the factors that affect the frequency of vital sign monitoring in the emergency department

THE FACTORS THAT AFFECT THE FREQUENCY

OF VITAL SIGN MONITORING IN THE

EMERGENCY DEPARTMENT

Authors: Kimberly D. Johnson, PhD, RN, CEN, Chris Winkelman, PhD, RN, Christopher J. Burant, PhD,Mary Dolansky, PhD, RN, and Vicken Totten, MD, Cleveland, OH

Introduction: Vital signs are an important component of thenursing assessment and are used as early warning signs ofchanges in a patient’s condition; however, little research has beenconducted to determine how often vital signs are monitored in theemergency department. Additionally, it has not been determinedwhat personal, social, and environmental factors affect thefrequency of vital sign monitoring. The purpose of this study wasto examine what factors may influence the time betweenrecording vital signs in the emergency department.

Methods: We performed a descriptive, retrospective chartreview of 202 randomly selected adult ED patients’ charts fromrepresentative times to capture a variety of ED levels ofoccupancy in an urban, Midwestern, teaching hospital.Descriptive and hierarchical regression analyses were used.

Results: The strongest predictor of the increased time betweenvital signs from the personal health factors was lower patient

acuity (Emergency Severity Index). This relationship remainedstrong even when social factors and environmental factors wereincluded. Increased length of stay and fewer routes ofmedications also had significant relationships to the increasedtime between vital sign monitoring.

Discussion: These findings are clinically importantbecause greater time between vital sign recordings canlead to errors of omission by not detecting changes in vitalsigns that could reveal changes in the patient’s condition.The findings of this study provide direction for futureresearch focusing on determining whether higher frequencyof vital signs surveillance contributes to higher quality careand linking quality of care to missing vital signs/inadequate monitoring.

Key words: Vital signs; Emergency department; Monitoring;Frequency of vital signs; Emergency Severity Index; Crowding

Vital signs are simple measurements of physiologicparameters that represent a set of objective dataused to determine general parameters of a patient’s

health and viability. These values influence the doctors’and nurses’ interpretation of a patient’s overall conditionand affect the course of treatment for each patient indivi-dually. Historically, vital signs have been considered as anintegral part of the nursing assessment and as an earlywarning sign of patient deterioration.1,2 Vital sign monitor-ing also may be used as a marker of nursing vigilanceor frequency of direct patient observation to evaluate thepatient’s condition or responses to interventions.

Vital signs are recorded at least once for every emer-gency patient and are monitored in the emergency depart-ment because changes can herald an imminent adversechange in the patient’s condition.3 Although vital signmonitoring is the most commonly performed task in emer-gency departments, there is limited information regardingthe optimal frequency with which vital signs should bemonitored. The majority of the literature addressing the

Kimberly D. Johnson, Member, Eastern Ohio Chapter ENA, is PostdoctoralFellow, VA Quality Scholar Program, Department of Veteran Affairs, FrancesPayne Bolton School of Nursing, Case Western Reserve University, Cleve-land, OH.

Chris Winkelman is Associate Professor, Frances Payne Bolton School ofNursing, Case Western Reserve University, Cleveland, OH.

Christopher J. Burant is Assistant Professor, Frances Payne Bolton School ofNursing, Case Western Reserve University, Cleveland, OH.

Mary Dolansky is Assistant Professor, Frances Payne Bolton School of Nur-sing, Case Western Reserve University, Cleveland, OH.

Vicken Totten is Director of Research, Emergency Medicine, University Hos-pitals Case Medical Center, and Assistant Professor, Case Western ReserveUniversity, Cleveland, OH.

For correspondence, write: Kimberly D. Johnson, RN, PhD, CEN, 5806Horning Rd, Kent, OH 44240; E-mail: [email protected].

J Emerg Nurs 2014;40:27-35.

Available online 23 October 2012.0099-1767/$36.00

Copyright © 2014 Emergency Nurses Association. Published by Elsevier Inc.All rights reserved.

http://dx.doi.org/10.1016/j.jen.2012.07.023

R E S E A R C H

January 2014 VOLUME 40 • ISSUE 1 WWW.JENONLINE.ORG 27

mailto:[email protected]

http://dx.doi.org/10.1016/j.jen.2012.07.023

frequency of vital sign monitoring is focused on inpatientsand is inconsistent in nature. Only 4 studies could belocated that addressed vital signmonitoring in the emergencydepartment.4-7

The frequency of obtaining vital signs depends on hos-pital policy, nursing judgment, or written physician orderand is commonly based on the patient’s acuity and chiefcomplaint. For example, primary stroke centers have guide-lines that require vital sign monitoring every 15 minutesduring the acute phases of care, and most intensive careunits require a minimum of hourly recorded vital signs.A report on rural ED care in the United States suggests thatin trauma admissions, vital sign monitoring should occurhourly8 and the Trauma Nursing Core Course guidelinesrecommend the ongoing assessment of vital signs. How-ever, there are no published standards of care or guidelineson the recommended frequency of obtaining vital signs forthe general ED population. No research has been publishedthat examines the frequency of vital sign monitoring byemergency nurses.

It has been suggested that social factors may affectvariations in patient care. Previous research reports thatfemale patients wait longer for and receive less pain med-ications.9 Other studies report that female patients receivea larger quantity and stronger dose of medication thantheir male counterparts.10 Mills et al11 found that non-white patients waited longer for and received less medica-

tion than their white counterparts. There are no dataabout the impact of insurance (Medicare, Medicaid, pri-vate, self-pay) in published reports related to disparatecare in the emergency department.

In addition to gender and race, environmental factorssuch as ED crowding have been shown to affect aspects ofcare in the emergency department including patient satis-faction levels,12-14 timeliness of medication administra-tion,15,16 and mortality rates.17-19 Furthermore, duringperiods of crowding, emergency nurses report perceiveddecreases in the quality of care provided to patients.20 How-ever, no studies have been reported that examine howcrowding specifically affects the nursing care provided.

The purpose of this study was to examine the fre-quency of vital sign monitoring and whether selected fac-tors (age, gender, ethnicity, insurance, number ofcomorbidities, number of over-the-counter [OTC] andprescription drugs, triage category) affect the frequency ofvital sign recording to provide guidance for the develop-ment of nursing policy regarding the frequency of vital signmonitoring. The second purpose was to determine whetherthese factors continue to influence the frequency of vitalsign monitoring in the presence of environmental/processfactors (crowding level, family presence, number ofroutes of medication administered in the emergency de-partment, length of stay) and to determine whether dispa-rities in care are present. The research questions were as

FIGURE

Model of nursing vigilance in emergency department.

RESEARCH/Johnson et al

28 JOURNAL OF EMERGENCY NURSING VOLUME 40 • ISSUE 1 January 2014

follows: (1) What are the personal health factors (numberof prescription medications, number of OTC medications,comorbidities, age, gender, triage category) that affect thefrequency of vital sign monitoring in the emergency de-partment? (2) What social factors (insurance status, ethni-city) affect the frequency of vital sign monitoring in theemergency department? (3) Does the effect of personal fac-tors on the frequency of vital sign monitoring in the emer-gency department change when environmental factors(family presence, crowding level, length of stay, number ofroutes of medications administered in emergency depart-ment) are taken into account?

Conceptual Model

The model of nursing vigilance in the emergency depart-ment that guided this study was based on the social eco-logic framework in which personal and environmentalfactors contribute to patient outcomes through nursing vig-ilance (Figure). This model was developed by use of themodel of vigilance of Meyer and Lavin21 to evaluate the ef-fect of social and environmental factors on the relationshipbetween personal factors and patient monitoring. In thisstudy, nursing vigilance is measured by the frequency ofmonitoring vital signs. Vital signs were used as a proxyfor nursing vigilance because they are an integral part ofthe nursing assessment and have been used as a decision-making tool that has influenced the course of patienttreatment.22 The effect on patient outcomes was notmeasured in this study.

Methods

A descriptive, retrospective chart review was performedafter we obtained approval from the hospital’s institutionalreview board. A strategic sampling strategy was used to cap-ture a variety of ED occupancies at an urban teaching hos-pital. The strategy captured 202 adult patients’ charts,randomly selected from randomly selected time periods.The emergency department at the study hospital wasequipped with 23 patient beds and cared for 70,000 pa-tients per year but was designed to care for only 40,000visits annually. For this study, the frequency of vital signmonitoring was calculated by dividing the total length ofstay by the number of times vital signs were recorded inthe chart. ED crowding, defined as any time when re-sources are inadequate to meet patient requirements,23

was measured by use of the Emergency Department WorkIndex (EDWIN). The EDWIN is an equation that incor-porates important components of the input/throughput/output model (eg, number of patients, acuity, numbersof physicians on duty, and bed availability) into a single

omnibus index. The EDWIN correlated well with nurseand physician assessments of crowding.12 A score of 1.7or higher is considered to indicate a crowded emergencydepartment. The triage category recorded in this studywas determined by use of the Emergency Severity Index,version 4 (ESI). The ESI has 5 levels to which patientscan be categorized. Level 1 indicates the most urgent cate-gory of patients who require immediate care because deathis imminent (ie, cardiac arrest), whereas level 5 is the leastacute category.24 This system of triage is endorsed by theEmergency Nurses Association and the American Collegeof Emergency Physicians. The ESI has been recommendedas a valid and reliable triage system.25

SAMPLE

Crowding levels were calculated at 4-hour intervals duringthe study using the EDWIN. We selected 165 charts froma possible 3,727 subjects from the crowded periods (ED-WIN >2) and 60 of a possible 73 subjects from non-crowded periods (EDWIN <2), for a total of 225 reviewedcharts. Although 225 records were requested, a total ofonly 212 charts were located for review from 4 one-weekperiods (January 11-17, March 8-14, June 14-20, andOctober 11-17) to provide a representative snapshot ofseasonal variations in ED occupancy. Of the 212 chartslocated, 202 met the inclusion criteria. The inclusion cri-teria included all the charts of patients assigned to a roomor hallway bed in the emergency department during per-iods with different crowding levels. Charts were excludedbecause the length of stay was less than 3 hours, becauseof missing triage assessments, because the patient assigneda triage category of 4 or 5 (lower acuity patients), or be-cause patients had incomplete baseline triage vital signs.Because of the shorter length of stay for the critically ill(eg, ESI 1), most patients with a high acuity were ex-cluded from the study.

POWER/EFFECT SIZE

By use of a power of 0.80 and the probability of a type Ierror of 0.05, the expected observable effect size for thisstudy was 0.95.26

DATA COLLECTION

Demographic variables, arrival information, assessmentdata, length of stay, and treatment information were col-lected for each subject by a single trained researcher. Pa-tient-related factors that potentially affect frequency ofvital sign monitoring were established a priori through dis-cussion with experts (including V.T. andM.D.) and a reviewof literature. A pilot study was conducted with 10 charts toensure that data points were available in the written records.

Johnson et al/RESEARCH


DATA ANALYSIS

Data analysis included descriptive analysis and hier-archic regression. Categorical, non-numeric variableswere recoded into dichotomous dummy variables and incor-porated as predictor variables. Three blocks of predictors (ie,personal health, social, and environmental factors) wereadded into the regression 1 step at a time. Data were

analyzed with SPSS software, version 16.0.27 Data were eva-luated for potential violations of the assumptions of regression.No adjustments were required.

Results

The mean EDWIN score was 2.56. Of the 168 time inter-vals, 142, or 85%, were crowded. The mean age was 47.5

TABLE 2Definitions of statistical terms

Statistical term Definition

Adjusted R2 The portion of the variance of the outcome that can be explained by the predictor variablesR2 change The amount of the variance of the outcome explained when more predictor variables are addedMulticollinearity When 2 or more predictor variables have strong correlations to each otherP value (P < .01) The probability of obtaining the same results by chance (P < .01 means there is less than a 1% chance of this

occurring randomly)

TABLE 1Characteristics of study sample

Variable Data

Age [mean (SD) (range)] (y) 47.5 (21.5) (18-97)Comorbidities [mean (SD) (range)] 2.52 (1.87) (0-9)Prescription medications [mean (SD) (range)] 3.26 (3.178) (0-14)OTC medications [mean (SD) (range)] 0.66 (1.037) (0-5)Length of stay in emergency department [mean (SD) (range)] (min) 405.1 (205.32) (47-1,407)Crowding level (EDWIN) [mean (SD) (range)] 10.5 (10.0) (1-29)No. of routes of medication administered in emergency department [mean (SD) (range)] 1.65 (1.14) (0-5)Frequency of vital sign monitoring [mean (SD) (range)] (min) 130.8 (94.84) (4-807)Payment method: Medicaid [No. (%)] 76 (36.9%)African-American ethnicity [No. (%)] 152 (74%)Family presence [No. (%)] 79 (39%)Admission diagnosis [No. (%)]Gastrointestinal 51 (24.8%)Neurologic 28 (13.6%)Pulmonary 26 (12.6%)Musculoskeletal 24 (11.7%)Cardiologic 22 (10.7%)

Female gender [No. (%)] 144 (69.9%)Triage category 2.56 (SD 0.512)1 (resuscitation) [No. (%)] 1 (<1%)2 (emergent) [No. (%)] 83 (41.7%)3 (urgent) [No. (%)] 118 (57.3%)

SD, Standard deviation.



TABLE 3Correlations among social, environmental, and personal health factors

Frequency

of vital

sign

recording

Gender Age Comorbidities OTC

medications

Triage

category

Prescription

medications

Ethnicity Insurance EDWIN Length

of stay

Family

presence

Gender –0.136a

Age –0.190b 0.110Comorbidities –0.253c –0.021 0.436c

OTCmedications

–0.004 –0.066 0.206b 0.337c

TriageCategory(ESI)

0.252c –0.217b –0.311c –0.336c –0.107

Prescriptionmedication

–0.223b 0.005 0.361c 0.670c 0.397c –0.215b

Ethnicity 0.037 0.002 –0.198b 0.031 –0.105 –0.003 –0.074Insurance 0.055 –0.091 –0.008 –0.144a –0.003 0.076 –0.124a –0.290c

EDWIN 0.112 0.058 0.021 0.054 –0.036 –0.175b 0.005 0.066 –0.005Length of stay 0.152a 0.031 –0.057 0.020 0.048 –0.061 0.006 0.118a –0.046 0.093Familypresence

–0.047 –0.057 –0.047 –0.044 –0.046 0.000 0.023 0.095 –0.020 0.014 0.005

No. ofroutes ofmedications

–0.147a 0.004 –0.022 0.089 0.069 –0.078 0.107 0.028 –0.087 0.054 0.204b –0.021

aP < .05.bP < .01.cP < .001.

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years (SD, 21.50 years; range, 17-94 years). Femalepatients accounted for 69.9% (n = 144) of thesubjects. Seventy-four percent (n = 152) were African Ame-ricans. Most patients reported being single (n = 144, 55.3%).Characteristics of the sample are available in Table 1.

INFLUENCE OF PERSONAL HEALTH FACTORS ONFREQUENCY OF VITAL SIGN MONITORING

The variables included in block 1 of the hierarchic regres-sion were (1) number of prescription medications, (2)number of OTC medications, (3) comorbidities, (4) age,(5) gender, and (6) triage category (ESI). By use of theenter method, a significant model emerged (F6,195 =4.541, P < .001) with an adjusted R2 = 0.096. (Table 2shows definitions of statistical terms.) Although significantcorrelations were present among predictors (Table 3),multicollinearity was not indicated. Several of the variables(number of prescription medications, comorbidities, age,gender, and triage category) had significant relationshipswith the frequency of vital sign monitoring (Table 4).However, the strongest predictor of the frequency of vitalsign monitoring was the ESI (t = 2.099, P = .037).

INFLUENCE OF SOCIAL FACTORS ON FREQUENCYOF VITAL SIGN MONITORING

Once the regression was completed on the personal factors,the social factors of insurance status and ethnicity were

added in block 2 of the hierarchic regression. The signifi-cance of the model remained unchanged with the additionof the social variables (F8,193 = 3.414, P = .001). However,the adjusted R2 decreased insignificantly to 0.088 (R2

change = 0.001, P = .859). There was no evidence of mul-ticollinearity in this model. None of the added social vari-ables correlated with the frequency of vital signmonitoring (Table 3), and the variables that contributedsignificantly to the model were unchanged from the pre-vious regression model, although the regression weightsvaried slightly (Table 4).

INFLUENCE OF ENVIRONMENTAL FACTORS ONFREQUENCY OF VITAL SIGN MONITORING

The third block in the hierarchic regression included envir-onmental factors: (1) family presence, (2) crowding level(EDWIN), (3) length of stay, and (4) number of routesof medications administered in the emergency department.The significance of the model remained unchanged withthe addition of the environmental variables (F12,189 =3.915, P < .001). However, the adjusted R2 increased sig-nificantly to 0.148 (R2 change = 0.075, P = .002). Therewas no evidence of multicollinearity in this model. Severalof the environmental factors had significant contributionsto the variance explained in this model. Triage category (t =2.486, P = .014) remained a predictor but at a higher sig-nificance level. Crowding level (t = 2.332, P = .021), length

TABLE 4Unstandardized and standardized βs and significance of social, environmental, and personal health predictor vari-ables for frequency of vital sign monitoring in 3-block hierarchic regression

Predictor variable Block 1 Block 2 Block 3

β (se) Standard β β (se) Standard β β (se) Standard β

Constant 94.20 (44.72) 84.82 (48.176) 69.24 (55.00)Gender –18.97 (14.55) 0.156a –18.95 (14.68) –0.091 –21.83 (14.226) –0.104Age –0.22 (0.33) –0.091 –0.18 (0.350) –0.041 –0.18 (0.339) –0.041No. of comorbidities –6.75 (4.93) –0.051 –7.16 (5.033) –0.141 –7.71 (4.884) –0.152Number of OTC medications 10.15 (6.68) –0.133 10.42 (6.740) 0.115 10.22 (6.541) 0.113Triage category 28.89 (13.8) –0.126 29.08 (13.832) 0.157 33.76 (13.581) 0.183No. of prescription medications –3.78 (2.80) 0.112 –3.62 (2.836) –0.121 –2.80 (2.759) –0.094Ethnicity 8.88 (16.130) 0.040 3.84 (15.756) 0.017Insurance 2.42 (16.052) 0.011 –0.86 (15.552) –0.004EDWIN 1.47 (0.630) 0.155Length of stay 0.08 (0.031) 0.180Family presence –11.81 (12.559) –0.062Routes of medication –13.73 (5.594) –0.185

aP < .05.



of stay (t = 2.663, P = .008), and number of routes of med-ications (t = –2.454, P = .015) were also found to have asignificant impact on the model (Table 3).

The length of time between vital signs was increasedby (1) higher (less acute) triage category, (2) increasedcrowding (higher EDWIN score), (3) increased length ofstay, and (4) fewer routes of medication administered dur-ing the ED stay. Triage category had the greatest impact onthe time between vital signs. Overall, for every increaseof 1 in the triage category (becoming less acute), the timebetween vital signs was increased by 34 minutes. Reflectingacuity, the time between vital signs increased by 5 secondsfor each increase of 1 minute in the length of stay (range,47-1,407 minutes) and decreased by 14 minutes when thenumber of medication routes delivered increased by 1. Inaddition, as the EDWIN score increased by 1, the length oftime between vital signs increased by 1.5 minutes.

Discussion

PERSONAL HEALTH FACTORS

Triage category had the greatest impact on the timebetween vital sign monitoring. Although the majorityof patients in this study were assigned triage category 2 or3, the results were consistent with previous studies wherea more acute (lower triage category) patient required moreresources28 as evidenced by more frequent vital signs. There-fore it seems reasonable that triage category (ESI) may be agood instrument to guide emergency nurses in determiningthe frequency of vital sign monitoring required for patients.

SOCIAL FACTORS

This study showed a lack of findings related to disparatecare in relation to social factors. No differences in thelength of time between vital signs were identified basedon age, gender, ethnicity, or type of insurance. How-ever, data were not collected to differentiate nursinghome patients from patients arriving from the community.Future research to determine whether care is disparate basedon nursing home residency may help in determining the bestmethods for caring for this patient population. This projectsuggests that vital sign monitoring was not based on com-mon social indicators.

ENVIRONMENTAL FACTORS

Every year, over 120 million patients in the United Statespresent to an emergency department, and often, thatemergency department is classified as crowded. Althoughcrowding did not have a significant correlation with thefrequency of vital sign monitoring, it did have a signifi-cant impact on the regression model. The time between

vital sign recordings increased by only 1.5 minutes per1-point increase in the EDWIN score. The data suggestthat there is a potential cumulative effect of increasedcrowding. As crowding levels (EDWIN) increased from1 to 29, the time between vital sign records could increaseby over 30 minutes. The results have a substantialimpact on the time between vital signs when the wholerange of EDWIN scores is included. This was consistentwith previous research that reported delays in patient careduring ED crowding.20,29,30 The clinical importance of anincrease of 30minutes between surveillance/recording of vitalsigns has not been determined. In patients with hemody-namic instability, 30 minutes may be an sufficient time inwhich to have a dangerous alteration in heart rate, blood pres-sure, or respiratory rate, resulting in a potential failure-to-res-cue situation.

One possible explanation for the small change betweeneach degree of crowding is teamwork among the ED staff.Previous research has shown that teamwork increases as theunit becomes more stressful, up to a certain level, and alsodecreases the occurrence of missed nursing care.31 Perhapsthe staff may pull together and work as a faster, more effi-cient team when the emergency department is crowded.Although the change is incremental and may be significantover larger variations, it is somewhat reassuring that onlysmall changes occur with small changes in crowding levels.

ED crowding has been correlated with adverse out-comes such as delayed cardiac intervention and medicationadministration, excess mortality rates, and perceivedlower quality of care,19,20,29,30,32 but its relationshipwith nursing care has never been examined. The resultsof this study show that the effect of crowding is statisti-cally significant, although the clinical significance needsto be examined further.

The length of time between vital sign assessmentsincreased with a longer length of stay. Because of the largerange within the sample, the time between vital sign assess-ments may be almost 2 hours longer in patients in the emer-gency department for long periods than for other patientswho are quickly discharged. This finding should not be sur-prising because often, during the patient’s stay, the emer-gency nurse may follow inpatient floor protocol andobtain vital signs less frequently (eg, every 4 hours) in stabi-lized patients awaiting inpatient beds. In this study the meantime between repetitions of recorded vital signs was 130minutes. A typical standard for vital sign frequency in an in-patient (non–intensive care unit) is every 4 hours, with awindow of observation 30 minutes before and after the hourmark considered a reasonable variation. In addition, moreacutely ill/injured patients may be admitted or die morequickly than stable patients.



Limitations

The major limitation of this study was that the data werelimited to handwritten notations because of the retrospec-tive design of the study. This study was conducted at a sin-gle institution, but there is no reason to believe that ouremergency department varied in culture from others. Thesample was limited to patients with triage categories 1, 2,and 3. Most critically ill patients (ESI 1) did not meet theinclusion criteria because of a short length of stay; resultsfrom this study cannot be generalized to ED patients withhigh acuity.

Implications for Nurses

Although environmental factors (ie, busyness of a crowdedemergency department) have the potential to decrease vig-ilance in patient monitoring, the results of this study showthat emergency nurses are capable of judging the appropri-ate frequency of vital sign monitoring. It is importantto have the ability to recognize when the departmentis crowded so that nurses can institute team principles(ie, situational monitoring). Perhaps developing a benchmarkfor the frequency of monitoring of vital signs would help thenurses during hectic times to communicate when teamwork isessential to provide safe, high-quality patient care.

More research is needed before establishing a standardof care related to frequency of vital sign monitoring basedon patient acuity and length of stay. Perhaps exploring thelinkages between assigned triage category and monitoringand processes within the nursing vigilance model would bebeneficial in establishing a standard of care for vital signmonitoring. Understanding the timing of vital sign mon-itoring after an intervention such as medication adminis-tration is also essential to determining the frequency ofvital sign monitoring and could further be explored withthis model.

Conclusion

It is possible that the frequency with which vital signs aremonitored can affect outcomes, increasing the vigilance ofmonitoring the patient’s condition. Vital sign monitoringalso may stand in as a marker of frequency of direct observa-tion of patients, which is needed to better evaluate patientcondition changes or patient responses to interventions.

This project showed that triage status (as measured bythe ESI) had the greatest contribution to determining thefrequency of vital sign monitoring in this population.However, it was also shown that environmental factors(length of stay and number of routes of medications admi-nistered) also affected that frequency. More evaluation of

ED processes and their links to patient outcomes is re-quired to understand the implications of complexities ofcare that occur in the emergency department. Measuresof outcomes do not tell us about the patient care process.Although knowing results is important, it is an insufficientstep toward improvement.33

Further testing of the model developed for this study willhelp providers to understand the factors that affectthe timeliness of patient care and help emergency nurses toidentify and address problem areas. Similarly, the effect ofcrowding on patients’ vital sign trends for the duration of theirED stay needs to be assessed to determine whether patientoutcomes are influenced by the presence of crowding andnot only by the care provided to them during these times.

There are limited data about the quality of nursingcare in the emergency department, and this project pro-vided important baseline data about the typical frequencyof vital sign monitoring and factors that influencedthe frequency of vital sign records. There is currently no na-tional standard recommending the frequency for monitoringof vital signs among patients in the emergency departmentexcept for selected conditions such as stroke or angina.5 It isnot known whether 2 hours between vital sign assessments isa reasonable time period for general ED patients. Providinghigh-quality care implies that emergency nurses need to en-sure that standards of care are maintained for all patient po-pulations regardless of the environment or circumstances.Understanding factors that influence care and maintainingoptimal care in suboptimal circumstances like ED crowdingare important to practice, education, and research. Despitethe limitations, the findings of this study are reasonable touse in guiding future studies of emergency departmentsand emergency populations with characteristics similar tothose reported herein.

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