1. How data can improve health care What we don’t know can hurt us Gov 2.0 September 10, 2009 Elliott Fisher, MD, MPH Director, Population Health and Policy The Dartmouth Institute for Health Policy and Clinical Practice 1

2. The current conundrum Rising costs a serious threat Medicare Trust Fund – runs dry 2017 Cost of family coverage projected to double ($12,298 to $23,842 by 2020) Two million Americans affected by medical bankruptcy each year Barriers Fear of rationing, of change, that lower cost = lower quality Ignorance of where the money’s going in health care of how to do better Data can help: four examples Regional variations in spending and quality: where’s the waste? Identifying high-performing systems -- from whom we can learn Supporting change in clinical practice Motivating change in health systems

3. Per-capita Medicare Spending Trends: 1992 to 2006 Miami5.0 Salem, OR2.3 E. Long Island4.0 Boston3.0 San Francisco2.4 Annual Growth Rate Source: Slowing the Growth of Health Care Spending: Lessons from Regional Variation. Fisher, Skinner, Bynum, New England Journal of Medicine, February 26, 2009 US Avg3.5 3

4. What does higher spending buy? (1) Fisher et al. Ann Intern Med: 2003; 138: 273-298 (2) Baicker et al. Health Affairs web exclusives, October 7, 2004 (3) Fisher et al. Health Affairs, web exclusives, Nov 16, 2005 (4) Skinner et al. Health Affairs web exclusives, Feb 7, 2006 (5) Sirovich et al Ann Intern Med: 2006; 144: 641-649 (6) Fowler et al. JAMA: 299: 2406-2412 Not better care or better outcomes 4 More “supply-sensitive services”

5. Lack of support for improvement, care management and coordination. Local organizational support: Develop virtual or real integrated systems to support practice. Payment system that rewards more care, increased capacity, high margin treatments, entrepreneurial behavior. Payment reform: foster accountability for capacity – and behavior: capitation or global shared savings. Underlying problemKey principles Moving forward Addressing the underlying causes of rising costs, poor quality 5 Failure to recognize role of local system (e.g. capacity) as cost-driver. Local accountability: Foster accountability for total costs – and capacity at local level. Assumption that more is better. Equating less care with rationing. Measurement: Comprehensive performance measures: outcomes, patient experience.

6. Data on regional performance Marked variations highlight opportunities to improve 6 Everett, WA Sacramento, CA La Crosse, WI Cedar Rapids, IA Temple, TX Portland, ME Sayre, PA Richmond, VA Asheville, NC Tallahassee, FL “How do they do that?” conference Lighter colors = lower spending Partici- pants Qualifying Regions All Others Medicare Spending $6,773$7,098$8,562 Hospital Days 1.521.561.90 Spending for Imaging (last 2 yrs) $633$662$843 Hospital Quality Score 95% 93%

7. Benchmarks of efficiency Potential savings from achieving high performance 7 Everett, WA Sacramento, CA La Crosse, WI Cedar Rapids, IA Temple, TX Portland, ME Sayre, PA Richmond, VA Asheville, NC Tallahassee, FL Category of Care Potential Savings Medicare spending 16% Hospital days17% Specialist visits36% “How do they do that?” conference Lighter colors = lower spending

8. How do they do that? Some preliminary insights Common elements across regions Shared aims; “accountable” to the community; Physician engagement as leaders; support for professional values Strong primary care foundation Use of data to support work; professional feedback; peer review Data feedback – a critical component Organizational accountability – integrated systems to support feedback Data itself – actual performance of providers

9. Data to improve quality Support for process management Ch 7. BC James, JS Lazar. A health system’s use of clinical programs to build quality infrastructure. In: Practice-Based Learning and Improvement Second Edition. EC Nelson, PB Batalden, JS Lazar, Eds. Intermountain Health Care Focus on managing defined clinical populations Care pathways defined by multi-disciplinary team Protocols implemented through EHR with process, outcome tracking and feedback Scientific review / updating on monthly basis.

10. Data to address variations Partners Healthcare Stafford RS Am J Heart 2003, 145:979-85. Physician level (n = 117) Low: 0.0% High: 24.6% Practice level (n = 10) Low: 1.0% High: 8.1% Baseline variation in ordering Impact of Individualized Feedback and Education.

11. Data to address variations Partners Healthcare May 29, 2008 Presentation at Federal Trade Commission Tom Lee, MD (Partners Healthcare System) (with permission)

12. And, perhaps, to stimulate change McAllen vs El Paso