CMS Research Update:
Challenges
g to Implementing
p
g Physician
y
Value-Based
Purchasing Initiatives
Mathematica Policy Research
2010 AcademyHealth Annual Meetings,
Meetings June 27
Disclaimer: The content of this presentation does not represent CMS policy
Introduction
2
Addressing the Policy Problem
 Continued high and rising cost of care in Medicare, coupled with
evidence of some poor quality health care
 A key to “bending cost curve” and improving quality
– Turn Medicare into a value-based p
purchaser (VBP)
(
)
• Payment reform: link payment directly to performance
• Provider feedback reports:
p
raise awareness/highlight
g g
opportunities for improvement
• Public reporting: consumers “vote with their feet”
–
CMS is developing VBP initiatives
• Hospitals,
p
, nursing
g homes,, home health agencies,
g
, dialysis
y
facilities
• Physicians and other professionals (2008 MIPPA; 2010 ACA)
3
Physician VBP Goals
 Improve quality of care
 Avoid unnecessary costs; reduce long-term cost growth
 Improve equity of health care delivery and outcomes
 Hold providers jointly responsible for the entire care
of a patient population
4
Affordable Care Act — The “Three-Legged Stool” for
Physician VBP
 PFS value-based modifier
– Focus on performance of individual physicians
 Accountable Care Organizations (ACOs)/Shared Savings
Program
g
– Focus on more integrated systems of care
 Research
– Focus on new pilots, demonstrations, exploration of new
delivery and payment models
5
CMS’ PVBP Efforts to Date Under Mathematica’s Contract

Design options for implementing a PVBP program, including
ACO features
–
–
–
–
–
–

Patient assignment and size
Organizational structure
Physician
y
mix
Performance measurement
Governance
Medical home capability
Develop and test cost/resource use/quality measures
within physician feedback reports
–
–
–
–
–
–
Risk adjustment for per capita/episode cost measures
Physician and medical group attribution rules
Minimum case sizes for statistical reliability/power
B
Benchmarks
h
k ffor assessing
i good/poor
d/
performance
f
Cost of service/utilization breakdowns for actionability
National scale-up for physician feedback reports
6
Today’s Presentation: Empirical Research
on Selected Challenges
 Identifying responsible/accountable providers for
Shared Savings/ACO Program
– Mai Pham, MD (Center for Studying Health System
Change)
 Risk adjustment of episode cost measures
– Eric Schone,, PhD (Mathematica)
(
)
 Actionable information for improving cost performance
– Tim
Ti Lake,
L k PhD (Mathematica)
(M th
ti )
7
Identifying the Responsible Provider
for Value-Based Purchasing
Hoangmai Pham and Genna Cohen
C t ffor St
Center
Studying
d i Health
H lth System
S t
Change
Ch
Tim Lake and Arkadipta Ghosh
Mathematica Policy Research
8
From Whom Does Medicare Purchase Value-Based
Care?
 Single provider
 Multiple providers at a single practice site
 Multiple providers may be in multiple sites but in a
single “practice” (PGP, ACO)
 Multiple
p p
providers in multiple
p “practices”
p
but in a single
g
organization (PGP, ACO)
 Multiple
p p
providers in multiple
p organizations
g
in a single
g
geographic area
9
What Is an Appropriate Provider “Unit”?
 Identifiable with existing or feasibly collected new data
– NPI,, TAXID/TIN,, new registration
g
process
p
 Can support reliable, meaningful performance measurement
– Minimum number of attributed beneficiaries (>/= 5
5,000)
000)
 Able to influence care delivery to improve performance
– Cohesive organization with collaborative relationships
– Includes broad continuum of care?
10
Using Claims to Define Candidate Physician
Organizations
Physician-Based
Organization
Definition
Integrated delivery
system
A Tax ID with a minimum of 500 physicians that include PCPs
and specialists/surgeons
Physician group
practice
i
A Tax ID that includes both PCPs and specialists/surgeons and meets a
certain
i patient
i
threshold
h h ld ((5,000,
000 10
10,000,
000 or 2
25,000
000 patients)
i
)
Primary care/medical
Home
A Tax ID with PCPs for whom the sum of attributed patients meets certain
thresholds (5,000, 10,000, or 25,000 patients)
Virtual physician
practice
A zip code with physician NPIs for which the sum of attributed patients
across all NPIs meets certain thresholds (5,000, 10,000, 25,000 patients)
Geographic market –
county
A county for which the sum of attributed patients across all NPIs meets
certain thresholds (5,000, 10,000, or 25,000 patients)
Geographic market –
An HRR for which the sum of attributed patients across all NPIs meets
hospital referral region certain thresholds (5,000,
(5 000 10,000,
10 000 or 25,000
25 000 patients)
11
Methods: The Case of Massachusetts
 Massachusetts Health Quality Partners Provider File
reveals actual organizations
– NPIs for every physician in the state
– Linked to identifiers for p
practice site, medical group,
g
p
and “health care network”
– “Networks” may represent PHO, IPA, large medical
group and
d correspond
d to
t regional
i
l service
i
organizations that contract with private health plans
– 2007 file included 13,617
13 617 physicians,
physicians >3,400
>3 400 practice
sites, 209 medical groups, and 11 networks
• Median of 2 physicians per site, 17 per medical
group, 675 per network
12
Analysis of Merged MHQP and Medicare data
 2006 claims for 5% random sample of U.S. providers
 Linked to MHQP data using NPIs (UPINs)
– 327 physicians across 320 actual practice sites,
102 medical groups, and 10 networks
– Multiplied number of observed NPIs per TAXID by 20
– Assigned each physician to a claims-based
organization, then assessed degree of agreement
between claims-based
claims based affiliations and actual
affiliations in MHQP data
13
Mean % of Other Physicians in a Physician’s Actual Organization
Who Are Also in the Same Claims-Based Organization
Actual organization based on MHQP data
Physician
organization based
on claims
Practice site
Medical group
Network
Integrated delivery
system
8.8
7.2
9.1
Physician group
practice
ti – 5,000
5 000
25.2
19.0
23.0
Primary care – 5,000
24.0
18.5
23.0
Virtual p
physician
y
practice – 5,000
52 5
52.5
32 2
32.2
15 2
15.2
County – 25,000
87.9
65.9
28.0
14
Median Number of (Whole or Partial) Actual Organizations
Represented in a Claims-Based Organization
Actual organization based on MHQP data
Physician
organization
based on claims
Practice site
Medical group
Network
8.5
2.0
1.5
Physician group
practice – 5,000
4
2
1
Primary care –
5,000
4
2
1
Virtual physician
group – 5,000
4
2
1
County – 25,000
18
9
3
Integrated delivery
system
15
Conclusions
 Claims-based provider organizations don’t correspond
well to actual provider organizations
 Particularly true for levels of organization that could be
most effective at improving care
 Extent of agreement between claims-based and actual
affiliation of p
physicians
y
with organizations
g
decreases as
the number of beneficiaries increases
 More than one medical group is typically assigned to a
claims-based organization, with working relationships
among the medical groups unclear
16
So Now What?
 Prospective self-assignment by providers (ACO program)
– Data options
• New database similar to MHQP
• Use only existing TAXIDs
 Prospective or retrospective assignment by Medicare
– Data options
• Modify claims to allow multiple group-level NPIs
• Retain
R t i currentt claims
l i
forms,
f
TAXIDs,
TAXID and
d NPIs
NPI
(no policy change)
17
Risk
Ri
k Adj
Adjusting
ti Episode
E i d Costs
C t for
f
Physician Feedback Reports
Eric Schone and Aparajita Zutshi
Mathematica Policy Research
18
Design of the Feedback Reports
 Phase I reports included information on relative
physician cost for attributed Medicare patients/
episodes
 Profiles were based on patients from 12 sites in the
Community Tracking Study
 Costs were risk adjusted and prices (unit costs)
standardized to eliminate regional price variation
19
Model Fit is Weak
Condition
R-square
Community-acquired pneumonia
0.0240
Heart failure
0.0079
COPD
0 0438
0.0438
AMI
0.0513
UTI
0.0402
Hip fracture
0.0200
Emphysema
0.0310
Coronary artery disease
0.0065
Prostate cancer
0.0087
Ch l
Cholecystectomy
t t
0 0126
0.0126
20
Severity Score is Sometimes Inversely Related
to Episode Cost
Condition
Community-acquired pneumonia
Heart failure
COPD
AMI
UTI
Hip fracture
HCC Score
Score^2
Coeff
1326.62
-73.81
T Stat
24.83
-10.86
Coeff
210 92
210.92
-4 98
-4.98
T Stat
16.72
-2.96
Coeff
979.71
-79.29
T Stat
59.52
-34.21
Coeff
-2085.92
120.44
T Stat
-14 53
-14.53
6 47
6.47
Coeff
504.67
-28.78
T Stat
37.43
-16.61
Coeff
-2151.76
188.48
T Stat
-12.30
8.14
21
Cost per Episode and Adjusted Cost per Episode
Are Highly Variable
22
Findings robust to the several variations in model
specifications
 Using individual HCCs as risk factors
 Using complications and comorbidities from
MS-DRG grouper (without POA indicator) as risk factors
 Adjusting for episode stage
 Changing Winsorization thresholds
 Excluding episodes that are too short or too long
 Using
U i logged
l
d costt as dependent
d
d t variable
i bl
23
Conclusions

Episodes for some conditions are very heterogeneous

Effects of prospective risk adjusters are weak and
sometimes backward

Risk adjustment can increase the dispersion
of episode costs

Analyzing
y g episodes
p
created from CMS data,, MaCurdy
y et al.
(2010) also found risk adjustment increased the proportion
of high cost outliers for a variety of conditions

Previous findings
– For current groupers, episode costs cannot be risk
adjusted by using existing severity measures (Thomas
2006)
– Variations in episode cost are driven by variation
in the number of episodes per patient (MedPAC 2006)
24
Implications
 To create reliable physician profiles based on CMS data
requires better risk adjusters and/or more homogeneous
episodes
 Groupers based on medical records data rather than
claims data should improve performance in both areas
 Electronic medical records offer access to necessaryy data
25
Designing
g g Actionable Feedback
Reporting on Medicare Cost
Performance
Tim Lake, Ellen Singer, Greg Peterson, and Stephanie Peterson
Mathematica Policy Research
26
Purpose of the Research
 Identify actionable cost-of-service (COS) category “drilldowns”
in physician feedback reports
– Analyze COS drivers of overall variation in cost measures
– Assess provider perspectives on clinically meaningful
COS categories
27
Methods
 Claims-based analysis
– Overall cost measures (Medicare Part A and B)
– COS categories (e.g., hospital, physician, lab services)
 Telephone discussions
– Individual physicians
– Medical group leaders
28
COS categories
 Evaluation and management
 Procedures
 Lab
 Imaging
 Outpatient hospital facility
 Inpatient hospital facility
 Post-acute (SNF, rehab, home health)
 Durable
D
bl medical
di l equipment
i
t
 All other
29
Claims Analysis Results
 Hospital and post-acute care use were main drivers
– Exception:
p
prostate
p
cancer costs
 Ambulatory service costs not strongly associated with
overall cost variation
 “Other” Part B services were an important cost driver
– Difficult to classify
30
Telephone Discussion Results
 Physicians prefer:
– Feedback on services they provide
– More detail in reports but in tailored ways
– Recommendations on how to improve
 Physicians are less interested in:
– Services viewed as outside their control, such as
hospitalization/post-acute care
 Leaders of large medical groups are interested
in feedback on full range of services
31
Conclusions
 Hospital, post-acute, and difficult-to-classify ambulatory
services are key drivers of Medicare costs
 But individual physicians do not believe that the use
of these services is in their control
 Medical group leaders want the most information
32
Implications
 Types of information fed back to individual physicians
versus medical groups/ACOs
 Tailoring feedback
 Need for information on how to improve cost
performance —including how to reduce the likelihood
of costly
y outcomes
33
For More Information
 Please contact:
– M
Mary Laschober
L
h b
• mlaschober@mathematica-mpr.com
– Myles Maxfield
• mmaxfield@mathematica-mpr.com
34