Hot off the press: Sleep and fatigue risk in emergency medicine physicians
Bibliographic record
Abstract
Emergency medicine is synonymous with shift work, and shift work is synonymous with fatigue. Poor sleep and chronic fatigue are often taken for granted in emergency medicine, but shift work is associated with increased rates of cancer, cardiovascular disease, and accidents.1 Even more concerning for most clinicians is the impact of fatigue on the care we provide for our patients. Even moderate levels of fatigue can impact performance similarly to being intoxicated with alcohol.2 Industrial studies indicate errors increase by as much as 30%–50% on night shifts.3 The evidence is more limited in medicine, but there are numerous studies focusing on resident physicians that tie fatigue to clinical errors, impaired cognition, reduced empathy, and increased interpersonal conflict.4 Here, we review the prospective observational study published by Fowler et al. in the March 2023 issue of Academic Emergency Medicine, providing critical analysis of the article and summarizing the social media discussion and a podcast in which the authors discuss their work.5 This is a prospective observational study that consists of a convenience sample of 17 emergency physicians from a single academic emergency department. Physicians wore a commercially available device that uses actigraphy to measure sleep quality and assess fatigue. A “ReadiScore” fatigue score was measured before and during clinical shifts. This score consists of three factors: sleep quality, sleep duration, and sleep efficiency (total sleep time divided by total time in bed). The authors conclude that sleep is an issue for many emergency physicians and that most physicians spend a significant proportion of their shifts in a fatigued state. This observational data set focuses on a clear question with important implications for emergency medicine. They use an objective, validated tool to measure sleep and fatigue, and the results are believable. There are several limitations to consider. This is a convenience sample that only included 17 physicians out of 131 working in this academic department. Physicians volunteering to participate in such a study might be significantly different from those who declined, resulting in selection bias and limiting generalizability. Furthermore, we know very little about the participants who volunteered. Were they known to have good or poor sleep prior to the study? Did they use any pharmacologic or nonpharmacologic sleep aids? How much caffeine did they consume on average? These and other baseline characteristics would have been very helpful to understand who volunteered for this study. Participants were not blinded to the hypothesis of the study, which raises the possibility of the Hawthorne effect, in that participants might have changed their usual sleeping habits because they knew they were being monitored. They were blinded to the ReadiScore for half of the study, thus limiting their ability to change their behavior in response to the score. This study utilizes a proprietary ReadiScore, which will be unfamiliar to most readers. Although the ReadiScore has been validated in prior research and is reported to be 93% accurate when compared to the criterion standard of polysomnography, the use of an unfamiliar score can make research difficult to interpret. An important distinction exists between clinically significant and statistically significant findings. Without knowing the minimal change in ReadiScore that is detectable clinically, the reported results are difficult to interpret. The scores vary slightly by shift starting time, but it is unclear whether the statistically significant differences have any clinical meaning. The lack of familiarity with this score is compounded by the lack of a comparison group. It would have been helpful to know how these physicians' scores compare to other physicians (perhaps those with only daytime office hours) or even with the same physicians during a vacation. Physicians spent almost one quarter of their time on shift in a fatigued state. Physicians slept a mean (±SD) of 6.8 (±1.8) h per night, but the quality of sleep was rated as poor (7.7/10, SD ±1.8). Shift start time only accounted for 1% of the variance among ReadiScores, although they did note a trend to lower ReadiScores with shifts that started both earlier and later in the day. Shift type (day, evening, night) was significantly associated with fatigue score, with night shifts being associated with higher fatigue scores. We are not surprised by the finding that emergency physicians spend a significant proportion of their time on shift in a fatigued state. However, for such a pervasive problem, sleep and fatigue receive relatively little attention in emergency medicine. Other professions have strict criteria describing when they are safe to work. There are still many gaps in our knowledge about the impacts of fatigue on patient safety, and the steps we can take to mitigate this problem, but it seems like this issue warrants greater attention from the emergency medicine community. Sandy Sea Bee (@desertchase): In our training, we all did the 24 hour shifts with the 3 hours of handover rounds followed by a half day of lectures then drive home (pray to not get in an accident) to try & get a few hours sleep to do it all again. My husband in aviation never understood how this was allowed. Gage says: I fear the medical industry policy makers (ie gov't) will look for simple solutions that looks good on the surface but has intended consequences downstream, because it is much more complicated. Emily Hirsh responds: This is very interesting to me! I agree with you that while the changes needed must occur at the systems level, ”easy“ changes (especially those made without input of the persons involved in the changes) are almost always fraught with negative downstream consequences, as you demonstrated in your comment. This is why I think the actual solutions will need to occur in stages, likely implemented more at the local level rather than nationally, with lots of input from EPs themselves, and with ongoing data collection, assessment of effects and consequences, and willingness to change strategy as needed. Dr. Brian Goldman (@NightShiftMD): This is a dirty little secret in much of clinical medicine. MDs are as reluctant to talk about their sleep as they are to talk about their errors. Josh Hargraves: “However, for most clinicians, it isn't the personal risk that bothers us.” At this point in my career, I think I disagree. I think most clinicians do not understand the personal risk physiological/psychological impact circadian disruption has on their day to day. Lauren Fowler responds: Josh, I agree. What I have experienced working with physicians is that they know they are fatigued, but they severely underestimate how fatigued they are. And when thinking about their fatigue, I am not sure they truly understand the consequences of this on their overall health and wellbeing. While more attention is being paid to the issues related to fatigue and sleep deprivation in medicine, it is still a long way to go to help educate physicians and everyone about the importance of sleep for overall health and wellbeing. Robin Mraz responds: … As a ER doc for 25 years, we actually do understand. The thing is, what are we supposed to do? ER is 24/7, 365. We really don't have a choice. Someone has to be there for our patients. Áine Yore, MD (@AineYoreMD): What is amazing to me is that study after study shows that fatigue critically impairs decision making and yet it's been really hard to show a relationship between factors that cause fatigue and patient harm. Dan: Humans are not at their best at 0330. Humans are not at their best when working twenty four hours straight. We have all experience [sic] the emotional fall off in those situations. It seems a fairly safe assumption that our cognitive skills fall off as well (there is evidence in the military medicine world that physical skills survive exhaustion much better than cognitive skills but I cannot recall the reference). Just think about the world of hurt that would befall a doc or a nurse who came to work with a blood alcohol of 0.08% yet routinely we work at that level of impairment (or much worse) from simple fatigue. We really need a paradigm shift if we want to address this. If we really valued healthcare, those who work unsocial hours (hate that term but it is what is used) should be strongly incentivized to work those hours so they do not have to work nearly as many hours so they can recover better. Lauren Fowler responds: Dan, I completely agree, and our group is hoping that some of the research we do will lead to meaningful change. At the very least, we are hoping to draw attention to possible mitigation and countermeasures for the fatigue, but ideally system change would happen. I worked with the military on fatigue research, and it was very similar. The higher ups didn't really pay attention when we said their pilots were tired, but when we told them the pilots were flying as if they had a .10 BAC, that got their attention. Hopefully this will have a similar effect! Emergency physicians frequently spend at least a portion of their shifts in a fatigued state, which may impact patient care. More research is needed to determine the impact of fatigue, and more importantly the systems-level interventions that might decrease fatigue with the goal of improving physician wellbeing and patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".