jack wennberg on unwarranted variation in medical practice - lessons from the usa

Tracking Medicine

John E. Wennberg

Small Area Variations in Health Care Delivery

New Hampshire

Massachusetts

New York

Quebec

From ‘ Science’ , 14 December 1973

Vermont Map from ‘Science’

Morrisville and Waterbury Center

Tonsillectomy rate per 10,000 children among 13 Vermont Hospital service areas

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50

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450Morrisville

1969

Tonsillectomy rate per 10,000 children among 13 Vermont Hospital service areas

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50

100

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450

Morrisville

Morrisville

1969 1973

1.5-2 x

2-3 x

1.5-2 x

2-3 x

Table 2.1. A Test of Consumer Contribution to Small Area Variations in Health Care Delivery: Randolph and Middlebury, VT

Middlebury, Randolph, Vermont Vermont

• Socio-economic characteristics• White 98% 97%• Born in VT or NH 59 61• Lived in area 20 or more years 47 47• Income level below poverty 20 23• Have health insurance 84 84• Regular place of physician care 97 99

• Chronic illness level • Prevalence 23% 23%• Restricted activity last 2 weeks 5 4• More than 2 weeks in bed last year 4 5

Table 2.1. A Test of Consumer Contribution to Small Area Variations in Health Care Delivery: Randolph and Middlebury, VT (continued)

• Access to physician• Contact with physician within year 73% 73%

• ‘ Post-access’ utilisation of health care• Hospital discharges per 1,000 132 220• Surgery discharges per 1,000 49 80• Medicare Part B spending per Enrollee ($) 92 142

Source: Adapted from Wennberg, J and Fowler, FJ. 1977, A Test of Consumer Contributions to Small Area Variations in Health Care Delivery, Journal of the Maine Medical Association, 68(8):275-279

Unwarranted variation in health care delivery:

• variation that can’t be explained by illness, medical evidence or patient preferences.

The three categories of unwarranted variation in health care delivery

• Effective care- evidence-based care that all with need should receive

• Preference-sensitive care

• Supply-sensitive care

Preference-sensitive care

• Involves tradeoffs − more than one treatment exists and the outcomes are different.

• Decisions should be based on the patient’s own preferences.

• But provider opinion often determines which treatment is used.

Knee replacement: an example of preference-sensitive careRatio of knee replacement rates to the U.S. average (2002-03)

1.30 to 1.78 (40)1.10 to < 1.30 (75)0.90 to < 1.10 (120)0.75 to < 0.90 (46)0.36 to < 0.75 (25)Not Populated

Knee replacement per 1,000 Medicare enrollees

1.0

3.0

5.0

7.0

9.0

11.0

1992-93 2000-01

Red dot = U.S. average: 4.03 5.64 40% increase

Relationship between knee replacement rates among hospital referral regions in 1992-93 & 2000-01

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0

Knee Replacement (1992-93)

Kne

e R

epla

cem

ent (

2000

-01)

R2 = 0.75

Determining the need for hip and knee arthroplasty: The role of clinical severity and patients’ preferences

‘…Among those with severe arthritis, no more than 15% were definitely willing to undergo (joint replacement), emphasizing the importance of considering both patients’ preference and surgical indications in evaluating need and appropriateness of rates of surgery.’

Bottom line implication:

• Clinical appropriateness should be based on sound evaluation of treatment options (outcomes research).

• Medical necessity should be based on informed patient choice among clinically appropriate options (high-quality, shared decision-making).

Supply-sensitive care

• The frequency of use is governed by the assumption that resources should be fully utilised, ie: that more is better.

• Specific medical theories and medical evidence play little role in governing frequency of use.

• In the absence of evidence, and under the assumption that more is better, available supply governs frequency of use.

Hip fractureR2 = 0.06

All medicalconditionsR2 = 0.54

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400

1.0 2.0 3.0 4.0 5.0 6.0Acute care beds

Dis

char

ge r

ate

Association between hospital beds per 1,000 and discharges per 1,000 among Medicare Enrollees: 306 hospital regions

R2 = 0.49Num

ber

of v

isits

to c

ardi

olog

ists

0.0

0.5

1.0

1.5

2.0

2.5

0.0 2.5 5.0 7.5 10.0 12.5 15.0

Number of cardiologists per 100,000

Association between cardiologists and visits per person to cardiologists among Medicare enrollees: 306 regions

Contrasting practice patterns in managing chronic illness during the last two years of life

Regions in highest and lowest utilisation quintiles

Resource input Lowest Quintile

Highest Quintile

Ratio H/L

Medicare $ per capita $38.300 $60,800 1.59

Physician Labor/1,000

All physicians 16.6 29.5 1.78

Medical specialists 5.6 13.1 2.35

Primary care doctors 7.4 11.5 1.55

Relationship between Resource Inputs and Outcomes:Highest versus lowest quintiles of spending

Cohort Health Outcomes

Survival: Worse Functional status: SameSatisfaction: WorsePerceived access: WorseObjective quality: Worse

Evaluating relative efficiency among academic medical centers

in managing chronic illness

End of life care at selected academic medical centers (deaths 2001-05)

Hospital Name

NYU Medical Center

UCLA Medical Center

Brigham and Women's

Johns Hopkins

Tufts-New England

Beth Israel Deaconess

Boston Medical Center

Massachusetts General

Cleveland Clinic

Mayo Clinic (St. Mary's)

University of Wisconsin

TotalMedicarespending

105,068

93,842

87,721

85,729

85,387

83,345

79,672

78,666

55,333

53,432

49,477

End of life care at selected academic medical centers (deaths 2001-05)

Hospital Name

NYU Medical Center

UCLA Medical Center

Brigham and Women's

Johns Hopkins

Tufts-New England

Beth Israel Deaconess

Boston Medical Center

Massachusetts General

Cleveland Clinic

Mayo Clinic (St. Mary's)

University of Wisconsin

TotalMedicarespending

105,068

93,842

87,721

85,729

85,387

83,345

79,672

78,666

55,333

53,432

49,477

Allphysicians

50.8

38.5

29.3

25.7

26.9

27.6

23.1

29.5

26.1

20.3

17.3

End-of-life care at selected academic medical centers (deaths 2001-05)

% of deathswith ICU

admission

35.1

37.9

26.2

23.2

28.5

23.5

28.6

22.5

23.1

21.8

16.1

Hospital Name

NYU Medical Center

UCLA Medical Center

Brigham and Women's

Johns Hopkins

Tufts-New England

Beth Israel Deaconess

Boston Medical Center

Massachusetts General

Cleveland Clinic

Mayo Clinic (St. Mary's)

University of Wisconsin

TotalMedicarespending

105,068

93,842

87,721

85,729

85,387

83,345

79,672

78,666

55,333

53,432

49,477

Allphysicians

50.8

38.5

29.3

25.7

26.9

27.6

23.1

29.5

26.1

20.3

17.3

End-of-life care at selected academic medical centers (deaths 2001-05)

% of deathswith ICU

admission

35.1

37.9

26.2

23.2

28.5

23.5

28.6

22.5

23.1

21.8

16.1

Average co-payments

(past 2 years)

$5,544

4,835

3,729

3,390

3,327

3,338

2,979

3,409

3,045

2,439

2,059

Hospital Name

NYU Medical Center

UCLA Medical Center

Brigham and Women's

Johns Hopkins

Tufts-New England

Beth Israel Deaconess

Boston Medical Center

Massachusetts General

Cleveland Clinic

Mayo Clinic (St. Mary's)

University of Wisconsin

TotalMedicarespending

105,068

93,842

87,721

85,729

85,387

83,345

79,672

78,666

55,333

53,432

49,447

Allphysicians

50.8

38.5

29.3

25.7

26.9

27.6

23.1

29.5

26.1

20.3

17.3

Composite hospital compare technical quality measures for selected academic medical centers (2005)

Hospital Name

NYU Medical Center

UCLA Medical Center

St Mary’s Hospital (Mayo)

University of Wisconsin

Quality Score

88.2%

83.9%

94.1%

91.9%

End-of-life care at selected academic medical centers (deaths 2001-2005)

Academic Medical Center

Spending per decedent

Full-time Equivalent Physicians/ 1000 decedents

ICU deaths per decedent

Copayment per decedent

NYU Med Ctr. $105,068 50.8 FTE 35.1% $5,444

UCLA Med Ctr. 93,842 36.5 37.9 4.835

Mayo Clinic 53,432 20.3 21.8 2,439

U. Wisconsin 49,477 17.3 16.1 2,059

Pathways to Reform

Example of supply-sensitive care

Evaluating relative efficiency in managing chronic illness

Example of preference-sensitive care

Good and bad variation

‘If all variation were bad, solutions would be easy. The difficulty is in reducing the bad variation which reflects the limits of professional knowledge and failures in its application, while preserving the good variation that makes care patient-centred.’

Professor Al Mulley BMJ 2010

Good variation: The NHS Atlas Model