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Table 18.2: Factors Associated with High 30-Day
Readmission Rates*
I. DISEASE/ILLNESS
A. Severity of the Index or Primary Illness/
Disease
1. Prior hospital admissions**
2. Prior emergency department visits**
3. Medications: new prescription of home
oxygen, chronic steroid use,
immunosuppressive drugs, others**
B. Comorbidity
1. Specific medical comorbidities: heart
failure, renal failure, dialysis,
immunosuppression, bleeding disorders,
anemia, AIDS
2. Increased number of comorbidities
3. Mental health/psychiatric illness
4. Dementia
C. Complexity or Problems Occurring During
Index Admission**
1. ICU admission
2. Length of hospitalization (longer LOS):
prolonged inpatient stay
3. More extensive or complicated surgery
(e.g., longer operating room time)
4. Post-operative complications
5. Complications of injury
6. Complications of procedures: such as
percutaneous coronary intervention
(PCI )
7. Postoperative or postprocedure
dysrhythmias
8. Laboratory abnormalities: hemoglobin
(anemia), sodium (hyponatremia),
hyperglycemia
9. Discharge from hospital other than to
home (e.g., skilled nursing facility,
rehabilitation facility)
II. PATIENT VARIABLES
A. Demographics***
1. Age (especially elderly)
2. Gender
3. Race
B. Ability to Do Activities of Daily Living/Need
for Assistance***
1. Activity
2. Functional impairment
3. Mobility
4. Exercise
C. Individual Risk Factors***
1. Smoking
2. Substance abuse
3. Obesity
D. Socioeconomic
1. Lack of social supports: family/caregivers
- Married vs. single (married has
spouse for support and care
giving)
2. Homeless
3. High poverty areas, lower household
income, type of insurance coverage
III. ACCESS TO CARE***
1. Geography (location: where you live)
2. Regional differences (by county, whether
urban/suburban/rural)
3. Access to care
4. Low per capita primary care
5. Health care utilization
IV. IN-HOSPITAL VARIABLES
A. Factors related to the care given during the
index or initial stay
1. Operative factors
2. Procedure factors
3. Treatment
4. Complications
B. Factors related to discharge process
1. Patient and family education prior to
discharge
C. Factors related to follow-up care
1. Transitions of care
2. Follow-up phone calls
3. Patient follow-up arranged prior to
discharge
4. Timely follow-up appointments
5. Needed service(s) arranged prior to
discharge: home health care, visiting
nurse, etc.
6. Home visits by a health care
provider
* This is likely not an all-inclusive list but gives some of the
factors associated with readmissions. Variables may be
added, deleted or changed when additional research
becomes available.
** These may be markers of disease complexity and/or
severity of illness.
*** Noted in some studies but not others, may apply to one
disease or condition but not others.
Hospital Readmissions
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20:37:32
readmissions.
24,34,42,43
Factors occurring during the
index admission related to the complexity of care –
for example, more extensive and/or complicated
surgery and/or prolonged operative time – have
been associated with greater risks of 30-day
readmissions.
27,37
Complications occurring from
procedures – such as percutaneous coronary intervention (PCI) or surgery related to a patient’straumatic injury – during the initial or index hospital
admission have been correlated with higher 30-day
readmissions rates.
26,27,34,37,44–48
Although there is
a wide range of complications from bleeding to
dysrhythmias, wound infections seem to be the
most common postoperative complication.
45,47,48
Laboratory abnormalities during the initial or
index visit are being evaluated for their effect on
30-day readmissions. Kidney in jury (e.g., abnormal creatinine and blood urea nitrogen) and
the degree or severity of kidney inj ury was associated with increased readmission rates.
30,31,47
Anemia has generally been correlated with higher
30-day readmission rates, especially for medical
patients.
25,29,49
For hyperglycemia there were
conflicting reports: one study found an association with readmission rates,
50
while another
did not.
51
Electrolyte abnormalities, specifically
hyponatremia,
52
and an elevated white blood cell
count
53
were independ ent predictors of a greater
readmission r ate.
Demographic Variables
Demographic variables are also correlated with
readmissions rates with older patients having higher
readmission rates in most studies,
18,26,28,34,54–56
but
not all studies.
43
Gender has mixed results with
some studies finding that females having higher
readmission rates,
32,34
while others report males
having significantly higherreadmissionratesthan
females,
18,57
and others noted that gender had no
effect.
43
Similarly, race has been associated with
increased readmission rates in African Americans
versus Caucasians according to some
reports
34,43,54,58,59
but not others,
24,27
with socioeconomic factors (poverty, location of service) possibly
accounting for some of the disparity for African
Americans having higher readmission rates than
Caucasians.
54,57
Individual Factors
Individual factors have been looked at.
Smoking,
32,48
substance abuse,17and “patient
noncompliance”
17,60
are correlated with higher
readmission rates. Most studies found that obesity
or increased body mass index was associated
with higher readmission rates,
27,54,61
while others
did not.
48
Functionality and ability to do activities of
daily living and mobility/exercise have also been
linked with 30-day readmissions. Measures of
frailty and diminished or dependent functionality
as indicated by lack of activity, lack of mobility,
and history of falls in the preceding 6 months are
predictors of increased readmissions.
26,62–64
Similarly, discharge destination, comparing discharge
to a nursing home or skilled nursing facility
instead of to home, has been linked to an increase
in readmission rates.
26,27
Socioeconomic Factors
Socioeconomic factors are noted to have an
effect.
54,57,65
Those with a lower median income
have higher 30-day readmission rates.
17,54,57
Patients from high poverty areas have been noted
to have higher readmission rates.
17,18
Medicaidenrolled patients have a higher rate of 30-day
readmissions than commercially insured
patients.
34,37,43,54,65–68
The homeless have higher
readmission rates.
69
Lack of social supports is a
negative variable. Those with a spouse for support
and care giving do better with fewer 30-day
readmissions than unmarried or widowed individuals in most studies.
24,43
AccesstoCare, Health Care Utilization
and Community Factors
Access to care has been evaluated. Health care
utilization, low per capita primary care/access
to care and geography/regional differences
(where you live by neighborhood, county, region,
or census region) may affect the readmission
rates.
17,18,65,68
Whether the patient lives in an
urban versus rural area has been linked with
readmission rates in some studies
65,68
but not
others.
45
The relationship between number of
health care providers and readmissions has been
evaluated and is inversely linked to the number of
general practitioners (e.g., a decrease in readmissions occurred with an increased per capita
number of general practitioners).
65
Proximity to
a health care provider/hospital or the distance to
care has been linked to readmission rates.
65,68
Sharon E. Mace
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20:37:32
Comparing emergent with elective admissions
shows higher readmission rates for those admitted
emergently.
54,68
As noted, the type of insurance
coverage has also been associated with readmission rates with patients having “Medicaid” and
“Medicare or State-financed insurance” more
likely than patients with private (or commercial)
insurance or health maintenance organization
insurance to be readmitted.
34,37,43,54,65–68
Hospital Factors
When hospital variables are looked at, the volume
may be a factor with higher-volume institutions
or high-performing hospitals having lower
readmission rates.
67,70
Adherence to best practices
has been advocated as a method to decrease
readmission rates and improve patient outcomes.
Of interest was the finding in this New England
Journal study that “high adherence to reported
surgical proce ss measures was only marginally
associated with reduced readmission rates.”
67
Readmission rates are higher at teaching hospitals
according to several studies.
10,67
Number of hospital beds, ownership type (for-profit, nonprofit
or government) and multihospital affiliation or
not had no effect on readmission rates according
to one study,
10
while another study found lower
readmission rates at larger (> 400 beds) hospitals
and at private nonprofit hospitals (vs. for-profit
or public hospitals).
67
Hospital variables can be categorized into three
groups: factors relevant to the operation or procedure or treatment for a given condition or illness,
factors applicable to the discharge process and
factors germane to follow-up care. Readmission
rates vary with the diagnosis at the index hospitalization.
71
The specific illness/diagnosis or type of
operation or procedure performed for a given condition or illness has been looked at with differences
in 30-day readmission rates noted according to the
types of procedures or surgery done (e.g., vascular
surgery, major joint replacement, bowel procedures, coronary-artery bypass grafting, lobectomy,
cardiac stent, others),
67,68
but whether a different
surgical approach or technique is associated with a
lower readmission rate is variable and may be
somewhat dependent on the specific technical procedure. This suggests that the technical approach
for each procedure or operation should be evaluated individually. For example, two reports found
no difference based on the type of operation
performed (e.g., laparoscopic vs. open procedure),
72,73
while others report that certain technical
aspects may make a difference in readmission
rates.
74
Regarding medical conditions, cardiac care
has been examined. Giving aspirin, beta blockers,
angiotensin-converting enzyme (ACE) or angiotensin receptor blocker (ARB) inhibitors at hospital discharge has been associated with a lower
30-day readmission rate.
34,75,76
The initial correct
antibiotic affected hospital readmission for pneumonia.
77
Moreover, it seems likely that the results
will be highly specific for a given procedure or
operation or illness/condition.
68,73,74
Problems
related to the discharge process and follow-up
care have also been implicated as a factor in
increasing the readmission rate.
10
Potential Solutions
Various programs focusing on the patient during
their admission have been suggested as a means
to decrease readmissions. Such programs often
involve targeting high-risk patients (e.g., frail
and elderly), a team management approach, active
case management, and/or units for the high-risk
(often elderly) patients and a focus on discharge
instructions and/or transitions of care and/or
follow-up care.
10,79–93
Achieving patient/family understanding of
discharge instructions – whether by using medication reconciliation and counseling, teach-back
mechanisms or other approaches – has been
advocated as one approach to decreasing
readmissions.
75,77,79,81,83
Improving transitions of care has been recommended as a method for lowering 30-day
readmission rates.
84,85
Follow-up phone calls to discharged patients
has been recommended.
79
Arranging timely
follow-up appointments prior to the patient’s discharge from the hospital has been suggested as
another promising approach.
79,86
Home visits by a health care practitioner –
whether a community paramedic, a nurse, community health worker, pharmacist, nurse practitioner, physician assistant, or physician – after the
patient’s discharge from the hospital may be one
avenue for decreasing readmissions.
86–91
Receipt
of hospice or home-based palliative care postdischarge was associated with lower odds of hospital readmission.
92
Hospital Readmissions
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20:37:32
Solutions may also be related to socioeconomic variables that may be beyond the reach of
an individual provider, hospital or even health
care system, but depend on societal as well as
individual factors.
Potential Solutions: Observation
Medicine
As noted, hospital variables include factors relevant to the operation or procedure or treatment
for a given condition or illness. This area of focus
could be based on “Best Practices” and standardized care, including care paths, protocols, and
order sets, as has been demonstrated to be effective with observation medicine when practiced in a
focused unit with specific criteria and set pathways. As proven in the evidence-based chapters
(both for disease-specific conditions and for agebased considerations), the correct application of
observation medicine shows much promise and
based on preliminary evidence could be extremely
valuable in achieving the goal of decreased 30-day
readmissions. Initial studies have found a
decrease in returns to the emergency department
(ED) and in readmissions to the hospital. For
example, in the study by Peacock et al., the 90day return visit to the ED for congestive heart
failure patients treated in the ED observation unit
was decreased by 64% over standard inpatient
care.
93
In the article by Roberts et al., the readmission rate was 4.8% for the ED chest pain unit
patients versus 6.1% for standard inpatient care.
94
The evidence-based chapters have other articles detailing the decreased readmissions,
improvement in quality-of-life measures, and
decreased morbidity and mortality with the use
of protocol-driven, standardized observation
units. (Chapters 80 and 81)
Summary
Hospital readmissions, which are common,
costly, and not unique to any one system or country, are a focus of health care and governmental
agencies in an attempt to control costs and
improve patient quality and outcomes. The etiology of readmissions appears to be multifactorial
and complex. Factors cited as contributing to
readmissions involve not only hospital variables
but also demographic, individual, socioeconomic
and community/geographic considerations.
The rate of 30-day preventable readmissions
is highly variable depending on the methodology
utilized to determine what is an avoidable or
preventable admission. The use of “avoidable”
readmissions as a metric for hospital payment
has been questioned because of the effect of socioeconomic factors and patient/disease variables
outside the control of a given hospital(s) or health
care system on some readmissions.
Recommendations for ways to decrease hospital readmissions have been issued. Various
solutionshavebeenproposedinanattemptto
decrease the readmission rate. Observation
medicine has been effective in reducing costs,
decreasing the LOS, increasing patient/family
satisfaction, decreasing ED return visits, decreasing readmissions, and improving patient care
and outcomes. (See evidence-based Chapters 80
and 81) It seems likely the promise of observationmedicinemaybeabletohaveapositive
impact on readmission rates.
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Sharon E. Mace
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Part III
Chapter
19
New Developments in Observation Medicine
Level of Care Determination
Medical Necessity Risk Stratification
Louis Graff IV, FACEP, FAAP
Medical Necessity determination is a central
task of the physician evaluating patients in the
emergency department (ED). Those identified as
having a serious dangerous disease or as likely
(moderate to high probability) of having a serious dangerous disease need admission for i npatient evaluation and treatment. Those identified
as having low probability of a serious dangerous
disease are appropriate for outpatient evaluation
and management in observation. Those iden tified as unlikely (very low probability) of having a
serious dangerous disease are appropr i ate for
discharge home and scheduled outpatient evaluation and management. Accurate disposition of
patients has great importance for high quality of
patient care and proper utilization of resources.
Threshold to Observe: Missed
Diagnosis Rate
Optimal threshold for observation is the amount
of observation with the lowest possible missed
diagnosis rate. The purpose of observation is to
provide additional services after the ED visit to
patients who might have a serious disease and
might suffer adverse effects if discharged home.
The missed diagnosis rate is the metric to judge
whether the threshold for observation is adequate
and is the percentage of a serious dangerous disease that the diagnosis missed at the initial visit. If
the missed diagnosis rate is not near zero, then the
physician’s threshold for observation is too high
and the physician will fail to place in observation
low-probability-of-disease patients, some of
whom have a serious disease and would be identified if observed. The rule-out evaluation rate is
the percentage of chief complaint patients who
are ‘ruled out’ for a serious disease. For example,
for chest pain (CP) patients and acute myocardial
infarction(MI), the rule-out evaluation rate is the
percentage of ED CP patients placed in observation or inpatient admission so they can get a full
rule-out evaluation for acute MI. In the example
illustrated in Figure 19.1, the optimal threshold
for observation is the CP evaluation rate at which
the missed MI diagnosis rate is very low.
20% 25% 30% 35% 40% 45% 55%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
1999
2000
2001
2002
2003
2004
1997
1998
2005
2006
2007
50%
Figure 19.1 Acute Coronary
Syndrome (ACS) Evaluation Rate vs
Acute Coronary Syndrome (ACS) Miss
Diagnosis Rate
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Performance improvement efforts focus on
lowering the physicians’ threshold for observati on
so they observe all patients who might have a
serious dangerous disease and lower the missed
diagnosis rate to near zero. For a syndrome (e.g.,
CP chief complaint) it is the rate of missed diagnosis and the rate of testing (e.g., for CP, it is ruleout MI testing rate). There is an average performance with a missed diagnosis rate of 2% to 5%
(e.g., 2–5% of patients with acute MI have their
diagnosis missed at the initial ED evaluation). The
ideal is zero-missed diagnosis rate with the goal
being best practice performance always on the
journey toward zero. With feedback to individual physicians of their cases with a missed diagnosis and feedback to the group of lessons
learned, both individuals and the entire group
of physicians can lower their threshold for observation (extended evaluations) rather than discharge home after the initial ED evaluation. In
this example (Figure 19.1) the group diagnostic
performance went from 2% to < 0.5% missed MI
rate as the threshold for observation lowered
with the percentage of ED CP patients with a
rule-out MI rate increased from 35% to 50%.
Threshold for Inpatient Admission:
Observation Usage Rate
Observation usage rate is the metric to judge
whether the threshold to admit patients to the
inpatient service is too low or too high. It is for
a given chief complaint the number of observation patients with final diagnosis of the chief
complaint divided by the number of observation
patients with final diagnosis of the chief complaint plus the number of admitted inpatient
patients with final diagnosis of the chief complaint. For example, the CP observation usage rate
is calculated by dividing the number of observation CP patients with a final diagnosis of CP by
the number of observation CP patients and the
number of inpatient admit patients with the final
diagnosis of CP. If the CP observation usage is
very high (e.g., 90%), then there is a quality-ofcare issue because the only patients being admitted are those with very high probability of disease.
If moderate probability of disease patients were
admitted as inpatients, then the CP observation
usage rate would be lower because there would
be patients evaluated in the inpatient service
who turned out to not have a serious dangerous
disease and were given a final diagnosis of CP. If
theCPobservationusageisverylow(e.g.,10%),
thenthereisautilizationissuebecausemany
patients with low probability of disease were
admitted. If patients with low probability of disease were observed rather than admitted as inpatients, then the CP observation usage would be
higher because there would be many evaluated
in the observation unit who turned out to not
have a serious disease and were given a final
diagnosis of CP.
The Utilization Review Process
Severity of illness (SI) and intensity of service (IS)
are the yin and yang of medical necessity (see
Chapter 66 Medical Necessity). There must be
documentation of both to justify acute inpatient
admission. Patients needing acute care hospitalization must have documentation by the clinician
that shows SI to the nurse who is performing
primary Utilization Review (UR) review using a
UR screening system (such as Interqual) or to the
physician (when there is no evidence of Medical
Necessity on primary UR review) who is performing the secondary UR review. Patients needing
acute care hospitalization must also have documentation by the clinician that shows IS to the
nurse performing primary UR review provision
or to the physician (when there is no evidence of
IS on primary UR review) performing the secondary UR review.
Primary UR review is performed by a UR
nurse using Interqual or other screening UR programs. The UR nurse identifies SI consistent with
acute inpatient hospitalization from documentation of serious objective findings. These can be
abnormal lab tests such as serum potassium less
than 2.0 or they can abnormal physical findings
such as low systolic blood pressure consistent
with shock. The nurse identifies IS consistent with
acute inpatient hospitalization from the orders for
therapy documented by the clinician.
Secondary UR review is performed by a UR
physician using his or her clinical judgment. The
UR physician examines the clinician’s documentation for what is documented in SI. This can be
the clinician documentation of an admitting diagnosis that requires inpatient hospitalization evaluation and treatment (e.g., acute MI). Or this can
be when the admitting clinician has concern
(moderate to high probability) that the patient
Louis Graff IV
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