Patient Welfare and Selective Contracting
in Kidney Transplantation
…patients should be free to seek out the providers with the
best track records given their unique circumstances. In the
current environment, where patients' treatments are determined
by the networks they are in, network providers are all but
guaranteed.
David H. Howard
Department of Health Policy and Management
Emory University
Michael E. Porter and Elizabeth Olmsted Teisberg
Harvard Business Review
Funding by NIDDK/NIH
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CIGNA LIFESOURCE Transplant NetworkMS
The nation's leading transplant program
Why lies at the core of consumers’ complaints about managed
care? To my mind, the answer is clear. Today’s well-educated,
assertive consumers want choice and control.
Each facility in our network is carefully chosen and must continue to meet
our stringent quality standards. We assess several factors, including:
Regina Herzlinger, J Health Politics Policy and Law 1999
•patient outcomes;
•staff training and experience;
•number of transplants performed;
•waiting periods;
•availability of housing arrangements and transportation for patients'
families;
•geographic location and accessibility; and
•availability of transplant organs in the geographic area of the facility.
In health care there is increasing control over access to medical
services. Insurance companies disallow patient free choice of
physicians, clinics and hospitals outside their approved network. If
there is a solution to this problem, it will take the form of changing
the incentive structure: empowering the consumer by channeling
third-party payment allowances through the patients or students
who are choosing and consuming the service.
Vernon Smith, Wall St. Journal 2006
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What is the Blue Quality Centers for Transplant (BQCT)
Program?
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Humana has authorized the National Transplant Network
(NTN) Site Selection Committee to establish guidelines by
which the NTN is to be developed and maintained.
The Blue Quality Centers for Transplant (BQCT) is a centers of
excellence bone marrow and organ transplant program offered through
participating Blue Cross Blue Shield Plans. All institutions selected as
BQCT centers of excellence must meet stringent Program criteria. BQCT
includes for covered transplants seven individual networks: heart; lung;
heart/lung; liver, lung, simultaneous kidney-pancreas; pancreas and bone
marrow (autologous and allogeneic).
•Transplant programs must be in continuous operation for a minimum
of one year, and ideally, for four years from the date of the first
transplant.
•Transplant team members should be in place and working together
for a minimum of one year.
•Transplant programs must meet volume guidelines for the previous
year or average of previous two years. (see below)
•Outcomes for a solid organ transplant program will be compared to
United Network for Organ Sharing (UNOS) or US Transplant,
whichever is most current, and must not be significantly below the
expected patient and graft survival at one year or three years.
Transplant programs must meet Medicare minimum patient survival
outcomes, as defined in the Federal Register for a specific solid organ
transplant.
How do you select and credential facilities?
Institutions are selected for the Blue Quality Centers for Transplant
(BQCT) Program based on their ability to meet defined clinical criteria
that are unique for each type of transplant. Panels of transplant surgeons
and physicians advise the Blue Cross and Blue Shield Association on
selection criteria, which are updated in response to medical advances.
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Direct method - Overview
Three approaches to studying the role of quality in selective
contracting:
• Direct method: Compare quality between in- and out-of-network
providers (Gaskin et al. 2002; Mukamel et al. 2002, Lindrooth et al.
2002).
• Obtain network lists from for Beechstreet, Aetna, Humana, United
Health, Cigna
• Merge with a list of all hospitals offering kidney transplant services w/
outcome data for each center
• Indirect method: Infer the role of quality in contracting decisions from
patient flows. (Escarce et al. 1999; Chernew et al. 1998; Feldman and
Sharfstein 2000).
• Compare outcomes between in-network and out-of-network facilities
• Very indirect method: Examine how the impact of hospital competition
on quality varies by payer type. (Kessler and McClellan 2000;
Gowrisankaran and Town 2003).
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2
Quality at in-network and out-of-network transplant centers for
five health plans
Quality: O-E graft survival
Health plan
% In-network
BeechStreet
United
Cigna
Humana
Aetna
21%
33%
29%
35%
38%
In-network Out-of-network
0.90
1.39
0.50
1.40
0.74
-0.58
-1.19
-0.92
-1.14
-1.28
Diff.
T-stat
1.47
2.58
1.42
2.54
2.02
1.66
4.00
1.96
3.69
3.16
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Sample construction
Indirect method - Overview
• Use data on registrations for patients added to the kidney transplant
waiting list
•
28,364 non-elderly adult (age 18-64) kidney transplant registrants.
•
Registered on the wait list between January 1, 2000 and October 31,
2002.
•
With private insurance or Medicare (no Medicaid).
•
Note that the choice of transplant center is made at the time of
registration, not the time of transplant.
•
The database includes the universe of candidates for deceased donor
transplants in the United States and 30 percent of living-donor transplant
recipients.
•
Choice sets were constructed using distance and historical registration
patterns.
• Create patient-level choice sets
• Merge with data on transplant center location and outcomes
• Compare impact of outcomes on choice between patients with private
insurance and Medicare
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Statistical analysis
Statistical analysis
Conditional logit model: fully interact
transplant center attributes
-quality
-distance
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Conditional logit model: fully interact
patient characteristics
-age
-sex
-race
-health status
-education
-insurance
transplant center attributes
-quality
-distance
patient characteristics
-age
-sex
-race
-health status
-education
-insurance
U ij = β 01q j + β 02 q j AGEi + β 03 q j MALEi + ... + β12 q j PRIVi +
β13 d ij + β14 d ij AGEi + β15 d ij MALEi + ... + β 26 d ij PRIVi + ε ij
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Differences between privately-insured registrants and
Medicare beneficiaries
Statistical analysis
Conditional logit model: fully interact
Insurance
Private
Medicare
transplant center attributes
-quality
-distance
patient characteristics
-age
-sex
-race
-health status
-education
-insurance
Age
Male
White
On dialysis
Diabetic
Cardiac condition
ADL limitation
Education: HS degree
Education: College degree
Education: Unknown
Employed
U ij = β 01q j + β 02 q j AGEi + β 03 q j MALEi + ... + β12 q j PRIVi +
β13 d ij + β14 d ij AGEi + β15 d ij MALEi + ... + β 26 d ij PRIVi + ε ij
N
48
59%
57%
70%
35%
15%
12%
56%
20%
22%
55%
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59%
38%
95%
41%
19%
17%
62%
8%
21%
21%
15,906
12,458
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Estimates from the conditional logit model
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Estimates from the conditional logit model
Center attributes
Center attributes
Variable
Quality
Variable
Distance
Quality
Distance
1.6671 (1.2792)
-0.9509 (0.0152)*
0.0000 (0.0000)
Level
×Age
-0.0161 (0.0165)
0.0000 (0.0000)
0.0017 (0.0005)*
×Male
0.0417 (0.3444)
0.0017 (0.0005)*
1.3929 (0.3596)*
-0.0011 (0.0005)*
×White
1.3929 (0.3596)*
-0.0011 (0.0005)*
0.0646 (0.4932)
0.0010 (0.0006)
×On dialysis
0.0646 (0.4932)
0.0010 (0.0006)
-0.2809 (0.3753)
-0.0021 (0.0005)*
×Diabetic
-0.2809 (0.3753)
-0.0021 (0.0005)*
-0.2267 (0.4314)
-0.0013 (0.0006)*
Level
×Age
1.6671 (1.2792)
-0.9509 (0.0152)*
-0.0161 (0.0165)
×Male
0.0417 (0.3444)
×White
×On dialysis
×Diabetic
-0.0013 (0.0006)*
×Cardiac condition
×ADL limitation
1.7232 (0.5084)*
0.0061 (0.0008)*
×ADL limitation
1.7232 (0.5084)*
0.0061 (0.0008)*
×Education: HS degree
-1.3803 (0.8673)
0.0026 (0.0008)*
×Education: HS degree
-1.3803 (0.8673)
0.0026 (0.0008)*
×Education: College degree
1.0463 (0.9872)
0.0033 (0.0010)*
×Education: College degree
1.0463 (0.9872)
0.0033 (0.0010)*
×Education: Unknown
-1.2771 (0.8987)
0.0022 (0.0009)*
×Education: Unknown
-1.2771 (0.8987)
0.0022 (0.0009)*
-0.5976 (0.3913)
-0.0026 (0.0005)*
3.0534 (0.3822)*
0.0035 (0.0005)*
×Cardiac condition
-0.2267 (0.4314)
×Employed
-0.5976 (0.3913)
-0.0026 (0.0005)*
×Employed
×Private insurance
3.0534 (0.3822)*
0.0035 (0.0005)*
×Private insurance
*P < 0.05
Standard errors are in parentheses.
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*P < 0.05
Standard errors are in parentheses.
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Quality-choice elasticity estimates
Estimates from the conditional logit model
Center attributes
Actual: 0.14 [95% CI: -0.21,- 0.07].
Variable
Quality
Distance
Level
×Age
1.6671 (1.2792)
-0.9509 (0.0152)*
-0.0161 (0.0165)
0.0000 (0.0000)
×Male
0.0417 (0.3444)
0.0017 (0.0005)*
×White
1.3929 (0.3596)*
-0.0011 (0.0005)*
×On dialysis
0.0646 (0.4932)
0.0010 (0.0006)
×Diabetic
-0.2809 (0.3753)
-0.0021 (0.0005)*
×Cardiac condition
-0.2267 (0.4314)
-0.0013 (0.0006)*
×ADL limitation
1.7232 (0.5084)*
0.0061 (0.0008)*
×Education: HS degree
-1.3803 (0.8673)
0.0026 (0.0008)*
×Education: College degree
1.0463 (0.9872)
0.0033 (0.0010)*
×Education: Unknown
-1.2771 (0.8987)
0.0022 (0.0009)*
×Employed
-0.5976 (0.3913)
-0.0026 (0.0005)*
×Private insurance
3.0534 (0.3822)*
0.0035 (0.0005)*
*P < 0.05
Standard errors are in parentheses.
One-standard deviation increase in graft failure rate for a hospital ⇒
7% decline in registrations
If all registrants were privately insured: -0.22 [95% CI: -0.29, -0.15].
One-standard deviation increase in graft failure rate ⇒
11% decline in registrations
If all registrants were in Medicare: -0.03 [95% CI: -0.10, 0.04].
One-standard deviation increase in graft failure rate ⇒
2% decline in registrations
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Porter & Teisberg’s Ingredients for Change
To sum up: evidence is consistent with plans’ claims.
No Restrictions to Competition and Choice
• No preapprovals for referrals or treatments
• No network restrictions
• Strict antitrust enforcement against collusion, excessive
concentration, and unfair practices
• Meaningful co-payments and medical savings accounts with
high deductibles, all of which will give consumers incentives
to seek good value
Caveats
No data on prices
Endogeneity?
Only observe static effects-dynamic effects may be more important
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Accessible Information
• Appropriate information on treatments and alternatives is
formally collected and widely disseminated.
• Information about providers‘ experience in treating particular
diseases and conditions is made available immediately.
• Risk-adjusted outcome data are developed and continually
enhanced.
• Some information is standardized nationally to enable
comparisons.
Porter & Teisberg’s Ingredients for Change
Porter & Teisberg’s Ingredients for Change
No Restrictions to Competition and Choice
• No preapprovals for referrals or treatments
• No network restrictions
• Strict antitrust enforcement against collusion, excessive
concentration, and unfair practices
• Meaningful co-payments and medical savings accounts with
high deductibles, all of which will give consumers incentives
to seek good value
No Restrictions to Competition and Choice
• No preapprovals for referrals or treatments
• No network restrictions
• Strict antitrust enforcement against collusion, excessive
concentration, and unfair practices
• Meaningful co-payments and medical savings accounts with
high deductibles, all of which will give consumers incentives
to seek good value
Accessible Information
• Appropriate information on treatments and alternatives is
formally collected and widely disseminated.
• Information about providers‘ experience in treating particular
diseases and conditions is made available immediately.
• Risk-adjusted outcome data are developed and continually
enhanced.
• Some information is standardized nationally to enable
comparisons.
Accessible Information
• Appropriate information on treatments and alternatives is
formally collected and widely disseminated.
• Information about providers‘ experience in treating particular
diseases and conditions is made available immediately.
• Risk-adjusted outcome data are developed and continually
enhanced.
• Some information is standardized nationally to enable
comparisons.
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