Business Case for SCOAP
Sean D. Sullivan, PhD Professor, Director
Pharmaceutical Outcomes Research and Policy Program University of Washington
Topics
• The health care expense problem – The Orzag Hypothesis – What is driving health care cost escalation?
• The quality/cost problem – The Orzag Solution – The disconnect between expenditures, technology and
outcomes – The path forward
• SCOAP as a regional “pilot test” of a local solution to health care cost/quality improvements – Case studies
Under 14 15‐24 25‐34 35‐44 45‐54 55‐64 65+
US Demographics 80%
70%
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
U.S.1950-1960
Source: U.S. Census, 2000
37%
‐4%
12%
18%
17%
35%
8%
3
Under 14 15‐24 25‐34 35‐44 45‐54 55‐64 65+
US Demographics
80%
70%
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
U.S.1960-1970
Source: U.S. Census, 2000
4% 9%
‐4%
13%
19%
21%
48%
4
Under 14 15‐24 25‐34 35‐44 45‐54 55‐64 65+
US Demographics
80%
70%
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
U.S.1970-1980
Source: U.S. Census, 2000 ‐11%
49%
11%
‐2%
17%
27%
20%
5
Under 14 15‐24 25‐34 35‐44 45‐54 55‐64 65+
US Demographics
80%
70%
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
U.S.1980-1990
Source: U.S. Census, 2000
4%
16%
47%
11%
‐3%
22%
‐13%
6
Under 14 15‐24 25‐34 35‐44 45‐54 55‐64 65+
US Demographics
80%
70%
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
U.S.1990-2000
Source: U.S. Census, 2000
12% 7%
‐8%
20%
49%
12%
15%
7
Under 14 15‐24 25‐34 35‐44 45‐54 55‐64 65+
US Demographics
80%
70%
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
U.S.2000-2010
Source: U.S. Census, 2000
‐1%
‐3%
‐13%
17%
46%
13% 9%
8
Under 14 15‐24 25‐34 35‐44 45‐54 55‐64 65+
US Demographics
80%
70%
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
U.S.2000-2020
Source: U.S. Census, 2000
7% 7
% ‐10%
3%
73%
54%
8%
9
Prepared by the UNC Institute on Aging
Increases in the Oldest Old U.S. Population Aged 85+ (in millions)
Sources of data: U.S. Census Bureau, “65+ in the United States: 2005,” December 2005; U.S. Census Bureau, U.S. Interim ProjecLons by Age, Sex, Race, and Hispanic Origin, 2004.
Demographics = unsustainable spending growth rates???
Source: Congressional Budget Office, “The Long-Term Budget Outlook,” December 2003 Assumptions: Excess cost growth of 2.5% for both Medicare and Medicaid; Social Security benefits paid as scheduled under current law
25
20
15
10
5
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
Fede
ral S
pend
ing
($ T
rillio
ns)
$265B $158B $455B
Medicare $13.2T
Medicaid $4.4T
$17.6T
$423B Social Security
$5.2T
42x
11x
CBO Projections for Social Security, Medicare, and Medicaid
11
Actual Projected
Projected growth rates as a percentage of GDP
Source: Committee for Economic Development (CED), “A New Tax Framework: A Blueprint for Averting a Fiscal Crisis” (Washington, DC; CED, 2005)
50
45
40
35
30
25
20
15
10
5
0
Perc
enta
ge o
f GD
P
1960 1970 1980 1990 2000 2010 2020 2030 2040 2050
Historical Spending
Historical Revenues
Federal Spending and Revenue Projections
12
Federal Spending Under CBO’s Alternative Fiscal Scenario – Health Care Will Bankrupt America
Percentage of Gross Domestic Product
Estimated Contributions of Selected Factors to Long-Term Growth in Real Health Care Spending per Capita, 1940 to 2000
Smith, Heffler, and Freeland (2000)
Cutler (1995)
Newhouse (1992)
Aging of the Population 2 2 2
Changes in Third-Party Payment 10 13 10
Personal Income Growth 11-18 5 65
Misdiagnosing the Problem
• Most discussions in media: aging and demographics
• Orzag Hypothesis: – Rising cost per beneficiary, not the
number or type of beneficiaries
Sources of Growth in Projected Federal Spending on Medicare and Medicaid
Percentage of GDP
VALUE
Cost Health Outcome
Medicare Spending per Beneficiary in the United States, by Hospital Referral Region, 2005
Source: Data from CMS
19
Variation in Medicare Spending
Source: Dartmouth Atlas of Health Care
20
Quality Variation Even within Medicare
Source: Dartmouth Atlas of Health Care
21
22
Higher Spending Does Not Necessarily Lead to Higher Quality
Source: Baicker and Chandra (Health Affairs 2004)
The Relationship Between Quality and Medicare Spending, by State, 2004
Composite Measure of Quality of Care
Source: Data from AHRQ and CMS.
Variations Among Academic Medical Centers
UCLA Medical Center
Massachusetts General Hospital
Mayo Clinic (St. Mary’s Hospital)
Biologically Targeted Interventions: Acute Inpatient Care
CMS composite quality score 81.5 85.9 90.4
Care Delivery―and Spending―Among Medicare Patients in Last Six Months of Life
Total Medicare spending 50,522 40,181 26,330
Hospital days 19.2 17.7 12.9
Physician visits 52.1 42.2 23.9
Ratio, medical specialist / primary care 2.9 1.0 1.1
Use of Biologically Targeted Interventions and Care-Delivery Methods Among Three of U.S. News and World Report’s “Honor Roll” AMCs
Source: Elliot Fisher, Dartmouth Medical School.
Concentration of Total Annual Medicare Expenditures Among Beneficiaries, 2001
Percent
Source: Data from CMS.
The Horizon of New Health Technologies
• Diagnostics: Virtual colonoscopy
• Devices: Computerized knee • Procedures: Breast MRI • Drugs: Biologics
Managed Healthcare Executive, August 2004
New Technology #2
Average Health Insurance Premiums and Worker ContribuCons for Family Coverage, 1999‐2008
Source: Kaiser/HRET Survey of Employer-Sponsored Health Benefits, 1999-2008.
$5,791
$12,680
117% Increase
119% Increase
Paths Toward Capturing the Opportunity
• Information – Comparative effectiveness research – Randomized control trials – Health Information Technology – Cost offsets and ROI
• Incentives – Better care, not more care – Coverage vs. differentiated payments
• Delivery Systems • Health Behavior
– Making it easy and simple to lead healthy lives – Managing chronic disease – Emphasizing prevention – Changing behavior and social norms among medical professionals
Connecting the Dots – Business Case for Quality (from clinical improvement to financial gain)
Improved Profits
Cost Improvements
Revenue Enhancements
Increased Bed Turnover
Reduced ALOS
Increased Quality
Static Bed Count
Increased Admits
2r3~6-2.4 2 K 6 66 Then a
miracle occurs
P 06511 HAQ 10+t n=1.\
Re-operative Complications Elective Colon Resection
Impact on Length of Stay
5
6
7
8
9
10
2006 2007 2008
Ave
rage
LO
S (d
ays)
Year
0
1
2
3
4
5
2006 2007 2008
Med
ian
LOS
(day
s)
Year
Colon Resection Gastric Bypass
Testing for Leak in OR Prevents Reoperation After OR
0%
20%
40%
60%
80%
100%
Q1 06 Q2 Q3 Q4 Q1 07 Q2 Q3 Q4 Q1 08 Q2 Q3 Q4
(Denominator)
SCOAP Changing Behavior Around Quality
0%
20%
40%
60%
80%
100%
Q1 06
Q2 Q3 Q4 Q1 07
Q2 Q3 Q4 Q1 08
Q2 Q3 Q4
Proper LN Evals in Cancer Blood Clot Prevention
0%
20%
40%
60%
80%
100%
Q1 06
Q2 Q3 Q4 Q1 07
Q2 Q3 Q4 Q1 08
Q2 Q3 Q4
Diabetes management
Avoiding Transfusion
The “Behavior Change” Model
Focus on clinician behavior change
Clinician-led Initiative
Process of care
Quality measures Efficiency measures
Outcomes
Business case for quality
More directly measureable-business case for efficiency/standardization
• LOS, time in OR • Expensive medication use • Exchange/substitute/standardize equipment, implants, devices • Within 72hr blood/radiologic testing
Case Studies
• Phase 1 – QI module: 50 hospitals in SCOAP • Phase 2 – business module: UWMC, Providence
Everett, Good Samaritan, Samaritan Healthcare • Focus:
– Uncaptured revenue related to pre-op testing • Unbilled charges $200-500,000/yr
– Supply avoidance through substitution • Savings $1.5-3 million/yr
Lessons from Pilot Phases
• Success but recognized challenges – Several “data programs” compete for hospital resources – Uncertainty at the hospital level about ability to change – Upfront costs of abstraction/dues
• 1, 5, 10K dues, 10-30K abstraction fees • SCOAP Solution
– Data abstraction is a service-no up front cost – Change intervention is a service-no up front cost – Option to have “risk” or “no-risk” contracts
• Fees determined through – Avoided costs – Enhanced revenue related to 72hr testing
How SCOAP Makes Financial Assessments
• Measure utilization of OR supplies before and after intervention – Units used x cost/unit at baseline and after SCOAP
• Measure utilization of post-surgical supplies before and after intervention – Units used x cost/unit at baseline and after SCOAP
• Measure blood and radiologic testing within 72 hours before and after intervention – Tests used x revenue/test at baseline and after SCOAP
SWOT
• Strengths – Sweet spot of quality improvement and cost reduction – Novel approach
• Clinician led behavior change • No risk to hospital
• Weaknesses • Opportunities
– What else would you like us to focus on • Threats
– Competitors – Internal programs – Supply chain management consulting groups – Administrative data
• Willingness to pay/What’s it worth? – Back end/no risk % of savings vs up-front fee
Find out more: www.scoap.org