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Perioperative Medicine  |   January 2018
Prevalence of Potentially Distracting Noncare Activities and Their Effects on Vigilance, Workload, and Nonroutine Events during Anesthesia Care
Author Notes
  • From the Center for Research and Innovation in Systems Safety, Institute for Medicine and Public Health, Vanderbilt University, Nashville, Tennessee (J.M.S., E.S.P., A.N.L., M.S.S., M.B.W.); Departments of Anesthesiology (J.M.S., A.N.L., M.S.S., M.B.W.) and Biostatistics (M.S.S.), Vanderbilt University School of Medicine, Nashville, Tennessee; Amazon, Seattle, Washington (D.A.); and Health Services Research Division and Geriatrics Research Education and Clinical Center, Veterans Affairs Tennessee Valley Healthcare System–Nashville Campus, Nashville, Tennessee (M.B.W.).
  • This article is featured in “This Month in Anesthesiology,” page 1A.
    This article is featured in “This Month in Anesthesiology,” page 1A.×
  • Corresponding article on page 6.
    Corresponding article on page 6.×
  • This work was in part presented at the 2011 American Society of Anesthesiologists Annual Meeting in Chicago, Illinois, October 19, 2011; the 2016 Annual Meeting of the Anesthesia Patient Safety Foundation in Phoenix, Arizona, September 7, 2016; and the 2017 International Symposium on Human Factors and Ergonomics in Health Care in New Orleans, Louisiana, March 6, 2017.
    This work was in part presented at the 2011 American Society of Anesthesiologists Annual Meeting in Chicago, Illinois, October 19, 2011; the 2016 Annual Meeting of the Anesthesia Patient Safety Foundation in Phoenix, Arizona, September 7, 2016; and the 2017 International Symposium on Human Factors and Ergonomics in Health Care in New Orleans, Louisiana, March 6, 2017.×
  • Submitted for publication May 27, 2015. Accepted for publication July 25, 2017.
    Submitted for publication May 27, 2015. Accepted for publication July 25, 2017.×
  • Address correspondence to Dr. Slagle: Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, 1211 21st Avenue South, Medical Arts Building, Suite 732, Nashville, Tennessee 37212. jason.slagle@vanderbilt.edu. This article may be accessed for personal use at no charge through the Journal Web site, www.anesthesiology.org.
Article Information
Perioperative Medicine / Clinical Science / Education / CPD
Perioperative Medicine   |   January 2018
Prevalence of Potentially Distracting Noncare Activities and Their Effects on Vigilance, Workload, and Nonroutine Events during Anesthesia Care
Anesthesiology 1 2018, Vol.128, 44-54. doi:10.1097/ALN.0000000000001915
Anesthesiology 1 2018, Vol.128, 44-54. doi:10.1097/ALN.0000000000001915
Abstract

Background: When workload is low, anesthesia providers may perform non–patient care activities of a clinical, educational, or personal nature. Data are limited on the incidence or impact of distractions on actual care. We examined the prevalence of self-initiated nonclinical distractions and their effects on anesthesia workload, vigilance, and the occurrence of nonroutine events.

Methods: In 319 qualifying cases in an academic medical center using a Web-based electronic medical chart, a trained observer recorded video and performed behavioral task analysis. Participant workload and response to a vigilance (alarm) light were randomly measured. Postoperatively, participants were interviewed to elicit possible nonroutine events. Two anesthesiologists reviewed each event to evaluate their association with distractions.

Results: At least one self-initiated distraction was observed in 171 cases (54%), largely during maintenance. Distractions accounted for 2% of case time and lasted 2.3 s (median). The most common distraction was personal internet use. Distractions were more common in longer cases but were not affected by case type or American Society of Anesthesiologists physical status. Workload ratings were significantly lower during distraction-containing case periods and vigilance latencies were significantly longer in cases without any distractions. Three distractions were temporally associated with, but did not cause, events.

Conclusions: Both nurse anesthetists and residents performed potentially distracting tasks of a personal and/or educational nature in a majority of cases. Self-initiated distractions were rarely associated with events. This study suggests that anesthesia professionals using sound judgment can self-manage nonclinical activities. Future efforts should focus on eliminating more cognitively absorbing and less escapable distractions, as well as training in distraction management.

What We Know about This Topic
  • Anesthesia providers sometimes engage in potentially distracting activity unrelated to patient care

What This Article Tells Us That Is New
  • Self-initiated potentially distracting activities were common and largely restricted to stable portions of cases

  • Potentially distracting activity did not impair vigilance and was not responsible for any adverse events

DURING low workload periods, some clinicians may engage in activities not related to patient care (e.g., social conversations, reading personal email, etc.), which may be potentially distracting. Anesthesia professionals can experience prolonged periods of time during the maintenance phase of routine clinical cases that are relatively uneventful, characterized largely by the monitoring of a stable patient. Early task analysis studies found that, during routine maintenance periods, the clinician may perform few observable clinical tasks. Such idle periods may occupy substantial parts (up to 40%) of routine cases.1–3  Such periods appear to be associated with lower cognitive and physical demands.4,5  As a result, clinicians may become bored and seek to occupy their minds or hands with tasks unrelated to the immediate care of the patient.
In research conducted 15 yr ago at two adjacent academic medical centers on the West coast,5  we described the incidence of reading during anesthesia care because self-initiated distractions (e.g., reading or listening to music) raise concerns about reduced vigilance, lower care quality, lack of professionalism, and increased medicolegal risk.5–8  Reading was observed in 35% of cases but exclusively during the maintenance phase when workload was lower. Reading did not appear to affect vigilance, as measured by the providers’ response to the illumination of an alarm light.
However, our findings had a theoretical basis. Low workload produces a low arousal state that can impair performance.9  In laboratory-induced boredom, increased effort is necessary to suppress distracting stimuli and a generalized feeling of fatigue,10  adding tasks to a monotonous job can decrease boredom, and dividing attention among several tasks (time sharing) can improve performance.11,12  In a simulated driving study, performing a secondary verbal task improved driving quality (e.g., improvements in lane-keeping performance) and neurophysiologic measures of alertness.13  Nonetheless, a limitation of our operating room (OR) study was the absence of any measure of impact of the self-initiated distractions we observed.
With the evolution of information and communication technology, there appears to be greater opportunities to be distracted in the OR.14  Anesthesia providers now have access to numerous new electronic ways to be distracted, from desktop computer Web browsing to cell phone tweeting. We therefore sought to replicate our previous study in this new milieu, with three important differences: (1) we conducted the study at a large Mid-South academic medical center where the anesthesia providers had ad lib access to the internet on their anesthesia record-keeping system and also all had cell phones and/or tablet computers with them in the OR; (2) we video recorded every study case; and (3) we queried each provider at the end of the case to detect the occurrence of nonroutine events (NREs), which is any event that deviates from expected or optimal care for a specific patient in that specific situation.15–17 
The goal was to elucidate the nature and incidence of potentially distracting non–patient care activities during anesthesia care in the era of ubiquitous computing and to investigate how such non–patient care activities affect patient care tasks, clinical workload, and vigilance, as well as their relationship to NREs.
Materials and Methods
To delineate the epidemiology of intraoperative distractions and elucidate their potential impact, we conducted intraoperative behavioral task analysis, videotaping, workload and vigilance assessments, and intraoperative internet usage tracking. For purposes of this study, we defined a distraction as any observable intraoperative activity that was unrelated to patient care or clinical work activities.
Participants
The focus of this study was the behavior of primary anesthesia providers, anesthesiology residents, and nurse anesthetists at a single academic medical center. In addition to obtaining institutional review board approval, we received approval from our institutional risk management and human resources attorneys. Study data were deidentified and kept secure. As part of the consent process, participants were informed about all aspects of the study except the focus of the study on intraoperative distractions and that we were recording their intraoperative internet use. After primary anesthesia providers gave written informed consent, 364 elective general anesthesia cases were studied from August 2007 to October 2009. On completion of the project’s data collection phase, all of the participants were notified (i.e., debriefed) of the nondisclosed aspects of the project and given the opportunity to withdraw their data from the study. Only 3 of 89 participants withdrew (their data) from the study after debriefing.
Direct Structured Observation
Before each study case, participants provided basic demographic data via a brief questionnaire that included their overnight sleep history. A single observer (E.S.P.), trained to reliable performance,18  collected all of the intraoperative data. The observer sat in the OR and categorized each clinician’s activities into 79 possible tasks that were bundled into nine larger task categories (manual; observing; conversing; recording; miscellaneous clinical tasks; and electronic communication tasks that were related either directly to the current case, were more generally work related and therefore not considered a nonclinical distraction, or that were a self-initiated personal or educational distraction). We only briefly summarize the procedures for task classification, observation methods, observer training, and reliability assessment, because they have been described in numerous previous publications.1,2,4,5,18,19  Observations began when the patient entered the OR and ceased when the patient departed the OR. No data were collected when a study participant was on break. The observer categorized observable tasks, noted events (e.g., surgical incision and closure), and initiated (on computer prompt) workload ratings and alarm light latency measurements. Each event or task occurrence was recorded by clicking with a mouse on the appropriate button on the computer display. The software then automatically logged the time and task initiated. If the participants were reading or reviewing paper or electronic case-related materials while in the process of record keeping (medical documentation), then this was categorized as recording and not reading.
If two tasks occurred simultaneously, multitasking was indicated with start/stop multitasking markers in the task analysis file. The times for all simultaneously performed tasks, as indicated by the multitasking markers, were counted until the end multitasking marker for both tasks. To ensure the correct calculation of the amount of time spent on tasks that were simultaneously performed (i.e., each task within the multitasking markers), the total time for the case (i.e., the denominator used for the percentage time calculations) was extended by the time of the overlapping tasks. All of the tasks within the multitasking markers were also given an additional occurrence to their tallies for the number of occurrences.
End of induction was defined as the time when the patient had been intubated and the endotracheal tube had been secured or when the anesthesia provider had told the surgeons that they could begin operating, whichever occurred first. Beginning of emergence was defined as occurring when the anesthesia provider shut off all of the anesthetic agents and began delivering 100% oxygen.
Each case studied was assigned a randomly generated case number to assure anonymity. All of the participant and patient identifiers were removed. Standardized forms were used to capture the relevant case summary data elements. A custom NRE database, written in open-source software, included patient characteristics, anesthesia, surgical and provider variables, and NRE information.
A total of 319 of the original 364 cases were selected for analysis based on the following criteria: a case duration of at least 0.75 h but no more than 6 h, patient American Society of Anesthesiologists (ASA) physical status score of 3 or lower, and complete case data. After this screening process, all cases containing personal or educational distractions were identified. As in our previous study,5  the distraction cases were further subdivided by time into periods when distractions occurred and periods when there were no distractions. To simplify data analysis, tasks performed during each case were grouped into nine predetermined aggregated categories, including manual, observing, conversing, recording, other clinical, case-related electronic communications tasks (e.g., anesthesia record keeping), work-related electronic communication tasks (but unrelated to the current case), and personal/educational distraction tasks.
Vigilance Measurement
The anesthesia provider’s vigilance was measured by the time it took to respond to the random illumination of a red alarm light, based on software prompts at 7- to 15-min random intervals. Depending on the OR and the clinical equipment configuration, the vigilance light was either placed atop the physiologic monitors or between the vital signs monitor and the gas analysis monitor.1,2  Participants were instructed to, either verbally or by hand signal (participant’s preference), indicate their detection of the illuminated light. The calculated response time between light illumination and its detection provided a measure of the response latency to a new (secondary) task demand. Because the illumination prompts occurred within random intervals, the number of response latency measurements depended on case duration.1,2 
Workload Measurements
Psychologic workload was assessed by the trained observer and by the participants themselves using a visual analog scale, ranging from 6 (no exertion) to 20 (maximum exertion).1,2  At random 8- to 12-min intervals, the computer prompted the observer to score the participant’s workload and then to query the participant as to his or her own workload rating.
Internet Use Tracking
To determine the incidence and nature of intraoperative internet browsing (i.e., nonclinical, case-specific, and/or general/educational clinical searches), excluding online record keeping, documentation, and paging, the browser’s history logs were collected from the anesthesia-dedicated workstation, labeled, and archived on a secure server immediately after each study case. Custom software created spreadsheets containing the browser page access history (each visited page’s domain, subdomain, and time of access). These data were time linked to other case-specific study data elements, deidentified, and imported into the study database. The browser logs were manually coded by the research team as either clinical/educational (e.g., PubMed) or nonclinical (e.g., ebay.com). Coded browsing data were then used to validate task-analysis data, which provided the actual time spent on internet use via direct observation and/or video review.
NRE Data Capture and Review
At the end of each case studied, the Comprehensive Open-ended Nonroutine Event Survey17  was administered to the anesthesia provider participant by trained observers. Possible NREs, identified based on the clinician’s response to nine yes or no questions, were then elucidated using open-ended questions about the nature of and potential contributors to the event. If not volunteered, the trained observers asked the provider about any events that the researcher may have observed during the case.
Two domain experts first independently reviewed each event and determined whether the reported event met the definition of a nonroutine event. If there was disagreement between expert reviewers, they would meet and have an open discussion of that particular event to reach consensus. On the rare occasion that agreement could not be obtained, a third expert reviewed the case and potential NRE. Unanimous agreement was reached for all of the events.
We used a screening process to identify potentially distracting activities that could have affected the occurrence, management, or outcome of identified NREs. We included all types of self-initiated personal/educational distractions (i.e., nonclinical distractions or non–patient care activities). As is detailed further in the Results section, there were 88 cases that contained both nonclinical distractions and at least one NRE. For each event, two clinician investigators (A.N.L. and M.B.W.) reviewed the complete time-sequence case spreadsheet that included all of the relevant tasks, distractions, and NRE information to identify those cases where there was a possibility that a distraction was related to the NRE. The videos of the resulting 42 cases were then reviewed with the results iteratively discussed until the two clinicians reached consensus as to whether any NRE was affected by a distraction.
Sample Size and Statistical Analysis
These data were collected as part of an Anesthesia Patient Safety Foundation–funded (Rochester, Minnesota) research project for which we performed a statistical power analysis based on a priori assumptions about the incidence of NREs reported postoperatively and of nonclinical distractions. We evaluated the power to detect an unadjusted association between the occurrence of one or more nonclinical distractions and one or more NREs using the Pearson chi-square test. Based on previous work, we estimated that the incidence of one or more nonclinical distractions was greater than 30% and that the incidence of one or more NREs was 35% among cases with distractions versus 20% for cases without distractions. Under these assumptions, approximately 350 cases are required to achieve 80% power at 5% type I error rate. In implementing the study, we collected 364 cases before debriefing subjects about the true nature of the study and ending data collection for the project. Only 319 of these cases met the inclusion criteria for the analyses that we conducted.
All of the quantitative data are summarized using the median, interquartile range, and range. For each task and task category, the percentage of total case time, average task duration, and other quantitative factors were compared by case type (distraction vs. nondistraction cases) using the Wilcoxon rank sum test and by distraction versus nondistraction periods within distraction cases using the Wilcoxon signed-rank test (i.e., a paired test). The Pearson chi-square test was used to compare categorical factors by case type.
The effects of case type and period type on self-reported workload and vigilance latency (dependent variables) were quantified using mixed-effects regression methods, adjusting for provider months of experience, duration of sleep in the most recent rest period, case duration, surgery type, and patient ASA classification. The effects of provider months of experience, sleep duration, and case duration were modeled using a four-knot natural spline. Two independent, nested random intercepts, indexed by provider and case, respectively, were used to account for heterogeneity in workload ratings and vigilance latencies among providers and cases. Vigilance and workload data were transformed using the natural logarithm to reduce skew. Residual diagnostics were examined to verify that this transformation was sufficient to satisfy the regression assumptions (i.e., residual normality).
P values less than 0.05 were considered statistically significant. Due to the complexity of the correlations between all of the test statistics, we did not attempt to make adjustments to preserve the family wise type I error rate.
Results
The distraction (n = 171; i.e., nonclinical distraction cases) and no distraction (n = 148) case cohorts did not differ significantly by case type or ASA status (P = 0.705 and P = 0.451, respectively; tables 1 and 2), with the majority of cases involving patients with an ASA status of 2 or 3. The average duration of distraction cases overall, as well as of the maintenance phases, was significantly longer than nondistraction cases (P < 0.001 and P < 0.001, respectively; table 1). During distraction cases, the periods with distractions (table 1) were much briefer than periods without any distractions (P < 0.001). Distractions during care were more common in anesthetics performed by certified registered nurse anesthetists than by anesthesiology residents (P = 0.005; table 1).
Table 1.
Study Case Characteristics
Study Case Characteristics×
Study Case Characteristics
Table 1.
Study Case Characteristics
Study Case Characteristics×
×
Table 2.
Study Cases by Type of Surgery
Study Cases by Type of Surgery×
Study Cases by Type of Surgery
Table 2.
Study Cases by Type of Surgery
Study Cases by Type of Surgery×
×
The occurrence of at least one nonclinical distraction was observed in 54% of the cases, although the distractions accounted for only approximately 2% (median) of total case time and 3% of total maintenance period time. The median activity duration (i.e., dwell time) of each distraction was 2.3 s (25% = 1.0 s; 75% = 4.1 s; table 3) before switching to another task. Almost half (49%) of the distractions were personal in nature, whereas approximately one fourth were educational in nature (the latter being much more common in residents than certified registered nurse anesthetists). The most prevalent type of distraction was internet browsing for personal (0.2 [0.0, 2.1%], 0 to 24% of case time, 14.7 [0.0, 33.9], 0- to 95.0-s dwell time or activity duration) or educational (0.0 [0.0, 0.8%], 0 to 9.3% of case time, 0.0 [0.0, 23.8], 0- to 117.5-s dwell time) purposes and accessing email (see table 3). Other distractions observed, albeit less commonly, included the use of a personal electronic device (e.g., for scheduling activities and viewing reminders), personal phone calls, conversations of a personal (e.g., chatting with nurses and/or surgeons) or educational nature (e.g., talking with the attending physician), answering or sending personal pages, and personal reading.
Table 3.
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases×
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases
Table 3.
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases×
×
In every case containing distractions, there were periods (e.g., induction or emergence) without any distractions. If one analyzes the periods in the case, a clearer picture of the role of distractions versus other activities emerges (table 4). All of the major types of patient care tasks consumed significantly more case time than any of the distractions (table 4). As seen in previous anesthesia task analysis studies dating back two decades,1,2,17,19,20  observing the physiologic monitors was consistently the most common task (median = 13% of total case), and anesthesia record keeping consumed approximately 12% of case time, albeit lower during periods containing distractions. In the largely episodic periods that contained distractions, approximately one third of case time was consumed with personal distractions, and very little time (median = 0.0%) was spent on educational distractions. During periods and cases with and without distractions, a substantial amount of time was spent on information-gathering activities that were related to work (e.g., checking the schedule to seek information about future cases) but not the current case (table 4).
Table 4.
Percentage of Time Spent on Grouped Task Categories
Percentage of Time Spent on Grouped Task Categories×
Percentage of Time Spent on Grouped Task Categories
Table 4.
Percentage of Time Spent on Grouped Task Categories
Percentage of Time Spent on Grouped Task Categories×
×
Vigilance and Workload
In unadjusted comparisons, both participant-reported (P = 0.003) and observer-scored workload ratings (P < 0.001) were significantly lower during distraction-containing cases than cases without any distractions (fig. 1). Similarly, in cases with distractions, both participant- and observer-reported workload measures were significantly lower (P < 0.001 and P < 0.001, respectively) during distraction periods than periods without any distractions (which typically included induction and emergence, periods notable for higher workload1,2 ). Adjusting for months of experience, case duration, sleep duration, patient ASA, surgical type, and case type (i.e., distraction vs. nondistraction cases), self-reported workload ratings were 11% lower (95% CI, 8 to 14%; P < 0.001) during distraction periods versus nondistraction periods. There was no evidence of an independent association between case type and self-reported workload ratings after adjusting for distraction versus nondistraction periods within a case (P = 0.302). In unadjusted analyses, vigilance latency was not different based on either case type (P = 0.235) or period type within distraction cases (P = 0.872). However, in adjusted analyses, vigilance latencies were longer by 27% (95% CI, 7 to 49%; P = 0.004) among nondistraction cases versus distraction cases, but this trend did not attain statistical significance in comparisons of distraction versus nondistraction periods after adjusting for case type (P = 0.069). Months of experience, case duration, and surgical type were all significantly associated with both self-reported workload and vigilance latency in adjusted analyses (table 5). Provider sleep duration and patient ASA class were not significantly associated with self-reported workload or vigilance latency.
Table 5.
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis×
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis
Table 5.
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis×
×
Fig. 1.
Workload ratings and vigilance. Psychologic workload was rated on a 6 to 20 scale. Distraction and nondistraction periods: N = 59. Vigilance light response latency is in seconds. Distraction and nondistraction periods: N = 74. All of the data are presented as medians and interquartile ranges. For periods within distraction cases, only cases with data for periods both with and without distractions were analyzed. ‡P < 0.01; §P < 0.001.
Workload ratings and vigilance. Psychologic workload was rated on a 6 to 20 scale. Distraction and nondistraction periods: N = 59. Vigilance light response latency is in seconds. Distraction and nondistraction periods: N = 74. All of the data are presented as medians and interquartile ranges. For periods within distraction cases, only cases with data for periods both with and without distractions were analyzed. ‡P < 0.01; §P < 0.001.
Fig. 1.
Workload ratings and vigilance. Psychologic workload was rated on a 6 to 20 scale. Distraction and nondistraction periods: N = 59. Vigilance light response latency is in seconds. Distraction and nondistraction periods: N = 74. All of the data are presented as medians and interquartile ranges. For periods within distraction cases, only cases with data for periods both with and without distractions were analyzed. ‡P < 0.01; §P < 0.001.
×
NREs and Distractions
Eighty-eight (52%) of the 171 distraction cases and 105 of the 148 nondistraction cases (71%) contained at least one NRE (P < 0.001). Fifty-two distraction cases (30%) and 53 nondistraction cases (36%) contained more than one NRE. The clinician experts determined that three self-initiated personal distractions (i.e., non–patient care activities) were temporally related to NREs (3.4% of distraction cases; table 6), although none of the distractions were believed to contribute to the occurrence of the event. However, one of these distractions occurred concurrently with and could have influenced event management.
Table 6.
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions×
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions
Table 6.
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions×
×
Discussion
Both nurse anesthetists and residents in an academic medical center, even when being observed, performed potentially distracting tasks of a personal and/or educational nature in a majority of cases. The most common distraction activity was personal internet use. These distractions almost always occurred during maintenance when clinical workload was lower, especially in longer cases. Vigilance was significantly better in cases with versus those without distractions.
Task disruptions apparently occur frequently in other clinical domains21–23  and may be important contributors to medical adverse events.22–26  However, one must distinguish self-initiated distractions from externally prompted distractions.27–29  Although interruptions are common in anesthesia (e.g., the surgeon asking for the OR table to be rotated), our study focused on self-initiated distractions. In our NRE analysis, few self-initiated distractions affected ongoing clinical tasks. These data suggest that self-initiated escapable distractions during low workload periods when the patient is stable may be less problematic than external interruptions.30  However, this depends on anesthesia professionals using sound professional judgment.
In the present study, distractions occurred almost exclusively (99.6%) during the maintenance phase, which is commonly characterized by lower workload tasks (e.g., patient monitoring).1,2  This may explain why vigilance, as measured by the random alarm light located in the monitoring array, was maintained during distraction periods. Similar findings have been seen in other domains, for example, in a 4-h experiment involving unmanned vehicle operators primarily searching for and destroying hostile targets with low task loads (i.e., one target per hour), operators were in a distracted state (i.e., engaged in other tasks such as talking to other participants or eating) for 45% of the time.31  Yet, the top performers, all of whom were in a distracted state for much of the experiment, did quite well. The authors ascribed this finding to a periodic switching strategy that allowed them to efficiently maintain their vigilance and respond effectively when needed.31  A similar effect has been seen with sleep-deprived anesthesiology residents monitoring for events during long simulated anesthetics.32 
The present study replicates but also extends the findings from our previous smaller study,5  which was conducted at a different academic medical center when intraoperative internet access was limited and ubiquitous electronic communication was unavailable. In the present study, every OR had a computer with unrestricted internet access and all of the anesthesia providers had intraoperative access to handheld electronic communication devices. Thus, it is not surprising that, when providers choose to be distracted, their distractions were more likely to be electronic in nature. Although the potential clinical value of various electronic communication technologies is well appreciated,33–36  their use has unintended consequences, including a greater degree of mental occupation by self-initiated distractions.
In this study, self-initiated distractions were considered temporally related to 3% of the events in distraction-containing cases. Although at least one distraction temporally proximate to an event was considered by the expert reviewers to be untimely, none were deemed to be causal. Similarly, Wax et al.37  reported that 171 anesthesia providers during 1,061 cases spent a median of 14 min (16%) per case on nonanesthesia electronic health record (EHR) computer activities. Greater non-EHR activities occurred in longer cases with a lower ASA status, cases under general anesthesia, and when attending physicians worked alone. Non-EHR usage time was not associated with significantly greater variability or abnormalities of patient vital signs.
Distractions can differ in terms of how they are initiated (external vs. self-initiated) and consequently how quickly and easily they can be ignored, controlled, or terminated.38–40  Thus, distractions that are under the anesthesia provider’s full control (e.g., internet searching and browsing) may be more conducive to multitasking (i.e., time sharing) with lower workload tasks, like monitoring and charting, during uneventful periods. The effectiveness of such a strategy will depend in part on how mentally absorbing the distractions are. More mentally absorbing distractions (e.g., playing a video game on a smart phone) will interfere with effective time sharing, thus degrading performance of essential clinical tasks.
Other Consequences of Distractions
Distractions can have other adverse effects. During clinical tasks, distractions can be disruptive if they require reorientation of attention. When switching from one task to another, the cognitive readjustment between tasks, referred to as a switch cost, slows responses and increases the risk of error.41  After an interruption, it is more difficult and takes twice as long to reorient to (resume) more complex compared with routine tasks.42  Additional effort is required to ignore distractions and remain attendant to current tasks and goals.43  According to the cognitive fatigue model, unplanned task disruptions can increase cognitive effort, produce information overload, and lead to fatigue and a more negative mood.44 
Driving while texting has been put forward as a comparison to texting while administering anesthesiology. One difference is that driving accidents can happen in seconds. In fact, a large proportion of automobile accidents by teen drivers have been found to have associated distractions within 6 s of the accident.45  The concurrent conduct of nondriving tasks, such as texting, is known to cause life-threatening accidents.46  Studies show that the human brain cannot perform two conscious activities simultaneously (even if on different sensory channels) but rather switches rapidly between tasks.47  Consistent with this, texting while driving substantially increases cognitive load and substantially decreases vigilance and response time, leading to performance decrements that are significantly worse than driving while drunk.48  Consequently, text messaging while driving is banned for all drivers in 46 states.49  Hand-held cell phone use is banned in 14 states and is banned for novice drivers in 38 states.49 
Role of Policies and Admonitions
With ad lib access to electronic distractions, there is more opportunity for potentially unprofessional behavior. An anesthetist recently self-published a novel in which he thanked the “many tardy surgeons” at his institution who “unwittingly helped [by allowing him to write his book in the OR] by making simple operations last for hours.”50  In a 2012 search of the 5,822 claims of intraoperative adverse events in the ASA Closed Claims database, there were 13 cases in which nonclinical distractions were an associated factor.51  An updated analysis in late 2016 found two additional cases (Karen Posner, Ph.D., Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington; personal communication). Malpractice carriers generally view all nonclinical distracting activities as risky and have explicitly advised providers to refrain from them.8  Some anesthesia residency programs forbid intraoperative distractions. The national anesthesiology and nurse anesthesia professional associations both have distraction policies.52,53 
Why do experienced anesthesia providers routinely initiate distractions during low workload periods of the maintenance phase of anesthesia care? Boredom stems from the need to maintain attention in the absence of relevant task information and is most likely to occur in semiautomatic tasks that prevent mind wandering but are not fully mentally absorbing. In an unpublished survey that we conducted years ago, almost 90% of anesthesia providers admitted to at least occasional boredom while administering anesthesia, presumably during what we called idle periods in previous studies.1,2  Humans typically mitigate boredom by altering tasks or by adding new tasks. Therefore, during idle periods, many anesthesia professionals add clinical or nonclinical secondary tasks to their routine. Given the inherent nature of the job, the performance of non–patient care tasks seems inevitable during idle periods. Thus, if access to electronic media were precluded, providers would likely alleviate boredom in other ways (e.g., listening to music, personal conversations, or reading paper materials).
Some self-initiated distractions (e.g., intraoperatively writing novels, video games, etc.) are unprofessional and potentially dangerous. Therefore, the choice, timing, and duration of any self-initiated distraction require good judgment and vigilance.
Study Limitations
This study has limitations. It was conducted in the main ORs of a single academic medical center in which internet connections were required to access the anesthesia EHR. Educational activities, including conversations with attending physicians, accounted for only a small fraction of all distractions. In nonacademic settings, more personal distractions may occur. The study site used a care team model where additional anesthesia providers were readily available to assist and provide regular breaks. The results may not generalize to other sites with different cultural, environmental, or technologic attributes. Future studies should examine the incidence of distractions in nonacademic and solo practice settings.
The results may have been affected by the observer’s presence. However, in numerous previous intraoperative observational studies, including those involving video recording of patient care, there has been little evidence of a Hawthorne effect.1,2,4,17,19,20  Although the providers did not know the purpose of the observations, it is reasonable to assume that the observer’s presence did not increase the incidence of distractions.
During the process of expert review and coding, which involved reviewing nonroutine event descriptions from surveys and videos, we may have missed some events. We may have incorrectly identified personal versus educational distractions (e.g., conversations that were inaudible).
In conclusion, at least one distraction was observed in more than half of a large corpus of anesthesia cases, with the most common distraction being personal internet use. Distractions only occurred during low workload periods, and a measure of vigilance was not adversely affected. A few NREs were associated with noncontributory distractions. Additional research should delineate the effects of distractions on the quality and safety of perioperative care.
Acknowledgments
The authors acknowledge the administrative and/or technical contributions of the following individuals: Neal Sanders, Ph.D. (Department of Health IT Data Analytics, Vanderbilt University Medical Center, Nashville, Tennessee), Steve Ampah, M.S. (Ph.D. student, Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama), Russ Beebe, B.A. (Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center), Andrew Kline, B.A. (Vanderbilt Comprehensive Care Clinic, Vanderbilt University Medical Center), Christine Goldsberry (Department of Anesthesiology, Vanderbilt University Medical Center), Kelly Peters, B.A. (Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center), and Callie Hanks, M.M.H.C. (Department of Performance Management and Improvement, Monroe Carell, Jr. Children’s Hospital at Vanderbilt, Nashville, Tennessee).
Research Support
Supported by a grant from the Anesthesia Patient Safety Foundation (Indianapolis, Indiana; to Dr. Slagle) and by internal institutional resources. Some of Dr. Weinger’s time was supported by the Geriatrics Research Education and Clinical Center, Veterans Affairs Tennessee Valley Healthcare System (Nashville, Tennessee).
Competing Interests
The authors declare no competing interests.
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Fig. 1.
Workload ratings and vigilance. Psychologic workload was rated on a 6 to 20 scale. Distraction and nondistraction periods: N = 59. Vigilance light response latency is in seconds. Distraction and nondistraction periods: N = 74. All of the data are presented as medians and interquartile ranges. For periods within distraction cases, only cases with data for periods both with and without distractions were analyzed. ‡P < 0.01; §P < 0.001.
Workload ratings and vigilance. Psychologic workload was rated on a 6 to 20 scale. Distraction and nondistraction periods: N = 59. Vigilance light response latency is in seconds. Distraction and nondistraction periods: N = 74. All of the data are presented as medians and interquartile ranges. For periods within distraction cases, only cases with data for periods both with and without distractions were analyzed. ‡P < 0.01; §P < 0.001.
Fig. 1.
Workload ratings and vigilance. Psychologic workload was rated on a 6 to 20 scale. Distraction and nondistraction periods: N = 59. Vigilance light response latency is in seconds. Distraction and nondistraction periods: N = 74. All of the data are presented as medians and interquartile ranges. For periods within distraction cases, only cases with data for periods both with and without distractions were analyzed. ‡P < 0.01; §P < 0.001.
×
Table 1.
Study Case Characteristics
Study Case Characteristics×
Study Case Characteristics
Table 1.
Study Case Characteristics
Study Case Characteristics×
×
Table 2.
Study Cases by Type of Surgery
Study Cases by Type of Surgery×
Study Cases by Type of Surgery
Table 2.
Study Cases by Type of Surgery
Study Cases by Type of Surgery×
×
Table 3.
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases×
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases
Table 3.
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases
Percentage of Time Spent on Non–patient Care Activities among Distraction Cases×
×
Table 4.
Percentage of Time Spent on Grouped Task Categories
Percentage of Time Spent on Grouped Task Categories×
Percentage of Time Spent on Grouped Task Categories
Table 4.
Percentage of Time Spent on Grouped Task Categories
Percentage of Time Spent on Grouped Task Categories×
×
Table 5.
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis×
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis
Table 5.
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis
Summary of Self-reported Workload and Vigilance Multiple Regression Analysis×
×
Table 6.
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions×
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions
Table 6.
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions
Three Cases Where Reported Nonroutine Events Were Deemed to Have Associated Self-induced Distractions×
×