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pdfORIGINAL CONTRIBUTION
Quality of Medical Care Delivered
to Medicare Beneficiaries
A Profile at State and National Levels
Stephen F. Jencks, MD, MPH
Timothy Cuerdon, PhD
Dale R. Burwen, MD, MPH
Barbara Fleming, MD, PhD
Peter M. Houck, MD
Annette E. Kussmaul, MD, MPH
David S. Nilasena, MD, MSPH, MS
Diana L. Ordin, MD, MPH
David R. Arday, MD, MPH
A
S CONCERN GROWS THAT ATtempts to control the cost of
health care will crowd out
quality, evidence has also
emerged that quality of care is and has
been far more uneven than previously
recognized. The public health report entitled Healthy People 20101 showed wide
gaps between public health performance goals and actual achievements
on many measures, including some
delivered by the fee-for-service (FFS)
health care system. Reviews, most notably by Schuster et al,2 showed that
there were major gaps in acute, chronic,
and preventive care almost everywhere that studies have been done.
More recently, a report from the Institute of Medicine showed serious problems of harm to patients from medical
errors.3 This kind of evidence was reflected in the recommendation of a recent presidential commission that quality of health care should become a major
national priority.4 Despite conditionspecific and managed care–specific reports, there has been no systematic pro-
Context Despite condition-specific and managed care–specific reports, no systematic program has been developed for monitoring the quality of medical care provided
to Medicare beneficiaries.
Objective To create a monitoring system for a range of measures of clinical performance that supports quality improvement and provides repeated, reliable estimates
at the national and state levels for fee-for-service (FFS) Medicare beneficiaries.
Design, Setting, and Participants National study of repeated, cross-sectional observational data collected in 1997-1999 on all Medicare FFS beneficiaries or on a representative sample of beneficiaries with a particular condition. Data were collected using medical record abstraction for inpatient care, analysis of Medicare claims for some
ambulatory services, and surveys for immunization rates. Separate samples were drawn
for each topic for each state.
Main Outcome Measures Beneficiary patients’ receipt of 24 process-of-care measures related to primary prevention, secondary prevention, or treatment of 6 medical
conditions (acute myocardial infarction, breast cancer, diabetes mellitus, heart failure,
pneumonia, and stroke) for which there is strong scientific evidence and professional
consensus that the process of care either directly improves outcomes or is a necessary
step in a chain of care that does so.
Results Across all states for all measures, the percentage of patients receiving
appropriate care in the median state ranged from a high of 95% (avoidance of sublingual nifedipine for patients with acute stroke) to a low of 11% (patients with pneumonia screened for pneumococcal immunization status before discharge). The
median performance on an indicator is 69% (patients discharged with heart failure
diagnosis who received angiotensin-converting enzyme inhibitors; diabetic patients
having an eye examination in the last 2 years). Some states (particularly less populous
states and those in the Northeast) consistently ranked high in relative performance
while others (particularly more populous states and those in the Southeast) consistently ranked low.
Conclusions It is possible to assemble information on a diverse set of clinical performance measures that represent performance on the range of services in a health
insurance program. These findings indicate substantial opportunities to improve the
care delivered to Medicare beneficiaries and urgently invite a partnership among practitioners, hospitals, health plans, and purchasers to achieve that improvement.
www.jama.com
JAMA. 2000;284:1670-1676
gram for monitoring the quality of
medical care provided to FFS Medicare beneficiaries.
Except for the clinical measures of
the Health Plan Employer Data and In-
1670 JAMA, October 4, 2000—Vol 284, No. 13 (Reprinted)
Author Affiliations: Health Care Financing Administration, Baltimore, Md.
Corresponding Author and Reprints: Stephen F.
Jencks, MD, MPH, S3-02-01, Health Care Financing
Administration, 7500 Security Blvd, Baltimore, MD
21244.
©2000 American Medical Association. All rights reserved.
PROFILE OF CARE FOR MEDICARE BENEFICIARIES
formation Set (HEDIS)5 and the Diabetes Quality Improvement Project
(DQIP)6 there is no clinical quality measure set in general national use. About
4 years ago, the Health Care Financing Administration (HCFA) began to
implement a program to measure and
track the quality of the care for which
Medicare pays. Simultaneously, HCFA
committed to using its peer review organization (PRO) contractors to systematically promote improved performance on the quality measures tracked
under this program using a voluntary,
collaborative, and nonpunitive educational strategy.7
This article describes the 24 initial
measures used in this program and reports the baseline values measured in
1997-1999. The Medicare measurement system we developed includes most
of the HEDIS clinical measures, but it addresses more conditions, measures more
elements of care, and measures the care
delivered to the 85% of Medicare beneficiaries who are covered under FFS.
The sampling frame provides statelevel results to target PRO activities,
evaluate PRO and HCFA effectiveness
in improving care, and create a national
picture of care under Medicare FFS.
Even though purchasers and beneficiaries are primarily interested in outcomes, we focused on measuring processes of care critical to outcomes rather
than on measuring outcomes themselves. Five reasons drove this choice:
(1) in comparison to outcomes of care,
there is more consensus on appropriate processes of care and the target rates
(nearly 100%); (2) measuring processes of care generally does not require the risk adjustment that has been
so controversial in comparisons of outcomes; (3) it is easier for providers, practitioners, and plans to identify and fix
the reasons why critical processes of care
were not carried out than to determine
why outcomes are not optimal; (4) many
important outcomes take years; and (5)
because significant, achievable improvements in outcomes are generally much
smaller in relative terms than improvements in processes, unrealistic sample
sizes are necessary to measure signifi-
cant improvements in outcomes. While
we report only process measures here,
HCFA intends to track outcomes, riskadjusted when possible, at the national
level for the targeted conditions.
METHODS
Clinical Topic
and Measure Selection
The clinical topics were selected using
5 criteria: (1) the disease is prevalent and
a major source of morbidity or mortality in the Medicare population; (2) there
is strong scientific evidence and practitioner consensus that there are processes of care that can substantially improve outcomes; (3) reliably measuring
the delivery of these processes is feasible; (4) there is a substantial “performance gap” between current performance and desirable performance; and
(5) there is at least anecdotal evidence
that PROs can intervene effectively to improve performance on the measures. Using these criteria, we adopted or developed 24 process-of-care measures
(TABLE 1) relating to primary prevention, secondary prevention, or treatment of acute myocardial infarction
(AMI), breast cancer, diabetes mellitus,
heart failure, pneumonia, and stroke.
Measures
Each measure is based on professionally developed, widely accepted practice guidelines that were translated into
measures either as part of a larger partnership (HEDIS and DQIP) or national
public health surveillance effort (Behavioral Risk Factor Surveillance System
[BRFSS]) or by HCFA staff in consultation with experts and relevant professional groups. Whenever possible, we
used measures that have wide acceptance and have been used and tested. The
detailed measure specifications and the
scientific evidence supporting each of
these measures is summarized on the
HCFA Web site.8
Acute Myocardial Infarction. We updated and/or expanded measures that
had been used for the Medicare Cooperative Cardiovascular Project.9,10
Heart Failure. We created measures
based on treatment recommendations
©2000 American Medical Association. All rights reserved.
from the American College of Cardiology/American Heart Association and the
Agency for Healthcare Research and
Quality, which were reviewed by clinical expert technical advisory panels and
extensively field tested by PROs.
Stroke. We adapted measures based
on treatment recommendations from
the American College of Chest Physicians, the American Heart Association, the National Stroke Association,
and the American Academy of Neurology; the measures were reviewed by
clinical expert technical advisory panels and extensively field tested by PROs.
Treatment of Pneumonia. We used
measures developed in collaboration
with the American Thoracic Society, the
Infectious Diseases Society of America,
and the Centers for Disease Control and
Prevention; the measures were reviewed by clinical expert technical advisory panels and extensively field
tested by PROs.
Prevention of Pneumonia. We used
outpatient immunization measures in
the BRFSS, which correspond both to
the HEDIS system and to commitments that HCFA has made to Congress under the Government Performance and Results Act and inpatient
measures corresponding to recommendations of the Advisory Committee on
Immunization Practices.
Breast Cancer. We adopted the
breast cancer screening measure used
in HEDIS,5 which measures the percentage of women aged 52 to 69 years
who have received a mammogram in
the past 2 years.
Diabetes. We selected those measures developed by the DQIP that can
be computed from claims data. Indicators based on chart abstraction were not
included because a representative
sample of office records is not currently available to PROs.
Data Sources and Sampling Frame
In all measures except immunization
status, the denominator or sampling
frame is patients enrolled in FFS
Medicare, and Medicare+Choice (managed care) plan members are excluded. All states in the United States
(Reprinted) JAMA, October 4, 2000—Vol 284, No. 13
1671
PROFILE OF CARE FOR MEDICARE BENEFICIARIES
were sampled, plus the District of Columbia and Puerto Rico.
Inpatient Measures (AMI, Heart
Failure, Atrial Fibrillation, Stroke,
Treatment of Pneumonia). We sampled
from Medicare hospital claims data in
each state for each condition. The discharges were eligible for selection only
if the principal diagnosis met the criteria for the target condition, except for
stroke prevention, for which we accepted any diagnosis of atrial fibrillation. We sampled the discharges for a
6-month period within each state. For
a third of the states, this period was
from April to October 1998; for another third of the states, July to December 1998; and for the remaining states,
October 1998 to March 1999. We
sampled up to 850 discharges for AMI,
pneumonia, and stroke, and up to 900
discharges for heart failure and used a
census of all discharges for states with
fewer than the targeted number of
discharges during the period. The
universe of eligible claims was first
sorted by age, race, sex, and hospital,
and cases were then sampled systematically from a random starting point.
Data for the performance measures were
Table 1. Quality Indicators for Care of Medicare Beneficiaries
Clinical Topic
Inpatient setting
Acute myocardial
infarction
Indicator
Short Name
Systematic random
sample of up to 750
inpatient records per
state
All Medicare patients with
principal discharge
diagnosis of heart failure
Systematic random
sample of up to 800
inpatient records per
state
Afibrillation
All Medicare patients with
any discharge diagnosis
of atrial fibrillation
Systematic random
sample of up to 750
inpatient records per
state
Antithrombotic prescribed at discharge
for patients with acute stroke
or transient ischemic attack
Antithrombotic
Avoidance of sublingual nifedipine
for patients with acute
stroke
Nifedipine
Systematic random
sample of up to 750
inpatient records per
state
Antibiotic within 8 h of arrival at hospital
Antibiotic time
Antibiotic consistent with current
recommendations
Blood culture drawn (if done) before
antibiotic given
Patient screened for or given influenza
vaccine
Patient screened for or given
pneumococcal vaccine
Antibiotic Rx
All Medicare patients with
principal discharge
diagnosis of stroke
(nifedipine and
antithrombotic) or
transient ischemic attack
(antithrombotic)
All Medicare patients with a
discharge diagnosis of
pneumonia
Flu immun
Pneu immun
All noninstitutionalized
persons aged $65 y
Behavioral Risk Factor
Surveillance System
Breast cancer
Influenza immunization every year
Pneumococcal immunization at least
once ever
Mammogram at least every 2 y
Mammography
All Medicare claims
Diabetes
Hemoglobin A1c at least every year
HbA1c
Eye examination at least every 2 y
Eye exam
Lipid profile at least every 2 y
Lipid profile
All female Medicare
beneficiaries aged
52-69 y
All Medicare patients with 2
ambulatory diagnoses or
1 inpatient diagnosis of
diabetes
Stroke
Pneumonia
Any setting
Pneumonia
Aspirin 24 h
Aspirin prescribed at discharge
Aspirin disch
Administration of b-blocker within 24 h
of admission
BB 24 h
b-blocker prescribed at discharge
ACE Inhibitor prescribed at discharge for
patients with left ventricular ejection
fraction ,40%
Smoking cessation counseling given
during hospitalization
Time to angioplasty, min
BB disch
Time to thrombolytic therapy, min
Evaluation of left ventricular ejection fraction
Lytic (min)
LVEF
ACE Inhibitor prescribed at discharge
for patients with left ventricular
ejection fraction ,40%
Warfarin prescribed for patients
with atrial fibrillation
ACEI in HF
Data Source
All Medicare patients with
principal discharge
diagnosis of acute
myocardial infarction and
no contraindications
Heart failure
Administration of aspirin within 24 h
of admission
Sampling Frame
for Denominator
ACEI in AMI
Smoking
PTCA (min)
Blood culture
Systematic random
sample of up to 750
inpatient records per
state
Flu screen
Pneu screen
All Medicare claims
*ACE indicates angiotensin-converting enzyme; PTCA, percutaneous transluminal coronary angioplasty; and HF, heart failure.
1672 JAMA, October 4, 2000—Vol 284, No. 13 (Reprinted)
©2000 American Medical Association. All rights reserved.
PROFILE OF CARE FOR MEDICARE BENEFICIARIES
abstracted from the hospital medical
records by 2 clinical data abstraction
centers (which are administratively independent of individual PROs) using
computerized abstraction tools with explicit criteria that were developed and
tested specifically for these measures.
The abstraction tools collected information on contraindications to the
treatment process being studied. Informed consent was not required because the data were collected for administration of the Medicare program,
not for research, and access to these data
is given to the program by law.
Influenza and Pneumococcal Immunization Rates. We used the BRFSS,11
which is coordinated by the Centers for
Disease Control and Prevention and
carried out by state health departments, to estimate statewide vaccination coverage. The BRFSS is a randomdigit-dialed telephone survey of the
noninstitutionalized adult population, and the estimates are for all persons older than 65 years; the national
sample is 26469 for this age group, with
a median state sample of 430 in 1997
(estimated from the 1997 BRFSS Public Use Data File12). The estimates therefore differ from those for other samples
by including beneficiaries who are enrolled in managed care and excluding
persons younger than 65 years old. Rate
estimates reported here are from the
1997 survey. Screening for or administration of influenza and pneumococcal vaccine for inpatients with pneumonia was ascertained from nursing
and physician notes and other information in the medical record.
Breast Cancer (Mammography). The
denominator was all women aged 52 to
69 years who were enrolled in Medicare FFS in both 1997 and 1998.
Whether a mammogram had been performed in the 2 years was determined
by whether Medicare had paid a claim
for a diagnostic or screening mammogram in that period.
Diabetes. The denominator was all
FFS beneficiaries aged 18 to 75 years
who had 2 outpatient claims or 1 inpatient claim with a diagnosis of diabetes mellitus during a 1-year period
starting January 1998-July 1998, with
the start date determined by the date
when the PRO’s contract began in that
state. Whether a service had been provided was determined by whether Medicare had paid a claim for the service.
Statistical Methods
For the inpatient measures, patients
found to have a clinical contraindication to the process of care were either
included as having received appropriate care (heart failure measures) or excluded from both the numerator and denominator (other appropriateness of care
measures). Reliability was calculated as
the percentage agreement on an indicator for 2 blinded, independent abstractions at different abstraction centers. Performance was calculated at the state level
for each of the measures. For 22 measures, results were calculated as the percentage of patients receiving appropriate care; for time to angioplasty or
thrombolytic therapy, the result was calculated as the median number of minutes from arrival at the hospital to beginning of angioplasty or thrombolytic
agent instillation. We primarily direct
our attention to variation among states
(including the District of Columbia and
Puerto Rico). We therefore calculated,
for each measure, performance of the
median state rather than a national average. We also calculated the rank of
each state on each performance measure and then calculated the average
rank for each state across the 22 measures (we excluded time to angioplasty
and time to thrombolytic therapy from
this calculation because the sample size
was too small in many states) and the
SD of the 22 ranks for each state. We
mapped the distribution of average ranks
to display geographic patterns.
RESULTS
Across the 4 inpatient conditions we obtained 94.3% to 99.2% of sampled
records (median, 95.3%). The reliability of measures based on medical record abstraction ranged from 80% to
95% with a median interrater reliability of 90%. TABLE 2 shows the number
of charts in the denominator of each rate
©2000 American Medical Association. All rights reserved.
in 2 ways: the individual rate or time
number is formatted in a type that reflects the number of charts used; the
Table also provides the median number of charts across all states. Even
though more than 700 records were obtained for each condition in most states,
the number of patients who qualified
for a particular indicator was rarely even
half that number and sometimes much
less. Table 2 shows 3 kinds of results:
(1) the performance of the median state
on each measure, (2) the average of
each state’s performance ranks across
the 22 measures, and (3) the rank of
each state among all states based on this
average rank. More detailed results are
available at the HCFA Web site.8
The performance rates in the median state for each of the 22 rate measures range from a high of 95% (avoidance of sublingual nifedipine in acute
stroke) to a low of 11% (patients with
pneumonia screened for pneumococcal immunization status before
discharge). When performance indicators are ranked by the rate in the median state, the median performance is
69% (patients discharged with heart failure diagnosis who received angiotensinconverting enzyme inhibitors; diabetic
patients having an eye examination in
the last 2 years). The range of rates for
each measure also varies widely across
the states, from a low of a 13-percentage point range for avoidance of sublingual nifedipine for patients with acute
stroke (Nevada, 86%; Wyoming, 100%)
to a high of a 54-percentage point range
for antibiotic administered within 8
hours of hospital arrival to patients with
an admission diagnosis of pneumonia
(Puerto Rico, 38%; Montana, 93%). The
median of the ranges for performance indicators (other than time to angioplasty and thrombolytic therapy) is 33
percentage points and the median interquartile range is 8 percentage points.
Table 2 shows the performance of each
state on each quality measure.
Table 2 also shows the average of the
ranking of each state compared with
other states on all of the performance
measures (except time to angioplasty
and thrombolytic therapy) and the SD
(Reprinted) JAMA, October 4, 2000—Vol 284, No. 13
1673
PROFILE OF CARE FOR MEDICARE BENEFICIARIES
Table 2. Rank and Performance on Medicare Quality Indicators by State*
41
Colorado
9
84
17
6
356
93
237
513
138
637
480
333
339
599
420
409
44 430
71
40
40‡
20‡
65
69
55
83
95
85
79
82
14
11
66
46
56
71
69
57
86
55
59
64
35
54
103§
65
62
50
80
96
87
77
79
13
10
63
48
55
58
63
48
96
78
73
82
38
53§
99§
51
91
53
86
93
87
75
93
24
17
58
39
52
69
56
56
86
63
68
69
53
41
94
71
65
56
81
92
82
78
88
27
22
73
59
57
67
65
56
75
78
55
62
57
24
58§
107§
52
64
50
78
92
88
78
82
6
4
61
39
50
57
67
43
35 (12)
85
84
59
68
66
41
36
108
62
65
44
75
87
84
66
86
10
6
66
50
54
65
70
61
18 (10)
86
90
65
76
74
48
39
79
64
72
57
84
94
86
85
85
20
19
74
53
55
78
68
51
Connecticut
Smoking
Lipid Profile
78
72
Eye Exam
75
64
HbA1c
154
85
Lytic (min)
211
84
ACEI in AMI
301
Median cases in
a state, No.
BB Disch
Mammography
California
Pneu Immun†
42 (12)
Flu Immun†
51
Pneu Screen
Arkansas
Flu Screen
87
Blood Culture
23 (13)
Antibiotic Rx
19
Antibiotic Time
Arizona
Nifedipine
87
Antithrombotic
78
24 (16)
Afibrillation
35 (12)
23
ACEI in HF
44
Alaska
LVEF
Alabama
PTCA (min)
25 (12)
BB 24 h
27
Aspirin Disch
Performance in
median state
State
Aspirin 24 h
Average (SD)
on 22 Indicators
Quality Indicators
Overall
Rank
25 627
6
15 (10)
91
91
68
75
75
41
37
108§
78
73
57
90
98
85
74
85
23
18
67
43
60
73
76
62
Delaware
14
20 (11)
85
86
62
73
71
70
41
50§
73
73
50
87
98
82
81
87
13
10
69
53
59
71
75
57
District of Columbia
34
31 (18)
97
83
74
93
74
27
50§
NA
71
76
54
80
99
77
73
67
22
16
54
32
52
60
69
52
Florida
40
34 (14)
77
78
60
69
71
29
36
131§
70
66
56
80
92
76
74
81
6
4
62
46
62
69
75
69
Georgia
47
37 (10)
79
80
62
69
68
34
34
104§
63
67
50
79
91
82
77
80
11
8
59
49
52
63
62
51
Hawaii
22
23 (16)
85
81
56
51
75
36
45§
75§
75
72
46
90
97
90
79
85
11
11
71
52
52
75
69
73
Idaho
21
23 (11)
88
85
70
73
59
56
43
133
54
87
57
80
98
89
78
83
13
10
66
50
53
77
68
59
Illinois
46
36 (10)
75
77
67
56
74
29
38
139
65
60
55
80
92
85
78
77
12
9
68
45
54
63
63
49
Indiana
30
28 (13)
84
87
61
70
68
54
21
161§
65
64
55
82
93
81
79
78
29
26
63
38
54
70
66
59
8
16 (9)
84
86
65
79
75
38
33
120§
53
71
57
83
99
87
78
86
21
19
70
52
60
81
80
61
Kansas
37
32 (15)
79
84
54
59
59
43
45
85§
58
70
51
76
89
89
77
87
19
11
62
44
58
76
75
50
Kentucky
36
32 (11)
80
83
64
74
70
36
31
119§
62
62
51
83
91
83
79
82
17
15
61
39
53
66
66
61
Louisiana
49
40 (11)
81
79
58
73
64
40
33
94
60
47
47
75
94
81
72
83
8
4
58
32
50
57
63
54
3
12 (10)
85
87
80
83
68
61
41
NA
66
72
61
87
97
88
78
86
39
19
72
50
66
76
78
60
27
25 (12)
86
84
68
76
79
40
54
168§
73
66
54
82
98
80
82
80
14
11
63
41
58
71
68
62
5
15 (11)
87
88
73
93
78
44
26
179§
76
63
64
86
96
86
82
86
13
9
66
53
63
80
78
58
Michigan
28
27 (10)
84
86
67
73
74
42
39
139§
69
62
51
79
96
84
71
81
19
14
64
46
64
72
64
55
Minnesota
4
14 (9)
90
89
66
83
80
39
40
97§
61
71
58
88
97
87
75
85
38
22
69
48
61
82
75
59
Mississippi
50
42 (11)
80
78
44
47
61
34
45
378§
58
60
46
75
98
85
78
81
10
5
61
46
47
52
61
39
Missouri
29
28 (12)
76
78
59
72
74
38
27
192
66
58
52
84
92
84
81
77
16
15
70
44
54
75
69
59
Montana
15
21 (14)
86
90
54
72
59
63
47
87
48
71
60
86
96
93
79
88
15
11
68
51
59
70
73
49
Nebraska
26
25 (10)
84
85
66
82
68
37
32
123§
60
66
58
84
89
87
82
82
15
12
66
50
56
75
78
56
Nevada
38
33 (15)
82
80
58
70
77
44
43
195§
77
71
42
78
86
86
80
76
11
10
57
54
50
71
64
62
1
10 (11)
88
90
76
90
81
49
39
418§
79
83
62
85
99
89
75
89
37
19
65
50
63
82
76
60
New Jersey
48
37 (12)
77
74
64
69
61
38
49
101§
72
73
55
73
96
79
74
80
12
8
61
34
50
62
72
66
New Mexico
32
29 (16)
85
87
54
62
77
50
44
118§
58
68
57
78
91
88
71
86
24
18
73
50
51
65
63
50
New York
31
28 (12)
83
81
67
72
75
49
37
140§
75
70
55
81
98
80
71
78
14
12
65
39
56
65
71
56
North Carolina
17
22 (11)
82
90
65
80
77
35
52§
108§
71
79
60
87
97
84
81
79
19
12
65
51
57
73
72
55
North Dakota
7
16 (16)
85
87
69
87
81
29
53
94
41
75
65
86
95
91
86
77
28
19
65
41
64
85
79
67
Iowa
Maine
Maryland
Massachusetts
New Hampshire
Ohio
33
31 (11)
87
86
63
73
72
27
53
72§
70
64
52
80
92
81
72
82
23
13
65
39
56
65
67
52
Oklahoma
42
35 (13)
79
79
46
63
69
25
38
84§
52
66
52
73
90
83
81
86
16
14
69
40
49
70
66
60
Oregon
10
19 (11)
87
84
69
77
70
53
40
102
59
69
57
78
94
90
79
88
14
13
70
56
59
79
71
52
Pennsylvania
18
22 (13)
82
81
71
88
84
42
39
191§
74
73
61
85
99
86
79
88
12
9
66
47
56
69
70
60
Puerto Rico
52
48 (8)
65
60
33
52
59
30
66
NA
44
59
31
73
98
38
55
65
7
5
42
34
45
41
54
43
Rhode Island
24
24 (16)
82
87
76
79
83
26
40
264§
77
80
59
88
95
80
84
81
10
7
68
43
58
71
77
55
South Carolina
35
32 (13)
80
80
58
70
59
24
54
559§
67
66
53
84
99
81
80
85
8
6
74
42
55
70
68
57
South Dakota
25
24 (14)
84
88
69
71
67
37
64
280§
51
65
61
84
90
91
85
84
14
14
66
41
57
78
75
60
Tennessee
39
34 (14)
83
84
56
67
67
44
26
106§
66
51
61
77
94
79
80
78
11
8
69
45
53
66
61
48
Texas
45
36 (14)
78
84
51
58
63
19
39
85§
64
62
45
72
90
80
80
84
12
8
68
44
51
73
68
66
Utah
56
20
23 (12)
83
90
58
68
79
51
50
153
57
78
57
86
92
88
85
82
19
17
66
49
55
79
69
Vermont
2
12 (10)
86
89
78
79
71
59
45
164§
71
77
58
87
98
89
82
89
33
17
70
52
63
83
75
56
Virginia
16
22 (13)
85
83
65
77
67
43
49
186§
77
74
61
90
97
86
84
82
11
10
68
54
55
74
71
60
Washington
13
20 (12)
86
88
67
66
76
60
46
121
63
80
50
83
94
88
73
86
21
16
70
52
59
82
72
59
West Virginia
43
35 (13)
84
85
53
65
64
43
33
212§
62
58
45
86
93
84
80
82
8
6
58
41
55
62
63
52
Wisconsin
11
19 (12)
85
88
70
85
65
42
61§
137§
67
75
60
84
95
87
78
81
23
17
66
43
60
83
74
63
Wyoming
12
19 (18)
91
95
70
62
90
66
35
157§
34
79
58
80
100
92
87
88
13
9
72
51
55
63
68
41
*For an explanation of the indicators, see Table 1. Values in tinted area indicate performance on each quality indicator as the percentage of patients receiving appropriate care
except for times to angioplasty and to thrombolytic therapy, which are reported in minutes. NA indicates not applicable because no cases were reported in that state. Key to
typeface: italic, 1 to 30 cases; regular, 31 to 100 cases; bold, 101-300 cases; bold italic, 301 or more cases.
†Estimates based on 1997 Behavioral Risk Factor Surveillance System public use file.
‡Includes states with a small number of cases (,10); interpret with caution.
§Based on a small number of cases (,10); interpret with caution.
1674 JAMA, October 4, 2000—Vol 284, No. 13 (Reprinted)
©2000 American Medical Association. All rights reserved.
PROFILE OF CARE FOR MEDICARE BENEFICIARIES
of these rankings; these averages of
rankings range from 10 to 48 because
no state is consistently at the top or bottom. Based on the average of the rankings, Table 2 shows the state’s rank
among all states and areas (range, 1-52).
The FIGURE shows that the rankings
tend to follow a geographic pattern with
northern and less populous states more
likely to rank high than southern and
more populous states.
COMMENT
Implications
Previous studies have reported results
using some of the individual measures
reported here,1-4,10 and HEDIS provides
a picture (albeit more limited) of care
in Medicare managed care, but we believe that this is the first study to provide a broad picture of quality of care in
FFS Medicare and the first to include
data that have been verified by chart abstraction of a national sample for several conditions. This study provides
strong evidence of a substantial opportunity to improve the care delivered to
Medicare beneficiaries. Available data
suggest that providing the services measured here could each save hundreds to
thousands of lives a year, but more
precise estimates of the effect of such
improvement on beneficiary health are
beyond the scope of this study.
The differences in average performance among states and regions are
modest compared with the overall need
for improvement. Nevertheless, the data
suggest real underlying geographic differences in the way care is delivered to
the Medicare FFS population. They also
suggest that variations among states on
individual measures are part of a larger
pattern and not simply local variation.
We do not yet understand the reasons
for these differences or whether aspects of the systems in high-performing states can be easily replicated in lowperforming states.
Limitations and Qualifications
These measures give a somewhat unbalanced picture of Medicare services.
They overrepresent inpatient and preventive services, underrepresent am-
bulatory care, and scarcely represent interventional procedures at all.
This article is generally limited to care
delivered in FFS Medicare. Nationally,
about 85% of Medicare beneficiaries are
cared for under FFS and about 15% under managed care, but in Arizona, California, Florida, and Pennsylvania more
than 25% of beneficiaries are enrolled in
managed care. Comparing HEDIS data
from managed care with this FFS data
presents technical problems that we have
not yet solved because denominators
and/or measure definitions differ in the
2 systems. However, the data reported
here for FFS do not differ dramatically
from the HEDIS data reported for Medicare managed care.13
This article is limited to national- and
state-level information. Information on
individual practitioners and providers requires a different and more efficient data
collection and reporting system designed to collect such voluminous data.
Even with practitioner- and providerlevel data, many practitioners and
providers treat too few patients with particular conditions to generate a meaningful sample size, and it will remain difficult to determine which practitioner is
responsible for delivering the process of
care that is measured.
We must also consider the extent to
which these measures fairly represent
quality of care for the services and
population addressed. There are 2 concerns: the validity of the measures as
representations of quality of care and
the accuracy of the data.
Each of the measures is based on both
strong science and professional consensus that delivering the service would either improve outcomes or be necessary
to services that would improve outcomes. Nevertheless, for almost all of the
services, there are circumstances in
which delivering them would be inappropriate. For the inpatient measures,
we included the major contraindications in our abstraction and computational algorithms, but there are likely to
be unusual circumstances that account
for a few cases of undelivered care. The
measures are designed to credit care as
appropriate if there is doubt, and we
©2000 American Medical Association. All rights reserved.
Figure. Average State Rank on 22 Medicare
Performance Measures
Quartile
Rank
First
Second
Third
Fourth
Puerto Rico (not shown) is in the fourth quartile.
know from PRO field experience with
the measures that valid, unmeasured
contraindications are not frequent.
Small numbers are a problem for some
inpatient measures, such as time to angioplasty and thrombolytic therapy, because a relatively small number of the
beneficiaries in our sample received
these services in some states. However,
the effect of small denominators is to increase the variation among states, not to
bias the median downward. We use surveys for influenza and pneumococcal immunization rates because many influenza immunizations are delivered
without claims being submitted to Medicare, and because there is no immediately feasible way to accurately determine pneumococcal immunization
status from existing Medicare claims data
files. Surveys, of course, may have recall and sampling bias, but this does not
appear to be a major problem for the
other measures.
If interrater reliability is 90%, the accuracy of the individual abstractor is
about 95% (each rater accounts for
about half of disagreements between
raters). The range of reliabilities is about
80% to 95%, suggesting that, even for
the most unreliable measure, abstraction errors would not account for a performance level below 90%.
Future Steps
We believe that this article and the
tracking system behind it establish a
(Reprinted) JAMA, October 4, 2000—Vol 284, No. 13
1675
PROFILE OF CARE FOR MEDICARE BENEFICIARIES
mechanism for HCFA to move beyond its historical focus on individual
cases and providers and to take responsibility as a purchaser for the care delivered to the population of Medicare
beneficiaries. Although it is customary to speak of holding providers, practitioners, and health plans accountable for the care they provide, it is at
least as important to hold purchasers,
whether Medicare or Medicaid or commercial or government employers, accountable for the quality of the care they
purchase, because they are making continual and important decisions that potentially balance quality against expenditures. As required by the Government
Performance and Results Act, HCFA is
beginning to assume this responsibility by reporting some of these measures to Congress as part of its annual
budget submission.
HCFA intends to extend the Medicare clinical performance tracking
system in 3 ways. First, for those measures based on medical record abstraction, we are now collecting a continuous sample large enough to provide
accurate trending of national data every
few months, although too small to provide state-level estimates more than every
few years. Second, we will collect enough
data to make accurate state-level estimates every 3 years (synchronous with
PRO contract cycles). This will allow us
to evaluate the success of each PRO in
meeting its major contractual requirement, which is to improve statewide performance on the measures. Third, we will
extend the system to include other settings, such as nursing homes, home
health agencies, and other providers and
to include other clinical priorities.
Obviously, pervasive gaps between
what is being done and what could be
done invite us to consider what policies
might lead to improvements. A future
article will describe the quality improvement strategy that HCFA is pursuing to
improve performance on these and other
measures. Recent reports3,4 have emphasized the importance of focusing on system failure rather than practitioner failure to working to close these performance
gaps. The United States has poured enor-
mous resources into practitioner training and very little into improving processes in the systems within which those
practitioners work, and it is time to
redress that balance. Available evidence
suggests that, at least for preventive
services, systems changes are more
effective than either provider or patient
education in improving provision of
services.14
The data should also remind us of the
need for partnership among HCFA, beneficiaries, practitioners, providers, and
health plans to achieve improvements.
The HCFA PROs are charged with promoting improvement. They now have
performance-based contracts with more
than $200 million a year for improving
performance on the measures reported. Their contracts hold them accountable for successful promotion of
improvement, and there is good evidence that they can contribute to significant improvement in care.10 Nevertheless, neither HCFA nor PROs deliver
care. They can only provide technical assistance to practitioners, providers, and
plans; take steps that will make it easier
for practitioners and providers to deliver and for beneficiaries to receive
needed care; and serve as conveners for
partnerships among local stakeholders. Only practitioners and providers can
make such systems changes as putting
appropriate standing orders in place, installing failure-resistant information systems, and designing processes that deliver critical services within the optimum
window of time. Segmenting improvement efforts according to payment
source is inefficient and counterproductive. Partnerships among all of the stakeholders, regardless of source of payment, can make improvement possible
and are urgently needed.
Funding/Support: All funding for this work was provided by the Health Care Financing Administration.
Disclaimer: The opinions herein are the authors’ and
not necessarily those of the Health Care Financing Administration.
Acknowledgment: The authors especially thank Joyce
V. Kelly, PhD, who coordinates the national PRO quality improvement efforts and Jeffrey Kang, MD, MPH,
without whom this work would not have been possible. We also thank the following individuals: Robert
Peterson, Stephanie Monroe, PhD, and Edwin D. Huff,
PhD, MA, for work on the sampling and sample validation for the inpatient measures; Marjorie Bedinger
1676 JAMA, October 4, 2000—Vol 284, No. 13 (Reprinted)
and James Michael, MS, for validation of the analytic
code and production of the inpatient measures; Cynthia G. Wark, RN, MSN, Martha J. Radford, MD, Harlan M. Krumholz, MD, Deron Galusha, MS, and Jennifer Lewis, BSN, for work on the acute myocardial
infarction measures; D. Jo DeBuhr, Edward P. Havranek,
MD, Harlan M. Krumholz, MD, Frederick A. Masoudi,
MD, and Debra Ralston for work on heart failure measurements; Wato Nsa, MD, PhD, Hui Jiang, MS, Dale
Bratzler, DO, MPH, Claudette Shook, RN, for work on
the pneumonia measurements; Marian Brenton, MPA,
Marc Hendel, MS, June Wilwert, RN, Timothy Kresowik,
MD, and Rebecca Hemann, BLS, for work on the stroke
measurements; Lawrence La Voie, PhD, Rebecca Rogers, and Gary E. Thoni for production of the mammography measurements; David Nicewander for work on
the diabetes measurements; and Kelly Westfall and Pam
Wolfe, MA, MS, for creating the Figure.
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©2000 American Medical Association. All rights reserved.
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