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Rehospitalization Data:
A Primer for Clinicians Prepared for Project STAAR
1
Prepared for Project STAAR
Stephen F. Jencks, M.D., M.P.H.Consultant in Healthcare Safety and Quality
410-708-1134 443-801-8348
steve.jencks@comcast.net
© 2010 Stephen F. Jencks
Purpose of this session:
To help clinicians, both hospital-based and in the
community, to understand how data can help
them in reducing preventable rehospitalizations.
2
© 2010 Stephen F. Jencks
At the end of this session you
should be able to:
• Explain what the rehospitalization effort is
about.
• Define some key terms.
• Discuss the causes of rehospitalization.
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• Discuss the causes of rehospitalization.
• Discuss the evidence on preventability.
• Discuss the relevance of risk adjustment.
• Discuss the uses of patient/caregiver input.
• Discuss measuring improvement.
© 2010 Stephen F. Jencks
Prologue:
Why rehospitalization matters
• Cost: ~$17 billion for Medicare and perhaps as
much again for others, 20%-40% avoidable.
• Suffering: 5 million rehospitalizations a year.
• Preventable: 1-2 million preventable with
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• Preventable: 1-2 million preventable with
current knowledge.
• Symptom of care fragmentation, which
strategies like the medical home also address.
• A form of unsafe care.
• Momentum for change, including legislation.© 2010 Stephen F. Jencks
Affordable Health Care for America
Act
• Reduces Medicare payments to hospitals with
“excessive readmissions”, starting in FY2011.
• Current CMS measures appear the likely
starting point.
5
starting point.
• Potential penalties rise to 5 percent by 2014.
• Secretary has growing discretion through
2014.
© 2010 Stephen F. Jencks
Other evidence of growing
momentum
• 500-1000 hospitals engaged in collaborative
projects to reduce rehospitalization
• 14 communities (QIOs)
• 3 states (STAAR)
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• 3 states (STAAR)
• Growing recognition that this is not just a
Medicare problem.
© 2010 Stephen F. Jencks
Causes of Preventable
Rehospitalization• About 90 percent of all rehospitalizations seem
to be related to the index hospitalization, and
not be part of a treatment plan.
• The majority of rehospitalizations are not for the
principal diagnosis of the original hospitalization
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principal diagnosis of the original hospitalization
(exceptions: chemotherapy and psychosis.)
• 70 percent of post-surgical hospitalizations are
for medical reasons – largely conditions like
pneumonia, heart failure, and gastrointestinal
that cause most hospitalizations in the elderly.© 2010 Stephen F. Jencks
Safety: A population at high risk
• 19.6% of live Medicare fee-for-service
discharges are rehospitalized within 30 days.
• Two-thirds of Medicare fee-for-service
medical discharges are rehospitalized or dead
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medical discharges are rehospitalized or dead
within a year.
• Half of Medicare fee-for-service surgical
discharges are rehospitalized or dead within a
year.
© 2010 Stephen F. Jencks
Aim of effort
• Many rehospitalizations result from care
system failures in the transition from hospital
to the next source of care.
• These care failures allow, and sometimes
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cause, clinical deterioration that leads to
rehospitalization.
• The failures reflect a lethal system design flaw.
• Our aim is to fix the system to prevent these
care failures so the patient does not
deteriorate and need rehospitalization. © 2010 Stephen F. Jencks
Terms
• Measurement system
• Rehospitalization rate
• Index/“At risk” discharge
• Rehospitalization or readmission
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• Rehospitalization or readmission
• Clinically related
• Potentially preventable
© 2010 Stephen F. Jencks
Rehospitalization rate
Rehospitalization rate =rehospitalizations
at-risk discharges
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at-risk discharges
Determining which discharges are at risk and
which rehospitalizations to count is complex and
requires a system, not just a formula.
© 2010 Stephen F. Jencks
Three Measurement Systems
• CMS (Yale). Complex, public domain.
• Strengths: Strong risk adjustment. NQF-
endorsed. In health care reform legislation.
• Weaknesses: Only 3 conditions; designed for
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• Weaknesses: Only 3 conditions; designed for
Medicare and hard to do on other data sets;
sample sizes too small to track change over
time; no exclusions for planned
rehospitalizations. Weaker than CMS and UHG
in tracking change/measuring differences.
© 2010 Stephen F. Jencks
Three Measurement Systems
• PPR (3M). Complex, proprietary.
• Strengths: model makes sense to clinicians;
widely known.
• Weaknesses: Many exclusions, some
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• Weaknesses: Many exclusions, some
debatable, produces low rates, not NQF
endorsed; stronger than CMS but weaker than
UHG in tracking change/measuring
differences.
© 2010 Stephen F. Jencks
Three Measurement Systems
• UHG (Pacificare). Simpler. Effectively public
domain.
• Strengths: simplicity, easy implementation;
NQF-endorsed.
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NQF-endorsed.
• Weaknesses: Almost no exclusions, even for
elective procedures and chemotherapy; risk
adjustment less sophisticated. Stronger than
CMS and UHG in tracking change/measuring
differences.
© 2010 Stephen F. Jencks
Index hospitalization =
“At risk” discharge
• Patients discharged dead are not at risk for
rehospitalization; patients transferred to
another hospital are the receiving hospital’s
risk.
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risk.
• Some measurement systems exclude
rehospitalizations from index discharges.
• Some exclude patients with diagnoses at very
high risk for rehospitalization such as major
malignancy with metastasis.
© 2010 Stephen F. Jencks
Rehospitalization/readmission
• Most systems use a 15-day or 30-day window.
• Most measurement systems do not count
rehospitalization for rehabilitation.
• 3M’s PPR system only counts
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• 3M’s PPR system only counts
rehospitalizations that their consultative
process has identified as plausibly
preventable.
• Other systems (CMS, UHG) count essentially
all rehospitalizations for whatever reason.© 2010 Stephen F. Jencks
Risk adjustment• For publication and payment you want a
method that produces valid comparisons. You
could miss classes of preventable
rehospitalizations without damage. Objective
definitions and risk adjustment are vital.
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definitions and risk adjustment are vital.
• For improvement you want a method that is
sensitive, even at the risk of false positives.
Comparability across providers and
communities is nice but not necessary. Risk
adjustment is not much help.
• © 2010 Stephen F. Jencks
Clinically related
1. A rehospitalization that is a product of the
disease process that caused the index
hospitalization, or, indirectly
2. A product of the care or risks associated with
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2. A product of the care or risks associated with
that hospitalization, or, therapeutically
3. Intended to complete care initiated during
the index hospitalization.
Note: 1 and 2 may be preventable. Preventing
Reason 3 is not within scope of the aim (Slide 5).© 2010 Stephen F. Jencks
4%
5%
6%
DA
Y R
EH
OSP
PIT
ALI
ZAT
ION
SPatterns of Rehospitalization for
Different Causes
ALL REHS
PNEUMONIA
19
0%
1%
2%
3%
0 10 20 30 40 50 60 70 80 90PE
RC
EN
T O
F TO
TAL
90
-DA
Y R
EH
OSP
PIT
ALI
ZAT
ION
S
DAYS AFTER INDEX DISCHARGE
4%
5%
6%
DA
Y R
EH
OSP
PIT
ALI
ZAT
ION
SPatterns of Rehospitalization for
Different Causes
ALL REHS
PNEUMONIA
RENAL FAILURE
20
0%
1%
2%
3%
0 10 20 30 40 50 60 70 80 90PE
RC
EN
T O
F TO
TAL
90
-DA
Y R
EH
OSP
PIT
ALI
ZAT
ION
S
DAYS AFTER INDEX DISCHARGE
4%
5%
6%
DA
Y R
EH
OSP
PIT
ALI
ZAT
ION
SPatterns of Rehospitalization for
Different Causes
ALL REHS
PNEUMONIA
RENAL FAILURE
PLACE STENT
21
0%
1%
2%
3%
0 10 20 30 40 50 60 70 80 90PE
RC
EN
T O
F TO
TAL
90
-DA
Y R
EH
OSP
PIT
ALI
ZAT
ION
S
DAYS AFTER INDEX DISCHARGE
PLACE STENT
4%
5%
6%
DA
Y R
EH
OSP
PIT
ALI
ZAT
ION
SPatterns of Rehospitalization for
Different Causes
ALL REHS
PNEUMONIA
RENAL FAILURE
CHEMOTHERAPY
22
0%
1%
2%
3%
0 10 20 30 40 50 60 70 80 90PE
RC
EN
T O
F TO
TAL
90
-DA
Y R
EH
OSP
PIT
ALI
ZAT
ION
S
DAYS AFTER INDEX DISCHARGE
CHEMOTHERAPY
PLACE STENT
A practical rule for clinicians
If a reason for rehospitalization is more frequent
in the days immediately after discharge then it is
likely that many of the rehospitalizations are
clinically related to heightened risk at the time
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clinically related to heightened risk at the time
of discharge.
© 2010 Stephen F. Jencks
Potentially preventable
• A rehospitalization is potentially preventable if
• a) it is clinically related to the index
hospitalization and
• b) many such rehospitalizations could have
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• b) many such rehospitalizations could have
been prevented by better acute, transitional,
or post-acute care.
• c) rehospitalizations for elective procedures or
for planned continuation of care are not
included here.© 2010 Stephen F. Jencks
25
6%
8%
10%
DA
Y R
EH
S O
N T
HIS
DA
YPattern of Rehospitalizations for Stroke
Daily and Cumulative
© 2010 Stephen F. Jencks
0%
2%
4%
0 5 10 15 20 25 30
PE
RC
EN
T O
F 3
0-D
AY
RE
HS
ON
TH
IS D
AY
Days from Discharge to REH
Application
• You can’t do curves like these with data
available at one hospital; you may be able to
get state or national data, but
• If you could, 90% of the reasons for
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• If you could, 90% of the reasons for
rehospitalization would have curves like
pneumonia.
• If you want to do a calculation like this, just
divide the number of rehospitalizations in
days 1-10 by the number in days 10-30.
© 2010 Stephen F. Jencks
Patient experience
• Discharge-related elements get terrible scores
on patient surveys.
• The patient and family/caregiver are the most
valuable source of information about how well
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valuable source of information about how well
the discharge worked and why
rehospitalization occurred.
• Medical records do not commonly record the
problems and failures.
© 2010 Stephen F. Jencks
Approach
• You need information that only patients and
caregivers can give you.
• Your admissions unit should be able to give
you a daily list of persons admitted in the last
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you a daily list of persons admitted in the last
24 hours who had been discharged in the last
30 days.
• Just go and talk to some of them about what
happened and why they are back.
• IHI has a tool to help.© 2010 Stephen F. Jencks
Using Public Data
• Only public data can tell you about
rehospitalization of patients in hospitals other
than the discharging hospital.
• You can do a great deal by working on and
reducing rehospitalizations in the discharging
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reducing rehospitalizations in the discharging
hospital, but public data can alert you to
classes of discharges that may be different.
• The manager of public data can usually tell
you the hospital(s) where most of your
patients are rehospitalized.© 2010 Stephen F. Jencks
Some things data can do for clinical
improvement
1. Tell you how many of your discharges are
rehospitalized (almost always more than
clinicians think)
2. In particular, inform surgeons, who are often
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2. In particular, inform surgeons, who are often
not informed when their patients are
rehospitalized for medical reasons.
3. Identify clinical (such as medication
problems) and nonclinical (such as failure of
follow-up)
© 2010 Stephen F. Jencks
• If we reduce rehospitalizations we expect to
reduce total hospitalizations and therefore,
also, at-risk discharges – that is, both the
numerator and the denominator of the rate.
• If we reduce at-risk discharges, changes in the
Measuring improvement.31
• If we reduce at-risk discharges, changes in the
rehospitalization rate become almost
uninterpretable.
• Over time, the number of rehospitalizations
will be more informative than the rate.
© 2010 Stephen F. Jencks
A practical algorithm for clinicians
• If the rate of rehospitalizations is declining,
you are making progress unless the total
number of at-risk discharges is rising.
• If the number of rehospitalizations is declining
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• If the number of rehospitalizations is declining
you are making progress unless the
population you serve is also declining.
• If you are caring for a population
rehospitalizations per person is generally a
good way to track progress.
© 2010 Stephen F. Jencks
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