The effect of emergency department crowding on clinically oriented outcomes.
ABSTRACT An Institute of Medicine (IOM) report defines six domains of quality of care: safety, patient-centeredness, timeliness, efficiency, effectiveness, and equity. The effect of emergency department (ED) crowding on these domains of quality has not been comprehensively evaluated.
The objective was to review the medical literature addressing the effects of ED crowding on clinically oriented outcomes (COOs).
We reviewed the English-language literature for the years 1989-2007 for case series, cohort studies, and clinical trials addressing crowding's effects on COOs. Keywords searched included "ED crowding,"ED overcrowding,"mortality,"time to treatment,"patient satisfaction,"quality of care," and others.
A total of 369 articles were identified, of which 41 were kept for inclusion. Study quality was modest; most articles reflected observational work performed at a single institution. There were no randomized controlled trials. ED crowding is associated with an increased risk of in-hospital mortality, longer times to treatment for patients with pneumonia or acute pain, and a higher probability of leaving the ED against medical advice or without being seen. Crowding is not associated with delays in reperfusion for patients with ST-elevation myocardial infarction. Insufficient data were available to draw conclusions on crowding's effects on patient satisfaction and other quality endpoints.
A growing body of data suggests that ED crowding is associated both with objective clinical endpoints, such as mortality, as well as clinically important processes of care, such as time to treatment for patients with time-sensitive conditions such as pneumonia. At least two domains of quality of care, safety and timeliness, are compromised by ED crowding.
- [Show abstract] [Hide abstract]
ABSTRACT: Background The Shorter Stays in Emergency Departments health target was introduced in New Zealand in 2009. District Health Boards (DHBs) are expected to meet the target with no additional funding or incentives. The costs of implementing such targets have not previously been studied.MethodA survey of clinical/service managers in ED throughout New Zealand determined the type and cost of resources used for the target. Responses to the target were classified according to their impact in ED, the hospital and the community. Quantifiable resource changes were assigned a financial value and grouped into categories: structure (facilities/beds), staff and processes. Simple statistics were used to describe the data, and the correlation between expenditure and target performance was determined.ResultsThere was 100% response to the survey. Most DHBs reported some expenditure specifically on the target, with estimated total expenditure of over NZ$52 m. The majority of expenditure occurred in ED (60.8%) and hospital (38.7%) with little spent in the community. New staff accounted for 76.5% of expenditure. Per capita expenditure in the ED was associated with improved target performance (r = 0.48, P = 0.03), whereas expenditure in the hospital was not (r = 0.08, P = 0.75).Conclusion The fact that estimated expenditure on the target was over $50 million without additional funding suggests that DHBs were able to make savings through improved efficiencies and/or that funds were reallocated from other services. The majority of expenditure occurred in the ED. Most of the funds were spent on staff, and this was associated with improved target performance.Emergency medicine Australasia: EMA 11/2014; · 0.99 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Since the beginning of power system restructuring and creation of numerous temporal power markets, transmission congestion has become a serious challenge for independent system operators around the globe. On the other hand, in recent years, emission reduction has become a major concern for the electricity industry. As a widely accepted solution, attention has been drawn to renewable power resources promotion. However, penetration of these resources impacts on transmission congestion. In sum, these challenges reinforce the need for new approaches to facilitate interaction between the operator and energy market players defined as the generators (power generation companies) in order to provide proper operational signals for the operator. The main purpose of this chapter is to provide a combination of a leader–follower game theoretical mechanism and multiattribute decision-making for the operator to choose his best strategy by considering congestion-driven and environmental attributes. First the operator (as the leader) chooses K strategies arbitrarily. Each strategy is constituted by emission penalty factors for each generator, the amount of purchased power from renewable power resources, and a bid cap that provides a maximum bid for the price of electrical power for generators who intend to sell their power in the market. For each of the K strategies, the generators (as the followers) determine their optimum bids for selling power in the market. The interaction between generation companies is modeled as Nash-Supply Function equilibrium (SFE) game. Thereafter, for each of the K strategies, the operator performs congestion management and congestion-driven attributes and emission are obtained. The four different attributes are congestion cost, average locational marginal price (LMP) for different system buses, variance of the LMPs, and the generators’ emission. Finally, the operator’s preferred strategy is selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed procedure is applied to the IEEE reliability 24-bus test system and the results are analyzed.Game Theoretic Analysis of Congestion, Safety and Security Networks, Air Traffic and Emergency Departments. 10/2014;
- Resuscitation 05/2013; 84(5):596-601. · 3.96 Impact Factor
PROGRESSIVE CLINICAL PRACTICE
The Effect of Emergency Department
Crowding on Clinically Oriented Outcomes
Steven L. Bernstein, MD, Dominik Aronsky, MD, Reena Duseja, MD, Stephen Epstein, MD,
Dan Handel, MD, MPH, Ula Hwang, MD, MPH, Melissa McCarthy, ScD, K. John McConnell, PhD,
Jesse M. Pines, MD, MBA, MSCE, Niels Rathlev, MD, Robert Schafermeyer, MD, Frank Zwemer, MD,
Michael Schull, MD, and Brent R. Asplin, MD, MPH, Society for Academic Emergency Medicine,
Emergency Department Crowding Task Force
Background: An Institute of Medicine (IOM) report defines six domains of quality of care: safety,
patient-centeredness, timeliness, efficiency, effectiveness, and equity. The effect of emergency depart-
ment (ED) crowding on these domains of quality has not been comprehensively evaluated.
Objectives: The objective was to review the medical literature addressing the effects of ED crowding on
clinically oriented outcomes (COOs).
Methods: We reviewed the English-language literature for the years 1989–2007 for case series, cohort
studies, and clinical trials addressing crowding’s effects on COOs. Keywords searched included ‘‘ED
crowding,’’ ‘‘ED overcrowding,’’ ‘‘mortality,’’ ‘‘time to treatment,’’ ‘‘patient satisfaction,’’ ‘‘quality of
care,’’ and others.
Results: A total of 369 articles were identified, of which 41 were kept for inclusion. Study quality was
modest; most articles reflected observational work performed at a single institution. There were no
randomized controlled trials. ED crowding is associated with an increased risk of in-hospital mortality,
longer times to treatment for patients with pneumonia or acute pain, and a higher probability of leaving
the ED against medical advice or without being seen. Crowding is not associated with delays in reper-
fusion for patients with ST-elevation myocardial infarction. Insufficient data were available to draw
conclusions on crowding’s effects on patient satisfaction and other quality endpoints.
Conclusions: A growing body of data suggests that ED crowding is associated both with objective clini-
cal endpoints, such as mortality, as well as clinically important processes of care, such as time to treat-
ment for patients with time-sensitive conditions such as pneumonia. At least two domains of quality of
care, safety and timeliness, are compromised by ED crowding.
ACADEMIC EMERGENCY MEDICINE 2009; 16:1–10 ª 2008 by the Society for Academic Emergency
Keywords: emergency department crowding, quality of care, patient safety
mergency department (ED) crowding was first
described nearly 20 years ago.1,2Reviews of
crowding’s history and causes are available.3
The Input-Throughput-Output conceptual model4has
become a widely accepted paradigm to understand the
various causes of crowding. Modern conceptions of
ª 2008 by the Society for Academic Emergency Medicine
PII ISSN 1069-6563583
From the Department of Emergency Medicine, Albert Einstein College of Medicine (SLB), Bronx, NY; the Department of Emer-
gency Medicine, Vanderbilt University (DA), Nashville, TN; the Department of Emergency Medicine, University of Pennsylvania
(RD, JMP), Philadelphia, PA; the Department of Emergency Medicine, Beth Israel Deaconess Medical Center (SE), Boston, MA;
the Department of Emergency Medicine, Oregon Health & Science University (DH, KJM), Portland, OR; the Department of Emer-
gency Medicine, Mount Sinai Medical Center (UH), New York, NY; the Department of Emergency Medicine, Johns Hopkins Uni-
versity (MM), Baltimore, MD; the Department of Emergency Medicine, Boston University (NR), Boston, MA; the Department of
Emergency Medicine, Carolinas Medical Center (RS), Charlotte, NC; the Department of Emergency Medicine, University of
Rochester (FZ), Rochester, NY; the Department of Emergency Medicine, Sunnybrook & Women’s College Health Sciences Center
(MS), Toronto, Ontario, Canada; and the Department of Emergency Medicine, Regions Hospital (BRA), St. Paul, MN.
Received July 16, 2008; revision received September 20, 2008; accepted September 22, 2008.
Presented at the Society for Academic Emergency Medicine (SAEM) Annual Meeting, Washington, DC, May 30, 2008.
Approved by the SAEM Board of Directors, July 9, 2008.
Address for correspondence and reprints: Steven L. Bernstein, MD; e-mail: email@example.com.
crowding suggest that crowding in the ED reflects
broader hospital crowding and inefficiencies in bed and
resource management. A 2003 report from the Govern-
ment Accountability Office (GAO) and a 2006 report by
the Institute of Medicine (IOM) note that bottlenecks in
output, such as the inability to transfer admitted patients
to inpatient beds, are a leading cause of crowding.5,6
The 2006 IOM reports represented a comprehensive,
landmark look at the past, present, and future of out-of-
hospital, hospital-based, and pediatric emergency care
in the United States. In response to these reports, in
2007 the Board of Directors of the Society for Academic
Emergency Medicine (SAEM) convened a task force to
examine the effects of ED crowding on patient-oriented
outcomes and emergency medicine education. Task
force members were appointed by the chair and chosen
for their history of scholarship in the field. Members
were not chosen to represent specific professional
organizations. This article represents the task force’s
examination of crowding’s effects on clinically oriented
The task force subcommittee charged with studying
patient outcomes convened three times: in person at
the May 2007 SAEM annual meeting, by conference call
in September 2007, and again in person at the October
2007 Scientific Assembly of the American College of
Because the IOM report on emergency care dis-
cussed quality of ED care using a framework contained
in its 2001 report,7this subcommittee chose to summa-
rize the literature on crowding and patient outcomes
using the same framework. This consists of six quality
of care domains: safety, timeliness, patient-centered-
ness, efficiency, effectiveness, and equity. High-quality
care, according to the IOM, performs well in all six
domains. Our analysis, therefore, reviews the evidence
of the clinical effects of crowding on the six domains.
Another recent review of crowding addressed causes
and solutions, as well as effects.8
We queried the Medline, Cochrane, and PsycNET
search engines, limited to English-language articles, for
the years 1989–2007 for case series, cohort studies, and
clinical trials addressing crowding’s effects on COOs.
Keywords searched included ‘‘emergency department
crowding,’’ ‘‘emergency department
‘‘mortality,’’ ‘‘time to treatment,’’ ‘‘patient satisfaction,’’
and ‘‘quality of care.’’ Abstracts of all articles were
reviewed by the first author and at least one other
author. Articles were retained for analysis if they repre-
sented cohort studies (prospective or retrospective) or
clinical trials with quantitative data and addressed a
clinically relevant endpoint that fits within one of the
IOM quality domains. Clinically relevant endpoints
included mortality, morbidity, treatment delays, patient
satisfaction, and process measures such as walkouts,
length of stay (LOS), and diversion. Reviews and edito-
rials were omitted. We did not prespecify a definition
of crowding, insofar as multiple measures are in use,
and no consensus yet exists on a quantitative definition
of crowding. A formal abstraction tool was not used;
agreement on inclusion or exclusion was by consensus
of the two reviewers.
Endpoints were chosen based on their clinical rele-
vance (e.g., mortality) or broad acceptance as relevant
measures of ED care quality (e.g., patients who leave
without being seen). Some measures may fit under
more than one quality domain (e.g., time to antibiotic
for patients with pneumonia may address both safety
and timeliness of care); we arbitrarily assigned each
measure to a single quality domain. In general, end-
points fall into three broad categories: mortality, time
to treatment, and other.
A total of 369 articles were identified, of which 41 were
kept for inclusion. These articles were largely single-
institution observational cohort studies; none were
randomized controlled trials. Several reflected data
pooled from multiple EDs, usually in a common geo-
graphic area. Hence, the strength of the evidence is
modest at best. Data from the studies are summarized
in Table 19–29and reviewed in detail under Discussion.
The next six sections address each quality domain and
are followed by a research agenda, review of findings,
limitations, and conclusions.
Safety and Effectiveness
The IOM defines safe care as ‘‘avoiding injuries to
patients from the care that is intended to help them’’7
and effective care as ‘‘providing services based on sci-
entific knowledge to all who could benefit and refrain-
ing from providing services to those not likely to
benefit (avoiding underuse and overuse).’’7These con-
cepts overlap in the context of effects of ED crowding
and are discussed together here.
Crowding and Mortality. Investigators from Australia,
Spain, and the United States have addressed the ques-
tion of whether patients experience higher mortality
rates when presenting during periods of ED crowding.
attempted to quantify the association between ED
crowding and 10-day mortality.9ED occupancy was
measured in 8-hour intervals coinciding with staffing
shifts and was calculated as the total number of patient
care hours during the shift divided by 8 hours. Based
on this measure, the peak 25% of shifts were consid-
ered ‘‘crowded’’ and were compared with the remain-
ing shifts. The groups were well matched for baseline
characteristics including season, age, shift, and referral
source. The relative risk of mortality at 10 days was
1.34 (95% confidence interval [CI] = 1.04 to 1.72) when
comparing crowded versus noncrowded shifts. Another
Australian study found that hospital and ED crowding
were associated with an increased 2-, 7-, and 30-day
mortality with statistically significant hazard ratios of
1.3, 1.3, and 1.2, respectively.10The adverse effect on
mortality occurred when midnight hospital occupancy
exceeded the 99th percentile distribution or when >20%
Bernstein et al.•ED CROWDING AND PATIENT OUTCOMES
Review of Selected Studies Examining Effect of Crowding on COOs*
IOM Quality DomainMain OutcomeStudy Effect of ED Crowding on Main Outcome
Safety and effectivenessMortality Richardson9
In a single ED, RR of mortality at 10 days was
1.34 (95% CI = 1.04 to 1.72).
In three hospitals, hazard ratios for mortality at
2, 7, and 30 days were 1.3, 1.3, and 1.2 for
patients admitted during periods of greater
ED and hospital occupancy.
In a single ED, weekly visit volume and ED
mortality rate correlated (p = 0.01).
In a consortium of 120 hospital ICUs, in-hospital
mortality when transferred to ICU > 6 hours
was 17.4% vs. 12.9% for transferred < 6 hours
(OR = 0.71; 95% CI = 0.56 to 0.89).
At a single public hospital ED, 11% of 186
patients who left without being seen were
hospitalized within the next week; three
required emergency surgery.
In a logistic model using data from 30 EDs,
waiting time, fraction of patients uninsured,
and teaching status had independent positive
associations with patients who left without
Of 694 patients admitted with pneumonia at a
single ED, 69% received antibiotics within
4 hours when waiting room occupancy and
ED LOS were in the lowest quartile vs. 28%
during the highest quartiles for both mea-
sures of crowding.
Of 405 patients admitted with pneumonia at a
single ED, antibiotic administration within
4 hours was less likely with a greater number
of patients (OR = 0.96 per additional patient;
95% CI = 0.93 to 0.99) and a greater number of
patients ultimately admitted (OR = 0.93 per
patient; 95% CI = 0.88 to 0.99) in the ED.
In 24 hospitals, ED LOS was inversely associ-
ated with probability of receiving antibiotics
for pneumonia within 24 hours (Spearman
q = )0.44, p = 0.04).
In the 25-ED region of Toronto, Ontario, Can-
ada, high crowding as measured by total out-
of-hospital time (associated with the propor-
tion of EDs on diversion) was 2.1 minutes
longer than during noncrowding (p = 0.004).
In Toronto, median door-to-needle times were
40, 45, and 47 minutes in conditions of no,
respectively (p < 0.001). Moderate and high
crowding conditions were associated with
longer median door-to-needle time (3.0 min-
utes, 95% CI = 0.1 to 6.0) and 5.8 minutes
(95% CI = 2.7 to 9.0), respectively.
Of 13,758 patients with severe pain at a single
ED, a multivariate logistic model showed that
analgesic nonadministration was indepen-
dently associated with waiting room number
(OR = 1.03 foreach
patient; 95% CI = 1.02 to 1.03) and occupancy
rate (OR = 1.01 for each 10% increase in
occupancy; 95% CI = 0.99 to 1.04).
In a single ED, occupancy >120% was associ-
ated with lower likelihood of documentation
of pain score (OR = 0.46; 95% CI = 0.21 to
0.98), but no difference in administration of
analgesia to older patients with hip fracture
(OR = 2.02; 95% CI = 0.89 to 4.62).
Not feeling informed about prolonged waits
was associated with greater dissatisfaction
(OR = 0.48; 95% CI = 0.39 to 0.57).
TimelinessTime to antibioticPines15
Time to thrombolysisSchull18
Time to analgesicPines20
ACAD EMERG MED•January 2009, Vol. 16, No. 1•www.aemj.org
or more of ED bays were occupied by patients waiting
for >8 hours for an inpatient bed.
Mortality rates among current ED patients have also
been found to correlate with crowding.11,30In a 4-year
study of data from a single ED, investigators from Bar-
celona, Spain, demonstrated a weak but positive corre-
lation between the weekly number of ED visits and
mortality rates in the ED (r = 0.18, p = 0.01). The same
authors also stratified the weekly number of ED visits
into moderate, intermediate, and high groups and dem-
onstrated that the mortality rate of ED patients was
greater during the high volume weeks.11These two
studies suffer from the fact that crowding was mea-
sured as the number of ED visits over the course of an
entire week; this is an inordinately long time interval
between consecutive 4- or 8-hour periods. In the United
States, patients admitted to the intensive care unit (ICU)
suffer higher hospital mortality rates when their ED
boarding times exceed 6 hours after the decision to
admit.12Excessive ED boarding times were associated
with an adjusted odds ratio (OR) of 0.71 (95% CI = 0.56
to 0.89) in a multivariate logistic regression model for
overall in-hospital survival. However, the two groups
were not specifically matched for disease category,
resulting in a significantly higher percentage of sepsis
patients in the ‘‘delayed’’ group and higher percentages
of multiple trauma, respiratory, and coronary artery
disease patients in the ‘‘nondelayed’’ group.
These observational studies indicate that ED crowd-
ing may be associated with higher mortality rates, both
during the initial ED visit and up to 30 days later. They
suggest a possible relationship between mortality and
crowding, but should be interpreted cautiously because
confounding due to variations in severity of illness
among patients presenting on crowded versus non-
crowded intervals may be difficult to control for and is
defined in variable terms. Moreover, none of these ser-
ies take into account other elements, such as nursing
and physician staffing.
Leaving without Being Seen.
sought to explain the reasons why patients leave before
being seen by a provider. Not surprisingly, almost half
of the group cite ‘‘fed up with waiting’’ as the major
reason for leaving, occasionally with serious conse-
quences.31Waiting times as long as 6 hours have been
associated with higher rates of ‘‘leaving without being
Several authors have
unscheduled return visits to the ED as a result of the
worsening of their initial medical condition, inadequate
initial care provided in the ED, or incomplete inpatient
IOM Quality DomainMain Outcome Study
Effect of ED Crowding on Main Outcome
72% reduction in diversion improved patient
ED LOS for admission >24 hours was associ-
ated with 10% increase in hospital LOS.
ED LOS and hospital LOS were <4 hours,
3.73 days; 4–8 hours, 5.65 days; 8–12 hours,
6.60 days; > 12 hours, 7.20 days (p < 0.001).
The corresponding excess hospital LOSs
(p < 0.001).
In a single ED, 817 chest pain patients were
admitted 904 times. ED LOS was not associ-
ated with total hospital LOS or other vari-
ables, but annual opportunity costs in lost
hospital revenue for chest pain patients was
$168,300 ($204 per patient waiting >3 hours
for a hospital bed).
In a single ED, inpatient LOS for patients admit-
ted during increasing quartiles of ED crowd-
ing was 5.00, 5.67, 5.81, and 5.85 days,
exclusive of ED LOS (p < 0.001).
Waiting time to see ED provider was longer at
hospitals in poorer neighborhoods: for every
$10,000 decline in per-capita income, patients
waited 10.1 minutes longer (95% CI = 1.8 to
18.4 minutes; p = 0.02).
Bivariate correlation was found between pro-
portion of uninsured using ED and waiting
time to see provider = 0.19 (p < 0.01) in 2000
1.96, and2.35 days
CI = confidence interval; ED = emergency department; ICU = intensive care unit; IOM = Institute of Medicine; LOS = length of
stay; NHAMCS = National Hospital Ambulatory Medical Care Survey; OR = odds ratio; RR = relative risk.
*Sections are organized using IOM-defined domains of quality.
Bernstein et al.•ED CROWDING AND PATIENT OUTCOMES
care or premature discharge. The evidence addressing
return to the ED more often than patients seen during
less crowded times yields mixed results. Looking at a
composite outcome of 72-hour returns, radiology over-
readings, and quality improvement cases, Bernstein
et al.32found that patients with these endpoints were
more likely to have initially been examined in the ED
during periods of crowding (p = 0.03). A more recent
study by the same author, from a different institution,
did not find an association between crowding on the
first ED visit and probability of admission during a
return visit within 72 hours.33Thus, the relationship
between crowding and subsequent likelihood of return-
ing to the ED for admission remains unclear.
There is evidence that patients who return to the ED
having been recently discharged from inpatient services
may exacerbate crowding by two mechanisms: by the
visit itself as well as by consuming more resources than
the average ED patient.34Baer et al.34found that, in a
single ED, patients discharged from an inpatient unit
within 7 days of the ED visit constituted 3% of all visits,
but had longer lengths of ED stay (6.58 hours vs.
5.22 hours), a higher admission rate (47% vs. 19%), and
higher charges ($1,415 vs. $391, all p < 0.001). Given
that the ED patient population consists of patients both
discharged and admitted to the hospital, it would
appear to make sense a priori that recently admitted
patients might be sicker and may consume more
resources than the average ED patient.
evidence is largely anecdotal and inconclusive. Trzeciak
and Rivers35conducted a literature review encompass-
ing 1990–2002 to describe the effect of ED crowding on
patient safety and public health. The study was largely
based on anecdotal reports, self-reports of adverse
events, and sentinel event analyses,36–38but one key
conclusion was that ‘‘. . . overcrowding in ED treatment
areas threatens public health by compromising patient
safety and jeopardizing the reliability of the entire US
emergency care system.’’35
Liu et al.39reported on inpatients who were boarded
in the ED, evaluating the frequency of errors and
adverse events for these patients. Of all boarded
patients, 28% had some error or adverse event in the
course of boarding. It is not clear how this rate com-
pares to all other hospital patients. In interpreting these
findings, ED boarding is a contributing factor to crowd-
ing, but is not solely responsible for the problem. In
summary, anecdotal evidence exists to link ED crowd-
ing with adverse events and error, but these relation-
ships requires further investigation.
ED crowding may contribute to medical
Errors andOther Adverse
The IOM defines timely care as ‘‘reducing waits and
sometimes harmful delays for both those who receive
and those who give care.’’7There is growing evidence
of the association between ED crowding and delays in
timely patient care. While some delays in ED care may
be considered an inconvenience and result in reduced
patient satisfaction, delays in care for time-sensitive
conditions such as pneumonia, acute myocardial infarc-
tion (AMI), stroke, sepsis, those requiring emergent
surgery (e.g., appendicitis, bowel obstruction), severe
trauma, and ambulance delays may result in poorer
patient care outcomes. Two processes that commonly
occur in the ED using time to care guidelines, delivery
of antibiotics within 4–6 hours for patients admitted
with pneumonia, and transfer of patients identified with
AMI to cardiac catheterization within 90–120 minutes
or thrombolysis within 30 minutes have been accepted
as standard measures of quality patient care.40
Recent studies have demonstrated the association
between patient-level exposure to ED crowding and
clinically significant delays in care. For patients with
community-acquired pneumonia, there are several stud-
ies that have associated ED crowding with delays in
delivery of antibiotics.15–17One was an ecologic study
correlating longer ED LOS for admitted patients with
the number of patients ultimately admitted who did not
receive antibiotics within 4 hours of arrival as recom-
mended bytheJoint Commission
PN-5b.16Two other studies have confirmed this associa-
tion at the patient level and have demonstrated that
exposure to ED crowding, when measured by ED LOS
and number of waiting room patients, was associated
with delays in antibiotic therapy.15,17Both studies dem-
onstrated that even at low levels of crowding, there
was a lower likelihood of receiving timely antibiotics.
Studies have demonstrated differences in ambulance
response times and time to thrombolysis in AMI on the
order of less than 10 minutes during times of high sys-
temwide diversion.18,19By contrast, when measured at
the individual hospital level, there was no association
between ED crowding when defined by ED LOS and
time to percutaneous intervention for patients with
AMI.17Another study of critically ill patients involving
those with severe sepsis and septic shock did not dem-
onstrate clinically associations between overall levels of
ED crowding and time to antibiotics or survival.41Pro-
longed ED LOS, however, has been associated with a
higher risk of death in patients admitted to ICUs from
the ED. These studies suggest that ED crowding may
have variable effects on timeliness of initiation of early
therapy for critically ill patients, while longer boarding
of patients in EDs prior to ICU transfer may increase
ED crowding has also been associated with delays in
analgesic therapy for patients with severe pain.20,21,42
One study demonstrated that higher ED patient occu-
pancy and more waiting room patients were associated
with delays in analgesia of greater than 1 hour both
from triage and from room placement time.20Another
study demonstrated similar delays of over 1 hour from
patient arrival to physician pain assessment, analgesia
ordering, and analgesia administration during periods
of high ED census, high number of boarders, and high
number of nonboarding patients.42Finally, a study of
hip fracture patients 50 years and older found that they
were less likely to receive analgesia during periods of
high ED patient census.21
Studies have demonstrated little effect of ED crowd-
ing on time to diagnosis for patients with appendicitis
ACAD EMERG MED•January 2009, Vol. 16, No. 1•www.aemj.org
or small bowel obstruction.43For the latter two studies,
patient insurance type and arrival during change of cli-
nician shift were significantly associated with delayed
care for these time-sensitive conditions.
A general observation of studies evaluating ED
crowding and time to care is that crowding is less
likely to affect patients who are identified as critically ill
during early ED assessment (i.e., those with acute ST-
shock). In these cases, the lack of an association or
small effect sizes suggest that such conditions, or the
related interventions, may be less sensitive to the nega-
tive effects of crowding. Conversely, patients with mul-
tistep processes of care, complex care coordination, or
those where well-developed protocols (e.g., ST-segment
elevation myocardial infarction) to speed care do not
exist may be more dramatically affected by ED crowd-
Finally, other time-related outcomes associated with
ED crowding include patient waiting times and ambu-
lance diversion. Prolonged patient wait times can result
in not only delays to care, but also reduced patient sat-
isfaction and patients leaving without being seen or
even eloping during medical evaluation. Studies have
demonstrated increased patient waiting times with
lower ratios of clinician staff to the number of waiting
room patients and ED census.28,44While ED census has
been found to increase patient waits, the level of com-
plexity of the ED patients may also dictate this associa-
Almost half of EDs in the United States reported
diverting ambulances at some point during 2002.46
While ambulance diversion itself has been used as a
measure of crowding for some studies, it is in fact an
outcome of crowding,47,48and several studies have
used diversion as an outcome to validate proposed
measures of ED crowding.48–50Ambulance diversion
from hospitals not only causes delays in transport and
care for patients with acute emergencies, but also
results in lost revenue for hospitals23,26,51,52and may be
associated with adverse outcomes.53–55ED crowding
has been associated with longer ambulance transport
times and longer response times for patients with chest
pain.18One study showed that one in eight patient
transports by paramedics were delayed (some over an
hour) waiting for an open ED gurney to receive the
patient.56A study from Canada found that each admit-
ted patient boarding in the ED caused an additional
6 minutes of ambulance diversion,57with similar find-
ings reported from Australia.58By comparison, increas-
ing the number of ICU beds has been shown to reduce
hospital time spent on diversion.59Diversion’s effects
on harder clinical endpoints such as mortality and in-
hospital complications require further study.
The IOM defines patient-centered care as ‘‘providing
care that is respectful of and responsive to individual
patient preferences, needs, and values and ensuring
that patient values guide all decisions.’’7
Patient Satisfaction. Patient satisfaction is widely used
to assess hospital services and is one measure of
patient perceptions and to better understand causes of
dissatisfaction at the individual, departmental, and sys-
tem levels. Satisfaction surveys typically ask about
duration of visit, the appearance of the facility, the ser-
vice provided by healthcare staff and ancillary services,
and the overall perception of quality.60,61
Prolonged waiting times, whether it is for nonurgent
care, for patients who perceive they have an emer-
gency, or prolonged holding while waiting for admis-
sion or discharge, are known to decrease patient
satisfaction. Inefficient hospital systems that fail to
move the patient through the ED visit frequently hear
patients and their families stress their dissatisfaction
with the ED visit. By instituting a variety of protocols
or system efficiencies, and by reducing patient wait
times, satisfaction scores improve.60
Sun et al.22surveyed approximately 2,300 patients
and noted that a significant number of patients were
dissatisfied when they were not told about the pro-
longed wait times or actually perceived that they had
prolonged wait times. In a follow-up article, they con-
cluded that their patient satisfaction survey replicated
their prior finding that satisfaction strongly predicts the
patient’s willingness to return.62Patient perception of
wait times, rather than actual LOS or time waiting to
be seen by a physician, was the source of dissatisfac-
In a study by Yancer et al.,23process improvement
teams implemented a series of hospitalwide initiatives
to decrease ambulance diversion, decrease wait times
in the ED, improve patient satisfaction scores, and
decrease risks to patient safety. The teams focused on
reducing crowding by looking at inpatient bed availabi-
lity. After process redesign, ambulance diversions fell
by 72%, average inpatient and ED LOS fell, the number
of boarders held in the ED decreased, and ED patient
satisfaction scores improved.
Rodi et al.63described the Institute for Healthcare
Improvement’s finding that reducing delays is critical to
improving all aspects of emergency care. Turnaround
time is a primary driver of satisfaction. Resources can
help improve delivery of care. Even at children’s hospi-
tals it was noted that prolonged LOS resulted in dissat-
isfaction and increased number of patients ‘‘left without
Garson et al.65asked patients whether they had a
preference for boarding in an inpatient unit or in the
ED setting. The patients’ preferences were for an inpa-
tient hallway over the ED. The implication is that trans-
fer of patients to hallway locations would reduce ED
LOS, improve safety, and patient satisfaction.
Patient safety is also affected by prolonged LOS and
overcrowding. Viccellio61reviewed the above article
that looked at whether patients preferred boarding in
the ED versus boarding on an inpatient floor. He com-
ments that his facility’s patient satisfaction improved
when patients were boarded on inpatient floors. The
patients who were boarded on the inpatient units had a
shorter time in moving to an actual inpatient bed than
did the patients boarded in the ED.66The boarding
of admitted patients in the ED may jeopardize their
Hospitals usethis togauge
Bernstein et al.•ED CROWDING AND PATIENT OUTCOMES
The IOM defines efficient care as ‘‘avoiding waste, in
particular waste of equipment, supplies, ideas, and
energy.’’7ED crowding may lead to less efficient care
if, for example, delays in the ED lead to complications
that require longer hospital stays. Compared to elective
admissions, patients who are admitted through the ED
are more expensive to care for.67Krochmal and Riley24
found that patients who had to spend at least 24 hours
in the ED had inpatient LOS that were about 10%
longer than patients who had shorter ED LOS.24A
study in three Australian hospitals found a similar cor-
relation between ED LOS and inpatient LOS.25How-
ever, a separate study by Bayley et al.26found no
association between ED LOS and inpatient LOS. Like
outcomes that could be associated with ED crowding,
the analyses may be confounded by the fact that sicker
patients may be rushed through the ED, but still have
long hospital LOS. Thus, the findings to date are likely
to underestimate the effects of ED crowding.
A more crowded ED has implications for the ability
of a hospital to deal with surge capacity. The American
College of Emergency Physicians defines surge capacity
as the ‘‘health care system’s ability to manage a sudden
or rapidly progressive influx of patients within the cur-
rently available resources at a given point in time.’’68
National policy for emergency-preparedness calls for
hospitals to accommodate surges of 500 new patients
per million population in a disaster and 50 patients per
million in other mass casualty incidents.69The compo-
nents to handle surge capacity are complex, and the
ability of hospitals to handle disaster surge capacity is
not well understood, but the decrease in the number of
U.S. EDs and growth in patient volume may have
diminished hospital surge capacity. According to an
American Hospital Association 2007 survey, nearly half
of all US EDs routinely function at or over 100% capac-
ity.70A study by Kanter and colleagues71in 2007 exam-
ined New York’s hospitals’ ability to respond to influx
of new patients. It found that even using underoptimis-
tic assumptions, 500 new patients per million age-spe-
cific population will often overwhelm existing hospital
resources, especially for an incident involving large
numbers of children. However, these estimates are
often based on administrative data and not functional
hospital capacity and hence are likely to underestimate
the effects of hospital and ED capacity on the ability to
deal with disaster surges.
The IOM defines equitable care as that which ‘‘does not
vary in quality because of personal characteristics such
as gender, ethnicity, geographic location, and socioeco-
nomic status.’’7The literature consistently shows that
ED crowding is more prevalent among hospital EDs
located in large, urban areas.5,6,46,72Crowding is also
more common among EDs located in poorer neighbor-
hoods,28with longer waiting times at safety-net hospital
EDs used disproportionately by uninsured persons.29
Because minorities and persons of lower socioeco-
nomic status are more likely to live in communities
where ED crowding occurs, they are disproportionately
impacted by ED crowding and any negative conse-
quences that crowding has on other quality care
Developing a Research Agenda
Many challenges in crowding research remain, particu-
larly in the domains of understanding its effects on clin-
ical care, on education of residents and students, and in
the design of interventions to mitigate crowding. There
has been much progress in the development of crowd-
ing metrics, although a universally accepted measure
Additional work is needed to identify adverse clinical
outcomes of ED crowding, with emphasis on new study
designs, clinical endpoints, and modeling approaches.
The effects of crowding on providers’ clinical decision-
making requires further study, as does crowding’s
effects on the ability to educate medical students and
residents and other providers in training. Newer tools,
such as simulation laboratories, may provide opportuni-
ties for work in this area.
crowding and ambulance diversion, or to mediate
their effects, are needed as well. The Urgent Matters
national program office of the Robert Wood Johnson
To develop more useful measures of crowding, we
suggest two goals: 1) identify an objective, quantifiable
measurement framework that represents normal patient
flow and 2) standardize measures related to patient
flow.75Standardizing the definitions76,77and developing
generalizable patient flow measures78would allow com-
parisons among different interventions, different ED
settings, and facilitate multicenter studies.
Measuring patient flow throughout an institution in
real time is challenging. Data from clinical information
systems will likely support this task. Many EDs are
electronic patient tracking boards and integrate data
from institutional information systems.79Current sys-
tems are optimized for supporting providers with
patient-oriented information, but may facilitate the
real-time monitoring and visualization of operational
efficiency measures, such as patient flow, provider
productivity, or turnaround times of ancillary services.
Improvements in information technology will not only
support various approaches to measure patient flow,
but will likely facilitate the design and testing of inter-
ventions to alleviate crowding.
Of note, the National Quality Forum is currently con-
sidering creation of quality measures for ED care.80
Some of these proposed measures indirectly reflect
crowding, such as ED LOS and time to admission deci-
We reviewed English-language literature only, because
we were not resourced to conduct searches in other
languages. Ofnote, crowding
English-speaking countries have been published in
English-language journals. We did not conduct a formal
systematic review, with two reviewers independently
ACAD EMERG MED•January 2009, Vol. 16, No. 1•www.aemj.org
grading each article for the strength of the evidence. In
addition, it would have been useful to have conducted a
formal risk-of-bias assessment.81Most of the articles
cited represent single-institution observational cohorts,
and no randomized controlled trials were identified.
Hence, the strength of the evidence is modest at best.
Finally, we did not search the gray literature, again
because of resource constraints. We do note that much
includes government reports such as those of the
GAO,5or the National Hospital Ambulatory Medical
Care Survey (NHAMCS) series.82Although invaluable,
these reports do not represent original studies of clini-
cally relevant endpoints in ED crowding. Nonetheless,
it is possible that by not examining the gray literature
A growing body of evidence exists to document the
adverse effects of ED crowding on clinically important
outcomes. Future work will continue to document these
adverse effects, but will increasingly focus on interven-
tions to prevent or alleviate crowding’s impact on qual-
ity of care.
1. Dickinson G. Emergency department overcrowding.
CMAJ. 1989; 140:270–1.
2. Gallagher EJ, Lynn SG. The etiology of medical
gridlock: causes of emergency department over-
crowding in New York City. J Emerg Med. 1990;
3. Bernstein SL, Asplin BR. Emergency department
crowding: old problem, new solutions. Emerg Med
Clin North Am. 2006; 24:821–37.
4. Asplin BR, Magid DJ, Rhodes KV, Solberg LI,
Lurie N, Camargo CA Jr. A conceptual model of
emergency department crowding. Ann Emerg Med.
5. Government Accountability Office. Hospital Emer-
among Hospitals and Communities. Washington,
DC: General Accounting Office, 2003.
6. Institute of Medicine. Hospital-Based Emergency
Care: At the Breaking Point. Washington, DC:
National Academies Press, 2006.
7. Institute of Medicine. Crossing the Quality Chasm:
A New Health System
Washington, DC: National Academy Press, 2001.
8. Hoot NR, Aronsky D. Systematic review of emer-
gency department crowding: causes, effects, and
solutions. Ann Emerg Med. 2008; 52:126–36.
9. Richardson DR. Increase in patient mortality at
overcrowding. Med J Aust. 2006; 184:213–6.
10. Sprivulis PC, Da Silva JA, Jacobs IG, Frazer AR,
Jelinek GA. The association between hospital over-
crowding and mortality among patients admitted
via Western Australian emergency departments.
Med J Aust. 2006; 184:208–12.
11. Miro O, Antonio MT, Jimenez S, et al. Decreased
department overcrowding. Eur J Emerg Med. 1999;
12. Chalfin DB, Trzeciak S, Likourezos A, Baumann BM,
Dellinger RP. Impact of delayed transfer of critically
ill patients from the emergency department to the
intensive care unit. Crit Care Med. 2007; 35:1477–83.
13. Baker DW, Stevens CD, Brook RH. Patients who
leave a public hospital emergency department with-
out being seen by a physician. Causes and conse-
quences. JAMA. 1991; 266:1085–90.
14. Stock LM, Bradley GE, Lewis RJ, Baker DW, Sipsey
J, Stevens CD. Patients who leave emergency
departments without being seen by a physician:
magnitude of the problem in Los Angeles County.
Ann Emerg Med. 1994; 23:294–8.
15. Pines JM, Localio AR, Hollander JE, et al. The
impact of emergency department crowding mea-
sures on time to antibiotics for patients with com-
16. Fee C, Weber EJ, Maak CA, Bacchetti P. Effect of
emergency department crowding on time to antibi-
otics in patients admitted with community-acquired
pneumonia. Ann Emerg Med. 2007; 50:501–9.
17. Pines JM, Hollander JE, Localio AR, Metlay JP. The
association between emergency department crowd-
ing and hospital performance on antibiotic timing
for pneumonia and percutaneous intervention for
myocardial infarction. Acad Emerg Med. 2006;
18. Schull MJ, Morrison LJ, Vermeulen M, Redelmeier
ambulance transport delays for patients with chest
pain. CMAJ. 2003; 168:277–83.
19. Schull MJ, Vermeulen M, Slaughter G, Morrison L,
Daly P.Emergency department
thrombolysis delays in acute myocardial infarction.
Ann Emerg Med. 2004; 44:577–85.
20. Pines JM, Hollander JE. Emergency department
crowding is associated with poor pain care for
patients with severe pain. Ann Emerg Med. 2008;
21. Hwang U, Richardson LD, Sonuyi TO, Morrison RS.
The effect of emergency department crowding on
the management of pain in older adults with hip
fracture. J Am Geriatr Soc. 2006; 54:270–5.
22. Sun BC,AdamsJ,
Brennan TA, Burstin HR. Determinants of patient
emergency care. Ann Emerg Med. 2000; 35:426–34.
23. Yancer DA, Foshee D, Cole H, et al. Managing
capacity to reduce emergency department over-
crowding and ambulance diversion. Jt Comm J
Qual Pat Saf. 2006; 32:239–45.
24. Krochmal P, Riley TA. Increased health care costs
associated with ED overcrowding. Am J Emerg
Med. 1994; 12:265–6.
25. Liew D, Liew D, Kennedy MP. Emergency depart-
ment length of stay independently predicts excess
inpatient length of stay. Med J Aust. 2003; 179:524–6.
Bernstein et al.•ED CROWDING AND PATIENT OUTCOMES
26. Bayley MD, Schwartz JS, Shofer FS, et al. The
financial burden of emergency department conges-
tion and hospital crowding for chest pain patients
awaiting admission. Ann Emerg Med. 2005; 45:
27. Bernstein SL, Yadav K, Wall S, et al. Association
between ED crowding and inpatient length of stay
[abstract]. Acad Emerg Med. 2008; 15:S201.
28. Lambe S, Washington DL, Fink A, et al. Waiting
times in California’s emergency departments. Ann
Emerg Med. 2003; 41:35–44.
29. Burt CW, Arispe IE. Characteristics of emergency
departments serving high volumes of safety-net
patients: United States, 2000. National Center for
Health Statistics. Vital Healt Stat 2004; 13.
30. Miro O, Sanchez M, Milla J. Hospital mortality
31. Rowe BH, Channan P, Bullard M, et al. Characteris-
tics of patients who leave emergency departments
32. Bernstein SL, Verghese V, Leung W, Lunney AT,
Perez I. Development and validation of a new index
to measure emergency department crowding. Acad
Emerg Med. 2003; 10:938–42.
33. Bernstein SL, Yadav K, Wall S, et al. Lack of associ-
ation between EDcrowding
34. Baer RB, Pasternack JS, Zwemer FL Jr. Recently
discharged inpatients as a source of emergency
department overcrowding. Acad Emerg Med. 2001;
35. Trzeciak S, Rivers EP. Emergency department over-
crowding in the United States: an emerging threat
to patient safety and public health. Emerg Med J.
36. The Lewin Group. Emergency department overload:
a growing crisis. The results of the American Hos-
pital Association Survey of Emergency Department
(ED) and Hospital Capacity. Falls Church, VA:
American Hospital Association, 2002.
37. Derlet RW, Richards JR. Overcrowding in the
nation’s emergency departments: complex causes
and disturbing effects. Ann Emerg Med. 2000;
38. The Joint Commission. Sentinel event alert, June
17, 2002. Available at: http://www.jointcommission.
Accessed Feb 11, 2007.
39. Liu SW, Thomas SH, Gordon JA, Weissman J. Fre-
patients boarding in the emergency department.
Acad Emerg Med. 2005; 12:49b–50b.
40. JointCommission. The
htm. Accessed Nov 8, 2007.
41. Pines JM, Goyal M, Band RA, Gaieski DF. ED
crowding has no impact on time to antibiotics or
and errors among
survival in septic patients receiving early goal-direc-
ted therapy [abstract]. Ann Emerg Med. 2007;
42. Hwang U, Morrison R, Harris B, Spencer N,
Richardson L. The association of ED crowding
factors with quality of pain management [abstract].
Acad Emerg Med. 2007; 14:s54.
43. Bickell NA, Hwang U, Anderson RM, Rojas M,
Barsky CL. What affects time to care in emergency
room appendicitis patients? Med Care. 2008; 46:417–22.
44. Kyriacou DN, Ricketts V, Dyne PL, McCollough MD,
Talan DA. A 5-year time study analysis of emergency
department patient care efficiency. Ann Emerg Med.
45. Schull MJ, Kiss A, Szalai JP. The effect of low-com-
plexity patients on emergency department waiting
times. Ann Emerg Med. 2007; 49:257–64.
46. Burt CW, McCaig LF, Valverde RH. Analysis of
ambulance transports and diversions among US
emergency departments. Ann Emerg Med. 2006;
47. Hwang U, Concato J. Care in the emergency
department: how crowded is overcrowded? Acad
Emerg Med. 2004; 11:1097–101.
48. Hoot NR, Zhou C, Jones I, Aronsky D. Measuring
and forecasting emergency department crowding in
real time. Ann Emerg Med. 2007; 49:747–55.
49. Epstein SK, Tian L. Development of an emergency
department work score to predict ambulance diver-
sion. Acad Emerg Med. 2006; 13:421–6.
50. McCarthy ML, Aronsky D, Jones ID, et al. The
emergency department occupancy rate: a simple
measure of emergency department crowding? Ann
Emerg Med. 2008; 51:15–24.
51. Falvo T, Grove L, Stachura R, et al. The opportunity
loss of boarding admitted patients in the emergency
department. Acad Emerg Med. 2007; 14:332–7.
52. McConnell KJ, Richards CF, Daya M, Weathers CC,
Lowe RA. Ambulance diversion and lost hospital
revenues. Ann Emerg Med. 2006; 48:702–10.
53. Velianoff GD. Overcrowding and diversion in the
emergency department: the health care safety net
unravels. Nurs Clin North Am. 2002; 37:59–66.
54. Asplin BR. Does ambulance diversion matter? Ann
Emerg Med. 2003; 41:477–80.
55. Brewer S. Study: Clogged Trauma Care Leads to
Deaths. Houston Chronicle. Houston, TX, 2002:A27.
56. Eckstein M, Chan LS. The effect of emergency
availability. Ann Emerg Med. 2004; 43:100–5.
57. Schull MJ, Lazier K, Vermeulen M, Mawhinney S,
Morrison L. Emergency department contributors to
ambulance diversion: a quantitative analysis. Ann
Emerg Med. 2003; 41:467–76.
58. Fatovich DM, Nagree Y, Sprivulis P. Access block
causes emergency department overcrowding and
ambulance diversion in Perth, Western Australia.
Emerg Med J. 2005; 22:351–4.
59. McConnell KJ, Richards CF, Daya M, Bernell SL,
Weathers CC, Lowe RA. Effect of increased ICU
capacity on emergency department length of stay
and ambulance diversion. Ann Emerg Med. 2005;
ACAD EMERG MED•January 2009, Vol. 16, No. 1•www.aemj.org
60. Purnell L. Reducing waiting time in emergency
department triage. Nur Manag. 1995; 26:64Q, 64T,
61. Viccellio P. Customer satisfaction versus patient
safety: have we lost our way? Ann Emerg Med.
62. Sun BC, Adams JG, Burstin HR. Validating a model
of patient satisfaction with emergency care. Ann
Emerg Med. 2001; 38:527–32.
63. Rodi SW, Grau MV, Orsini CM. Evaluation of a fast
track unit: alignment of resources and demand
length of stay for emergency department patients.
Qual Manag Health Care. 2006; 15:163–70.
64. Goldman RD, Macpherson A, Schuh S, Mulligan C,
Pirie J. Patients who leave the pediatric emergency
department without being seen: a case control
study. CMAJ. 2005; 172:39–43.
65. Garson C HJ, Rhodes KV, Shofer FS, Baxt WG,
Pines JM. Emergency department patient prefer-
ences for boarding locations when hospitals are at
full capacity. Ann Emerg Med. 2008; 51:9–12.
66. Greene J. Emergency department flow and the
boarded patient: how to get admitted patients
upstairs. Ann Emerg Med. 2007; 49:68–70.
67. Melnick GA, Serrato CA, Mann JM. Prospective
payments to hospitals: should emergency admis-
sions have higher rates? Health Care Finan Rev.
68. American College of Emergency Physicians. Health
care system surge capacity recognition, prepared-
ness and response [editorial]. Ann Emerg Med.
69. U.S. Department of Health and Human Services.
National Bioterrorism Hospital Preparedness Pro-
gram. Available at: http://www.hhs.gov/aspr/opeo/
hpp/. Accessed Mar 14, 2008.
70. American Hospital Association. The state of Amer-
ica’s hospitals: taking the pulse. Available at: http://
HospitalsChartPack2007.ppt. Accessed Mar 17, 2008.
71. Kanter RK, Moran JR. Hospital emergency surge
capacity: an empiric New York statewide study.
Ann Emerg Med. 2007; 50:314–9.
72. Burt CW, McCaig LF. Staffing, capacity, and ambu-
lance diversion in emergency departments: United
States, 2003–04. Advance Data. no. 376. Hyattsville,
MD: National Center for Health Statistics, 2006.
73. Wilson MJ, Siegel B, Williams M. Perfecting Patient
Flow: America’s Safety Net Hospitals and Emer-
National Association of Public Hospitals, 2005.
74. Wilson MJ, Nguyen K. Bursting at the Seams:
Improving Patient Flow to Help America’s Emer-
gency Departments. Washington, DC: Urgent Mat-
75. Asplin BR. Measuring crowding: time for a para-
digm shift. Acad Emerg Med. 2006; 13:459–61.
76. Solberg LI, Asplin BR, Weinick RM, Magid DJ.
Emergency department crowding: consensus devel-
opment of potential measures. Ann Emerg Med.
77. Welch S, Augustine J, Camargo CA Jr, Reese C.
Emergency department performance measures and
benchmarking summit. Acad Emerg Med. 2006;
78. Asplin BR, Flottemesch TJ, Gordon BD. Developing
models for patient flow and daily surge capacity
research. Acad Emerg Med. 2006; 13:1109–13.
79. Aronsky D, Jones I, Lanaghan K, Slovis CS. Sup-
porting patient care in the emergency department
with a computerized whiteboard system. J Am Med
Inform Assoc. 2008; 15:184–94.
80. National Quality Forum. National Voluntary Consen-
sus Standards for Emergency Care. Available at: http://
Accessed Sep 10, 2008.
81. Higgins JP, Green S. Cochrane Handbook for Sys-
(updated February 2008). The Cochrane Collabora-
82. Pitts SR, Niska RW, Xu J, Burt CW. National Hospi-
tal Ambulatory Medical Care Survey: 2006 Emer-
Statistics Reports; No. 7. Hyattsville, MD: National
Center for Health Statistics, 2008.
Bernstein et al.•ED CROWDING AND PATIENT OUTCOMES