Time of day effects on the incidence of anesthetic adverse events
We hypothesized that time of day of surgery would influence the incidence of anesthetic adverse events (AEs). Clinical observations reported in a quality improvement database were categorized into different AEs that reflected (1) error, (2) harm, and (3) other AEs (error or harm could not be determined) and were analyzed for effects related to start hour of care. As expected, there were differences in the rate of AEs depending on start hour of care. Compared with a reference start hour of 7 am, other AEs were more frequent for cases starting during the 3 pm and 4 pm hours (p < 0.0001). Post hoc inspection of data revealed that the predicted probability increased from a low of 1.0% at 9 am to a high of 4.2% at 4 pm. The two most common event types (pain management and postoperative nausea and vomiting) may be primary determinants of these effects. Our results indicate that clinical outcomes may be different for patients anesthetized at the end of the work day compared with the beginning of the day. Although this may result from patient related factors, medical care delivery factors such as case load, fatigue, and care transitions may also be influencing the rate of anesthetic AEs for cases that start in the late afternoon.
Time of day effects on the incidence of anesthetic adverse
M C Wright, B Phillips-Bute, J B Mark, M Stafford-Smith, K P Grichnik, B C Andregg, J M Taekman
See end of article for
M C Wright, PhD,
Anesthesiology, Box 3094,
Duke University Medical
Center, Durham, NC
27710, USA; melanie.
Accepted for publication
5 April 2006
Qual Saf Health Care 2006;15:258–263. doi: 10.1136/qshc.2005.017566
Background: We hypothesized that time of day of surgery would influence the incidence of anesthetic
adverse events (AEs).
Methods: Clinical observations reported in a quality improvement database were categorized into
different AEs that reflected (1) error, (2) harm, and (3) other AEs (error or harm could not be determined)
and were analyzed for effects related to start hour of care.
Results: As expected, there were differences in the rate of AEs depending on start hour of care. Compared
with a reference start hour of 7 am, other AEs were more frequent for cases starting during the 3 pm and 4
pm hours (p,0.0001). Post hoc inspection of data revealed that the predicted probability increased from
a low of 1.0% at 9 am to a high of 4.2% at 4 pm. The two most common event types (pain management
and postoperative nausea and vomiting) may be primary determinants of these effects.
Conclusions: Our results indicate that clinical outcomes may be different for patients anesthetized at the
end of the work day compared with the beginning of the day. Although this may result from patient related
factors, medical care delivery factors such as case load, fatigue, and care transitions may also be
influencing the rate of anesthetic AEs for cases that start in the late afternoon.
ealth care is a 24 hour a day operation. Factors such as
time on the job, effects of circadian rhythms, and issues
related to demand, scheduling, and staffing may all
have an effect on patient care over the course of a day.
Research has revealed that human performance is adversely
affected by sleep deficit, circadian rhythm disruption, and
long work hours, leading to decrements in cognitive and
psychomotor performance and increased risk of accidents.
Fatigue is believed to be a greater problem in transportation
accidents than drugs and alcohol combined, contributing to
15–20% of all transportation accidents.
and simulator studies, and evaluation of clinical data have
revealed damaging effects of fatigue, both for the patient and
the healthcare worker.
Decisions about scheduling,
demand, and staffing can result in variations in workload
over the course of the day that may be reflected in care. In
addition, staffing and scheduling decisions may create
specific times of day that are associated with potentially
risky events such as care transitions.
Research evaluating relationships between time of day and
clinical performance, as defined by the occurrence of adverse
events (AEs) or patient outcomes, is limited. In anesthesia,
this type of research is partially hindered by a relatively low
frequency of adverse outcomes.
One exception is a
prospective study of cases of unintended dural puncture in
obstetric epidural anesthesia that identified a greater risk of
unintended dural puncture for epidural placement performed
at night than during daytime.
There is also little research
evaluating clinical performance over multiple times of day.
Several studies have compared night performance with day
and sleep deprived practitioners with well
but few have considered potential
time of day effects such as the early morning and afternoon
circadian troughs or start and end of shift.
We suspect that clinical performance may vary throughout
the day due to effects of time on the job, circadian lows, and
times of transition. Periods that include circadian lows (3–
5 am, 3–5 pm) and transfers of patient care from one
anesthesia team to another (7 am, 4–6 pm) are times of
day that may be related to qualitative changes in operating
room performance and the incidence of AEs. Identifying
periods of relatively impaired operating room performance is
an important step in applying human factors principles to the
improvement of patient care in this environment.
The Duke University Medical Center Department of
Anesthesiology maintains a perioperative database that
serves as a patient record and as a tool for assessing and
improving quality of care. Specific clinical and administrative
events are documented in the database as ‘‘quality improve-
ment’’ (QI) events. These data provide an opportunity for a
retrospective analysis of the incidence of AEs with respect to
time of day. The primary objective of this evaluation was to
determine whether time of day affects the number of AEs
that occur perioperatively. We hypothesized that time of day
of surgery would influence the incidence of anesthetic
adverse events (AEs).
The Saturn Information System is a perioperative database
and charting tool through which anesthesia providers and
perioperative nurses electronically record and track a
patient’s clinical progress. The Saturn database includes
patient demographic data, surgical and anesthetic plans, and
patient notes, including QI event descriptors and provider
entered text. The selection of QI event descriptors and the
addition of comments are under the discretion of the
anesthesia care team (self-reports) in the operating room
and preoperative and postoperative care units.
This study is based on data from 130 912 operating room
cases recorded in Saturn between 1 May 2000 and 4 August
2004. The data include all anesthetic procedures completed at
Duke University Medical Center during that time, including
both inpatient and ambulatory procedures for adult and
pediatric patients. All data were de-identified before transfer
Abbreviations: AE, adverse event; PONV, postoperative nausea and
vomiting; QI, quality improvement
to the research team and were therefore exempt from
The independent variable we selected to assess the effect of
time of day on the incidence of AEs was the start hour of
care. The following characteristics of the patient, procedure,
and operating room environment were expected to affect the
incidence of AEs and were included in our analyses as
Age of patient (in months).
Sex of patient.
Global assessment of patient health (American Society of
Anesthesiologists (ASA) physical risk classification).
Emergency or non-emergency/scheduled procedure.
Duration of anesthesia care (in minutes).
Complexity of the procedure (ASA base units—a measure
of case difficulty used for billing purposes).
Activity level of the OR suite (number of operating rooms
Although of interest, details such as the experience level of
the anesthesia provider, the case load of the attending
anesthesiologist, and the makeup of the care team could not
be determined from the de-identified data set.
Response variables for our analyses were anesthetic AEs.
These were determined from the self-reported QI events
recorded in the perioperative database. Based on Leape’s
definition of preventable and non-preventable AEs,
that indicate an error or failure to adhere to a standard of
care can be considered preventable AEs. We originally
planned to separately analyze events which may be pre-
ventable, events which cause some harm to the patient, and
non-preventable events. However, due to the nature of the
database, we were not able to definitively distinguish
preventable and non-preventable events. We were, however,
able to separate the data into three categories of events. These
included error (preventable events), harm (events which
resulted in harm to patients), and other AEs. We included the
category of ‘‘other AEs’’ to classify adverse events that could
not be definitively described as preventable or causing harm
based on the information available, but were likely to be
associated with error, harm, or an increased risk of either
error or harm. Detailed definitions of the categories used by
the research team are provided in table 1.
When QI events are entered into the database at the point
of care by a certified registered nurse anesthetist (CRNA),
anesthesia resident, or staff anesthesiologist, the practitioner
selects from a list of perioperative events and also has an
opportunity to enter additional text describing the QI event.
Five experienced anesthesiologists reviewed the QI event
labels available in the database and came to a consensus (in a
face to face meeting) regarding whether each QI event label
represented error, harm, or other AE (table 2). The panel of
reviewers also identified the QI events that represented
administrative delays because we believed that there may be
an association between delays and AEs. We validated the
assignment of QI events into these categories by comparing
the automated classification by event labels with manual
classification by four experienced anesthesiologists (JT, JM,
KG, MSS) using both the event labels chosen by the
practitioner and associated text. Data from 148 cases showed
that, for the largest portion of the events (83%), two or three
of the reviewers made no change to the classification that
would have been made based on the event label alone. In
addition, the reviewers very rarely agreed to change from an
adverse event category to no event or delay (four cases, 2.6%)
and they never agreed to change from either the error or
harm category to the less definitive other AE category. Based
on these results, we concluded that an analysis of the data
based on categorization by QI event labels was unlikely to
attain different results than an analysis that included expert
review of each case that contained text descriptions.
Analysis of data
Because of the binary nature of the AE response variables, the
data were subjected to multiple logistic regression analysis.
The multiple regression model included start hour of care as
an independent variable with the patient and procedural
factors included as covariates. In order to minimize the
degrees of freedom in the statistical model, the ASA physical
status classification was reduced to two categories of low risk
(ASA classification 1 or 2) and high risk (ASA classification 3,
4, or 5). Hour of day was treated as a categorical variable as
were ASA status, sex, and emergency (yes or no). Age in
Table 1 Definitions of event categories
An error is defined using an adaptation of the definition posed by
The Australian Council for Safety and Quality in Health Care
Shared Meanings project
(Merry, personal communication,
‘‘The failure to complete an action as intended or the unintentional
use of a wrong plan to achieve an aim.’’
This definition includes both errors due to a deficiency in
knowledge or a failure in judgment or decision making (e.g.
‘‘mistakes’’ or ‘‘errors of judgment’’) and errors which are an
incorrect execution of a correct action sequence (e.g. ‘‘slips’’ or
‘‘technical errors’’). Errors may occur by doing the wrong thing
(commission) or by failing to do the right thing (omission).
Because it is difficult to determine the intent of a practitioner, we
will assume the ‘‘standard of care’’ as the underlying assumption
of intent. Therefore, any incident that represents a deviation from
‘‘standard of care’’, whether it is an error of judgment or a
technical error, will be classified as an error. For our purposes,
‘‘standard of care’’ is defined in accordance with NC statutes as
care that is ‘‘… in accordance with the standards of practice
among members of the same health care profession with similar
training and experience situated in the same or similar
communities…’’ (1975, 2
Sess, c 977, s 4).
Harm includes any untoward medical occurrence in the patient that
is not reasonably expected or common, based on the procedure
being conducted. The instance of harm may or may not have a
causal relationship with treatment. Harm includes any unfavorable
incident or unintended sign (including an abnormal laboratory
finding), symptom, or disease temporally associated with the
procedure for which the patient is receiving anesthetic care,
whether or not considered related to the procedure. This includes
emotional distress, psychological trauma, invasion of privacy,
embarrassment, loss of social status or employment, or any
economic impact considered related to the conduct of the
procedure. This category does not include delays of an
administrative nature (see ‘‘Delay’’ below). Our definition is
similar to an incident of ‘‘Harm’’ or ‘‘Loss’’ for the patient, as
defined by the Shared Meanings project:
‘‘Harm: Death, disease, injury, suffering, and/or disability
experienced by a person’’ (see loss)
‘‘Loss: Any negative consequence, including financial,
experienced by a person(s) or organisations(s)’’.
This category is included for other perioperative events that may
have some association with error or harm but do not provide
sufficient evidence of either error or harm based on the above
definitions. This category includes, for example, events that are
sometimes due to error or sometimes result in harm; events that
sometimes occur concurrently with either error or harm; or events
that may be associated with an increased risk for error or harm.
This category does not include delays of an administrative nature
(see ‘‘Delay’’ below).
This category is included to track administrative delays. These
include events that delay the start of the procedure or the transfer
of the patient. Examples include: late arrival of the surgeon,
anesthesiologist, other care team members or the patient;
readiness problems associated with equipment, operating room,
or other care units; and lateness of laboratory results or other
necessary information. This category is NOT intended to reflect
changes in procedure length or anesthesia care associated with
other perioperative events of the categories error, harm, or other
AEs as defined above.
Time of day and adverse events 259
months, surgery duration in minutes, and surgery complexity
in ASA base units were treated as continuous variables.
Since there were fewer night time cases, initial review of
the data suggested that power would be maximized by
grouping events into eight 3 hour intervals over a full
24 hour day. We also conducted a separate hour by hour
comparison of the non-emergency (scheduled) procedures
over the 12 hours of the regular work day (6 am to 6 pm). In
the 12 hour analysis, the category of error was excluded due
to an insufficient number of observations.
The response variables for four separate analyses were (1)
harm, (2) error, (3) other AEs, and (4) delays. A review of the
frequency of specific event types revealed a proportionally
high frequency of postoperative nausea and vomiting
(PONV) in the harm category (35% of harm events) and a
proportionally high frequency of pain management issues in
the other AEs category (49% of other AEs). Post hoc analyses
therefore included analyses of the response variables PONV
and pain management. We also analyzed harm with PONV
events excluded and other AEs with pain management events
excluded. Our goal in this analysis was to understand the
degree to which these specific event types may be driving the
overall results for harm and other AEs.
To further describe significant time of day effects,
comparisons were made using odds ratio estimates. We
selected the start hour time period with the highest frequency
of starts (7 am hour or 6–9 am time period) as the reference
point for all comparisons.
Cases with missing start or end times were excluded from the
analysis dataset, reducing the sample from 130 912 to
107 620 cases. The frequency distribution and means of the
covariates are shown in table 3, and the frequencies of all
case start times for each start hour are shown in fig 1. The
dataset was further reduced to 90 159 cases for the multiple
regression analysis because of missing covariate data. From
this dataset, the following frequencies were observed for the
Harm (including PONV events): 667
Other AEs (including pain management events): 1995
Pain management: 1102
As expected, the multiple logistic regression analysis
revealed a number of significant effects of covariates on
AEs and delays in both the 24 hour analysis of all cases and
the work day analysis of non-emergency cases. The results for
the 24 hour analysis are shown in table 4.
Table 3 Covariate proportions (for categorical
variables) and means (for continuous variables)
Variable Proportion Mean (SD)
Emergency 8% emergency
Sex 52% female
ASA physical status 62% low risk
(ASA 1 and 2)
Age 48 (23) years
Duration 2.7 (2.0) hours
Complexity 6.7 (3.9) ASA base
OR suite activity level 34 (7) OR rooms in
Total number of cases started
Average number of case
starts per day
Case start time
Figure 1 Frequency of case starts throughout the day (daily averages
assume 365 day/year operation).
Table 2 QI events assigned to AE categories
122 Delayed recognition
126 Unintentional extubation
133 Delayed recognition
173 Wrong medication/wrong
177 Inadequate preoperative
114 Unplanned outpatient
115 Unplanned admission to ICU
121 Inability to intubate
124 Trauma to airway
125 Damage to teeth
131 Significant hypoxemia
134 Severe bronchospasm
136 Pulmonary aspiration
146 Confirmed myocardial infarction
147 Pulmonary edema/CHF
148 Cardiac arrest
152 Excessive block
153 Adverse event following block
155 Post dural puncture headache
161 Prolonged sedation
162 Prolonged neuromuscular
163 Central nervous system
164 Peripheral neurologic deficit
165 Patient awareness
171 Significant hyperthermia
172 Significant (unintended)
174 Drug/transfusion reaction
176 Prolonged nausea/vomiting
191 Eye injury
192 Skin/soft tissue injury
195 Other injury/catastrophe
204 Emergency tracheotomy or
211 Wound infection
212 Other infection/sepsis
213 Deep vein thrombosis
214 Pulmonary thromboembolism
215 Postoperative oliguria/anuria
216 New postoperative need for
417 Postoperative nausea and
127 Unanticipated difficult
128 Other airway
132 Significant hypercapnia
137 Other respiratory
141 Significant hypertension
142 Significant hypotension
143 Significant tachycardia
144 Other major arrhythmia
145 Suspected myocardial
149 Other cardiovascular
151 Failed/inadequate block
154 Unintentional dural puncture
156 Other regional
166 Other neurological
175 Problem with vascular access
181 Equipment problem (describe)
194 Staff injury (describe)
217 Other postoperative
416 Pain management
418 Failed discharge criteria
808 Unclassified (please describe)
301 No/incomplete surgical consent
302 No green sheet (no
preoperative assessment paperwork)
303 Late arrival of patient
304 Waiting for results
305 Time for regional anesthesia
306 OR not ready
307 Surgeon unavailable
308 Anesthesia unavailable
309 Anesthesia detained in
310 Other reason for delay
401 No assigned intermediate
402 No assigned SD bed
403 No assigned ICU bed
404 Room not ready: occupied
405 Room not ready: not clean
406 No transporter
407 Floor nurse unavailable
408 PACU nurse admitting 2nd
409 Waiting for surgeon: orders
410 Waiting for surgeon: MO1B
(waiting for surgeon to sign
postoperative care unit patient
411 Waiting for surgeon: RX
412 Waiting for surgeon: other
413 Waiting for anesthesiologist
414 Waiting for lab results
415 X-ray to be taken or read
419 Delayed discharge: other
ICU, intensive care unit; CHF, congestive heart failure; OR, operating room; PACU,
postoperative care unit; SD, stepdown.
260 Wright, Phillips-Bute, Mark, et al
In the multiple logistic regression analysis over a 24 hour
day, the incidence of other AEs was significantly influenced
by the 3 hour time period in which surgery began
(p,0.0001). The predicted probability of other AEs (with
all covariates held constant) is shown in fig 2. Visual
inspection of the graph suggests a higher probability of AEs
in the late afternoon and early evening hours than in
morning and early afternoon cases. Odds ratio estimates
confirm the afternoon effect, indicating that cases that began
between 3–6 pm had a higher probability of other AEs than
cases that started during the reference time of 6–9 am (point
estimate 1.48, 95% Wald confidence limit 1.19 to 1.84). Odds
ratio estimates also indicate that, compared with the
reference time of 6–9 am, there was a lower probability of
events for cases starting later in the morning (9 am–noon)
(point estimate 0.68, confidence limit 0.68 to 0.85) or in the
early afternoon (noon–3 pm) (point estimate 0.88, confi-
dence limit 0.77 to 0.99).
Analysis of hourly starts of scheduled cases over the
workday (6 am–5 pm) revealed significant effects of start
time for harm (p,0.01) and other AEs (p,0.0001).
Considering the effects on harm, odds ratio estimates
revealed significant differences from the reference start time
of 7 am for the 8 am hour only (point estimate 0.62,
confidence limit 0.45 to 0.85). However, visual inspection of
the predicted probability of harm (fig 3) reveals trends
toward an increase in events for cases that start in the late
afternoon. For example, the predicted probability of harm is
three times higher for cases that start at 3 pm (1.0%) than for
those starting at 8 am (0.3%). Compared with the 7 am
reference time, odds ratio estimates suggest trends toward a
greater number of events for the 2 pm and 3 pm start hours
(2 pm point estimate 1.38, confidence limits 0.97 to 1.98;
3 pm point estimate 1.53, confidence limits 0.98 to 2.40).
Considering the effects on other AEs, the predicted
probability of an event indicated an increased risk for cases
starting in the late afternoon (fig 3). Odds ratio estimates
revealed a higher probability of other AEs for cases beginning
in the 3 pm and 4 pm hours than for those beginning at the
reference hour of 7 am (3 pm point estimate 1.49, confidence
limit 1.15 to 1.93; 4 pm point estimate 1.68, confidence limit
1.16 to 2.42). Odds ratio estimates also revealed that cases
starting in the 6 am hour and all hours between 8 am and
1 pm had a lower probability of an event than cases starting
in the 7am hour (with the strongest effect seen at 9 am with
a point estimate of 0.58 and confidence limits of 0.48 to 0.70).
In examining specific data points for the size of the effect, the
predicted probability of other AEs increased from a low of
1.0% for cases starting at 9 am to a high of 4.2% for cases
starting at 4 pm.
Because of the high proportion of pain management events
in the other AEs data set and PONV events in the harm
dataset, we analyzed these specific events for time of day
effects as post hoc analyses. The analyses revealed significant
effects for both pain management (p,0.0001) and PONV
(p,0.0001) in both the 24 hour and work day analyses. For
the most part, these effects mirrored the effects seen in the
general analyses of other AEs and harm. There was an
increased probability of pain management events for cases
starting in the late afternoon (2, 3, and 4 pm) compared with
7 am. There was also a decreased probability of pain
management for cases starting in the mid morning (8 am–
noon) compared with 7 am. For PONV, there was an
increased probability of events for cases starting at 2 pm
compared with 7 am and a decreased probability of events in
the late morning (8–10 am) compared with 7 am.
For the work day analysis, removal of pain management
and PONV events from the other AEs and harm datasets
reduced the strength of the time of day effects such that they
were no longer significant (p = 0.13 for harm; p = 0.18 for
other AEs. Time of day had a significant effect on other AEs
over a 24 hour day (p,0.05). In this case, the midday
probability of an event (9 am–noon and noon–3 pm) was
lower than the reference time of 6–9 am. In general, the
overall rates of occurrence with these high frequency events
excluded were very low (around 0.2% for harm and 0.6% for
other AEs) and the confidence intervals were very large (of
the order of 1.5 to 2.5% with, for example, a point estimate of
1.00 and confidence limits of 0.59 to 1.71 at 6–9 pm).
In both the 24 hour analysis and the work day analysis,
delays were significantly affected by hour of day (p,0.0001).
Table 4 Effects of covariates on adverse events and
delays in the 24 hour analysis of all surgical cases
Increased probability of an AE
associated with: For the following event categories:
Higher patient age Other AE, Delay
Female sex Harm, Other AE
High ASA status Harm*, Other AE, Delay
Longer case duration Error*, Harm, Other AE, Delay
Emergency cases Delay
Higher complexity cases Other AE, Delay
Greater number of OR rooms
Harm**, Other AE, Delay
*p,0.05, **p,0.01, or p,0.001 (Wald x
Case start time
of another AE
Figure 2 Predicted probability of other AEs in 3 hour time increments
throughout the day. Notes: (1) Error bars indicate upper and lower
bounds of 95% confidence intervals. (2) Filled circles represent data
points that are significantly different from the reference time of 6–9 am
(represented by a cross). (3) Assumes covariates set to male sex, low
ASA physical status rating, non-emergency, duration 143 minutes,
number of OR rooms 34, ASA base units 6.
Case start time
of an adverse event
Other adverse events
Figure 3 Predicted probability of harm and other AEs for scheduled
cases over the work day (6 am–5 pm). Notes: (1) Error bars indicate
upper and lower bounds of 95% confidence intervals. (2) Filled symbols
represent data points that are significantly different from the reference
time of 7 am (represented by a cross). (3) Assumes covariates set to male
sex, low ASA physical status rating, non-emergency, duration
143 minutes, number of OR rooms 34, ASA base units 6.
Time of day and adverse events 261
The predicted probabilities of delays from both analyses are
shown in fig 4. Delays appear to increase substantially over
the work day. Predicted probability increases from just over
5% in the morning hours to approximately 30% in the late
The results of our analyses support our hypothesis that AEs
are influenced by the time of day of surgery. We identified a
small but significant increase in AEs in the early morning
compared with late morning and early afternoon hours. This
effect was robust throughout a number of different analyses
and event types. We also identified a significant and sizable
increase in AEs in the late afternoon compared with early
morning. Post hoc analyses revealed that this effect may have
been driven primarily by the most frequent events of PONV
and pain management.
In addition to the significant effects of AEs, there was a
significant and sizeable increase in administrative delays in
the late afternoon. This suggests that there may be a
relationship between administrative delays and AEs that
requires further investigation.
There are a number of reasons why AEs may occur more
often at the end of the day, including (1) end of day fatigue,
(2) afternoon circadian lows, (3) care transitions, (4) change
in makeup of the care team, (5) changes in case load, (6)
physiological changes in the patient, or (7) other unrecog-
nized factors. Some of these same factors might also be
relevant as an explanation for the increased AE rate in the
These data were collected at our medical center where all
anesthesia care is supervised by a faculty physician anesthe-
siologist and delivered primarily by either an anesthesia
resident or a CRNA. The attending anesthesiologist may
supervise up to four CRNAs or up to two residents in separate
operating rooms. The work day generally begins between 6
and 7 am and ends between 3 and 6 pm for most attendings
and residents. However, each day several attendings stay
later to finish cases or remain on a late call schedule that
ends between 6 and 9 pm. Both attendings and residents
work a distinct night call schedule. CRNAs work 12 hour
shifts of 7 am to 7 pm, 11 am to 11 pm, or 7 pm to 7 am.
Scheduling details are visually depicted in fig 5.
Because of this scheduling system, times of transition in
anesthesia care are most likely to occur around 6–8 am and
3–7 pm. Changes in the makeup of the care team may also
occur during these times. In particular, a greater fraction of
the case load may be covered by CRNAs rather than by
residents during the 3–7 pm time frame. There also may be
increases in case load per attending physician associated with
the 3–7 pm time frame as supervision transitions to fewer
late call anesthesiologists. Our finding of a substantial
increase in delays in the late afternoon also suggests a
potential problem of workload at this time. In contrast to the
6–8 am time of transition when well rested anesthesiologists
arrive to begin the day, physicians supervising cases during
the 3–7 pm transition times are usually physicians who are
continuing cases, taking over new cases, and who began their
work day between 6 and 7 am.
Arbous et al
identified specific issues associated with both
transitions and the makeup of the patient care team that
affect postoperative mortality and coma. They found an
increased risk of perioperative death associated with intrao-
perative change of one anesthesiologist by another. In
addition, the risk of severe morbidity and mortality was
reduced by (1) direct availability of an anesthesiologist (via
intercom rather than phone or pager), (2) the presence of a
full time (versus part time) anesthetic nurse, and (3) the
presence of two individuals at emergence and termination of
anesthesia. While there are some relevant differences in the
model for anesthesia care in the United States and Europe—
for example, differences in training and responsibilities of
anesthesia nurses and requirements for anesthesiologists to
be present at critical points such as induction and emer-
gence—it is likely that, as case load increases, anesthesiol-
ogists may be faced with difficult choices about where their
presence is most needed or when they should call for help.
This study has some limitations. Firstly, it is based on non-
anonymous self-reports. Although the database is used as the
official perioperative record which requires providers to be
diligent in reporting significant events, they may be biased in
24 hour day
Case start time
of a delay
Figure 4 Predicted probability of delays by time of day. Notes: (1)
Error bars indicate upper and lower bounds of 95% confidence intervals.
(2) Filled symbols represent data points that are significantly different
from the reference time (represented by a cross). (3) Assumes covariates
set to male sex, low ASA physical status rating, non-emergency,
duration 143 minutes, number of OR rooms 34, ASA base units 6.
Scheduled and unscheduled cases Unscheduled casesUnscheduled cases
Attending night call Attending work day Attending night call
CRNA night shift CRNA day shift CRNA night shift
Resident night call Resident work day Resident night call
Midnight 3 am 6 am 9 am Noon 3 pm 6 pm 9 pm Midnigh
Attending late day call
CRNA late day shift
Figure 5 Approximate Duke University Medical Center anesthesia staff schedules over a 24 hour day. Note: Medium grey areas represent flexible
start and stop boundaries as well as times of overlapping coverage and transition.
262 Wright, Phillips-Bute, Mark, et al
how they document events or in their decisions whether or
not to report minor incidents. They may be more likely to
select QI event labels that describe the event more generally
or are seen as patient related events rather than labels that
suggest clinician error and indicate a cause or possible blame.
There also may be an increase in documentation associated
with cases in which there are transitions, either for purposes
of providing key information to oncoming providers or for
providing a clear historical record of whether events occurred
before or after the transition.
Secondly, a reduction of over 30% in our sample size due to
missing data calls into question the robustness of the
database for the purposes of these analyses. Although we
were unable to identify specific causes of missing data, we
have no reason to believe that there are any systematic biases
associated with missing data that would affect the results of
Lastly, we were unable to clearly determine which events
were preventable. This limits our ability to determine
whether or not the time of day effects have underlying
causes that can be controlled. For example, circadian effects
on the patient such as changes in the sensitivity to pain
propensity for PONV could partly explain our results.
Most studies on the effects of fatigue on clinical outcomes
have focused on sleep deprivation or disrupted circadian
rhythms—for example, looking at post call effects
comparing night time with daytime performance.
not aware of any other studies that have revealed decrements
in clinical outcome associated with the beginning or end of
the work day. This study presents evidence of a significant
and sizeable increase in the incidence of anesthetic AEs in the
late afternoon hours. It is unclear whether the increase in
AEs was due to (1) problems related to increased case load
and delays at these times, (2) effects of caregiver fatigue after
many hours on the job, (3) problems that occur because of
transitions, (4) increased reporting during times of transi-
tions, or (5) other unidentified factors such as changes in the
makeup of the anesthesia care team or physiological changes
in the patients. Future research should focus on identifying
the causes of increases in anesthetic AEs in the late
afternoon. After these causes are identified, strategies to
reduce or eliminate these events should be evaluated.
The authors thank the APSF Scientific Evaluation Committee for
their input regarding our methods and ongoing enthusiasm for this
project; Richard Adrian (Duke Health Technology Solutions
Perioperative Information Systems Group) for his support in
retrieving the perioperative data; and Terry Breen (Duke University
Medical Center, Department of Anesthesiology) for his assistance in
interpreting the quality improvement information and participation
in categorizing QI events.
M C Wright, B Phillips-Bute, J B Mark, M Stafford-Smith, K P Grichnik,
B C Andregg, J M Taekman, Department of Anesthesiology and the
Duke University Human Simulation and Patient Safety Center, Duke
University Medical Center, Durham, North Carolina, USA
This research was funded by a grant from the Anesthesia Patient Safety
Foundation (APSF), Indianapolis, IN, USA.
Competing interests: none declared.
Preliminary results of this project were presented at the Healthcare
Systems Ergonomics and Patient Safety Conference in Florence, Italy on
1 April 2005 (Wright MC, Andregg BC, Mark JB, et al. Effects of time of
day and surgery duration on adverse events in anaesthesia. In: Tartaglia
R, Bagnara S, Bellandi T, eds. International Conference on Healthcare
Systems Ergonomics and Patient Safety. Taylor & Francis, 2005, 377–
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