.0001 0.0001
of transports, as rated on a VAS scale (mean increase of 15.8, 95% CI 13.8–17.9). The results of the hierarchical linear model showed four factors significantly associated with the overall level of post-mission (VAS) fatigue (Table 3): pre-mission fatigue levels, duration of the mission, overnight missions, and mission difficulty. One other factor that approached significance was transporting via helicopter (p = 0.09). Factors that were not significant included the amount of sleep clinicians reporting having had in the previous 24 hours, the number of previous shifts worked, whether clinicians were working on-call or rostered, whether they were physicians or flight nurses, and whether they were members of the general or pediatric transport team. Post-mission VAS and SP fatigue ratings (Figure 1) were highly correlated (r = 0.86, 95% CI 0.84–0.89, p < 0.001), and while pre-mission SP fatigue ratings influenced post-mission ratings (SP), clinicians with low pre-mission fatigue could still finish at 6 or 7 on the SP scale (Figure 2). The results of the hierarchical logistic model (Table 4) showed that the odds of being severely fatigued by the
FIGURE 2. Relationship between pre-mission and post-mission fatigue, as measured on the Samn-Perelli (SP) fatigue scale. This boxplot illustrates medians and interquartile range. The whiskers extend to measurements less than 1.5 times the interquartile range from the box.
end of the mission rose by 2.19 for every step higher on the SP fatigue scale at which clinicians started out (95% CI 1.43–3.34). For each hour extra the mission lasted the odds of severe fatigue increased by 1.32 (95% CI 1.12–1.56). The odds for night missions (spanning midnight to 6 a.m.) were increased 4.05 times and the odds of severe fatigue for the most difficult mission were 4.71 that of the easiest mission but this was not statistically significant (p = 0.06). Clinician performance was also related to fatigue, with poorer self-rated performance correlating to higher levels of post-mission fatigue (VAS fatigue rating, r = −0.4, 95% CI −0.5 to −0.3, p < 0.001; and SP fatigue rating r = −0.34, 95% CI –0.42 to −0.25, p < 0.001).
DISCUSSION This is the first study we are aware of that has prospectively mapped fatigue in an interhospital air transport TABLE 4. Hierarchical Logisitic Regression with Severe Post-mission Samn-Perelli Fatigue Levels (6 or 7) as the Outcome Odds ratio 95% LCL 95% UCL
FIGURE 1. Relationship between Samn-Perelli (SP) and VAS ratings of fatigue. This boxplot illustrates medians and interquartile range. The whiskers extend to measurements less than 1.5 times the interquartile range from the box.
Pre-mission fatigue (SP scale 1–7) Mission duration (hours) Night mission Mission difficulty (0–1)
P-value
2.19
1.43
3.34
< 0.0001
1.32 4.05 4.72
1.12 1.52 0.91
1.56 10.77 24.5
0.0001 0.001 0.06
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setting. We found that air medical clinicians reported high levels of fatigue on a relatively frequent basis, with fatigue increasing significantly over the course of a mission. It is not clear what the clinical significance of this increase in fatigue was but research from patient settings estimate a change of 10 mm on a 100mm fatigue scale to be the minimum clinically important difference.23,24 In this study an increase of 10 mm would be caused by an increase in the pre-mission fatigue of 18 mm, 5 hours extra duration, a night mission, or a very difficult mission. These missions could be considered of moderate duration, averaging 5.7 (±2.8) hours including total travel time and handover back in the ICU. This duration is longer than is likely in emergency settings but shorter than missions typically seen in long-haul military or international repatriation critical care settings.2 There are few studies from this environment, but our results are consistent with research from pre-hospital transport settings showing clinician fatigue is common, and can often be severe.25–28 It may depend on how services are organized and delivered, however; for example, in some emergency air medical settings clinicians have the opportunity for significant rest and sleep during duty periods, and fatigue may even decline over long-duration duty periods.1 In contrast to emergency medical services (EMS) settings the clinicians in this study were often leaving routine clinical duties or being called in from home to undertake missions. So it is interesting to note their apparent burden of fatigue is only equivalent to the lowest reports from EMS literature, which estimate severe clinician fatigue as being between 10 and 55%.27 One explanation might be that many EMS workers finish a shift at one agency only to start a shift at a different organization,26 which is not common practice for the hospital-based services reported on here. The prospective nature of this study design also meant that fatigue was being reported in “real time” during clinical shifts as opposed to recalling “fatigue while at work.” There were a number of influences on the level of fatigue clinicians reported and whether or not it was severe. A third of all mission reports involved overnight work (between midnight and 6 a.m.), which was independently predictive of severe fatigue in this study. This is not surprising, given the known effects of circadian rhythm on night-time sleepiness and function.29 The impact of pre-mission fatigue was also significant, with many clinicians starting missions relatively fatigued. However, even clinicians who started out nonfatigued still experienced extreme tiredness or exhaustion by mission end (Figure 2). Research from primary air medical transport environments has tended to conceptualize workload in terms of overall shift duration and have shown it to have little effect.1,16,17 In contrast we attempted to capture mission-specific workloadrelated factors within shifts, by taking account of fac-
41 tors such as mission duration and difficulty, and found that they influenced clinician fatigue significantly. Research from other transport settings, such as rail, also highlights the importance of considering actual workload during shifts, in addition to sleep length and overall work hours and timing, when managing fatigue.6 In this study the number of previous shifts worked, and the amount of sleep clinicians reported in the 24 hours previous to their missions, did not impact significantly on post-mission fatigue, when adjustment was made for pre-mission fatigue. This implies that despite the key role sleep is known to play in affecting or managing fatigue4 it would not influence the level of postmission fatigue if clinicians who started “fatigued” were not included in the transport crew. It also indicates the influence of mission workload, represented by factors such as mission duration and difficulty, on post-mission fatigue. There are clinical implications for these results, in that the levels of fatigue reported suggest at least some level of performance decrement. Clinicians’ own rating of their performance was inversely related to their level of fatigue, and the wording of the Samn-Perelli descriptors illustrated that for 11.2% of missions reported on, there was at least one clinician working who was so fatigued he/she was finding it difficult to concentrate or function effectively by mission end. It is also notable that when asked to recall the 6-month period leading up to this study clinicians reported undertaking a total of 22 missions, which they retrospectively recognized should have been declined in light of the impact of fatigue. Work from other health-care settings has shown that fatigue impacts adversely on clinically relevant tasks, for example, reducing attention and vigilance, impairing decision making, and reducing the quality of communication.4,30,31 Clear associations between fatigue and medical error have also been described,4,26,32,33 with one study from the EMS setting reporting the odds of committing an error or adverse event as 2.2 times greater in fatigued than nonfatigued clinicians.26 While we did not attempt to measure error here, fatigued clinicians did report worse overall performance. Work from wider occupational settings concludes that there is clear evidence for the link between fatigue and safety outcomes,34 although they note that the relationship may be complex.7,35,36 There are also operational implications for these results in terms of service organization and fatigue risk management. This cohort averaged 7 hours sleep in the 24 hours preceding each mission, which is the lowest recommended daily allowance for healthy adults.37 If sleep is repeatedly restricted to less than 7 hours a night the result can be significant daytime cognitive dysfunction, though this does vary between individuals.38 While we did not employ a comprehensive validated tool to measure overall sleep quality, a single question answered at enrollment showed that
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many clinicians experienced regularly disturbed sleep due to factors outside the workplace, and a number of flight nurses (17%) reported concerning levels of susceptibility to daytime sleepiness (ESS scores greater than 10). Still, their rate of concerning daytime sleepiness was comparable to that of a New Zealand general population, where 15% of people reported ESS scores greater than 10,39 and significantly less that the rate of 38%, which was noted in a study of EMS workers.40 The predictive role of pre-mission fatigue has implications for services where transport clinicians are routinely leaving other clinical duties, or working oncall and outside routine hours. It would also clearly be a concern in services where clinicians are likely to have additional employment; for example, reports from some prehospital and air medical settings suggest 34–81% of clinicians are employed in more than one organization.25,41,42 The influence of pre-mission fatigue also highlights the relevance of fatigue risk management for this clinical setting. Most fatigue riskmanagement systems would include a policy of selfidentification for clinicians who feel too fatigued to carry out their clinical duties safely.43 While 3 flight nurses from this cohort had declined to undertake a mission in the previous 6 months on the grounds of fatigue, many missions had been undertaken that clinicians reported they should have declined. When factors unique to the air ambulance cabin environment (although not measured here) are also considered including noise and physical restriction of movement, as well as periods of monotony and clinical isolation, it is clear that air medical clinicians are particularly vulnerable for fatigue.
LIMITATIONS This study had several strengths, with a prospective design and results derived from actual workload in a clinical setting. However, there were limitations in that the sample size was relatively small, and the subjective ratings of fatigue were not linked to objective measures of performance or reports of error. On the other hand, evaluating fatigue very close to when there was risk of a fatigue-related error provides valuable insight into how clinicians felt in the clinical field, as opposed to asking them to recall. Sleep over the previous 24 hours was measured from self-report rather than using an objective means such as actigraphy, and mission difficulty was explored with a single question, the answer to which is subjective and might be influenced by a variety of factors both operational and individual. It is also possible that the observed association between post-mission fatigue and mission difficulty could be to some extent reverse causation. This study should be interpreted in its specific context of clinicians who were often working on-call or leaving clinical roles in the ICU. There were flight nurses who were older and more experienced than
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the physicians, and team members whose familiarity with transporting patients together was variable, even though they all worked in the same ICU. Mission duration and team characteristics may also vary from those generally seen in primary air medical settings. Therefore, these findings should not be generalized outside an interhospital air transport setting with similar characteristics. In addition, this study was performed in the summer months and we do not know whether the results would differ in seasonal conditions where there is less daylight. Future designs that focus on determining the impact of fatigue in air transport settings would be strengthened by the inclusion of objective measures including cognitive function, psychomotor vigilance, and sleep, if feasible methodologies for collecting such data in a busy clinical setting could be developed. Such research might be done more practically in a simulated air medical environment where the impact of high levels of fatigue on in-flight clinical performance might also be more safely examined. Consideration of nontechnical or human factor aspects of performance would also add value, given their direct contribution to safe and efficient clinical care.44
CONCLUSION This study demonstrated that clinicians who undertook interhospital transports of even moderate duration experienced high levels of fatigue on a relatively frequent basis. The degree of fatigue they reported was influenced by prior fatigue, the length or difficulty of their transport mission, and working during the night. Although fatigue-limiting and performanceenhancing routines (such as adequate pre-mission rest or sleep, breaks in monotony, breaks and rest periods during missions) might benefit medical crews in longer mission air ambulance workplaces, these are a challenge to implement given the nature of singlepatient transfer logistics and the prospect that clinicians may work in more than one organization. In summary, significant fatigue was reported by air medical crews on active patient transport missions. We conclude that fatigue prevention or fatigue resistance measures could positively affect air medical clinicians in this context, though this would need to be tested. Further study is also needed to assess the effects of mission fatigue on performance measures for air medical crews on longer missions.
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23.
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Myers JA, Psirides A, Hathaway K, Larsen PD. Air transport by the Wellington Flight Service: a descriptive analysis of interhospital transfers over a 5-year period in the Wellington region of New Zealand. N Z Med J. 2012;125:19–28. Lockley SW, Landrigan CP, Barger LK, Czeisler CA. Harvard Work Hours Health and Safety Group. When policy meets physiology: the challenge of reducing resident work hours. Clin Orthop Rel Res. 2006;449:116–27. Dawson D, Chapman J, Thomas MJW. Fatigue-proofing: a new approach to reducing fatigue-related risk using the principles of error management. Sleep Med Rev. 2012;16:167–75. Dorrian J, Baulk SD, Dawson D. Work hours, workload, sleep and fatigue in Australian Rail Industry employees. Appl Ergon. 2011;42:202–9. Folkard S, Akerstedt T. Trends in the risk of accidents and injuries and their implications for models of fatigue and performance. Aviat Space Environ Med. 2004;75:A161–7. Gaba DM, Howard SK. Fatigue among clinicians and the safety of patients. N Engl J Med 2002;347:1249–55. Gander P, Purnell H, Garden A, Woodward A. Work patterns and fatigue-related risk among junior doctors. Occup Environ Med. 2007;64:733–8. Lockley SW, Barger LK, Ayas NT, Rothschild JM, Czeisler CA, Landrigan CP; Harvard Work Hours Health and Safety Group. Effects of health care provider work hours and sleep deprivation on safety and performance. Jt Comm J Qual Patient Saf. 2007;33:7–18. Muecke S. Effects of rotating night shifts: literature review. J Adv Nurs. 2005;50:433–9. Gander PH, Signal TL, Van den Berg MJ, Mulrine HM, Jay SM, Mangie J. In-flight sleep, pilot fatigue and psychomotor vigilance task performance on ultra-long range versus long range flights. J Sleep Res. 2013;22:697–706. Gregory KB, Winn W, Johnson K, Rosekind MR. Pilot fatigue survey: exploring fatigue factors in air medical operations. Air Med J. 2010;29:309–19. Powell DM, Spencer MB, Petrie KJ. Fatigue in airline pilots after an additional day’s layover period. Aviat Space Environ Med. 2010;81:1013–7. Powell DMC, Spencer MB, Holland D, Broadbent E, Petrie KJ. Pilot fatigue in short-haul operations: effects of number of sectors, duty length, and time of day. Aviat Space Environ Med. 2007;78:698–701. Allen TL, Delbridge TR, Stevens MH, Nicholas D. Intubation success rates by air ambulance personnel during 12-versus 24hour shifts: does fatigue make a difference? Prehosp Emerg Care. 2001;5:340–3. Thomas F, Hopkins RO, Handrahan DL, Walker J, Carpenter J. Sleep and cognitive performance of flight nurses after 12-hour evening versus 18-hour shifts. Air Med J. 2006;25:216–25. Johns MW. A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep. 1991;14(6):540–5. International Civil Aviation Organization (ICAO). Fatigue Risk Management Systems Manual For Regulators. ICAO Doc 9966, Montreal, 2012. Samn SW, Perelli LP. Estimating aircrew fatigue: a technique with implications to airlift operations. Technical report no. SAM-TR-82–21. USAF School of Aerospace Medicine; Brookes AFB, TX, 1982. Powell D, Spencer MB, Holland D, Petrie KJ. Fatigue in twopilot operations: Implications for flight and duty time limitations. Aviat Space Environ Med. 2008;79:1047–50. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2012. Available from www.R-project.org/. Wells G, Li T, Maxwell L, MacLean R, Tugwell P. Determining the minimal clinically important differences in activity, fatigue, and sleep quality in patients with rheumatoid arthritis. J Rheumatol. 2007;34:280–9.
43 24. Schwartz AL, Meek PM, Nail LM, et al. Measurement of fatigue: determining minimally important clinical differences. J Clin Epidemiol. 2002;55:239–44. 25. Patterson PD, Suffoletto BP, Kupas DF, Weaver MD, Hostler D. Sleep quality and fatigue among prehospital providers. Prehosp Emerg Care. 2010;14:187–93. 26. Patterson PD, Weaver MD, Frank RC, et al. Association between poor sleep, fatigue, and safety outcomes in emergency medical services providers. Prehosp Emerg Care. 2012;16:86–97. 27. Patterson PD, Weaver MD, Hostler D, Guyette FX, Callaway CW, Yealy DM. The shift length, fatigue, and safety conundrum in EMS. Prehosp Emerg Care. 2012;16:572–6. 28. Sofianopoulos S, Williams B, Archer F. Paramedics and the effects of shift work on sleep: a literature review. Emerg Med J. 2012;29:152–5. 29. Van Dongen HP, Dinges DF. Circadian rhythms in fatigue, alertness, and performance. In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine, 3rd ed. Philadelphia: WB Saunders; 2000:391–9. 30. Gander P, Millar M, Webster C, Merry A. Sleep loss and performance of anaesthesia trainees and specialists. Chronobiol Int. 2008;25:1077–91. 31. Howard SK, Gaba DM, Smith BE, Weinger MB, Herndon C, Keshavacharya S, Rosekind MR. Simulation study of rested versus sleep-deprived anesthesiologists. Anesthesiology. 2003;98:1345–55. 32. Landrigan CP, Czeisler CA, Barger LK, Ayas NT, Rothschild JM, Lockley SW; Harvard Work Hours, Health and Safety Group. Effective implementation of work-hour limits and systemic improvements. Jt Comm J Qual Patient Saf. 2007;33(11, Suppl):19–29. 33. West CP, Tan AD, Habermann TM, Sloan JA, Shanafelt TD. Association of resident fatigue and distress with perceived medical errors. JAMA. 2009;302:1294–300. 34. Williamson A, Lombardi DA, Folkard S, Stutts J, Courtney TK, Connor JL. The link between fatigue and safety. Accid Anal Prev. 2011;43:498–515. 35. Dorrian J, Roach GD, Fletcher A, Dawson D. Simulated train driving: fatigue, self-awareness and cognitive disengagement. Appl Ergon. 2007;38:155–66. 36. Gander P, Hartley L, Powell D, et al. Fatigue risk management: organizational factors at the regulatory and industry/company level. Accid Anal Prev. 2011;43:573–90. 37. National Centre for Chronic Disease Prevention. CDC: Sleep and Sleep Disorders. [Internet]. Atlanta Centers for Disease Control and Prevention. 2013. [Cited 2014 April 29]. Available from www.cdc.gov/sleep/ 38. Banks S, Dinges DF. Behavioral and physiological consequences of sleep restriction. J Clin Sleep Med. 2007;3: 519–28. 39. Harris R. Obstructive sleep apnoea syndrome: symptoms and risk factors among Maori and non-Maori adults in Aotearoa [Thesis]. Department of Public Health, Wellington, NZ: University of Otago; 2003. 40. Fernandez A. An Assessment of the Relationship between Emergency Medical Services Work-life Characteristics, Sleepiness, and the Report of Adverse Events. Thesis, Dept of Public Health, Ohio State University; 2011. Available from etd.ohiolink.edu/ 41. Frakes MA, Kelly JG. Off-duty preparation for overnight work in rotor wing air medical programs. Air Med J. 2005;24:215–7. 42. Frakes MA, Kelly JG. Sleep debt and outside employment patterns in helicopter air medical staff working 24-hour shifts. Air Med J. 2007;26:45–9. 43. Lerman SE, Eskin E, Flower DJ, et al. Fatigue risk management in the workplace. J Occup Environ Med. 2012;54:231–58. 44. Flin RH, O’Connor P, Crichton M. Safety at the sharp end: a guide to non-technical skills. Farnham, UK: Ashgate; 2008.
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ABSTRACT
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. Key words: fatigue; transportation of patients; air ambulances; medical staff
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 SamnPerelli 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
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INTRODUCTION The impact of fatigue is an important consideration for the air medical clinical environment: air medical clinicians work in clinical isolation and in a role that frequently involves high pressure, overnight work, and extended durations of duty.1–3 When critically ill patients require transport via air ambulance, clinicians must plan and deliver advanced levels of care under challenging conditions,1 with the added impact of factors from the aviation environment, such as altitude, restricted space and equipment, noise, and vibration.2 Fatigue is influenced by factors such as circadian rhythm, duration of sleep, and work hours.4 There are many published reports focused on understanding and managing the risks associated with fatigue in shift workers, particularly in safety-critical fields, where the additional influence of workload has also been noted.5–7 Research specific to health care has linked extended shifts, lack of recovery time, and night work to increased rates of fatigue and medical error8–10 and lower quality patient care.11 In aviation the risks associated with fatigue have been well recognized and there are a number of studies examining factors related to long or successive duty periods, circadian disruption, and high mental workload,12–15 but research has focused almost exclusively on cockpit crew. There is some work from prehospital air transport settings, which suggests that overall shift length does not impact adversely on cognitive or technical skills performance in air medical clinicians.1,16,17 However, that research did not account for actual workload during duty, with the clinicians in those studies obtaining significant rest and sleep during extended duty periods. It is likely that fatigue is a significant issue for air medical clinicians, in light of the nature of their work and the high workload associated with planning and delivering care in such a unique clinical environment.
Received March 17, 2014 from Occupational and Aviation Medicine, University of Otago, Wellington, New Zealand (JAM, MFH, RFG, DMCP), Anesthesia and Intensive Care Medicine, Ume˚a University Medical Faculty, Ume˚a, Sweden (MFH), and Department of Public Health, University of Otago, Wellington, New Zealand (NFP). Revision received May 8, 2014; accepted for publication May 23, 2014. The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper. We thank the Research Office and Flight Service clinicians from the PICU (Auckland, NZ), and also the Wellington ICU Flight Service and clinicians for their invaluable help in undertaking this study. Address correspondence to Julie Myers, Department of Medicine, University of Otago Wellington, PO Box 7343, Wellington 6242, New Zealand. E-mail: [email protected]. doi: 10.3109/10903127.2014.936633
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However, very little has been published that documents or clarifies the impact of fatigue in an air medical clinical environment. The objective of this research was to prospectively assess the levels and patterns of fatigue in air medical transport teams, with a specific focus on workload or missions actually undertaken. We hypothesized that air medical clinicians experience fatigue during missions, and that the degree of fatigue is influenced by multiple mission factors.
In both teams it was common for clinicians to extend their duties when conducting an air medical transport whether it was through extending a routine shift or returning “on-call” (after hours or on a rostered day off) to carry out a patient transport. However, it was not common practice for hospital-based clinicians to undertake additional transport work at outside agencies, and at both facilities there are duty limits and fatigue risk-management policies in place.
Procedure
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METHOD Study Population The study protocol was approved in advance by the Human Ethics Committee of the University of Otago [Ref no. 12/233]. All participants were clinicians (flight nurses and medical registrars) who worked in one of two New Zealand-based specialist interhospital transport teams, one pediatric team, and one general team, which transported mainly adult cases. The population and geographical characteristics of New Zealand, together with centralized tertiary care services, give rise to interhospital transport teams that are primarily staffed by intensive care clinicians: nurses with advanced training in critical care flight nursing and physicians from specialist training programs for intensive care, pediatrics, anesthetics, or emergency medicine. The clinicians often leave routine clinical duties to retrieve high-dependency or critical care patients from smaller regional hospitals when tertiary care is required. The pediatric transport team for this study always included a physician and at least one flight nurse. All clinicians in the pediatric team were based in the pediatric intensive care unit (PICU) but scheduled to cover flight duty on specific shifts if the need for retrieval arose, and also to provide on-call cover. These clinicians were therefore often flying “off the floor,” leaving routine clinical duties in the PICU if a patient transport was required during a scheduled shift. All clinicians in the general transport team were based in a general intensive care unit (ICU), with physicians usually flying “off the floor” when retrievals were required, but with flight nurses generally alternating ICU clinical shifts with designated patient transport shifts. The general team did not always transport with a physician on board if patients were nonventilated and deemed to be relatively stable, in which case the clinical team might consist of two flight nurses or a single flight nurse as sole clinician. An additional crew person also provided logistical and practical support for the patient transports, but while they may have had a background in emergency services or rescue, they were not designated members of the medical team.
All clinicians who were designated as members of the PICU and general ICU transport teams were approached by the research team with initial information about the purpose and requirements of the study. They were informed that participation was voluntary and that no individual information, whether provided at initial enrollment or during the study, would be identifiable or available to clinical supervisors or managers. Participants provided informed consent and completed an initial enrollment questionnaire, which requested demographic details and information about the extent of their clinical air medical experience. There was one general question about whether their sleep at home was regularly affected by living with children or other housemates. There were also two general questions about whether they had declined to go on any patient transports in the last 6 months because they were too fatigued, and how often over the last 6 months they had completed a transport mission but thought they should have declined because they were too fatigued. They also completed the Epworth Sleepiness Scale (ESS) for which scores greater than ten are indicative of excessive daytime sleepiness.18 The study took place over a 4-month period from November 2012 to March 2013. Fatigue report forms were attached to routine paperwork so clinicians who were enrolled in the study could complete a personal fatigue report form at the beginning and end of every patient transport mission. Attempts were made to maximize objective reporting in what may have been a sensitive situation by assignment of a unique study ID number, which allowed clinicians to return coded fatigue reports into closed return boxes in ICU clinical areas, once each transport mission was fully complete. Return boxes were cleared by research staff and no individual flight or clinician data were available to flight service operations or management.
Fatigue Report Form Data The operational definition of fatigue for this study was based on the definition from the International Civil Aviation Organization (ICAO), which states that “fatigue is a physiological state of reduced mental or physical performance capability resulting from sleep
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38 loss or extended wakefulness, circadian phase, or workload (mental and/or physical activity) that can impair a crew member’s alertness and ability to safely operate an aircraft or perform safety-related duties.”19 Fatigue was rated on the 7-point Samn-Perelli (SP) fatigue scale.20 This well-established scale was originally developed for U.S. aircrew and is recommended for use in fatigue risk-management systems by the International Civil Aviation Organization.19 It has also been used extensively in aviation and transport research.6,12,14,15,21 Subjects are asked to rate their fatigue on a scale where 1 = fully alert and wide awake; 2 = very lively, responsive but not at peak; 3 = OK, somewhat fresh; 4 = a little tired, less than fresh; 5 = moderately tired, let down; 6 = extremely tired, very difficult to concentrate; 7 = completely exhausted, unable to function effectively. Ratings were also made on a 100-mm visual analog scale (VAS) with “alert” at the low end of the scale up to “drowsy” at the upper limit. Other data collected on each fatigue report form included sleep in the previous 24 hours, in-mission sleep time, whether clinicians were on call or on a rostered shift, team composition for the mission, whether they were a physician or flight nurse, and whether the transport was fixed-wing or helicopter. These specialist teams sometimes also transport by road for weather or logistical reasons. All patients required intensive care transport, but they were noted as category A acuity if they were ventilated or needing other forms of advanced life support, such as pacing, vasoactive medication, or an intra-aortic balloon pump. They were noted as category B if they were high dependency, having generally undergone an acute incident requiring intensive care or specialist treatment with the potential for deterioration to the level where advanced life support may be required. Examples included patients with acute myocardial infarction, subarachnoid hemorrhage, spinal injury, or multitrauma. Clinicians were also asked to provide a single rating for the overall difficulty of each mission taking everything into account, including weather, logistical issues, patient condition, or unexpected events. They rated this on a 100-mm VAS from “straightforward” to “very difficult.” Similarly, self-rated performance was rated on a 100-mm VAS from “poor” to “excellent.” Mission duration was measured in hours and defined as the time from when direct preparation for the transport started, including mission planning and equipment packing, right through to when the mission was fully complete. This included transport on both the outward and inward leg of the mission and full handover to the receiving ICU clinical team. Day missions were defined as those which started at 6 a.m. or later and were fully completed by midnight of the same day, with all other missions classified as night missions.
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Statistical Analysis Post-mission SP fatigue scores of 6 or 7 were categorized as severe based on previous literature.6,20 The nature of the study meant that repeated flights were undertaken by the same individuals. The baseline characteristics of study participants and a weighted reflection of the individual mission fatigue reports they returned are reported descriptively (Table 1). In the analysis of our primary outcome of interest, the post-mission fatigue level, we allowed for the repeated measures by grouping the missions by individual in hierarchical models. As outlined above, post-mission fatigue was measured by two scales, a categorized SP scale and a continuous VAS scale. We therefore used a multivariate hierarchical logistic model on the SP scale and multivariate hierarchical linear model on the VAS scale. All potential covariates (listed in Table 2) were included in the initial models and these were then reduced by backward elimination. This type of model thus gives results on the outcome of post-mission fatigue, mutually adjusted for various factors, importantly including pre-mission fatigue. To keep the models consistent across both outcomes, mission difficulty was not eliminated for the logistic model despite its borderline significance of p = 0.06. A sensitivity analysis disregarding the data from three clinicians who had previously refused missions due to fatigue was also undertaken. The statistical analysis was done in R version 15.2 using the nlme package.22
RESULTS In total 62 clinicians with a wide range of transport experience were recruited to participate in the study (Table 1). The average (SD) age of flight nurses was 40 (±7) years compared to 33 (±5) for the physicians. The average (SD) air medical transport experience of flight nurses was 74 (±69) months in contrast to the physicians who had an average of 7 (±22) months experience. At enrollment more flight nurses than physicians reported routinely disturbed sleep outside of work, and returned ESS scores greater than 10. On their pre-study enrollment questionnaires three clinicians reported declining to go on a patient transport mission in the 6 months leading up to this study due to fatigue. These data also showed that in the 6 months leading up to the study clinicians reported undertaking a total of 22 transports which they should, in retrospect, have declined. There were 403 fully complete fatigue report forms returned from 277 different transport missions. With 331 missions in total tasked to these services during the study period, this represented 84% of missions for which fatigue reports were returned (Table 2). However, the overall estimated return rate for fatigue reports was 73% based on the total number of
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TABLE 1. Baseline characteristics of participating air medical clinicians and fatigue reports returned Flight nurses (n = 29)
Characteristic
Age in years, mean (±SD) Female participants, n (%) Experience in months, mean (±SD) Regular sleep disturbance, n (%) Epworth score >10, n (%) Declined a mission in past 6 months due to fatigue, n (%) Should have declined a mission in past 6 months due to fatigue, number of occasions (%)
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a
40 (±7) 26 (90) 74 (±69) 12 (41) 5 (17) 3 17
Fatigue reportsa (n = 258)
Physicians (n = 33)
33 (±5) 15 (45) 7 (±22) 5 (15) 2 (6) 0 5
209 (87) 94 (39) 63 (26) 37 (14) 80 (32)
Fatigue reports (n = 145)
53 (44) 19 (16) 4 (3) 0 11 (7.5)
Approximately 3% of fatigue reports returned did not include an ID number from which baseline characteristics could be reported.
critical care patient transport missions undertaken over the period of the study, the number of clinicians required for each mission, and an assumption that all transport clinicians were enrolled in the study. Flight nurses returned 258 fatigue reports (64%) and physicians returned 145 fatigue reports (36%), with 61% of the reports from missions undertaken by fixed-wing aircraft, 19% by helicopter, and the remaining 20% by road. The average (SD) duration of missions was 5.7 (±2.8) hours with 32% of reports from night missions. The average (SD) amount of sleep clinicians reported having in the 24 hours preceding their mission was 7 (±1.6) hours and average (SD) in-mission sleep time (reported) was 4 (±19) minutes. On 28% of the fatigue reports returned clinicians were
working on-call, and for a further 50% the clinicians had left a PICU or ICU clinical shift to undertake the mission. On 85% of reports returned there was more than one clinician on the mission and for almost half (46%), the patients being transported were category A, requiring ventilation or advanced life support. The median (IQR) number of missions undertaken by each clinician was 4 (2–7) (Table 2). On 1 in every 12 of the fatigue reports returned (8.2%) clinicians reported severe post-mission fatigue (that is, levels of 6 or 7 on the SP scale). As a proportion of missions for which data was returned there was at least one clinician reporting severe fatigue on 11.2% of missions. Fatigue increased significantly from the start to the end
TABLE 2. Summary of Mission Fatigue and Associated Characteristics Characteristic
Part 1. Total number of missions reported on (n = 277) Missions undertaken per clinician, median (IQR) Missions where clinicians reported severe fatigue, n (%) Night missions (inclusive of a period between midnight and 6 am), n (%) Two or more clinicians on the mission, n (%)
Number
4 (2–7) 31 (11.2) 86 (31) 217 (78)
Part 2. Summary of data from fatigue report forms (n = 403) 247 (61) General 156 (39) Pediatric 244 (61) Fixed-wing Transport mode, n (%) 78 (19) Helicopter 81 (20) Road Night mission (inclusive of a period between midnight and 6 am), n (%) 130 (32) Mission duration, mean hours (±SD) 5.7 (±2.8) Sleep during mission, mean minutes (±SD) 4 (±19) Two or more clinicians on the mission, n (%) 343 (85) Sleep in the 24 hours previous to the mission, mean hours (±SD) 7 (±1.6) On-call from home, n (%) 112 (28) Transport mission undertaken “off the floor,” n (%) 204 (50) Rostered for flight duty only, n (%) 87 (22) Patient acuity category A, n (%) 186 (46) Consecutive previous shifts worked, mean (±SD) 2 (±1.3) VAS fatigue pre-mission, mean (±SD) 21 (±18) VAS fatigue post mission, mean (±SD) 37 (±24) Increase between pre- and post-mission VAS fatigue, mean (95% confidence interval) 15.8 (13.8–17.9)∗ SP fatigue score ≥6 pre-mission, n (%) 1 (0.2) SP fatigue score ≥6 post-mission, n (%) 33 (8.2) VAS self-rated performance, mean (±SD) 78 (±16) VAS self-rated mission difficulty, mean (±SD) 28 (±27)
Range
1–28
Retrieval service, n (%)
1.25–20 0–240 2–14
0–9 0–78 0–100
16–100 0–100
∗ p < 0.001. Total number of transport missions tasked to the services during the study period, n = 331; number of clinicians required for transport missions during the study period, n = 550.
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TABLE 3. Hierarchical Linear Regression with Fatigue Level as Measured on a VAS Scale (0–100) as the Outcome
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Pre-mission fatigue (VAS scale 0–100) Mission duration Night mission Mission difficulty (0–1)
Estimated effect size per unit
95% LCL
95% UCL
P-value
0.57
0.46
0.68
< 0.0001
2.11 10.49 11.7
1.24 6.55 4.8
2.98 14.42 18.6
< 0.0001 < 0.0001 0.0001
of transports, as rated on a VAS scale (mean increase of 15.8, 95% CI 13.8–17.9). The results of the hierarchical linear model showed four factors significantly associated with the overall level of post-mission (VAS) fatigue (Table 3): pre-mission fatigue levels, duration of the mission, overnight missions, and mission difficulty. One other factor that approached significance was transporting via helicopter (p = 0.09). Factors that were not significant included the amount of sleep clinicians reporting having had in the previous 24 hours, the number of previous shifts worked, whether clinicians were working on-call or rostered, whether they were physicians or flight nurses, and whether they were members of the general or pediatric transport team. Post-mission VAS and SP fatigue ratings (Figure 1) were highly correlated (r = 0.86, 95% CI 0.84–0.89, p < 0.001), and while pre-mission SP fatigue ratings influenced post-mission ratings (SP), clinicians with low pre-mission fatigue could still finish at 6 or 7 on the SP scale (Figure 2). The results of the hierarchical logistic model (Table 4) showed that the odds of being severely fatigued by the
FIGURE 2. Relationship between pre-mission and post-mission fatigue, as measured on the Samn-Perelli (SP) fatigue scale. This boxplot illustrates medians and interquartile range. The whiskers extend to measurements less than 1.5 times the interquartile range from the box.
end of the mission rose by 2.19 for every step higher on the SP fatigue scale at which clinicians started out (95% CI 1.43–3.34). For each hour extra the mission lasted the odds of severe fatigue increased by 1.32 (95% CI 1.12–1.56). The odds for night missions (spanning midnight to 6 a.m.) were increased 4.05 times and the odds of severe fatigue for the most difficult mission were 4.71 that of the easiest mission but this was not statistically significant (p = 0.06). Clinician performance was also related to fatigue, with poorer self-rated performance correlating to higher levels of post-mission fatigue (VAS fatigue rating, r = −0.4, 95% CI −0.5 to −0.3, p < 0.001; and SP fatigue rating r = −0.34, 95% CI –0.42 to −0.25, p < 0.001).
DISCUSSION This is the first study we are aware of that has prospectively mapped fatigue in an interhospital air transport TABLE 4. Hierarchical Logisitic Regression with Severe Post-mission Samn-Perelli Fatigue Levels (6 or 7) as the Outcome Odds ratio 95% LCL 95% UCL
FIGURE 1. Relationship between Samn-Perelli (SP) and VAS ratings of fatigue. This boxplot illustrates medians and interquartile range. The whiskers extend to measurements less than 1.5 times the interquartile range from the box.
Pre-mission fatigue (SP scale 1–7) Mission duration (hours) Night mission Mission difficulty (0–1)
P-value
2.19
1.43
3.34
< 0.0001
1.32 4.05 4.72
1.12 1.52 0.91
1.56 10.77 24.5
0.0001 0.001 0.06
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setting. We found that air medical clinicians reported high levels of fatigue on a relatively frequent basis, with fatigue increasing significantly over the course of a mission. It is not clear what the clinical significance of this increase in fatigue was but research from patient settings estimate a change of 10 mm on a 100mm fatigue scale to be the minimum clinically important difference.23,24 In this study an increase of 10 mm would be caused by an increase in the pre-mission fatigue of 18 mm, 5 hours extra duration, a night mission, or a very difficult mission. These missions could be considered of moderate duration, averaging 5.7 (±2.8) hours including total travel time and handover back in the ICU. This duration is longer than is likely in emergency settings but shorter than missions typically seen in long-haul military or international repatriation critical care settings.2 There are few studies from this environment, but our results are consistent with research from pre-hospital transport settings showing clinician fatigue is common, and can often be severe.25–28 It may depend on how services are organized and delivered, however; for example, in some emergency air medical settings clinicians have the opportunity for significant rest and sleep during duty periods, and fatigue may even decline over long-duration duty periods.1 In contrast to emergency medical services (EMS) settings the clinicians in this study were often leaving routine clinical duties or being called in from home to undertake missions. So it is interesting to note their apparent burden of fatigue is only equivalent to the lowest reports from EMS literature, which estimate severe clinician fatigue as being between 10 and 55%.27 One explanation might be that many EMS workers finish a shift at one agency only to start a shift at a different organization,26 which is not common practice for the hospital-based services reported on here. The prospective nature of this study design also meant that fatigue was being reported in “real time” during clinical shifts as opposed to recalling “fatigue while at work.” There were a number of influences on the level of fatigue clinicians reported and whether or not it was severe. A third of all mission reports involved overnight work (between midnight and 6 a.m.), which was independently predictive of severe fatigue in this study. This is not surprising, given the known effects of circadian rhythm on night-time sleepiness and function.29 The impact of pre-mission fatigue was also significant, with many clinicians starting missions relatively fatigued. However, even clinicians who started out nonfatigued still experienced extreme tiredness or exhaustion by mission end (Figure 2). Research from primary air medical transport environments has tended to conceptualize workload in terms of overall shift duration and have shown it to have little effect.1,16,17 In contrast we attempted to capture mission-specific workloadrelated factors within shifts, by taking account of fac-
41 tors such as mission duration and difficulty, and found that they influenced clinician fatigue significantly. Research from other transport settings, such as rail, also highlights the importance of considering actual workload during shifts, in addition to sleep length and overall work hours and timing, when managing fatigue.6 In this study the number of previous shifts worked, and the amount of sleep clinicians reported in the 24 hours previous to their missions, did not impact significantly on post-mission fatigue, when adjustment was made for pre-mission fatigue. This implies that despite the key role sleep is known to play in affecting or managing fatigue4 it would not influence the level of postmission fatigue if clinicians who started “fatigued” were not included in the transport crew. It also indicates the influence of mission workload, represented by factors such as mission duration and difficulty, on post-mission fatigue. There are clinical implications for these results, in that the levels of fatigue reported suggest at least some level of performance decrement. Clinicians’ own rating of their performance was inversely related to their level of fatigue, and the wording of the Samn-Perelli descriptors illustrated that for 11.2% of missions reported on, there was at least one clinician working who was so fatigued he/she was finding it difficult to concentrate or function effectively by mission end. It is also notable that when asked to recall the 6-month period leading up to this study clinicians reported undertaking a total of 22 missions, which they retrospectively recognized should have been declined in light of the impact of fatigue. Work from other health-care settings has shown that fatigue impacts adversely on clinically relevant tasks, for example, reducing attention and vigilance, impairing decision making, and reducing the quality of communication.4,30,31 Clear associations between fatigue and medical error have also been described,4,26,32,33 with one study from the EMS setting reporting the odds of committing an error or adverse event as 2.2 times greater in fatigued than nonfatigued clinicians.26 While we did not attempt to measure error here, fatigued clinicians did report worse overall performance. Work from wider occupational settings concludes that there is clear evidence for the link between fatigue and safety outcomes,34 although they note that the relationship may be complex.7,35,36 There are also operational implications for these results in terms of service organization and fatigue risk management. This cohort averaged 7 hours sleep in the 24 hours preceding each mission, which is the lowest recommended daily allowance for healthy adults.37 If sleep is repeatedly restricted to less than 7 hours a night the result can be significant daytime cognitive dysfunction, though this does vary between individuals.38 While we did not employ a comprehensive validated tool to measure overall sleep quality, a single question answered at enrollment showed that
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many clinicians experienced regularly disturbed sleep due to factors outside the workplace, and a number of flight nurses (17%) reported concerning levels of susceptibility to daytime sleepiness (ESS scores greater than 10). Still, their rate of concerning daytime sleepiness was comparable to that of a New Zealand general population, where 15% of people reported ESS scores greater than 10,39 and significantly less that the rate of 38%, which was noted in a study of EMS workers.40 The predictive role of pre-mission fatigue has implications for services where transport clinicians are routinely leaving other clinical duties, or working oncall and outside routine hours. It would also clearly be a concern in services where clinicians are likely to have additional employment; for example, reports from some prehospital and air medical settings suggest 34–81% of clinicians are employed in more than one organization.25,41,42 The influence of pre-mission fatigue also highlights the relevance of fatigue risk management for this clinical setting. Most fatigue riskmanagement systems would include a policy of selfidentification for clinicians who feel too fatigued to carry out their clinical duties safely.43 While 3 flight nurses from this cohort had declined to undertake a mission in the previous 6 months on the grounds of fatigue, many missions had been undertaken that clinicians reported they should have declined. When factors unique to the air ambulance cabin environment (although not measured here) are also considered including noise and physical restriction of movement, as well as periods of monotony and clinical isolation, it is clear that air medical clinicians are particularly vulnerable for fatigue.
LIMITATIONS This study had several strengths, with a prospective design and results derived from actual workload in a clinical setting. However, there were limitations in that the sample size was relatively small, and the subjective ratings of fatigue were not linked to objective measures of performance or reports of error. On the other hand, evaluating fatigue very close to when there was risk of a fatigue-related error provides valuable insight into how clinicians felt in the clinical field, as opposed to asking them to recall. Sleep over the previous 24 hours was measured from self-report rather than using an objective means such as actigraphy, and mission difficulty was explored with a single question, the answer to which is subjective and might be influenced by a variety of factors both operational and individual. It is also possible that the observed association between post-mission fatigue and mission difficulty could be to some extent reverse causation. This study should be interpreted in its specific context of clinicians who were often working on-call or leaving clinical roles in the ICU. There were flight nurses who were older and more experienced than
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the physicians, and team members whose familiarity with transporting patients together was variable, even though they all worked in the same ICU. Mission duration and team characteristics may also vary from those generally seen in primary air medical settings. Therefore, these findings should not be generalized outside an interhospital air transport setting with similar characteristics. In addition, this study was performed in the summer months and we do not know whether the results would differ in seasonal conditions where there is less daylight. Future designs that focus on determining the impact of fatigue in air transport settings would be strengthened by the inclusion of objective measures including cognitive function, psychomotor vigilance, and sleep, if feasible methodologies for collecting such data in a busy clinical setting could be developed. Such research might be done more practically in a simulated air medical environment where the impact of high levels of fatigue on in-flight clinical performance might also be more safely examined. Consideration of nontechnical or human factor aspects of performance would also add value, given their direct contribution to safe and efficient clinical care.44
CONCLUSION This study demonstrated that clinicians who undertook interhospital transports of even moderate duration experienced high levels of fatigue on a relatively frequent basis. The degree of fatigue they reported was influenced by prior fatigue, the length or difficulty of their transport mission, and working during the night. Although fatigue-limiting and performanceenhancing routines (such as adequate pre-mission rest or sleep, breaks in monotony, breaks and rest periods during missions) might benefit medical crews in longer mission air ambulance workplaces, these are a challenge to implement given the nature of singlepatient transfer logistics and the prospect that clinicians may work in more than one organization. In summary, significant fatigue was reported by air medical crews on active patient transport missions. We conclude that fatigue prevention or fatigue resistance measures could positively affect air medical clinicians in this context, though this would need to be tested. Further study is also needed to assess the effects of mission fatigue on performance measures for air medical crews on longer missions.
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