Decision-based
Evidence Making
Mark D. Smith, MD MBA
National Health Policy Conference
Washington, DC
February 7th, 2006
TRIP Funding Priorities, or:
An odd analogy from the provinces
Mark Smith, MD, MBA
The California HealthCare Foundation
Translating Research Into Practice Conference
Washington, DC
July 24th, 2003
Translating Research Into Practice Conference
Federal funding of basic and clinical science
swamps HSR
2004 Annual Budget, in Millions
30,000
25,000
20,000
15,000
10,000
5,000
0
NIH
AHRQ
Translating Research Into Practice Conference
An odd analogy
`
2003 payroll: $50 million
2003 payroll: $150 million
Dollars spent per win, 2000-2003
A’s: $446,000
Yankees: $1,396,000
ratio: 1 / 3.13
Sources: MLB.com and USA Today baseball salary database
Translating Research Into Practice Conference
The secrets of the As’ success
• Fast
• Cheap
• Fanatically devoted to
practical R & D
• Cunning
Translating Research Into Practice Conference
The A’s v. HSR
A’s success
• Fast
• Cheap
• Practical R&D
• Cunning
Incentives to HS Researchers
• Sloooowwwwww
• Expensive
• Uncontaminated
experiments
• Field of Dreams
Ford makes … Cars
The health care
system makes … Visits
Health Services
Research makes …
Tenure
Translating Research Into Practice Conference
What do we need more of ?
• Meaningful definitions when
framing research questions
• Quick turnaround
• Research questions driven more by
operational stakeholders’ priorities
• Expertise in the management
sciences as applied to health care
• Permanent research infrastructure
What drives selection of research
topics and methods?
•
•
•
•
•
Money
Researcher interest and skills
Researcher incentives
Data availability
Potential for publication of findings.
Problematic Researcher Attitudes
• Modeled on biochemical research –
researchers’ muse
• Observational Arrogance towards
delivery system
– town/gown ; “LMD”
– “only 65% of patients got …”
Typical Research Incentives
Are for projects to be:
•
•
•
•
Big
Long
Expensive
Dedicated staff
–Neutron bomb – nothing left of
value to the clinical enterprise
What data on patients are available?
•
•
•
•
•
•
Age
Sex
“Race”
Income proxies (e.g. zipcode)
Co-morbid conditions …
Etc.
What patient attributes are meaningful?
• Risk aversion
• Where on diffusion curve
• Assertiveness
What clinician attributes are meaningful?
• Risk aversion
• Where on diffusion curve
• Income elasticity
Research topics: ask the users
•
•
•
•
•
Medical Directors
Chiefs of staff
Department Chairs
Benefits purchasers
State/local legislators and
regulators
• Clinicians
The Role of Health care IT
IT: the new silver bullet?
The current state of
health care IT
Fast, cheap machines
Connected by
Slow, expensive people
The greatest contribution of
modern IT in health care:
The ability to measure and report
quality and the outcomes of policy
decisions in speedily and
economically
What do we need?
•
•
•
•
Relevance
Speed
“good enough” precision
Analytical attributes and skills unfamiliar
to many epidemiologists, health
services and policy researchers
• Integrated care/research IT platforms
Decision-based
Evidence Making
Mark D. Smith, MD MBA
National Health Policy Conference
Washington, DC
February 7th, 2006
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