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Dr Jack Wennberg, founder and director of the Dartmouth Institute for Health Policy and Clinical Practice, and founding editor of the Dartmouth Atlas of Health Care, gives his perspective on the challenges faced by the health system in England in reducing unwarranted variation.
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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
0
50
100
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450Morrisville
1969
Tonsillectomy rate per 10,000 children among 13 Vermont Hospital service areas
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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
0
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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
