2010 ARM
2010 ARM
Hospital Readmissions
The Challenge of Modeling Acute
Inpatient Rehospitalizations
Eugene A. Kroch, Ph.D.
John Martin, M.P.H.
Michael Duan, M.S.
June 27, 2010
2010 ARM
TOPICS
 Significance and Setting
 Health Care Reform
 Modeling and Risk Adjustment
 Socio-Demographics
 Discussion
2
2010 ARM
TERMS: Avoidable,
Avoidable Preventable,
Preventable Ambulatory care
sensitive
• Preventable
P
t bl (A
(Avoidable)
id bl ) H
Hospitalizations
it li ti
The supposition is that, since treatable chronic illness are responsible
for many such hospitalizations, their existence represents a failure of
the health care system.
• Ambulatory Care Sensitive Conditions
The assumption is that such hospitalizations can be averted by
ambulatory care, principally primary care, and that their existence is
evidence
id
ffor iinadequate
d
t access tto primary
i
care.
• Poverty and Social Determinants of Disease
In fact, to the extent that admissions are in excess of some norm, they
are primarily related to poverty and associated social determinants,
and the strategies
g
that must be deployed
p y will have to respond
p
to these
needs.
3
2010 ARM
Alt
Alternative
ti D
Definitions
fi iti
off R
Readmissions
d i i
Def-1:
Def 2:
Def-2:
Def-3:
Def-4:
Def 5:
Def-5:
Readmission within 30 days,
Readmission within 30 days
days,
Readmission within 30 days,
Readmission within 30 days,
Readmission within 30 days
days,
Diagnosis Groups
Septicemia
Lung Cancer
AMI
Ischemic Heart Diseases
Cardiac Dysrhythmias
Heart Failure
O l i
Occlusion,
cerebral
b l arteries
t i
Pneumonia
obstruction, intestinal w/o her
F
Femur
Fractures
F t
regardless cause of hospitalization
in the same broad hospital product line
in the same narrow hospital product line
in the related broad hospital product lines
in the related narrow hospital product lines
Def 1
16.6
17.1
12.7
10.4
11.4
20.0
17 0
17.0
12.9
14.6
19 3
19.3
Readmission Rate (%)
Def 2
Def 3
Def
3.4
1.8
1.4
1.1
1.4
0.7
1.8
0.9
1.2
0.7
9.3
3.5
15
1.5
10
1.0
5.9
2.2
5.2
2.3
26
2.6
12
1.2
4
3.1
2.7
4.6
3.8
4.5
13.1
72
7.2
8.2
7.3
35
3.5
Def 5
2.0
1.5
2.0
1.5
2.0
5.6
17
1.7
2.3
0.1
27
2.7
4
2010 ARM
Rates are Highest for Cancer
(Sorting on all 3-digit ICD-9 diagnosis codes)
ICD9
170
200
194
V56
186
147
205
171
148
204
202
201
146
150
Diagnosis
Neop, mlig bone/articular cartilage
Lymphosarcoma and reticulosarcoma
Neoplasm Malignant,
Neoplasm,
Malignant endocrine glnd
Encounter for dialysis/cathet care
Neoplasm, Malignant, testis
Neoplasm, Malignant, nasopharynx
Leukemia, myeloid
Neop, mlig, connective/soft tissue
Neoplasm, Malignant, hypopharynx
Leukemia, lymphoid
Neop mlig
Neop,
mlig,lymphoid/histiocytic
lymphoid/histiocytic
Hodgkin's disease
Neoplasm, Malignant, oropharynx
Neoplasm, Malignant, esophagus
Cases Readmission Rate (%)
7205
4081
56.6
12334
5345
43.3
2028
859
42 4
42.4
3217
1223
38.0
2368
882
37.2
1661
597
35.9
27821
9571
34.4
8789
2958
33.7
1267
371
29.3
41234
11802
28.6
65873
18659
28 3
28.3
10006
2671
26.7
3313
883
26.7
13587
3619
26.6
5
2010 ARM
Highest Rates When Sorting on PDx
(Sorting on principal diagnosis codes only)
ICD-9 Diagnosis
Cases Readmission Rate (%)
V58
Aft
Aftercare/enctr
/
t for
f other
th and
d unspec proc 65728
37270
56 7
56.7
V56
Encounter for dialysis/cathet care
2706
1075
39.7
204
Leukemia, lymphoid
5118
1807
35.3
200
Lymphosarcoma and reticulosarcoma
3576
1257
35.2
205
Leukemia, myeloid
8488
2536
29.9
170
Neop, mlig bone/articular cartilage
1474
425
28.8
282
Anemias, hereditary hemolytic
29063
8248
28.4
284
Anemia, aplastic & oth bone mrw flr
10211
2846
27.9
288
88
Diseases
seases o
of white
te b
blood
ood ce
cells
s
18957
895
5215
5
5
27.5
5
202
Neop, mlig,lymphoid/histiocytic
13538
3691
27.3
572
Abscess and disease sequelae, liver
20212
5425
26.8
201
Hodgkin's
Hodgkin
s disease
1837
488
26 6
26.6
203
Myeloma/immunoprolif neop, multiple
6907
1804
26.1
585
Kidney disease, chronic
5156
1277
24.8
643
Vomiting excessive
Vomiting,
excessive, in pregnancy
13505
3285
24 3
24.3
404
Disease, HTN heart/chronic kidney
16681
3945
23.6
6
2010 ARM
Basic Fact
Readmission rates are condition specific.
So, in comparing hospital wide readmission rates
the most important risk adjustment comes from
the relative distribution of patient conditions.
7
2010 ARM
TOPICS
 Significance and Setting
 Health Care Reform
 Modeling and Risk Adjustment
 Socio-Demographics
 Discussion
8
2010 ARM
Readmissions
• Potentially-Preventable
P t ti ll P
t bl R
Readmissions
d i i
Among Medicare beneficiaries, 17.6% of hospitalizations resulted in
readmission within 30 days, accounting for $15 billion in spending,
and 84% of these were classified by MedPAC as potentially
preventable.
• Health Care Reform Legislation
Health care reform builds off of the notion that excess readmissions
can be prevented
prevented, that regulators can accurately estimate the
“excess,” and that they can reliably adjust for risk.
• Peter Orszag
g ((Director of OMB 11-10-09)
“The Senate finance legislation includes incentives for hospitals to
avoid unnecessary readmissions that will lead to higher quality and
lower costs over time.”
time.
9
2010 ARM
MedPAC Recommendations
• Confidentially report readmission rates and resource use
around hospitalization episodes to hospitals and
physicians.
• Reduce payments to hospitals with relatively high
readmission rates for select conditions and also allow
shared accountability between physicians and hospitals.
• Report on the feasibility of virtual bundling, for
encouraging efficiency around hospitalization episodes.
• “Readmission
Readmission is generally more likely the more severely
ill a patient is, even within the same DRG. Refined DRGs
that better account for severity of illness should help in
adjusting for this factor.”
10
2010 ARM
PPACA (HR 3590) Reform Provisions
•
Up to 5% cut to all DRGs for readmissions over expected (transitioned
b t
between
FY 2013 and
d 2015)
•
The Secretary will define
– national preventable readmissions benchmarks
benchmarks,
– hospital-specific preventable readmissions rates, and
– “excess” preventable readmissions by condition.
• Should use condition-specific measures endorsed by the National Quality Forum
unless otherwise necessary.
• Calculations would be risk adjusted for patient‘s severity of illness, other patient
characteristics and differences in case types.
• N
No administrative
d i i t ti or jjudicial
di i l review
i
permitted
itt d ffor th
the measures or paymentt
methodology.
• Directs the Secretary to monitor inappropriate changes in admission practices
and authorizes the Secretary to penalize providers that avoid risky patients.
11
2010 ARM
Reformed Payment Formula
Conditions
C
diti
iincluded:
l d d
Initially 3 conditions (AMI, CHF, pneumonia), expanded in 2013 to the
rest of the 7 identified by MedPAC (COPD, CABG, PTCA, “other”
vascular) that account for 30% of readmissions at 15 days
Time period for readmission:
A time period
period, e
e.g.,
g 30-days
30 days, consistent with the specified measure
Adjustment:
Downward adjustments in Medicare payments is to be based on the
lesser of:
Ratio of Payments for “Excess” readmissions
Payments for ALL admissions
or
payments
y
in 2013 , increasing
g to 5% in 2015
1% of base DRG p
12
2010 ARM
TOPICS
 Significance and Setting
 Health Care Reform
 Modeling and Risk Adjustment
 Socio-Demographics
 Discussion
13
2010 ARM
Adequate Risk Adjustment?
• Health Care Reform builds off of the notion that excess
preventable hospitalizations can be prevented, that
regulators
l t
can reliably
li bl adjust
dj t ffor risk,
i k and
d th
thatt regulators
l t
can accurately estimate the “excess.”
• CMS uses a model to assess readmission in three focus
areas: heart failure, heart attack, and pneumonia.*
• Ability to discriminate among patients (those that are likely
to be readmitted versus those that are not) is low.
– C-statistic = 0.63 ((at best)) on a scale from 0.50 ((no discriminatoryy
power) to 1.00 (perfect discrimination)
*CMS
CMS www.hospitalcompare.hhs.gov
www hospitalcompare hhs gov
14
2010 ARM
Modeling Approach
CareScience
The Wharton School
Clinical
Grouping
Demographic
Factors
Weighted
Comorbid
Conditions
Regression
Models
Patient
Specific Risk
*Pauly MV
MV, Brailer DJ
DJ, and Kroch EA
EA. Measuring Hospital Outcomes from a Buyer’s Perspective (1996)
(1996).
American Journal of Medical Quality. 11 (3): 112-122.
15
2010 ARM
Forming Clinical Groupings
Guided by clinical and statistical factors
• Major
M j di
diagnoses (ICD
(ICD-9)
9) are separated
t d
• Severe diagnoses are separated
• Closely related diagnoses are rolled up
• Low volume - low severity diagnoses are rolled
up into 18 broad diagnosis groupings
• Results in 142 clinical strata
16
2010 ARM
Risk Assessment:
Clinical Factors
– Chronic Disease/Comorbidity
– Principal Diag (terminal digits)
– Valid Procedures
– Urgency
g
y of Admission
– Specialized (e.g. Neonate weight)
Patient risk factors
Patient Selection Factors
– Travel Distance to Facility
– Payor class
– Admission Source (e.g. Transfer in)
– Discharge
g Disp.
p ((e.g.
g Transfer out))
– Facility type (e.g. acute care)
Demographic
D
hi F
Factors
t
– Age
– Gender
– Household Income
– Race
– Time trend
17
2010 ARM
Calibration Data for 2009 Discharges
Βeta values
Client Calibration Data Base
•
Includes all of Premier’s acute-care hospitals
• Data Range July 2006 - June 2008
•
17.3 million discharges
•
615 acute
care facilities (as identified by
M di
Medicare
ID)
 Represents
p
about 20% of all US annual acute care
discharges
j
of Raw values begins
g
with
 Risk Adjustment
Client Calibration Data Base
patient severity
y
 Values to account for and isolate p
are derived from data base (Beta values)
18
2010 ARM
Discriminatory Power is Moderate
1.0
C Stat = 0.773
C-Stat
0 773
09
0.9
0.8
07
0.7
C-Stat = 0.682
0 682
Sensitivvity
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1 - Specificity
Full Model ROC
Principal Diag ROC
19
2010 ARM
Best Fitting Diagnoses
ICD-9 code
V57
18
715
969
436
721
965
851
V58
434
800
852
820
11
431
808
331
722
2
Diagnosis
Cases Readmissions Rate (%)
Rehabilitation
222,504
31,615
14.2
Factors Influencing Health Status
36,378
3,659
10.1
Osteoarthrosis and allied disorders
325,075
35,278
10.9
Poisoning by psychotropic agents
28,710
5,267
18.3
Disease,, acute cerbvas,, ill-defined
672
94
14.0
Spondylosis and allied disorders
46,549
4,142
8.9
Poisoning analgesics/antirheumatics
25,025
3,519
14.1
Laceration and contusion, cerebral
9,228
848
9.2
Aftercare
65,637
37,254
56.8
Occlusion, cerebral arteries
149,975
25,545
17.0
Skull_Fracture (includes 801)
17,829
1,336
7.5
Hemorrhage, intracranial post-inj
27,673
3,842
13.9
Femur_Fracture (includes 821)
140,073
27,034
19.3
Complications of Pregnancy and Chi 1,777,788
71,935
4.0
Hemorrhage, intracerebral
28,380
3,983
14.0
Fracture, pelvis
22,249
3,383
15.2
Degeneration, other cerebral
30,432
4,565
15.0
Di d
Disorders,
iintervertebral
t
t b l di
disc
153 202
153,202
9 506
9,506
62
6.2
Neoplasma
258,244
26,310
10.2
20
R-Sq
0.329
0.181
0.173
0.168
0.165
0.160
0.151
0.147
0.136
0.131
0.130
0.129
0.124
0.121
0.117
0.115
0.108
0 107
0.107
0.103
2010 ARM
Worst Fitting Diagnoses
ICD-9 code
285
707
584
599
042
414
507
491
540
428
764
398
571
592
Diagnosis
Cases Readmissions Rate (%)
Anemia other & unspecified
Anemia,
29 395
29,395
5 774
5,774
19 6
19.6
Ulcer, chronic, skin
24,745
4,719
19.1
Renal failure, acute
131,673
23,665
18.0
Disorder urethra/urinary tract oth
Disorder,
132 497
132,497
20 644
20,644
15 6
15.6
HIV disease
22,915
4,439
19.4
Disease, oth chronic ischemic heart 471,420
49,249
10.4
Pneumonitis due to solids/liquids
64 875
64,875
11 711
11,711
18 1
18.1
Bronchitis, chronic
163,519
30,657
18.7
Appendicitis, acute
98,029
4,268
4.4
Failure heart
Failure,
370 788
370,788
74 221
74,221
20 0
20.0
Immature_Neonates (764, 765, V213 184,170
5,970
3.2
Disease, other rheumatic heart
13,962
2,870
20.6
Disease and cirrhosis,
cirrhosis liver,
liver chrn
37 861
37,861
7 984
7,984
21 1
21.1
Calculus, kidney and ureter
57,944
5,119
8.8
21
R-Sq
0 034
0.034
0.033
0.031
0 031
0.031
0.030
0.029
0 028
0.028
0.028
0.027
0 027
0.027
0.026
0.026
0 025
0.025
0.019
2010 ARM
Alternative Readmissions Revisited
Def-1:
Def-2:
Def-3:
Def
3:
Def-4:
Def-5:
Readmission within 30 days,
Readmission within 30 days,
Readmission within 30 days
days,
Readmission within 30 days,
Readmission within 30 days,
Diagnosis Groups
Septicemia
L
Lung
C
Cancer
AMI
Ischemic Heart Diseases
Cardiac D
Dysrhythmias
srh thmias
Heart Failure
Occlusion, cerebral arteries
Pneumonia
obstruction, intestinal w/o her
Femur Fractures
regardless cause of hospitalization
in the same broad hospital product line
in the same narrow hospital product line
in the related broad hospital product lines
in the related narrow hospital product lines
Def 1
16.6
17 1
17.1
12.7
10.4
11 4
11.4
20.0
17.0
12 9
12.9
14.6
19.3
Readmission
R
d i i R
Rate
t (%)
Def 2
Def 3
Def
3.4
1.8
14
1.4
11
1.1
1.4
0.7
1.8
0.9
12
1.2
07
0.7
9.3
3.5
1.5
1.0
59
5.9
22
2.2
5.2
2.3
2.6
1.2
4
3.1
27
2.7
4.6
3.8
45
4.5
13.1
7.2
82
8.2
7.3
3.5
Def 5
2.0
15
1.5
2.0
1.5
20
2.0
5.6
1.7
23
2.3
0.1
2.7
22
2010 ARM
Poor Fit at Diagnosis Level
Diagnosis Groups
Septicemia
Lung Cancer
AMI
Ischemic Heart Diseases
Cardiac Dysrhythmias
Heart Failure
Occlusion, cerebral arteries
Pneumonia
obstruction, intestinal w/o her
Femur Fractures
Def 1
16.6
17.1
12.7
10.4
11.4
20.0
17.0
12 9
12.9
14.6
19.3
Septicemia
Lung Cancer
AMI
Ischemic Heart Diseases
Cardiac Dysrhythmias
Heart Failure
Occlusion,, cerebral arteries
Pneumonia
obstruction, intestinal w/o her
Femur Fractures
0.05
0.05
0.04
0 03
0.03
0.04
0.03
0.13
0.05
0.04
0.12
Readmission Rate (%)
Def 2
Def 3
Def 4
3.4
1.8
3.1
1.4
1.1
2.7
1.4
0.7
4.6
1.8
0.9
3.8
1.2
0.7
4.5
9.3
3.5
13.1
1.5
1.0
7.2
59
5.9
22
2.2
82
8.2
5.2
2.3
7.3
2.6
1.2
3.5
Model Performance (R-Square)
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.02
0 01
0.01
0 00
0.00
0 02
0.02
0.00
0.00
0.02
0.02
0.01
0.02
0.01
0.00
0.03
0.02
0.01
0.03
230.01
0.02
0.03
0.02
0.01
0.02
Def 5
2.0
1.5
2.0
1.5
2.0
5.6
1.7
23
2.3
0.1
2.7
0.01
0.02
0.01
0 01
0.01
0.01
0.01
0.01
0.01
0.00
0.02
2010 ARM
Patient Factors – part 1
Variable
AGE
Severity A
Severity B
Severity C
Severity D
Severity D
SOURCE (ER‐ref)
TYPE
CANCER
Description
Estimate
0.018
minimal
‐0.231
moderate
0.020
significant
0.070
severe
0 200
0.200
Phys Referral
‐0.052
Clinic Referral
0.124
HMO Referral
‐0.234
Hospital Transfer ‐0.320
SNF T
SNF Transfer
f
‐0.142
0 142
Amb Surg Ctr
‐1.037
Elective
‐0.131
Malignant
0.453
Base General Model
Odds Ratio StdErr
Odds Ratio
Wald
p‐value
1.018 0.002
68.078
0.000
0.793 0.028
68.144
0.000
1.020 0.008
6.010
0.014
1.072 0.013
26.909
0.000
1 221 0.018
1.221
0 018 125.774
125 774
0 000
0.000
0.949 0.034
2.297
0.130
1.132 0.081
2.356
0.125
0.791 0.183
1.642
0.200
0.726 0.057
31.544
0.000
0 867 0.106
0.867
0 106
1 813
1.813
0 178
0.178
0.355 0.597
3.013
0.083
0.877 0.038
12.089
0.001
1.573 0.056
66.449
0.000
24
2010 ARM
Patient Factors – part 2
Variable
Description
SNF Inter Care Fac
Inter Care Fac
DISCHARGE Other Inst
(Ref‐home)
Home health
Home IV Meds
Self Pay
Medicaid
PAYER (Ref BC/BS
Medicare) Commercial
HMO
Unknown
INCOME
i $10 000
in $10,000
Estimate
0.687
0 547
0.547
0.716
0.302
0.859
‐0.343
0 103
0.103
‐0.298
‐0.297
‐0.164
‐0.115
‐0.025
0 025
Base General Model
Odds Ratio StdErr
Wald
p‐value
1.988 0.033 434.123
0.000
1 727 0.089
1.727
0 089
37 342
37.342
0 000
0.000
2.046 0.063 128.261
0.000
1.353 0.031
93.174
0.000
2.360 0.298
8.310
0.004
0.710 0.057
36.146
0.000
1 108 0.039
1.108
0 039
6 955
6.955
0 008
0.008
0.742 0.062
23.250
0.000
0.743 0.047
40.657
0.000
0.849 0.028
35.770
0.000
0.891 0.074
2.409
0.121
0 975 0.001
0.975
0 001
15 643
15.643
0 000
0.000
25
2010 ARM
Patient Factors – previous hospitalizations
Variable
Description
1
Previous
Previous 2
Admissions
3
4
Estimate
0.545
0 920
0.920
1.260
1.615
Modefied General Model
Odds Ratio StdErr
Wald
p‐value
1.725 0.026
449.059
0.000
2 510 0.041
2.510
0 041
511 894
511.894
0 000
0.000
3.524 0.065
372.388
0.000
5.027 0.077
436.997
0.000
•Minimal improvement in model fit and
discriminatory power
•Highly confounded with other patient factors
•Not a “pure” patient effect (presumably)
26
2010 ARM
Summary
 Patient-level modeling to control for readmission risk
i moderate
is
d t att the
th hospital
h
it l llevel.
l
 Hospital discrimination rests mostly on the
distribution of principal diagnoses.
g of most individual diagnoses
g
 Patient-level modeling
(HF, AMI, PNE) has very little discriminatory power.
 Using our best models to control for patient
readmission risk does not leave the remaining
p
variation in readmission rates across hospitals
attributable to hospital performance.
 One left out variable set is socio-economic
socio economic
demographics
27
2010 ARM
TOPICS
 Significance and Setting
 Health Care Reform
 Modeling and Risk Adjustment
 Socio-Demographics
 Discussion
28
2010 ARM
Role of Socio-Demographics
Current approaches ascribe variability in hospital
readmission rates primarily to differences in
patient medical risk and hospital performance.
These approaches do not adequately account
for the effect of patient socio
socio-demographic
demographic and
community factors that influence health care
utilization and outcomes.
R. Bhalla and G. Kalkut, “Could Medicare Readmission Policy Exacerbate Health Care
System Inequity?”
Inequity? Annals of Internal Medicine, November 30, 2009
29
2010 ARM
REHOSPITALIZATION RATES
30
2010 ARM
Geographic
Geog
ap c Variation
a at o in Poverty
o e ty Rates
ates
Richard Cooper, 2009
31
2010 ARM
Trends in Readmissions
Kozak, L. J., M. J. Hall, and M. F. Owings.
Trends in avoidable hospitalizations,
1980-1998. Health Affairs 2001;20 (2): 225-32
32
2010 ARM
Other Evidence on Poverty & Readmissions
• In Manitoba Canada, residents from the lowest income neighborhoods
had 1.5-2.5-fold greater rates of hospitalizations than their counterparts
i hi
in
higher
h iincome areas (R
(Roos).
)
• Across Canada, ACS hospitalization rates for those younger than 75
years of age in the lowest-income
lowest income neighborhoods were more than
twice as high as the rate in the highest-income neighborhoods (CIHI).
• In a national study by AHRQ, individuals residing in the poorest large
urban areas had hospitalization rates 27% higher than those living in
wealthier urban communities (AHRQ).
• Preventable hospitalizations were 2-fold greater among elders in the
lower third of income and 1.7-fold greater for elders with grade school
vs. college degrees (Blustein).
• Average admission rates in low-income ZIP codes were as much as
3.7 times greater than in higher-income areas, with individual lowincome ZIP codes 20-fold higher. More than 80% of the variation
among ZIP codes
d in
i Buffalo
B ff l and
d Newark
N
k was explained
l i db
by th
the per centt
of low-income persons (Billings, 1996).
33
2010 ARM
The Challenge
“Preventable” hospitalizations have been proposed as indicators of poor health
plan performance. However, we found that preventable hospitalizations among
M di
Medicare
b
beneficiaries
fi i i are more common att lower
l
socioeconomic
i
i status
t t
(SES).
Race
Odds Ratio
Black
1.23
Other
1.40
Failure to consider
White
1.00
patients’
i
’ SES
Education level
Odds Ratio
characteristics may
Grade school
1.69
lead to the false
High school
1 09
1.09
conclusion
l i that
th t care is
i
College
1.00
of poor quality.
Income (tercile)
Odds Ratio
L
Low
1 96
1.96
Middle
1.33
High
1.00
Blustein, Hanson and Shea, Health Affairs, 1998
34
2010 ARM
TOPICS
 Significance and Setting
 Health Care Reform
 Modeling and Risk Adjustment
g p
 Socio-Demographics
 Discussion
35
2010 ARM
Discussion
Although readmission-related policies may prove
to be a transformational force in health care
reform, their incorrect application in facilities
serving vulnerable communities may increase
health care system inequity.
Policy options may possibly mitigate this
potential. But the science may not be adequate
to support that goal.
36
2010 ARM
Questions and Discussion
For more info or a copy
of this presentation:
Eugene Kroch,
Kroch PhD
Senior Fellow, Leonard Davis Institute of
Health Economics, University of Pennsylvania
ekroch@wharton.upenn.edu
Vice President and Chief Scientist
Scientist, Premier
Healthcare Informatics
Eugene_Kroch@PremierInc.com
g
@
37