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