JONA
Volume 47, Number 1, pp 41-49
Copyright B 2017 Wolters Kluwer Health, Inc. All rights reserved.
T H E J O U R N A L O F N U R S I N G A D M I N I S T R A T I O N
The Effect of Reported Sleep,
Perceived Fatigue, and Sleepiness on
Cognitive Performance in a Sample of
Emergency Nurses
Lisa A. Wolf, PhD, RN, CEN, FAEN
Cydne Perhats, MPH
Altair Delao, MPH
Zoran Martinovich, PhD
OBJECTIVE: The aim of this study is to explore
the relationship between reported sleep, perceived
fatigue and sleepiness, and cognitive performance.
BACKGROUND: Although evidence suggests that
fatigue and sleepiness affect the provision of care in
inpatient units, there is a lack of research on the sleep
patterns of emergency nurses and the effects of dis-
turbed sleep and fatigue on their cognitive abilities
and susceptibility to medical errors.
METHODS: A quantitative correlational design was
used in this study; in each of 7 different statistical models,
zero-order relationships between predictors and the
dependent variable were examined with appropriate
inferential tests.
RESULTS: Participants reported high levels of
sleepiness and chronic fatigue that impeded full
functioning both at work and at home.
CONCLUSIONS: Although high levels of self-reported
fatigue did not show any effects on cognitive function,
other factors in the environment may contribute to
delayed, missed, or inappropriate care. Further research
is indicated.
Studies of worker fatigue in the military and com-
mercial trucking industries1,2 suggest that long hours,
especially extending into overnight, can be hazardous
in terms of the potential for errors due to sleepiness
and reduced vigilance. Medical providers, both nurses
and physicians, are at risk because of the need for
around-the-clock coverage and the number and type
of decisions they must make in a given time span; in
1989, the Bell Commission Report cited sleep depri-
vation in medical house officers as a major contrib-
utor to the 1984 death of Libby Zion, an 18-year-old
who died at New York Hospital, leading to a restruc-
turing of the hours involved in medical residencies.3
The long (up to 36 hour) shifts of the medical resi-
dents in charge of her care and the consequences of
their fatigue on the decisions that were made were
cited as factors in her death. As a result of their inves-
tigation of the case, the Bell commission recommen-
ded limiting the work hours of medical residents to
less than 80 hours a week and no more than 24 hours
in a row, and subsequent research has led to similar
recommendations for the nursing workforce.4,5
Emergency care settings are chaotic environ-
ments, where there is high patient turnover, constantly
changing priorities and frequent changes in patient
condition. High demand work settings are associated
with increased fatigue, which can impair nurses_ at-
tentiveness and ability not only to recognize potential
errors they might commit but also to recognize and
mitigate the errors of others, inc ...
JONAVolume 47, Number 1, pp 41-49Copyright B 2017 Wolters .docx
1. JONA
Volume 47, Number 1, pp 41-49
Copyright B 2017 Wolters Kluwer Health, Inc. All rights
reserved.
T H E J O U R N A L O F N U R S I N G A D M I N I S T R A
T I O N
The Effect of Reported Sleep,
Perceived Fatigue, and Sleepiness on
Cognitive Performance in a Sample of
Emergency Nurses
Lisa A. Wolf, PhD, RN, CEN, FAEN
Cydne Perhats, MPH
Altair Delao, MPH
Zoran Martinovich, PhD
OBJECTIVE: The aim of this study is to explore
the relationship between reported sleep, perceived
fatigue and sleepiness, and cognitive performance.
BACKGROUND: Although evidence suggests that
fatigue and sleepiness affect the provision of care in
inpatient units, there is a lack of research on the sleep
patterns of emergency nurses and the effects of dis-
turbed sleep and fatigue on their cognitive abilities
and susceptibility to medical errors.
METHODS: A quantitative correlational design was
used in this study; in each of 7 different statistical models,
2. zero-order relationships between predictors and the
dependent variable were examined with appropriate
inferential tests.
RESULTS: Participants reported high levels of
sleepiness and chronic fatigue that impeded full
functioning both at work and at home.
CONCLUSIONS: Although high levels of self-reported
fatigue did not show any effects on cognitive function,
other factors in the environment may contribute to
delayed, missed, or inappropriate care. Further research
is indicated.
Studies of worker fatigue in the military and com-
mercial trucking industries1,2 suggest that long hours,
especially extending into overnight, can be hazardous
in terms of the potential for errors due to sleepiness
and reduced vigilance. Medical providers, both nurses
and physicians, are at risk because of the need for
around-the-clock coverage and the number and type
of decisions they must make in a given time span; in
1989, the Bell Commission Report cited sleep depri-
vation in medical house officers as a major contrib-
utor to the 1984 death of Libby Zion, an 18-year-old
who died at New York Hospital, leading to a restruc-
turing of the hours involved in medical residencies.3
The long (up to 36 hour) shifts of the medical resi-
dents in charge of her care and the consequences of
their fatigue on the decisions that were made were
cited as factors in her death. As a result of their inves-
tigation of the case, the Bell commission recommen-
ded limiting the work hours of medical residents to
less than 80 hours a week and no more than 24 hours
in a row, and subsequent research has led to similar
recommendations for the nursing workforce.4,5
3. Emergency care settings are chaotic environ-
ments, where there is high patient turnover, constantly
changing priorities and frequent changes in patient
condition. High demand work settings are associated
with increased fatigue, which can impair nurses_ at-
tentiveness and ability not only to recognize potential
errors they might commit but also to recognize and
mitigate the errors of others, including physicians.6,7
The work schedules of emergency nurses (ENs) are
characterized by increasing demands, irregular shifts,
and long hoursVall of which challenge their mental
and physical stamina. Although 12-hour shifts are
JONA � Vol. 47, No. 1 � January 2017 41
Author Affiliations: Director, Institute for Emergency Nursing
Research (Dr Wolf); Senior Research Associate (Mss Perhats
and Delao),
Emergency Nurses Association, Des Plaines; and Research
Assistant
Professor (Dr Martinovich), Department of Psychiatry,
Northwestern
University Feinberg School of Medicine, Evanston, Illinois.
The authors declare no conflicts of interest.
Correspondence: Dr Wolf, Institute for Emergency Nursing
Research, Emergency Nurses Association, 915 Lee St, Des
Plaines,
IL 60016 ([email protected]).
Supplemental digital content is available for this article. Direct
URL citations appear in the printed text and are provided in the
HTML and PDF versions of this article on the journal_s Web
site
5. making medical errors and potentially causing harm to
patients.13-15 The research of Rogers and colleagues8
demonstrated that the 12-hour shift is past the point
where safe decision making may be expected, and
both the Agency for Health Research and Quality4
and the Institute of Medicine5 have since recommended
limiting the length of nurses_ shifts. Although evidence
suggests that both mental and physical fatigue and
sleepiness affect the provision of care in inpatient
units,16 there is a lack of research on the sleep patterns
of ENs and the effects of disturbed sleep and fatigue
on their cognitive abilities and susceptibility to making
medical errors. The purpose of this study is to explore
the relationship between reported sleep quality, per-
ceived fatigue and sleepiness, and cognitive perfor-
mance in a sample of ENs.
Methods
Before recruitment of subjects, institutional review
board approval was obtained and the study was
approved (Chesapeake Research Review, Columbia,
Maryland). A Certificate of Confidentiality from the
National Institutes of Health was obtained to further
protect the respondents_ anonymity given the sensi-
tive nature of the question. A quantitative correla-
tional design was used to explore the relationship
between nurses_ reported sleep patterns, perceived
sleepiness and fatigue, and their ability to carry out
timed cognitive tasks. As part of a 35-item online
questionnaire, participants performed 3 medication
dosage calculations and correlations between fatigue
and cognitive performance were measured in the
6. same survey. Predictors were selected based on
empirically based associations with work fatigue
and its impact on task performance.17 In each of 7
different statistical models, zero-order relationships
(ie, simple relationships that do not hold constant
other predictors) between each of the predictors and
the dependent variable were examined with appro-
priate inferential tests.
Sample
Of the 2,419 ENs who were recruited via e-mail
and social media, 2010 met eligibility criteria and
consented to participate. Eligibility criteria required
that study participants were English speaking, currently
licensed ENs working at least 1 shift per week in a
US emergency department (ED). Among those who
consented, 504 survey respondents were excluded
because they did not meet study criteria (eg, worked 1
shift or less in the past 30 days, did not complete survey
in 1 sitting), resulting in a total sample of 1506 ENs.
Research Questions
On the basis of the existing body of evidence, we hy-
pothesized that ENs_ patterns and quality of sleep,
sleepiness, and fatigue would have an attenuating
effect on their performance in timed cognitive skill
tests. To investigate these effects, we posed the fol-
lowing research questions:
Q1: Is there a relationship between ENs_ day-
time sleepiness and performance in timed cognitive
tests?
Q2: Is there a relationship between ENs_
8. within the ESS18 (Cronbach_s ! = .88-.74 in 4 different
groups of subjects).
Q2 Instrument: We included 2 subscales (9-item
subscale: Bhow often have you had trouble sleeping[
and 6-item subscale: Bhow often have you used the
following substances to help you get to sleep[) from
the Pittsburgh Sleep Quality Index (PSQI), which is a
19-item well-validated self-rated questionnaire for
evaluating subjective sleep quality over the previous
month19 (Cronbach_s ! = .83). We developed a third
8-item subscale to assess Bhow often has it been difficult
to do the following things because you were fatigued[
(internal consistency and reliability coefficient = .85).
Q3 Instrument: The Occupational Fatigue Ex-
haustion Recovery (OFER15) Scale is a self-report
questionnaire of 15 questions, which form 3 sub-
scales: chronic fatigue, acute fatigue, and intershift
recovery, respectively. The OFER Scale produces
comparable scores between 0 and 100 for each
subscale, with cut-points into levels of low, low/
moderate, moderate/high, and high on each subscale
used to compute quartiles of scale score distribution.
The subscales have high internal reliability (9.84)
and face, construct, and discriminant validity.20
Timed cognitive performance was measured by
the speed and accuracy for 3 weight-based medication
calculations that are frequently performed in emer-
gency nursing practice. The first 2 medication ques-
tions required calculation of the initial bolus and the
total dose for a thrombolytic medication order for an
older adult patient with a diagnosis of ischemic stroke.
The 3rd medication question required calculation of
9. the 1st dose of an antibiotic for a pediatric patient
with a diagnosis of strep throat. Survey participants
were instructed to complete the survey in 1 sitting and
without interruption. This was done so that we could
determine the speed and accuracy of the medication
calculations in relation to their perceived state of fatigue
at that time. The online survey software recorded start
and stop times for the medication calculations and for
completion of the questionnaire. Seven people were
eliminated from the study sample because their
response times were extreme outliers. All results were
scored and compiled in an SPSS database (version 22;
Armonk, New York) for analysis.
Predictors
Predictors were selected because of a presumed impact
of work fatigue on cognitive performance. Interval-
scaled predictors included the standardized items
(based on analytic sample norms) from each of the
3 instruments used in this study. We examined 29
categorical predictors including work schedules
(total hours in the past 30 days), shift types (length
Table 1. Participants" Demographics
(n = 1506)
Percentage of Survey
Respondents
Gender
Male 15.5
Female 84.5
Intersex 0.1
Missing 0
10. Age
18-24 2.0
25-34 23.0
35-44 24.0
45-54 24.9
55-64 25.0
Q65 1.1
Missing 0
Primary ED role
Staff nurse 68.9
Charge nurse 16.3
Case manager 0.1
Clinical coordinator 1.8
Clinical educator 2.7
Clinical nurse specialist 0.6
Director 1.4
Manager 3.1
Nurse practitioner 1.6
Trauma coordinator 0.8
Other 2.7
Missing 0
Years of experience Mean (SD)
In nursing 17.5 (12.5)
In emergency nursing 12.8 (10.1)
In current ED 8.2 (8.4)
In all areas of emergency care,
excludingnursing (eg, tech, etc)
5.9 (9.3)
Table 2. Facility Characteristics
Percentage of
Survey Respondents
12. for 2 interval-scaled predictors were also obtained for
untransformed scores.
Data Analysis
Correlational analyses were performed using speed
and accuracy of the medication dosage calculations
as the dependent variable. Speed and accuracy data
were examined via 7 statistical models: OLS regres-
sion model predicting time in minutes on the page;
Cox regression model predicting time to fully correct
response (3/3 questions); Cox regression model predict-
ing time to partially correct response (2/3 questions);
3 logistic regression models predicting the probability
of a correct response for each individual medication
dosage calculation; and a polytomous logistic regres-
sion (PLUM) model predicting the number of correct
responses (of 3).
In each of 7 different statistical models, zero-
order relationships (ie, simple correlations or simple
contrasts of group vs full sample) between each pre-
dictor and the dependent variable (time and accuracy
of response) were examined with appropriate inferen-
tial tests (relative ratio, odds ratios, Wald z tests, P
values). Probability estimates were calculated for each
group and for the full sample. Inferential tests con-
trasted each group_s estimated probability of a correct
response to the medication calculation versus all other
groups combined (ie, contrasting each group versus the
full sample) and results were reported with associated
Wald z tests and P values.
Results
Descriptive Statistics
13. In this sample of 1506 participants who provided valid
responses on all items, it took an average of 21.6 minutes
to complete the 34 items in the online questionnaire. Cog-
nitive performance was measured by 3-timed medica-
tion dosage calculations (see Table, Supplemental
Digital Content 1, http://links.lww.com/JONA/A499).
Overall, respondents averaged 3.78 minutes to com-
plete the problem set and the average number of cor-
rect answers was 1.95 of 3. More specifically, the
percentages with 0, 1, 2, or 3 correct answers were
11.2% (n = 168), 17.4% (n = 262), 36.5% (n = 549),
and 35.0% (n = 527), respectively.
Work Schedules
The primary work schedule for this group of nurses
consisted of 12-hour shifts (74%), with a mean
shift length of 11 T 2 hours and an average of 152 T
50 hours worked in the preceding 30 days. Survey
data indicate that the mean number of shifts worked
in that period was 13 T 4, with 30% of the nurses
working all those shifts consecutively and 65%
working at least some consecutive shifts. The re-
ported numbers of extended work shifts ranged from
0 (39%), 1 to 3 (30%), 4 to 10 (25%), and 11 to
24 (5%). The primary reason given for working an
extended shift was to meet patient care needs (47%).
Sleep, Fatigue, and Cognitive Performance
Study participants reported varying levels of sleepi-
ness, sleep disturbance, and fatigue as measured by
their responses on the 3 scaled instruments. Most nota-
bly, 4 of every 10 nurses reported levels of daytime
sleepiness above the upper limit of normal (an ESS
15. Seven different statistical models were used to examine
zero-order relationships between each of the predictors
and the dependent variable (speed and accuracy of
response). Analysis of the interval-scaled predictors for
sleep patterns, sleep quality and fatigue (ESS, modified
PSQI, OFER15) did not detect any significant zero-
order effects within any of the 7 models (Table 4).
Nurses_ work schedules, caregiver status, and working
a 2nd job did show a significant effect on cognitive
performance. Nurses who worked the 7 AM to 7 PM
shift (n = 511) showed significantly poorer response
accuracy on several indicators (Table 5). These sub-
jects had a 25% lower probability (time-adjusted) of a
fully correct response set versus other nurses (mean, 1.87
vs 1.95; odds ratio [OR], 0.78; z = j2.45, P = .14). They
were also more likely to give an incorrect answer for the
pediatric dose calculation (mean, 41.3% vs 47.3%; z =
j3.36, P = .001). In contrast, nurses who worked the
7 PM to 7 AM shift (n = 418) had better time-adjusted
accuracy estimates based on the 2 Cox regression
models. Their probability of generating a fully
correct response was 1.27 times greater (z = 2.48,
P = .013). Similar effects were found for subjects
with a 2nd part-time or full-time job (n = 426) and
for those who were primary caregivers of children
(n = 541), with both groups completing the problem
set faster and more accurately than those with no
additional job or caregiver responsibilities.
Discussion
Based on the existing body of evidence, we hypoth-
esized that ENs_ sleep pattern, sleep quality, and
Table 3. Activities of Daily Living (ADL): Functional Impact of
16. Fatigue (Modified PSQI Subscale)
In the past month, how often has it been difficult for you to do
the following things because you were fatigued?
(valid percent; internal reliability and consistency coefficient =
.85)
Activity Never Difficult, % Sometimes Difficult, % Frequently
Difficult, % Always Difficult, %
Engage in social activities 14.8 46.9 28.1 10.2
Manage stress 17.5 52.9 23.4 6.2
Attend to family needs 25.8 48.2 21.4 4.6
Eat regular meals 21.9 35.7 29.4 13.0
Provide safe patient care 52.7 43.0 3.8 0.5
Safely operate a motor vehicle 61.2 33.8 4.3 0.7
Exercise 10.8 32.4 34.0 22.8
Attend to personal needs 27.6 45.4 21.6 5.4
Table 4. Effect of Sleep and Fatigue on Responses to
Medication Problem Set: 7 Statistical Models
for Interval Scale Predictorsa (n = 1506)
DV1: Correlation
With Time (Minutes
to Complete
Problem Set)
DV2: RR
of Fully
Correct
Response (3/3)
DV3: RR
17. of Partially
Correct
Response (2/3)
DV4: %
Correct
(Activase
Initial)
DV5: %
Correct
(Activase
Total)
DV6: %
Correct
(Amoxicillin
1st Dose)
DV7:
Number
Correct
(1-3)
r RR RR OR OR OR OR
Daytime sleepiness and fatigue
Daytime sleepiness 0.00 1.02 1.00 1.06 0.96 1.01 1.02
Chronic fatigue 0.04 0.93 0.95 0.95 0.97 0.95 0.95
Acute fatigue j0.01 1.02 1.02 0.97 1.02 1.00 1.00
Persistent fatigue 0.01 1.01 0.99 1.02 0.98 1.03 1.01
19. to fatigue (Table 3). Twelve-hour work shifts were the
norm among our study participants (74% of reported
shifts), with an average shift length of 11 hours. Thus,
any extension of work schedules places these nurses
at increased risk of potential harm to patients and
themselves.8-11 Of particular concern is the finding
that nearly half (46.8%) of study participants reported
that their fatigue made it difficult to provide safe
patient care at least some of the time.
Previous researchers have reported higher error
rates among nurses who worked rotating shifts21 and
night shifts.13 In contrast, nurses in our study who
worked the 7 AM to 7 PM day shift showed signifi-
cantly poorer response accuracy when compared
with nurses who worked any other shift (Table 5).
Participants who reported having specific stressors (ie,
being a primary caregiver or working a 2nd full or
part-time job in addition to their ED employment)
answered the problem set with greater speed and
accuracy than did those who did not have those res-
ponsibilities. These unexpected findings stimulate a
number of alternative hypotheses that have been raised
in the literature and are worth further consideration.
Effects of Job Demands and High-Stress
Environments
There is a substantial body of literature on the rela-
tionship between job performance, fatigue, and ex-
posure to repetitive, high levels of stress without
adequate recovery between episodes.9,13-24 Our
sample of ENs reported high levels of acute fatigue,
low intershift recovery, and difficulties with manag-
ing their daily routine (eg, eating regular meals, driv-
20. ing safely, providing safe patient care), consistent
with previous research that suggests that workers
Table 5. Effect of Shift Type on Responses to Medication
Problem Set: 7 Statistical Models for
Categorical Predictors
Survey question: During the past month, on which shift did you
primarily work in your ED? (n = 1506)
DV1: Time in Minutes to
Complete Problem Set
DV2: Relative RiskVFully
Correct Response (3/3)
DV3: Relative RiskVPartially
Correct (2/3)
n Mean t P RR Wald z P RR Wald z P
7 AM-3 PM 98 3.82 0.20 .842 1.07 0.38 .706 1.09 0.73 .463
7 AM-7 PM 511 3.90 1.36 .174 0.75 j3.04 .002 0.89 j1.85 .065
7 PM-7 AM 418 3.58 j1.94 .052 1.27 2.48 .013 1.15 2.06 .040
11 PM-7 AM 21 4.30 1.00 .316 0.45 j1.59 .112 0.71 j1.21 .226
11 AM-11 PM 136 3.63 j0.76 .446 1.08 0.54 .587 1.10 0.88 .380
3 PM-11 PM 31 3.61 j0.39 .694 1.81 2.40 .016 1.12 0.52 .606
3 PM-3 AM 51 3.67 j0.31 .758 0.96 j0.15 .883 0.93 j0.40 .687
DV4: % Correct
(Activase Initial)
DV5: % Correct
(Activase Total)
DV6: % Correct
22. work-related stress and fatigue, with adequate re-
covery being a key mediating factor.24-26 Additional
results from 2 Australian studies found that the psycho-
logical strain that nurses experience in high-paced,
demanding jobs and lower levels of fulfilling non-
work activities were associated with poorer sleep quality
and reduced ability to recover between shifts.27,28
Similarly, the unexpected finding that 12-hour
night shift workers performed better than 12-hour
day shift workers could be a function of differences in
environmental stress both within and between hospitals.
Possibly, the night shift manages fewer interruptions
from other hospital departments and fewer distractions
owing to comparatively fewer workplace and personal
demands than are experienced on the day shift. Re-
search suggests that fatigue, along with inadequate rest
and recovery between ED shifts, may reduce ENs_
ability to be vigilant and recognize the potential for
error.29 In his examination of the effects of sleep depri-
vation on cognition, Killgore17 concluded that sleep
loss has a differential effect on specific cognitive and
emotional processes, with some indication that higher-
level cognitive capacities (eg, memory, judgment,
decision-making) might remain degraded despite resto-
ration. The frequent extension of 12-hour shifts,
inadequate time off between shifts, and constant
exposure to demanding work may have contributed
to difficulties with performing some nursing tasks
(eg, medication calculations) within our sample of
ENs, nearly half of whom reported that fatigue made
it difficult for them to provide safe patient care.
Variations in nurses" vigilance between day and
night shifts and/or over the duration of individual
shifts may have contributed to performance differ-
23. ences in our sample. Surani and colleagues14 found
that vigilance improved over the duration of the shift;
in particular, fast reaction times were significantly
shorter in floor nurses after shift than before it. The
authors concluded that Bthere may also be impair-
ment related to the intensive care unit (ICU) setting,
where higher patient acuity requires even more
vigilance,[ suggesting that the high-acuity ED envi-
ronment may have an attenuating effect on ENs" job
performance. Because we did not collect data on these
types of parameters, we were not able to assess similar
effects that could have contributed to our findings.
Given the considerable costs of work-related fatigue (eg,
injuries), further research is warranted using chronobio-
logical objective sleep measures to more effectively
evaluate the effects of sleep deprivation30 as well as
recommended strategies (eg, work hour limits)31
that could improve the health and safety of the EN
workforce and the patients they care for.
Limitations
The sample was a self-selecting group of ENs who
reported on their sleep patterns and levels of fatigue;
no objective measures of fatigue were collected. We
also did not collect data on environmental factors that
may have affected results. In addition, although the study
instruments have been widely used, to our knowledge,
they have not been used to measure work-related fatigue
among ENsVa subgroup that reports high levels of acute
and chronic fatigue that may be above those found in the
general nursing population. As an example, researchers
found a greater discrepancy in test-retest ESS scores
among patients with possible sleep-disordered breathing
than has been previously reported in studies that
24. examined reproducibility only in normal subjects.32
Similarly, high levels of acute and chronic fatigue within
our study population could have resulted in limited
ability to detect effects of poor sleep quality and fatigue
on ENs" cognitive function. Moreover, because we did
not collect data on variables such as work environment,
vigilance, or the timing of survey completion (eg,
whether study participants completed the medication
problem set during midshift, postshift, or on a day off),
we can neither support nor reject the null hypothesis
that work-related fatigue has an attenuating effect on
ENs_ cognitive function.
Conclusions
A significant percentage of our sample of 1,506 ENs
reported high levels of sleepiness and chronic and
acute fatigue that impeded full functioning both at
work and at home. Although we could not determine
from this study whether levels of self-reported fatigue
affect cognitive function, participants did report
difficulty with providing both self-care and patient
care. Other factors in the environment may contrib-
ute to the effects of EN fatigue and resultant delayed,
missed, or inappropriate care. Further research on the
effect of ENs_ work schedules, physical and emo-
tional fatigue, patterns and quality of sleep, and work-
place conditions on their ability to safely and effectively
perform their jobs in the high-demand environment
of the ED is required. There is compelling evidence
to suggest that fatigue reduction interventions could
contribute to overall improvements in the delivery of
care, including reduction in medical errors.31 Nurse
administrators may consider a fatigue management
reduction system as described by Lerner et al in the
26. Ulmer R. Long hours and fatigue: a survey of tractor-trailer
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JONA � Vol. 47, No. 1 � January 2017 49
33. · The network sits behind a gateway router and firewall.
Antivirus is in use, but is not automatically updated across the
company. Employees often work remotely and only use their
login and password to gain access to the corporate systems.
· There is a Director of IT who has a full time staff of 5
employees, one of which does security duties part time.
There are a few known issues with their IT infrastructure and
organization:
· Recently, a number of PCs and office equipment has been
stolen out of the office.
· It’s at the data owner’s discretion as to whether or not to
secure their data files or folders. Many do not secure their
files, while some lock them so only they have access. There
have been rumors that customer data and intellectual property
have been lost.
· Two employees recently left your company and went to your
biggest competitor, where they just landed a contract with your
largest account.
· Vendors are allowed access to the site and computers without
authorization or supervision.
· Onsite staff at each location provides IT support part time
along with their other responsibilities. Password resets are done
by giving out a generic password — Chiefs2017.
You are an independent auditor brought in by Dalton, Walton, &
Carlton’s management. They’ve tasked you with conducting an
audit of their entire IT infrastructure, organization, and
processed.
Bellevue University CYBR 515 as of: August 2017
