The Accreditation Council for Graduate Medical Education first adopted duty hour standards for trainees in 2003 (1) in order to protect patients from errors by fatigued physicians and to protect trainees from the dangers of sleep deprivation. Following the initial work hour regulations, studies demonstrating evidence for both of these concerns were published (2–4). Further work hour reforms were instituted in 2010 and again in 2017 (5, 6). Accordingly, academic centers have been forced to significantly restructure staffing models given less work hour availability of trainees. With this change in staffing models, a greater burden of medical responsibilities may have shifted to attending physicians and advanced practitioners (7–10). Since duty hour reform has been instituted, patient outcomes have not clearly changed (11–14) and the effect on trainees appears to be mixed (14–18).
In contrast, less emphasis has been put on the downstream effects of work hour reforms on providers other than residents. In particular, the work and sleep of physicians who have completed training is seldom studied (19–21). Excessive work hours may result from a combination of obligations (clinical, administrative, research, and teaching), limited resources, and altruism. Even though senior physicians may be less prone to errors given greater clinical experience; sleep deprivation studies confirm errors in decision-making even in easy and familiar circumstances (22–25).
Minimal research has been conducted to measure work and sleep patterns in ICU physicians. Critically ill patients often have extreme and immediate needs with instability leaving little margin for error; they often require intense decision-making, urgent and unpredictable procedures, and communication and coordination among caregivers. Accordingly, some ICUs have implemented 24/7 in-hospital intensivists or telemedicine (26–28). In the academic setting, these staffing models can supply direct supervision, may reduce intensivist burnout, and may improve nursing satisfaction (29–31). In ICUs with a low-intensity daytime staffing model, addition of a nighttime intensivist may reduce mortality (32).
Our academic, tertiary care medical ICU (MICU) conducted a prospective, randomized study of nighttime staffing that examined patient outcomes (the Study to Understand Nighttime Staffing Effectiveness in a Tertiary Care ICU [SUNSET-ICU] trial) (33). In this observational study conducted during the trial, we measured sleep, work, and behavioral alertness in faculty and fellows during an ICU rotation in which weeks of work were randomized to in-hospital nighttime faculty intensivist staffing versus control.
MATERIALS AND METHODS
We conducted an observational study of work and sleep patterns in pulmonary and critical care (PCC) faculty and fellows rotating through the MICU at the Hospital of the University of Pennsylvania (HUP) from January 2012 to December 2012. Details of the parent randomized trial, which measured the effects of nighttime staffing models on ICU length of stay and patient outcomes, have been published previously and are detailed below (33). In the context of this parent study, we measured sleep, work, and behavioral alertness in faculty and fellows. The protocol was approved by the University of Pennsylvania Institutional Review Board (Approval 814878), and the trial was registered at ClinicalTrials.gov NCT01434823. All subjects gave written informed consent. Results of this study have been presented previously in abstract form (34).
Setting and Participants
The MICU is a 24-bed academic unit that is “closed” (mandatory intensivist as the primary provider) (35) and is consistently staffed with residents, fellows, and advanced practice providers (APPs—inclusive of Nurse Practitioners and Physician Assistants). Eligible participants included all faculty (intensivists) and PCC fellows who were assigned rotations in the MICU during 2012. There were no exclusion criteria.
Randomization and Interventions
As detailed in the parent study (33), there was weekly randomization of a nighttime staffing model, stratified by 2-week faculty rotation. Daytime staffing comprised two teams, each with six residents, an APP, a fellow, and an attending; daytime staffing was unchanged between the two models (Supplemental Table 1, Supplemental Digital Content 1, http://links.lww.com/CCM/E531). During the control staffing model, each team was represented by one or two in-hospital resident physicians during nighttime hours with the support of the team’s PCC fellow and faculty member, each available by phone. Generally, residents called the fellow first, and calls were escalated from the fellow to the faculty based on informal agreements and individual discretion. Both were available to return to the hospital if necessary. Three nights per week (in order for the fellow to have a both a weeknight off and a 36-hr period off from clinical duties) the attending alone was available for calls. In the intervention nighttime staffing model, the same cohort of in-hospital residents from both teams was fully supervised by an in-hospital nighttime intensivist (7 pm to 7 am) who was not otherwise involved in daytime patient care. The PCC fellow and the daytime intensivists did not have any expected nighttime duties after they signed out to the nighttime intensivist.
All subjects completed a baseline demographic assessment and Pittsburgh Sleep Quality Index (PSQI) (36). The PSQI is a validated self-administered measure of sleep quality and habits over the preceding month. Subjects completed the PSQI on day 1 of their MICU rotation in addition to providing their basic demographic data.
Subjects completed daily sleep and work logs detailing hours worked, calls received overnight, and sleep patterns. They additionally reported on their subjective sleepiness (using the Karolinska Sleepiness Scale, a 9-point verbally-anchored scale ranging from 1, “very alert,” to 9, “very sleepy, great effort to keep awake, fighting sleep”) (37) and their subjective self-assessment of well-being (e.g., alertness, stress, physical exhaustion) by marking Visual Analog Scales (VASs). Subjects additionally reported caffeine intake and exercise.
Participants wore a wrist actigraph with accelerometer and light sensors (Actiwatch Spectrum; Phillip Respironics, Murrysville, PA) in order to continuously track rest and activity patterns. Actigraphy data were collected in 1-minute epochs and stored in the watch until downloaded at 1-week intervals. Similar actigraphy devices have been validated and applied to study sleep patterns in physicians on call (38–43).
Subjects also completed a validated 3-minute psychomotor vigilance test (PVT) brief form on a designated machine (PVT-192; Ambulatory Monitoring, Ardsley, NY) to assess behavioral vigilance (44–46). They took these tests on weekday afternoons between 3 and 6 pm during the last 9 months of the study. The PVT measures alertness based on reaction time to stimuli presented at random 2- to 5- second interstimulus intervals.
The primary outcome was total sleep time (7 pm on one day to 7 pm the next day) as measured by actigraphy with supplemental data from sleep logs. Secondary outcomes were clustered in three domains: work hours, sleep, and behavioral vigilance. In regards to work hours, secondary outcomes included total work duration (hours per 24 hr period starting at 7 am), in-hospital work duration (hours worked in the hospital per 24 hr period starting at 7 am), home work duration (hours worked at home between leaving the hospital and returning to the hospital), nighttime work duration (hours worked between 7 pm and 7 am), and number and duration of overnight calls. Secondary sleep outcomes included number of awakenings per night and subjective assessments of sleepiness and alertness. PVT outcomes included response speed (reciprocal response time), median reaction time, the number of false starts (responses without a stimulus or response times < 100 ms), the number of lapses of attention (response times ≥ 355 ms), and 10% fastest and slowest reaction times.
The sample size for this study was predetermined based on the nighttime intensivist trial duration and the fixed rotation schedule for each attending and PCC fellow. The analyses were unadjusted, testing for differences between the intervention and control groups. The unit of analysis was the fellow or faculty day. To account for the correlation among participants (e.g., multiple observations and rotation assignments), all analyses used mixed effects models with random intercept. Intervention and control groups were compared with the least square means. All reported p values are two-sided. The a priori level of significance was set at 0.05. All statistical analyses were conducted with SAS 9.3 (SAS Institute, Cary, NC) and StataSE 13.1 (StataCorp, College Station, TX).
All faculty and fellows assigned to a daytime MICU team rotation during the 1-year study period participated in the SUNSET-ICU Sleep Study (n = 13 fellows, n = 20 faculty). See Table 1 for baseline characteristics of faculty and fellows. Faculty median age was 38.5, and fellow median age was 32. Baseline sleep quality for faculty and fellows, as assessed by the PSQI, were similar; however, fellows’ median score met the conventional threshold (of 5) indicating “poor sleep quality.” The most common areas of difficulty with sleep included subjective sleep quality, sleep duration, sleep disturbances, and daytime dysfunction. Faculty estimated a greater number of weekly work-hours than fellows, but similar hours worked per day.
The primary study outcome, daily total sleep time, and secondary outcomes of sleep and work are listed in Table 2. Daily sleep time was increased for faculty and fellows in the intervention staffing model—6.7 hours versus 6.4 in faculty intervention versus control and 6.7 versus 6.0 for fellows (both p < 0.001). Overnight phone calls, duration of overnight work, and overnight awakening periods differed significantly between intervention and control periods in both faculty and fellows, demonstrating that there was separation between groups (Table 2). In-hospital work duration did not differ between intervention and control staffing models for faculty or fellows (faculty, 11.2 vs 11.0 hr; fellows, 12.6 vs 12.5 hr; intervention vs control, respectively). However, total work done at home differed. Faculty worked 0.2 hours during the intervention versus 0.6 hours during the control; fellows worked 0.1 versus 1.0 hour, respectively (each p < 0.001). Behavioral vigilance as assessed by PVT demonstrated no significant differences in response speed, reaction time, lapses of attention, or false starts for faculty or fellows between staffing models (Supplemental Table 2, Supplemental Digital Content 2, http://links.lww.com/CCM/E532).
Sample double-plots of actigraphy data for an individual faculty and fellow participant are demonstrated in Figure 1. For Figure 1, the control staffing model was in place for week 1, and the intervention staffing model was in place for week 2. Interruptions in sleep (depicted as breaks in the medium blue areas) were common overnight during week 1 for both faculty and fellow. The median time of day for time of awakening, getting out of bed, arrival at work, and departure from work during work days for faculty and fellows were similar regardless of the staffing model (Supplemental Table 3, Supplemental Digital Content 3, http://links.lww.com/CCM/E533).
Fellows reported more trouble falling asleep in the control compared with the intervention group (16.6% vs 9.7%; p = 0.02) but faculty demonstrated no difference (Table 3). Both faculty and reported a higher frequency of nocturnal awakening and more difficulty resuming sleep during control weeks compared with intervention periods (faculty 32.3% control vs 10% intervention; fellows 34.9% vs 15.4%; both p < 0.0001). Faculty and fellows both reported significantly better sleep quality during the intervention (approximately a 10/100 point difference on the VAS) and each group felt more refreshed in the morning (five of 100 points difference for fellows, 12 for faculty). In summary, faculty scored less sleepiness, physical and mental exhaustion as well as more alertness during the intervention. Fellows scored less physical exhaustion and stress as well as more alertness during the intervention.
Coffee intake, soda intake, and energy drinks did not differ between faculty and fellows in the two staffing models (Supplemental Table 4, Supplemental Digital Content 4, http://links.lww.com/CCM/E534). Exercise and naps were uncommon and did not differ by staffing model for faculty or fellows.
In this single-center observational study of sleep and work in an academic MICU, we found that faculty and fellows work long hours each week irrespective of nighttime intensivist staffing. Fellows did work approximately 45 minutes less per day in the intervention model, but faculty work hours did not change. Daily sleep time was modestly increased for both faculty (20 min) and fellows (45 min) in the nighttime intensivist model. Although these statistically significant durations may seem small, they were accompanied by complementary, small improvements to sense of well-being in terms of sleepiness, alertness, and physical exhaustion.
A novel aspect of this study is that it reports work and sleep hours of nontrainee ICU physicians, which are rarely reported, let alone formally measured. Sleeping 6 hours per night has been reported by other physician groups and mirrors reports of sleep in the general population but is not ideal (47–51). In other professions, working 80 or more hours per week is unusual, so this may be a good reason to evaluate ICU staffing (47, 48, 52). This combination of work and sleep may be risky; however, we did not measure any difference in behavioral alertness. Similarly, no patient-level differences were detected in the parent randomized trial (including length of stay, mortality, and readmissions) (33).
Changes in cognitive performance or motor function between study arms were not detected by the PVT (39, 40). There are several plausible explanations. First, it is important to note that total sleep time in a 24-hour period is predictive of PVT performance, rather than the type of sleep fragmentation produced by being on-call (53). Additionally, given morning work demands, we elected to test faculty and fellows between 3 and 6 pm. This, coupled with only 9 months of sampling, limited our power. It is also possible that intensivists have altered circadian rhythms (from stress or chronic partial sleep deprivation) or chose critical care, in part because of their natural ability to function well despite stress and sleep deprivation (54–56). Interestingly, the effects of sleep deprivation on fatigue and mood are known to be greater than the effects on cognitive performance or motor function (57, 58). Perhaps, then, it is not surprising that the intervention staffing model was associated with a better sense of well-being when compared with the control.
Even when a colleague covered in hospital at night, clinicians in our study were minimally awake when at home. The group spent little time exercising; they experienced intermittent awakenings and inability to fall back asleep despite coverage. This may be a result of incomplete implementation, challenges of continuity of care, or our youthful faculty workforce. Assessments of work behaviors are crucial due to the now recognized commonality of burnout among ICU providers (59). Although we did not directly measure burnout in our study, a prior pilot study has shown that burnout may be reduced by nighttime staffing (30). Based on our observations, earlier nighttime transitions may be an opportunity to better impact work and sleep, and perhaps even intensify well-being. Alternatively, our physicians may need strategies to learn how to effectively care for patients yet limit their daily work and prioritize healthy sleep habits.
Limitations of this study include the self-reported nature of many data elements, including work hours and well-being outcomes. It is possible that our finding of improved well-being is the result of a placebo effect or due to subject-expectancy effect (subjects are aware that they are being studied and perhaps unconsciously try to have the study demonstrate what they think it should demonstrate). However, this is less likely given that, at the time, the future of nighttime intensivist staffing at the HUP was predicated on the results of the parent study, not this observational data. There are other variables which were not formally studied, including nighttime intensivists’ sleep, work, and alertness; burnout of participants; differential effect by sex; faculty productivity; trainee education and autonomy; the multidisciplinary team’s levels of collaboration, communication, understanding, and conflict; and finances. The perception of nurses experiencing this clinical trial has only been modestly explored but suggests perceived value (31). Finally, generalizability is limited given the small number of attendings and fellows on service during this study, which occurred in the context of a randomized clinical trial conducted in a tertiary academic environment.
Given the absence of improved patient outcomes studied during the randomized controlled trial, nighttime MICU intensivist staffing at HUP was abandoned after the trial completed in 2013. However, in 2017, HUP reversed course (in a highly controversial decision) and resumed nighttime intensivist staffing. This decision was driven by clinical expansion; continuous ICU bed strain yielded longer ICU wait times and a geographically distinct MICU was added that was exclusively staffed by advanced practitioners with variable experience levels. In our current state, the nighttime intensivists serve as primary clinicians in the new ICU, consultants for both of the established trainee-based MICU teams, on-site experts for bed flow and referring hospital communication, and participants in non-MICU clinical emergencies and procedures. Our participants are volunteers motivated by hands-on clinical care and the additional “per shift” remuneration. We intend to continue a volunteer approach assuming all shifts can be scheduled. This approach has allowed us to avoid adding challenging work hours on two known vulnerable populations: parents of young children and older clinicians. However, debates on equity and sustainability continue.
We acknowledge that one study and our proposed solutions may not fit others. From these experiences, we have learned that standardized data are powerful to guide administrative decision-making. We now conduct short, voluntary surveys of professional fulfillment and well-being across all ICU clinicians during service and nonservice blocks (60). These data has served as a means to gauge associations of burnout and fulfillment with existing models and will allow us to measure the effects of pilot projects in schedule design. We encourage others to do the same. In teaching hospitals, trainee education (and sometimes) autonomy are tracked through standardized rotation evaluations. These data should be reviewed with staffing models in mind. Even with all of this, we and others have many questions about nighttime work duties that cannot be answered with this dataset, or our current approach. Repeated questions arise on sustainability, academic productivity, administrative duties, and career advancement.
Other health systems have embraced telemedicine for nighttime staffing including a cross-health system ICU electronic ICU (e-ICU) (61) or contract-based e-ICUs, and, most creatively, temporary relocation of local clinicians to an e-ICU based in a time zone 12 hours apart from local time (NCT02895997). These approaches may work very well assuming existing on-site talent for hands-on care, particularly procedures and ultrasound.
This study demonstrates that a change in staffing models from a daytime intensivist model with fellow/faculty availability by phone at night to a daytime intensivist model with a separate in-hospital nighttime intensivist was associated with a reduction in fellow work-hours and a modest increase in attending and fellow sleep duration. Intervention periods were associated with fewer overnight calls, frequency and duration of awakenings, and self-perceived improvements in alertness and reductions in physical exhaustion. Although the parent randomized trial and systematic analyses of in-hospital nighttime intensivist models have not been proven to change patient outcomes (32, 33, 62), there may be improvements in the well-being of daytime intensivists. As our experience demonstrates, sites should structure themselves to conduct deliberate data collection to guide administrative decision-making. Although no single model will likely be successful, we all should pursue and measure whether our staffing models create an aggregate benefit for well-being, durable benefits against burnout, and valuable impacts on patient and nurse satisfaction as well as trainee education. This all must be done while considering costs. Given our workforce strain, our known high degree of burnout, and our objectively measured intense work hours, further discussions and data about ICU staffing models are highly relevant.
We thank Kyle Smith for his assistance with data entry and Adrian J. Ecker for his assistance with actigraphy data analysis. We also thank all faculty and fellows who participated in this study.
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