Following the Money:
Factors Associated with the
Cost of Treating High-Cost
Medicare Beneficiaries
Jim Reschovsky, Ctr. for Studying Health Syst. Chg.
Jack Hadley, George Mason University
Cynthia Saiontz-Martinez, Social & Scientific Systems
2010 Academyy Health Annual Research
Meeting, Boston, MA
Background
 Medicare facing fiscal crisis
 Health reform ma
may help bend the cost curve,
c r e
but full impacts well down the road
 Successful reform likely to depend on changes
in treatment of high-cost
high cost beneficiaries
• 25% of beneficiaries account for 75% of spending
• 5% of beneficiaries account for 43% of spending
• Most high-cost patients have multiple chronic conditions
Objectives
j
 Identify key factors associated with costs of
treating high-cost beneficiaries (top 25%):
• Patient
• Physician/practice
y
p
• Market
- Supply
- Structure
• Care patterns (fragmentation)
• Medicare fees
 Bottom 75% used as comparison
p
group
g
p
Construction of Analysis
y
File
2004-05 CTS
Phys.
y Surveyy
(N=5,600)
Construction of Analysis
y
File
2004-05 CTS
Phys.
y Surveyy
(N=5,600)
UPIN
2004-06 Medicare
Claims,,
(N=2.7 mill./yr.)
Construction of Analysis
y
File
2004-05 CTS
Phys.
y Surveyy
(N=5,600)
UPIN
2004-06 Medicare
Claims,,
(N=2.7 mill./yr.)
Exclusions (MA,
new enrl.)) (2.2
(
million/yr)
Construction of Analysis
y
File
2004-05 CTS
Phys.
y Surveyy
(N=5,600)
Each bene.
attributed to
USOC phys.
UPIN
2004-06 Medicare
Claims,,
(N=2.7 mill./yr.)
Exclusions (MA,
new enrl.)) (2.2
(
million/yr)
Construction of Analysis
y
File
2004-05 CTS
Phys.
y Surveyy
(N=5,600)
Each bene.
attributed to
USOC phys.
UPIN
2004-06 Medicare
Claims,,
(N=2.7 mill./yr.)
Construct
Analysis
Sample:
Exclusions (MA,
new enrl.)) (2.2
(
million/yr)
USOC in CTS site
CY 2006 claims
12 mo. lookback for decedents
Construction of Analysis
y
File
2004-05 CTS
Phys.
y Surveyy
(N=5,600)
Each bene.
attributed to
USOC phys.
Estimate
Cost=f(HCC)
Cost
f(HCC)
model
UPIN
2004-06 Medicare
Claims,,
(N=2.7 mill./yr.)
Construct
Analysis
Sample:
Exclusions (MA,
new enrl.)) (2.2
(
million/yr)
USOC in CTS site
CY 2006 claims
12 mo. lookback for decedents
Use predicted values to define high-cost (top
25%) and low
low-cost
cost (bottom 75%) beneficiary
samples (N=401K, 1.21 Mill, resp.)
Dependent Variable: Annual
Standardized Costs
 Measure of service utilization
 Includes full payment to providers
• Medicare paym’t. + patient cost sharing +
reimbursements from other insurers
 Standardization (single value for each distinct service):
• Eliminates geographic payment differences
• Policy driven differences in payment (e.g. DSH, GME)
• Different payment methods for classes of providers (e.g.
(e g
CAHs)
• Differences in payment over time (relevant for 2006
decedents
Explanatory
p
y Variables
 Patient health – CMS-HCC model
 Other beneficiary characteristics (e.g. imputed inc.)
 Physician/Practice characteristics
• USOC physician specialty (all)
• Other variables if USOC a CTS respondent
• CTS USOC dummy
 Market supply
 Market structure
 Local ((county)
y) care fragmentation
g
index
 Medicare fee levels
Sample
p Characteristics
Variable
Mean Std. Costs
Pred. High
High-Cost
Cost Benes.
Pred. Low-Cost
Low Cost Benes.
$47,647
$7,115
$41,921
$
,
((88%))
$5,806
$
,
(82%)
(
)
Mean Age (yrs.)
79.1
76.3
Mean # HCCs
6.7
1.6
Rec’d. serv. in mult.
Census Divisions
25.5%
20.5%
PCP is
i USOC
65 7%
65.7%
73 1%
73.1%
$36,409
$39,860
Ph /1000 pop iin Ct
Phys./1000
Cty.
23
2.3
22
2.2
Hosp. Beds/1000 pop.
4.8
4.6
Fragmentation index
41 8
41.8
42 2
42.2
Relative Medicare fees
$76.0
$75.3
Mean Medicare p
payments
y
Predicted income
Services used by predicted high- and
low cost beneficiaries differs
low-cost
Category of costs
Percent of Total Standardized Costs
Pred. High-Cost Benes.
Pred. Low-Cost Benes.
Ambulatory
23.3%
51.4%
Hospital
43.8%
26.2%
Post-acute
19.5%
8.2%
Other
13.4%
14.2%
Key
y Multivariate Results
 Patient health/demographic variables explain
most of the variation for both groups.
• R2=.52
52 (high
(high-cost);
cost); .23
23 (low-cost)
(low cost)
• Receiving care in mult. Census divisions assoc. with
higher costs (+4% for high-cost,
high-cost +12% for low-cost)

Few physician/practice factors significant
• Medical
M di l spec. as USOC (+4%
( 4% relative
l ti PCP USOC,
USOC +2%
2%
among low-cost)
• Inadeq.
Inadeq time for office visits (+3.5%:
(+3 5%: major problem vs
vs. no
problem; NS for low-cost)
 Supply of phys.,
phys hosp
hosp, SNF,
SNF HHA,
HHA hospice
weakly associated with cost
Key
y Multivariate Results,, con’t.
 % of for profit providers assoc. with higher
costs.
• Significant, but elasticities low (η=.01-.05 for hosp, HHA,
SNF)
 County mean fragmentation assoc. with higher
costs among low-cost benes. only: η = .38
 Higher Medicare fees assoc. with higher costs,
• η =.23 (high-cost), .41 (low-cost)
Conclusions & Policy
y Implications
p
 Geographic cost variations
ariations not dri
driven
en b
by pro
provider
ider
supply
• Questions wisdom of geographic-based
geographic based policies
 High
g % receiving
g care in multiple
p Census divisions
relevant to design of payment/delivery reforms
(P4P, ACOs, PCMH, etc.)
• Relevant to provider attribution and effectiveness
• Beneficiary
y incentives may
y be needed
 Inadequacy of time spent with patients as a cost
driver suggests potential benefit from PCMH model
Conclusions & Policy Implications,
con’t.
con’t
 Reforms may not be able to achieve much cost
g simply through
g reduced care
savings
fragmentation
 Physician payment reform can alter treatment
patterns
• But incentives need to be significant in magnitude
Conclusions & Policy Implications,
con’t.
con’t
 Designing effective reforms for high-cost benes
will be difficult
• Delivery reforms may be most effective reducing costs for
low-cost beneficiaries
• Need to target specific interventions for very sick,
complex patients
- Past demonstrations showed mixed results
- No one size fits all approach will work