Reducing variation in hospital admissions from the emergency department for low mortality conditions may produce savings

1. At the Intersection of Health, Health Care and Policy
doi: 10.1377/hlthaff.2013.1318
33, no.9 (2014):1655-1663Health Affairs
Low-Mortality Conditions May Produce Savings
Reducing Variation In Hospital Admissions From The Emergency Department For
Amber K. Sabbatini, Brahmajee K. Nallamothu and Keith E. Kocher
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2. By Amber K. Sabbatini, Brahmajee K. Nallamothu, and Keith E. Kocher
Reducing Variation In Hospital
Admissions From The Emergency
Department For Low-Mortality
Conditions May Produce Savings
ABSTRACT The emergency department (ED) is now the primary source for
hospitalizations in the United States, and admission rates for all causes
differ widely between EDs. In this study we used a national sample of ED
visits to examine variation in risk-standardized hospital admission rates
from EDs and the relationship of this variation to inpatient mortality for
the fifteen most commonly admitted medical and surgical conditions. We
then estimated the impact of variation on national health expenditures
under different utilization scenarios. Risk-standardized admission rates
differed substantially across EDs, ranging from 1.03-fold for sepsis to
6.55-fold for chest pain between the twenty-fifth and seventy-fifth
percentiles of the visits. Conditions such as chest pain, soft tissue
infection, asthma, chronic obstructive pulmonary disease, and urinary
tract infection were low-mortality conditions that showed the greatest
variation. This suggests that some of these admissions might not be
necessary, thus representing opportunities to improve efficiency and
reduce health spending. Our data indicate that there may be sizeable
savings to US payers if differences in ED hospitalization practices could
be narrowed among a few of these high-variation, low-mortality
conditions.
A
dmitting a patient to the hospital
from the emergency department
(ED) is one of the more expensive,
routine decisions made in health
care. Emergency providers deter-
mine whether a patient requires hospitalization
or can safely be discharged home about 350,000
times a day1
in the approximately 5,000 US EDs,2
resulting in almost twenty million annual admis-
sions to hospitals.3
The Centers for Medicare and
Medicaid Services (CMS) reported that hospital
carerepresented about 30 percent of the$2.7 tril-
lion in total expenditures for 2011—the largest
share of health care spending.4
Appropriate admissions from the ED should
be both emergent and necessary, dictated by a
patient’s diagnosis and clinical presentation.
Certain life-threatening diagnoses, such as acute
myocardial infarction, almost always require
hospitalization. Yet the majority of admissions
from the ED are for intermediate-severity con-
ditions such as acute exacerbations of chronic
diseases (for example, asthma exacerbation),
with significant variability in presentation.5
In
these cases, appropriate dispositions are not al-
ways clear, allow for provider and patient discre-
tion, and could be influenced by the local prac-
tice culture and resources.6
For example, it is
estimated that up to half of patients with conges-
tive heart failure in the ED could be discharged
home after a period of observation and treat-
ment. However, many of these patients are ad-
mitted, leading to greater resource use.7
As EDs
are now the primary source through which pa-
doi: 10.1377/hlthaff.2013.1318
HEALTH AFFAIRS 33,
NO. 9 (2014): 1655–1663
©2014 Project HOPE—
The People-to-People Health
Foundation, Inc.
Amber K. Sabbatini is an
instructor of emergency
medicine at the University of
Washington, in Seattle.
Brahmajee K. Nallamothu is
an associate professor of
cardiovascular medicine at the
University of Michigan; a core
investigator at the Center for
Clinical Management
Research, Ann Arbor Veterans
Affairs Medical Center; a
faculty member at the Center
for Healthcare Outcomes and
Policy; and a faculty member
at the Institute for Healthcare
Policy and Innovation, all in
Ann Arbor.
Keith E. Kocher (kkocher@
umich.edu) is an assistant
professor in emergency
medicine at the University of
Michigan; a faculty member at
the Center for Healthcare
Outcomes and Policy; and a
faculty member at the
Institute for Healthcare Policy
and Innovation.
September 2014 33:9 Health Affairs 1655
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3. tients with acute illnesses are hospitalized, sur-
passing direct admissions from ambulatory set-
tings,3,8
differences in admission practices have
important implications for the quality and fi-
nancing of the health care system.
Recent studies have demonstrated that all-
cause admission rates are highly variable across
individual providers and EDs.6,9
This variation
may ultimately represent the overuse, underuse,
or misuse of hospital services. As a result, under-
standing which conditions show the greatest var-
iation in ED admission practices may lead to
improvements in efficiency, as well as cost sav-
ings.We therefore examined a national sample of
ED visits to compare differences in admission
rates for the fifteen most commonly hospitalized
medical and surgical conditions. Our goal was to
identify conditions that showed the greatest var-
iation and, thus, represent possible targets for
standardizing admission practices. We then
sought to understand how this variation affects
national health spending by exploring potential
savings related to narrowing differences in ad-
mission rates for each condition.
Study Data And Methods
Data Source And Study Population We used
the 2010 Nationwide Emergency Department
Sample (NEDS) database to conduct the analy-
sis.10
NEDS is part of the Healthcare Cost and
Utilization Project (HCUP), a family of longitu-
dinal databases on hospital care in the United
States, sponsored by the Agency for Healthcare
Research and Quality. It is the largest all-payer
database of ED visits in the United States, cap-
turing information on over twenty-nine million
ED visits from twenty-eight states, with weights
to produce national estimates.
All ED visits except for patients who died in the
ED were included in the analysis. For the pur-
poses of this study, we considered visits resulting
in transfer as admissions, given the high likeli-
hood of admission at the receiving hospital.11
Observation-status cases were not included in
this analysis because they are not tracked in
NEDS.12
Clinical Classifications Software catego-
ries13
identified ED visits for the fifteen most
commonly admitted medical and surgical condi-
tions. NEDS also includes linked inpatient infor-
mation for ED visits that resulted in admission,
from which we derived total inpatient charges
and in-hospital mortality.
Outcome Measures Primary outcomes for
this study were unadjusted and risk-standard-
ized admission rates. To ensure valid results,
we excluded EDs with fewer than thirty cases
for each condition from our calculation of admis-
sion rates, as small sample sizes provide inade-
quate representations of health care use.
Survey weights were used to generate national
estimates of the number of ED visits and admis-
sions, observed inpatient mortality, mean in-
patient charges, and total national charges asso-
ciated with hospitalizations for each condition.
Observed inpatient mortality rates were exam-
ined to provide clinical context for the average
associated severity of each condition.
Data Analysis Mixed-effects, hierarchical lo-
gistic regression was used to adjust for the sever-
ity of patient case-mix and clustering of admis-
sions among hospitals. Covariates in the model
included age, sex, Elixhauser comorbidities (a
list of thirty comorbid conditions associated with
inpatient hospital mortality),14
primary payer
(uninsured, private, Medicaid, Medicare, oth-
er), and median income of the ZIP code in which
the patient resides. This approach assumes that
after adjustment for patient case-mix, the re-
maining variation is due to institutional or com-
munity factors that influence an ED’s propensity
to admit.
Risk-standardized admission rates for each
condition were derived from regression models
by dividing the number of predicted admissions
foreach ED, given that institution’s specific case-
mix, by the expected number of admissions had
those patients been treated at the average ED.
This predicted-to-expected ratio was then multi-
plied by the mean admission rate for the repre-
sentative sample to determine the risk-standard-
ized admission rate. This method of indirect
standardization has been well described in the
literature as a means for case-mix adjustment
when comparing outcomes among hospitals.15,16
Variation is expressed as the ratio of observed
and risk-standardized admission rates for EDs at
the seventy-fifth to twenty-fifth percentiles. The
seventy-fifth and twenty-fifth percentiles were
used instead of ninetieth and tenth percentiles
to provide a more conservative estimate of varia-
tion. Variation between the ninetieth and tenth
percentiles is available in the online Appendix.17
We also calculated the coefficient of variation for
the distribution of risk-standardized admission
rates, which is a unitless measure of dispersion
generated by dividing the standard deviation
by the mean rate, allowing for comparison be-
tween conditions with widely differing mean ad-
mission rates. The correlation between the
coefficient of variation and observed inpatient
mortality was then examined, to assist in under-
standing the relationship between variation in
admission practices and risk of poor clinical
outcomes.
To illustrate the impact of variation in ED ad-
mission rates on national health care spending,
we estimated annual national charges under
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4. three different utilization scenarios that narrow
variation: (1) if EDs with risk-standardized ad-
mission rates above the median hypothetically
decreased their hospitalization rates to the me-
dian for each condition; (2) if EDs with risk-
standardized admission rates in the top quartile
decreased admissions to the seventy-fifth per-
centile; and (3) if EDs with risk-standardized
admission rates in the top quartile decreased
admissions to the seventy-fifth percentile while
EDs with risk-standardized admission rates in
the bottom quartile increased admissions to
the twenty-fifth percentile. This latter scenario
is to account for the possibility that some EDs
may also be inappropriately underadmitting pa-
tients for their case-mix.
NEDS contains information on hospital
charges only and does not provide hospital-
specific cost-to-charge ratios. Therefore, our
analysis is largely limited to charge data. Howev-
er, we also estimated national health spending
by generating an overall cost-to-charge ratio of
0.30 for all US hospitalizations. This ratio is
calculated from health expenditure data provid-
ed by HCUP18
by dividing total national costs
($371.7 billion) by total national charges
($1.224 trillion) for hospital care.
Data management and analysis were per-
formed using Stata software (version 12.1 MP).
Additional technical details regarding the NEDS
database, the approach to modeling, and calcu-
lation of health expenditures are in the online
Appendix.17
The Institutional Review Board of
the University of Michigan evaluated this study
and classified it under “not regulated” status.
Limitations Our results should be interpreted
in the context of the following limitations. First,
this analysis was descriptive and intended to
provide a broad picture of variation in ED admis-
sions and, in particular, the implications on
health care spending associated with these dif-
ferences. Our consideration of health spending
under the three utilization scenarios does not
imply that there is an optimal rate of ED admis-
sions that can be inferred from this study.
Second, our description of variation and
health spending is at the ED (hospital) level after
controlling for patient case-mix. Many factors
not assessed in this study can influence an
ED’s admission rate in addition to patient fac-
tors, such as social conditions, market forces,
the medicolegal environment, local access to
care,6
and providers’ training and experience.
Addressing all of these factors might not be prac-
tical to target with policy or quality improvement
efforts.
Third, although we controlled for severity of
case-mix in our estimates by using accepted tech-
niques to produce risk-standardized admission
rates, we recognize that there is likely some por-
tion of unmeasured severity of illness inherent in
using administrative data. For example, patients
who are admitted may have a greater number of
comorbidities listed than patients who are dis-
charged from the ED, which could affect risk
adjustment. As a result, certain EDs may be ap-
propriately high admitters and would be unable
to reduce hospitalizations without adversely af-
fecting patient outcomes and safety. Conversely,
some EDs may be inappropriately low admitters,
discharging patients who would have benefited
from hospitalization. Any interventions to ad-
dress systematic overuse, underuse, or misuse
of hospital admissions will necessarily have
consequences for health spending. Ultimately,
variation in admissions must be interpreted in
the context of longitudinal outcomes such as
patient mortality, unanticipated return visits
to the ED, or readmissions, which this study
was not designed to evaluate.
Finally, our estimates of national health
spending are not meant to serve as a formal
economic analysis of net expenditures and do
not account for increases in outpatient resource
utilization that occur by shifting care to ambula-
tory settings, such as costs associated with ob-
servation, office, and home-based care. We are
also only able to report estimates derived from
hospital charges, adjusted for an overall cost-to-
charge ratio for US hospitalizations, which may
overestimate the impact on national expendi-
tures. Therefore, if ED admission rates could
be reduced for certain conditions, true spending
reductions to the health system will be less than
our current estimates. However, this work pro-
vides an important starting point to use in un-
derstanding the scale of national health spend-
ing that could be affected by narrowing
variation.
Study Results
Study Population There were 28,539,883
visits among 961 hospital-based EDs in 2010
(Exhibit 1). Overall, 15.4 percent of the ED visits
resulted in admission, with a mean accompa-
nying charge of $34,826. Just over half of the
visits (55.6 percent) were by females, with an
average age of 38.8 years and 21.6 percent carry-
ing two or more comorbidities. The majority of
visits were for patients who had private insur-
ance (30.8 percent), followed by Medicaid
(25.6 percent), and then Medicare (20.9 per-
cent). Uninsured patients made up 18.1 percent
of the visits. Clinical conditions in the top fifteen
admitted medical and surgical diagnoses ranged
from chest pain (965,432 total visits and an
average of 1,002 cases per ED) to acute renal
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5. failure (82,595 total visits and an average of 137
per ED).
Admission Practices There was substantial
variation in unadjusted ED admission rates
(see online Appendix Exhibit 1)17
and risk-
standardized admission rates (see online Appen-
dix Exhibit 2)17
for many of the conditions stud-
ied. Observed differences in admission rates
ranged from a low of 1.02 for sepsis to 2.60
for chest pain at the seventy-fifth and twenty-
fifth percentiles (Exhibit 2). After risk adjust-
ment, the degree of variation between EDs wid-
ened. Those conditions showing the greatest var-
iation included chest pain (risk-standardized
admission rate ratio: 6.55), soft tissue infection
(risk-standardized admission rate ratio: 3.40),
and asthma (risk-standardized admission rate
ratio: 3.07). In contrast, higher-severity condi-
tions with less diagnostic and prognostic uncer-
tainty, such as sepsis (risk-standardized admis-
sion rate ratio: 1.03), acute myocardial infarction
(risk-standardized admission rate ratio: 1.06),
and acute renal failure (risk-standardized admis-
sion rate ratio: 1.10), showed the smallest differ-
ences. There was a strong inverse correlation
(Pearson correlation: −0.71) between observed
inpatient mortality and the magnitude of varia-
tion in risk-standardized admission rates as
quantified by the coefficient of variation. For
example, chest pain demonstrated wide varia-
tion (risk-standardized coefficient of variation:
1.04) and low observed inpatient mortality
(0.05 percent). Condition-specific variation be-
tween the ninetieth and tenth percentiles is
Exhibit 1
Emergency Department (ED) Visit- And Hospital-Level Characteristics Of The Study Population, 2010
Characteristica
ED visit level
(N = 28,539,883)
Hospital levelb
(N = 961)
ED disposition admitted 15.4% 12.4%
Mean inpatient charge (SD) $34,826 (53,046) $31,856 (17,138)
Patient characteristics
Mean age, years (SD) 38.8 (24.2) 39.9 (5.6)
Percent female 55.6 55.1
Percent with 2 or more comorbidities 21.6 19.3
Primary payer (%)
Private 30.8 31.1
Medicare 20.9 23.0
Medicaid 25.6 24.7
Uninsured 18.1 16.7
Other 4.6 4.7
Household income (%)
$39,999 or less 32.9 35.4
$40,000–$49,999 27.8 30.9
$50,000–$65,999 22.1 19.1
$66,000 or more 17.2 12.3
Primary diagnosis [CCS category] Number of visits (% of total visits) Average number of cases per ED
Chest pain [102] 965,432 (3.4) 1,002
Soft tissue infections [197] 755,649 (2.6) 813
Asthma [128] 429,853 (1.5) 481
COPD [127] 425,803 (1.5) 461
Urinary tract infection [159] 699,940 (2.5) 749
Fluid and electrolyte disorders [55] 204,658 (0.7) 231
Biliary tract disease [149] 149,161 (0.5) 196
Cardiac dysrhythmias [106] 311,992 (1.1) 348
Diabetes with complications [50] 176,884 (0.6) 216
Pneumonia [122] 377,213 (1.3) 406
Congestive heart failure [108] 215,305 (0.8) 254
Stroke [109] 132,585 (0.5) 185
Acute renal failure [157] 82,595 (0.3) 137
Acute myocardial infarction [100] 117,403 (0.4) 165
Sepsis [2] 182,844 (0.6) 272
SOURCE Authors’ analyses of data from the 2010 Nationwide Emergency Department Sample database. NOTES SD is standard
deviation. CCS is Clinical Classifications Software. COPD is chronic obstructive pulmonary disease. a
Unweighted data. b
Hospital-
level characteristics represent averages per hospital of the visit-level information.
Emergency Department Use
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6. shown in online Appendix Exhibit 3.17
Impact On Health Spending Average charges
per hospitalization for each condition ranged
from $18,162 for chest pain to $64,086 for acute
myocardial infarction (Exhibit 3). There were an
estimated $266.6 billion in total charges for
admissions related to the fifteen conditions stud-
ied, representing $80.0 billion in national health
costs (assuming an overall estimated cost-to-
charge ratio of 0.30). For the top five conditions
Exhibit 2
Variation In Emergency Department Admissions Among Hospitals, By Clinical Condition, 2010
Clinical condition
Observed
inpatient
mortality
rate (%)
Observed
admission
rate ratioa
Risk-standardized
admission
rate ratioa,b
Risk-standardized
coefficient
of variationb,c
Chest pain 0.05 2.60 6.55 1.04
Soft tissue infections 0.32 2.27 3.40 0.92
Asthma 0.26 2.01 3.07 0.86
COPD 1.19 2.10 2.84 0.66
Urinary tract infection 0.77 2.06 2.82 0.87
Fluid and electrolyte disorders 1.29 1.72 2.62 0.57
Biliary tract disease 0.49 1.55 2.09 0.47
Cardiac dysrhythmias 1.00 1.54 1.94 0.46
Diabetes with complications 0.56 1.48 1.78 0.42
Pneumonia 3.08 1.43 1.75 0.38
Congestive heart failure 2.83 1.23 1.39 0.24
Stroke 7.28 1.09 1.14 0.15
Acute renal failure 4.03 1.06 1.10 0.09
Acute myocardial infarction 5.07 1.04 1.06 0.13
Sepsis 14.72 1.02 1.03 0.05
SOURCE Authors’ analyses of data from the 2010 Nationwide Emergency Department Sample database. NOTES Conditions are
presented in descending order from most to least variable by their risk-standardized admission rate ratio. COPD is chronic
obstructive pulmonary disease. a
Ratio compares the admission rates at the seventy-fifth to twenty-fifth percentile of emergency
departments. b
Adjusted for age, sex, comorbidities, primary payer, and income. c
Pearson correlation between the riskstandardized
coefficient of variation and observed inpatient mortality shows strong inverse correlation (−0.71).
Exhibit 3
National Estimates Of Emergency Department (ED) Visits And Charges Related To ED Admissions, By Clinical Condition, 2010
Admissions from ED National charges ($ billions)
Clinical condition Number Percent
Average charge
per admission ($) All hospitalsa
Low-admitting EDsb
High-admitting EDsc
Chest pain 703,115 16.2 18,162 10.3 4.0 6.3
Soft tissue infections 487,001 14.3 22,772 10.4 2.6 7.8
Asthma 343,814 17.8 19,770 6.3 1.6 4.7
COPD 601,153 31.7 25,681 14.6 4.8 9.8
Urinary tract infection 529,007 16.9 22,123 11.0 3.1 7.9
Fluid and electrolyte disorders 381,105 41.2 20,210 7.1 2.1 5.0
Biliary tract disease 357,188 53.9 38,819 13.1 3.9 9.2
Cardiac dysrhythmias 603,619 42.7 30,353 16.9 5.2 11.7
Diabetes with complications 446,367 55.7 28,597 12.1 3.1 9.0
Pneumonia 929,515 54.3 31,476 26.7 12.2 14.5
Congestive heart failure 823,067 84.7 34,394 26.5 7.5 19.0
Stroke 556,514 92.8 47,034 22.3 7.3 15.0
Acute renal failure 347,470 94.5 34,479 11.4 4.9 6.5
Acute myocardial infarction 508,526 96.7 64,086 28.0 10.3 17.7
Sepsis 816,853 98.4 63,876 49.9 20.1 29.8
SOURCE Authors’ analyses of data from the 2010 Nationwide Emergency Department Sample (NEDS) database. NOTES Results were calculated from survey weights
provided in the 2010 NEDS. Conditions are presented in descending order from most to least variable by their risk-standardized admission rate ratio. COPD is
chronic obstructive pulmonary disease. a
Total national charges related to all hospitalizations from the emergency department. b
Charges related to EDs that admit
below the median risk-standardized admission rate. c
Charges related to EDs that admit above the median risk-standardized admission rate.
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7. exhibiting the greatest variation in risk-
standardized admission rates, we estimated total
charges of $52.6 billion ($15.8 billion in costs).
Asthma was the least-expensive condition
($6.3 billion in national charges) and sepsis
the most ($49.9 billion in national charges).
Exhibit 4 shows estimated reductions in
national charges based on three utilization sce-
narios. Under the first scenario—if higher-
admitting hospitals were to admit at the median
rate—we estimated that there would have been
$16.9 billion less in charges (for a cost savings of
$5.1 billion) to US payers in 2010 for the five
most variable conditions. Under the second sce-
nario, if hospitals in the top quartile reduced
admissions to the seventy-fifth percentile, we
estimatedthat there would have been $7.0 billion
less in charges (for a cost savings of $2.1 billion)
for these same five conditions. Under the third
scenario, if hospitals below the bottom quartile
also increased admissions to the twenty-fifth
percentile, the reduction in charges would be
estimated at $2.8 billion (for a cost savings of
$0.8 billion).
Most conditions studied achieved cost savings
under all three scenarios. However, for a handful
of conditions, with chest pain being the only
high-variation one, raising the bottom quartile
and lowering the top quartile of hospital risk-
standardizedadmission rates actuallyresulted in
net increases in spending.
Discussion
Among a national sample of EDs, we found sub-
stantial variation in risk-standardized hospital
admission rates for many commonly admitted
conditions. In particular, chest pain, soft tissue
infections, asthma, chronic obstructive pulmo-
narydisease, andurinarytract infectionsshowed
the greatest variation and, therefore, may repre-
sent the best opportunity to improve the efficien-
cy of ED admission practices and result in poten-
tial cost savings. In contrast, diagnoses such as
sepsis, acute myocardial infarction, and stroke
showed markedly less variation across EDs, rep-
resenting consistent practice patterns, probably
in response to these conditions’ high mortality
and less diagnostic ambiguity.
These data also highlight the magnitude of
health spending that could be affected with a
change in ED practices. We found national
charges to payers in excess of $266 billion per
year for the fifteen conditions studied, with high-
mortality, time-sensitive diagnoses such as sep-
sis and acute myocardial infarction representing
the greatest cost burden to the health care sys-
tem. However, these conditions also presented
Exhibit 4
Potential Annual Reductions In National Charges For Emergency Department (ED) Admissions, By Clinical Condition, Under Different Utilization Scenarios
Annual reduction in national charges ($ billions)
Clinical condition
Current national
charges ($ billions)
Scenario 1:
high-admitting
at mediana
Scenario 2: top
quartile at 75th
percentileb
Scenario 3: top quartile at
75th percentile and bottom
quartile at 25th percentilec,d
Chest pain 10.3 3.3 1.3 +0.8
Soft tissue infections 10.4 3.9 1.8 1.3
Asthma 6.3 2.2 0.9 0.7
COPD 14.6 3.9 1.3 0.4
Urinary tract infection 11.0 3.6 1.7 1.2
Fluid and electrolyte disorders 7.1 1.8 0.6 0.2
Biliary tract disease 13.1 2.7 1.1 0.5
Cardiac dysrhythmias 16.9 3.3 1.2 0.6
Diabetes with complications 12.1 2.2 0.9 0.6
Pneumonia 26.7 3.2 0.9 +0.3
Congestive heart failure 26.5 2.3 0.5 0.2
Stroke 22.3 0.6 0.1 +0.2
Acute renal failure 11.4 0.2 <0.1 +0.1
Acute myocardial infarction 28.0 0.3 <0.1 +0.2
Sepsis 49.9 0.3 <0.1 +0.3
SOURCE Authors’ analyses of data from the 2010 Nationwide Emergency Department Sample (NEDS) database. NOTES Results were calculated from survey weights
provided in the 2010 NEDS. Conditions are presented in descending order from most to least variable by their risk-standardized admission rate ratio. a
Potential
reduction in charges if higher admitting EDs (those with risk-standardized admission rates above the median) admitted at the median rate for each condition.
b
Potential reduction in charges if EDs with risk standardized admission rates in the top quartile admitted at the seventy-fifth percentile. c
Potential reduction in
charges if EDs with risk-standardized admission rates in the top quartile admitted at the seventy-fifth percentile and those with riskstandardized admission rates
in the bottom quartile admitted at the twenty-fifth percentile. d
Plus signs indicate overall increase in national health expenditures under scenario 3.
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8. little opportunity to realize meaningful spend-
ing reductions. Instead, high-variation, low-
mortality conditions represented the greatest
source of potential savings. Our results suggest
that there may be sizeable reductions in health
spending if higher-admitting EDs were able to
reduce their admissions for a handful of these
more variable conditions.
Studies that have explored population-level
regional variation in admission practices have
found that differences are not fully explained
by patient characteristics but rather are influ-
enced by hospital capacity.19,20
For example,
the regional supply of inpatient beds seems to
explain geographic variation in hospital use
more than the disease burden of the underlying
population. Furthermore, populations with
higher rates of hospitalization do not appear
to have improved outcomes or quality of care.21,22
Recent literature has demonstrated substantial
variation in overall admission rates in the ED.
One study examined a group of three EDs and
compared provider- and hospital-level differenc-
es,9
while another study performed a hospital-
level analysis to examine institutional and com-
munity factors influencing the overall admission
rate.6
Both found a two-to-threefold difference in
adjusted rates of overall hospitalization. Similar
patterns of variation exist for ED patients with
pneumonia and congestive heart failure.23,24
To our knowledge, this study is the first exam-
ining adjusted ED admission rates to identify
specific conditions with the greatest degree of
variation and to explore its implications for
health spending. The substantial variation in
ED admission practices observed in this study,
not explained by case-mix, represents a major
source of inefficiency in the health care system
and is particularly relevant because EDs are now
the primary venue through which US patients
are hospitalized.3,8
In addition, the fact that
high-variation conditions with the greatest po-
tential to reduce health spending also carry a low
risk of mortality may suggest that many of these
admissions could be effectively treated in set-
tings other than hospital inpatient units.
However, further work will be required to de-
termine the optimal rate of ED admissions for
each clinical condition, as this variation could
represent systematic overuse, underuse, or mis-
use of hospital services. For example, in scenario
3 we found that increasing admission rates for
EDs in the lowest admission quartile resulted in
greater net charges for some conditions. These
conditions generally were high-mortality, time-
sensitive conditions; variation here may indicate
that some hospitals have admission rates that are
inappropriately low. As a result, optimizing pa-
tient outcomes may mean increasing hospital-
izations for these conditions, but health care
expenditures could also rise.
One unexpected finding is that there was also
an increase in national spending under scenario
3 for chest pain, which is the only high-variation,
low-mortality condition that showed this pat-
tern. This unique trend for chest pain admis-
sions should not necessarily imply underuse of
inpatient services. Instead, it likely reflects pat-
terns of formal observation care, where patients
are not admitted to the hospital but placed in
observation status. Given the proliferation of
protocol-driven observation units and other
pathways that specifically target and standardize
care for patients with chest pain,25
it is plausible
that many hospitals have already driven their
chest pain admission rates to the minimum. In-
creasingadmission ratesfor these hospitals may,
therefore, be inappropriate.
While health spending related to variation in
ED admission practices is substantial, potential
savings will be directly tied to avoiding expensive
inpatient care for a selected group of discretion-
ary admissions. This will require both reducing
unnecessary admissions as well as creating ap-
propriate, less expensive alternatives to hospi-
talization.26
Innovative replacements for admis-
sion will ultimately depend on the local delivery
system and resources but could include develop-
ing efficient observation care options, expedit-
ing outpatient follow-up, expanding capacity to
deliver hospital-type care in the home,27,28
and
improving discharge planning and coordination
capabilities from the ED setting.29,30
For exam-
ple, hospitals are increasing the use of protocol-
ized observation services for ED patients with
certain conditions, such as the routine manage-
ment of chest pain.31
These pathways have the
capacity to deliver equivalent outcomes at lower
costs.25,32,33
However, substantial barriers to the
effectiveness of these solutions will need to be
overcome, including the current fee-for-service
payment model that rewards use, markets that
may contain excess hospital capacity, and liabili-
ty concerns regarding medicolegal risks. In ad-
dition, any alternative treatment options will al-
so be accompanied by some additional costs.
Therefore, accurately assessing true health sys-
tem savings will depend on accounting for this
additional spending, which this study cannot
address.
Our data suggest that developing alternatives
to hospitalization may offer the greatest gains
for conditions that demonstrate high variability
in ED admission practices and a low risk of mor-
tality such as chest pain, soft tissue infections,
asthma, chronic obstructive pulmonary disease,
and urinary tract infections. Other common con-
ditions that demonstrated more modest degrees
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9. of variation in admission practices, such as car-
diac dysrhythmias, pneumonia, and congestive
heart failure, may still provide opportunities for
improved efficiency given the large accompa-
nying hospital spending for these diagnoses
found in our study. However, these conditions
will require even greater sensitivity when assess-
ing a particular patient’s suitability for an alter-
native treatment pathway, given the higher risk
of short-term mortality.
A major challenge to changing ED admission
practices is the need for tools and evidence that
support emergency providers in making cost-
efficient and safe dispositions that ensure pa-
tients are appropriately selected for observation
or outpatient management.26
Any potential re-
duction in spending should be dependent upon
achieving an optimal admission rate that main-
tains patient welfare. There is surprisingly
sparse high-quality literature to assist clinicians
seeking to make evidence-based admission deci-
sions. Specifically, there are few comparative ef-
fectiveness studies evaluating important differ-
ences in outcomes and costs between traditional
hospitalization versus alternatives such as dis-
charge, home health services, or observation for
common ED conditions. Examples of such inves-
tigations include the prospective application of
the pneumonia severity index as a risk-stratifi-
cation tool to guide clinical decision making
around admission for community-acquired
pneumonia.34
When such tools are developed,
it will also be critical to ensure that they are
disseminated and appropriately applied.35
Although serving as a useful starting point for
making ED dispositions, clinical decision rules
also have limitations. They generally do not ac-
count for nonclinical factors that can influence
an emergency provider’s disposition decision,36
a decision that reflects a complex interplay be-
tween a patient’s clinical presentation, ability to
access timely outpatient primary and specialty
follow-up, family concerns, time of day or day of
week, and the adequacy and safety of a patient’s
living situation. Therefore, solutions also re-
quire addressing the local health care delivery
environment and underlying socioeconomic
conditions of the surrounding population. In
addition, discharging an emergency patient
may carry substantial medicolegal liability, as
the brief ED visit can lead to missed diagnoses
and poor outcomes.36,37
As a result, clinicians
may have lower thresholds for admission in or-
der to diminish this perceived risk.38
Conclusion
In the coming years, the ED will continue to play
a vital role in hospital admission decisions,
thereby influencing a substantial portion of
health spending and the allocation of both inpa-
tient and outpatient resources. Efforts aimed at
improving cost-efficiency in admissions should
seek to leverage the ED into a workshop for de-
veloping innovative strategies for care coordina-
tion and alternatives to acute hospitalization,
particularly around a selected group of high-
variation, low-mortality conditions that show
the greatest potential impact on health spend-
ing. However, this approach should be balanced
with a better understanding of the optimal rate
of ED admissions that maintains overall patient
safety. Ideally, policy makers and administrators
will begin to view the ED not as a cost center to be
avoided but as an opportunity to enhance the
quality of care for patients with acute health
needs. ▪
This article was presented as an
abstract at the Society of Academic
Emergency Medicine national meeting,
May 2014, in Dallas, Texas, and at the
AcademyHealth Annual Research
Meeting, June 2014, in San Diego,
California. Keith Kocher is an occasional
consultant for Magellan Health Services
and advises on emergency medicine
issues, including imaging use.
NOTES
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