Decision-basedEvidence Making

Mark D. Smith, MD MBA

National Health Policy ConferenceWashington, DC

February 7th, 2006

Translating Research Into Practice ConferenceWashington, DC

July 24th, 2003

Mark Smith, MD, MBAThe California HealthCare Foundation

TRIP Funding Priorities, or:An odd analogy from the provinces

0

5,000

10,000

15,000

20,000

25,000

30,000

NIH AHRQ

2004 Annual Budget, in Millions

Translating Research Into Practice ConferenceFederal funding of basic and clinical science

swamps HSR

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

• Fast

• Cheap

• Practical R&D

• Cunning

• Sloooowwwwww• Expensive• Uncontaminated

experiments• Field of Dreams

A’s success Incentives to HS Researchers

Translating Research Into Practice Conference

Ford makes … Cars

The health care system makes …Visits

Tenure

Health Services Research makes …

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

Translating Research Into Practice Conference

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

• Big

• Long

• Expensive

• Dedicated staff–Neutron bomb – nothing left of

value to the clinical enterprise

Are for projects to be:

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 ofhealth 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-basedEvidence Making

Mark D. Smith, MD MBA

National Health Policy ConferenceWashington, DC

February 7th, 2006