1. Policy Implications of Mapping Healthcare Outcomes
John D Rockefeller JD MPH
Associate Dean and Lecturer
Geisel School of Medicine
Dartmouth College
4TH LATIN AMERICAN MEETING ON THE RIGHT TO HEALTH AND HEALTH SYSTEMS, Bogotá, Colombia. April 2 to 4, 2014

2. 1973 – Measuring Health Care in Vermont
Wennberg J, Gittelsohn A. Small area variations in health care delivery. Science 1973;182:

3. 1993: From the ashes of the Clinton Health Care Reform was born the Dartmouth Atlas of Health Care
The work I will present comes from the Dartmouth Atlas of Health Care working group which includes my colleagues Wennberg, Fisher, and Skinner

4. 2014: The Dartmouth Atlas of Health Care
The Dartmouth Atlas of Health Care provides national public reporting of health system performance over time through the lens of variation in utilization, cost, quality, and patient experience.
The Atlas highlights variation, its causes, and its consequences in order to provide target audiences with compelling data to effect positive changes in the health care system.
6 billion Medicare claims a year x many many years of data
= lots of terabytes of data
The work I will present comes from the Dartmouth Atlas of Health Care working group which includes my colleagues Wennberg, Fisher, and Skinner
Current Funders
Robert Wood Johnson Foundation
California HealthCare Foundation
Charles H. Hood Foundation

5. Total Population = 305 million
Medicare is terrifically important, but so too are other populations: (U.S. Population by Insurance Type – 2010)
Age
< 19
19-25
26-34
35-44
45-54
55-64
≥ 65
Medicaid
Medicare fee-for-service
Private/commercial
Medicare “HMO”
Uninsured
Population:
79m
30m
37m
40m
44m
37m
39m
Total Population = 305 million

6. The Dartmouth Atlas of Health Care
Leadership Group
Faculty (a dynamic cohort)
Julie Bynum, MD MPH
Nancy Morden, MD MPH
Shannon Brownlee, MS
Chiang-hua Chang, PhD
Therese Stukel, PhD
Jeff Munson, MD
John Erik-Bell, MD MS
Douglas Staiger, PhD
James Weinstein, MD MS
Phil Goodney, MD MS
David Goodman, MD MS (Co-PI)
Elliott Fisher, MD MPH (Co-PI)
Jonathan Skinner, PhD
John Wennberg, MD MPH (Founder)
Kristen Bronner, MA (Managing Editor)
Scott Chasan-Taber, PhD
(Director of Atlas Analytics)
The Amazing Staff
Elisabeth Bryan, MS
Thomas Bubolz, PhD
Donald Carmichael, MDiv
Julie Doherty
Jennifer Dong, MS
Daniel Gottlieb, MS
Jia Lan, MS
Martha Lane, MA
Stephanie Raymond, MA
Nancy Marth, MS
Sally Sharp, SM
Jeremy Smith, MPH
Yunjie Song, PhD
Dean Stanley, RHCE
Andrew Toler, MS
Stephanie Tomlin, MPA
Rebecca Zaha, MPH
Weiping Zhou, MS

7. Price-adjusted Medicare spending per beneficiary among hospital referral regions (2010)
$10,420 to
13,830
(61)
9,770
to <
10,420
(62)
8,920
(60)
8,100
6,910
Not populated

8. What causes the variation in spending? What is the right rate?
How can we make fair comparisons?
Are there different causes of variation in utilization?
How can we improve the value of health care?

9. Percent of Medicare diabetics with eye exams hospital service areas (2010)
Effective care

10. tdi.dartmouth.edu
October 15, 2013

11. Use of drugs to treat osteoporosis following fragility fracture among hospital referral regions ( )
Effective care 17 .4 to 28
.1%
(48) 15 .2
to <
.4%
(50) 13 .8
.2%
(46) 12 .3
.8% 6
.3%
Insufficient data
(64)
Not populated

12. Use of beta-blockers 7-12 months following discharge for AMI (2008-10)
Effective care 92 %
or More
(0) 84
to <
(42) 76
(164) 68
(86)
Less than 68%
(13)
Insufficient data
(1)
Not populated

14. Quality Dartboards for large Northern New England hospital service areas Children < 18 yrs – all payer claims data, average

15. Quality Dartboards for large Northern New England hospital service areas Children < 18 yrs – all payer claims data, average

16. Variation in Effective Care
The choice of service is dictated by strong evidence of effectiveness for almost all targeted patients.
The benefits almost always outweigh any adverse effects.
Risk adjustment is often not necessary.
The right rate is usually obvious.

17. TURP for BPH discharges per 1,000 male Medicare enrollees (2007) age-sex-race adjusted
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Ratio of TURP for BPH rate to U.S. average
Idaho Falls, ID 2.79
Panama City, FL 2.56
Gulfport, MS 2.46
Boston, MA 1.19
St. Louis, MO 1.07
Camden, NJ 1.07
Houston, TX 1.05
Los Angeles, CA 1.04
Manhattan, NY 1.03
Philadelphia, PA 1.01
Indianapolis, IN 1.00
East Long Island, NY 0.82
Orlando, FL 0.77
Atlanta, GA 0.71
Dallas, TX 0.62
Salinas, CA 0.24
Transurethral Resection of Prostate
Red dots indicate highest 3, lowest, and HRRs with at least 300,000 FFS Medicare beneficiaries 17

18. Preference-Sensitive Care
Involves tradeoffs.
Scientific uncertainty often substantial.
The effect of supply (e.g. physicians) is variable.
Patient and provider values are often different.
Decisions that should be based on the patient’s own preferences.
Decision quality is improved through shared decision-making and decision aids.

20. Percent of cancer patients dying in hospital among academic medical centers and NCI Cancer Centers (2010) adj. for age-sex-race, cancer type, non cancer conditions 10 15 20 25 30 35 40 45 50 55
Percent dying in hospital
Lenox Hill Hospital (New York, NY) 49.8
Maimonides Medical Center (Brooklyn, NY) 48.1
New York Methodist Hospital (Brooklyn, NY) 47.3
Mount Sinai Hospital (New York, NY) 46.9
Beth Israel Medical Center (New York, NY) 44.9
Allegheny General Hospital (Pittsburgh, PA) 16.6
Univ Hospitals of Cleveland (Cleveland, OH) 16.5
Univ of Kentucky Hospital (Lexington, KY) 16.5
Akron General Medical Center (Akron, OH) 12.2
St. Luke's Hospital (Bethlehem, PA) 11.5

21. Capacity (i.e. supply) is often located without respect for need
There is virtually no relationship between regional physician supply and health needs.
2.0
4.0
6.0
8.0
10.0
12.0
3.0
9.0
15.0
18.0
Acute Myocardial Infarction
Rate per 1,000 Medicare Enrollees
age-sex-race adjusted
Cardiologists per 100K
We can also look at more specific indicators of population disease burdens. CAD disease and its major acute events, AMI vary more than 4-fold across regions.
Shouldn’t we expect that more cardiologists would be found in regions with higher AMI rates?
We don’t find this.
Source: Wennberg, et al. Dartmouth Cardiovascular Atlas

22. Physician Supply and Physician Visits age-sex-race adj. Cardiologists
= 0.49
Number of Visits per beneficiary
0.0
0.5
1.0
1.5
2.0
2.5
5.0
7.5
10.0
12.5
15.0
Number of Cardiologists per 100,000
Again, capacity matters. In 1996, the number of cardiologists per 100,000 residents varied from fewer than two to more than 12— more than six-fold; visits to cardiologists varied about five-fold. About half of the variation in visits rates is explained by the numbers of cardiologists per 100,000 residents. The R2 of the association is .49.
This association shouldn’t be surprising. On average, a region with twice as many cardiologists per 1,000 has twice as many hours available for patients to visit cardiologists.
In terms of value for services, we have classified the regions according to costs.

23. Head CT Scans per 1,000 Children (2007-10, age-sex-payer adj.)3 5 7 9 11 13 15 17 19 21
Head CT scans per 1,000 children 14 .7 to 19
(13) 12 .3
to <
(14) 10 .5 8 .9 4 .2
Insufficient data
(1)
Not populated
Bangor, ME 11.1
Portland, ME 9.7
Lebanon, NH 8.9
Burlington, VT 8.4