Public Reporting of Long
Term Care Quality:
The US Experience
Vincent Mor, Ph.D.
Brown University
1
Background
Long history of scandals regarding long term care
quality, particularly nursing homes
While preference for and supply of “community
based” alternatives have grown in US, all
acknowledge residentially based long term care
must be part of any system
Home Health less scrutinized but many worry
about care adequacy since hard to inspect
2
Background
Institute of Medicine Report in 1987 served as
basis for nursing home reform also adopted by
home care
Uniform Resident Assessment Instrument created
in 1991 and became the basis for the creation of
performance measures designed to stimulate
quality competition through public reporting
Home Health Outcome and Assessment
Information Set (OASIS) emerged independently
3
Background (cont.)
Using RAI Nursing Home Quality Measures
tested, revised and published as “Nursing Home
Compare” since 2002
More recent efforts to create composite measure
incorporating Inspection results, Staffing Levels
and Quality Measures have been widely
promulgated
Home Health Quality Measures developed and
tested and published as Home Health Compare
since 2004
4
Purpose
Summarize US Experience with Development of
Long Term Care Quality Measures
Review Conceptual and Technical Issues Facing
the Construction of Long Term Care Quality
Measures
Review Literature on Effects of Public Reporting
of Quality Measures in Long Term Care
5
The Nursing Home Resident
Assessment Instrument (RAI)
1986 Institute of Medicine Report on Nursing
Home Quality Recommended a Uniform RAI to
Guide Care Planning --MDS
OBRA ‘87 Contained Nursing Home Reform Act
Including RAI Requirement
A 300 Item, Multi-Dimensional RAI Tested for 2
Years
Mandated Implementation in 1991
6
Clinical Planning Basis of the MDS
Assessment Profile in Given Domain
“Triggers” Potential “Risk” Status
Resident Assessment Protocol Reviewed to
Determine Presence of Problem or High
Risk of Problem
Care Planning and Treatment Directed to
the Problem
Data Quality Contingent upon conduct of
Clinical Care Planning Process
7
MDS Background
MDS Version 2.0 Introduced in 1996
Admission, Short Term and Quarterly
Reassessments done on all Residents
Inter-State Variation with some requiring
additional data
Since 1998 all MDS records are computerized
and submitted to Centers for Medicare &
Medicaid
8
9
10
MDS: Putting Practice into
Research
11
CMS Quality Measures
“The quality measures, developed under CMS
contract to Abt Associates and a research team
led by Drs. John Morris and Vince Mor, have
been validated and are based on the best research
currently available. These quality measures meet
four criteria. They are important to consumers,
are accurate (reliable, valid and risk adjusted),
can be used to show ways in which facilities are
different from one another, and can be influenced
by the provision of high quality care by nursing
home staff.” CMS Web Site
12
CMS Quality Measures - Long Term
Percent of Long-Stay Residents Given Influenza Vaccination During the Flu
Season
Percent of Long-Stay Residents Given Pneumococcal Vaccination
Percent of Residents Whose Need for Help With Daily Activities Has Increased
Percent of Residents Who Have Moderate to Severe Pain
Percent of High-Risk Residents Who Have Pressure Sores
Percent of Low-Risk Residents Who Have Pressure Sores
Percent of Residents Who Were Physically Restrained
Percent of Residents Who are More Depressed or Anxious (Looks back 30 days)
Percent of Low-Risk Residents Who Lose Control of Their Bowels or Bladder
Percent of Residents Who Have/Had a Catheter Inserted and Left in Their Bladder
Percent of Residents Who Spent Most of Their Time in Bed or in a Chair
Percent of Residents Whose Ability to Move in and Around Their Room Got
Worse
Percent of Residents with a Urinary Tract Infection (Looks back 30 days)
Percent of Residents Who Lose Too Much Weight (Looks back 30 days)
13
Physical FunctioningOctober/December 2009
State
National
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
ADL
Worse
14.9%
17.4%
11.6%
14.2%
13.9%
10.4%
15.3%
15.4%
13.4%
13.8%
12.7%
Bed
Bound
4.7%
5.3%
6.5%
4.2%
4.9%
7.1%
3.0%
2.5%
1.7%
4.1%
4.6%
Move
Worse
14.7%
14.6%
11.7%
12.6%
14.6%
11.6%
14.9%
16.3%
13.2%
15.5%
12.5%
Decline in
ROM
6.6%
10.9%
5.4%
5.5%
5.5%
6.2%
6.4%
4.8%
5.4%
8.1%
5.2% 14
Psychotropic Drug UseOctober/December 2009
State
AntiPsychotics
Overall
AntiPsychotics
LOW Risk
Anti-Anxiety
Agents
National
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
18.6%
11.2%
15.9%
17.9%
19.2%
16.8%
18.6%
23.7%
13.6%
20.2%
12.2%
15.6%
4.7%
14.0%
15.6%
15.8%
14.0%
15.1%
21.2%
12.4%
17.8%
10.1%
23.1%
21.5%
27.2%
21.1%
21.5%
20.4%
18.1%
22.7%
13.4%
23.3%
27.5%
15
CMS Quality Measures – Short stay
Percent of Short-Stay Residents Given Influenza
Vaccination During the Flu Season
Percent of Short-Stay Residents Who Were Assessed
and Given Pneumococcal Vaccination
Percent of Short-Stay Residents With Delirium
Percent of Short-Stay Residents in Moderate to Severe
Pain
Percent of Short-Stay Residents With Pressure Sores
16
Home Health Quality Measurement
OASIS began as a cooperative effort between
home health agencies and researchers to develop
simple “outcome” measures to track patients’ rate
of improvement while in care
University of Colorado researchers worked with
large Visiting Nurse Services to develop and test
CMS then funded multiple large demonstrations
to implement the tool and use for quality
measurement and case-mix reimbursement
17
Outcome Based Quality Improvement
Distinct measures of change in patient
functioning, resolution of symptoms and ability
to manage independently collected at the start
and “end” of care (OR every 60 days)
Most Medicare home health is short term
Measures tested and revised with extensive case
mix adjustment to allow for comparison across
agencies and states
18
Risk-adjusted Home Health Outcome Report for
Improvement of Activities of Daily Living
EXAMPLE:
Percent of Patients in Home Health Care whose
ability to [Groom, Bathe, Dress Upper and Dress
Lower Body] themselves improves between start
of care and discharge
19
CMS OASIS Report – 2009
Rates of Improvement in ADL
State
Grooming
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
71.0%
67.0%
67.0%
68.0%
71.0%
70.0%
69.0%
68.0%
75.0%
69.0%
71.0%
Upper
Dressing
73.0%
66.0%
69.0%
69.0%
72.0%
71.0%
69.0%
69.0%
79.0%
69.0%
73.0%
Lower
Dressing
75.0%
56.0%
67.0%
70.0%
70.0%
70.0%
68.0%
70.0%
79.0%
68.0%
74.0%
Bathing
68.0%
59.0%
64.0%
65.0%
67.0%
64.0%
62.0%
62.0%
72.0%
66.0%
67.0%
20
Risk-adjusted Home Health Outcome
Report for Utilization Outcomes
Percent of patients who have received emergency
care prior to or at the time of discharge from
home health care.
Percent of patients who are discharged from
home health care and remain in the community
Percent of patients who are admitted to an acute
care hospital for at least 24 hours while a home
health care patient.
21
Risk-adjusted Home Health Outcome
Report
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Any
Emergent
Care
23.0%
20.0%
25.0%
24.0%
18.0%
23.0%
27.0%
20.0%
21.0%
18.0%
22.0%
Discharged
to
Community
64.0%
71.0%
67.0%
64.0%
72.0%
69.0%
65.0%
70.0%
71.0%
70.0%
68.0%
Acute Care
Hospital
33.0%
25.0%
29.0%
32.0%
25.0%
26.0%
32.0%
26.0%
26.0%
26.0%
29.0%
22
Conceptual Issues Inherent in
Applying Quality Indicators
Requires “shared” interpretation of Quality
Assumes all Providers have same goals
Assumes Measured Quality Domains are Important
Indicators are NOT Quality per se, BUT often used as
evidence in and of themselves
Assumes Facilities Accountable for most of the variation in
the Indicator (e.g. outcomes)
Assumes Facilities Know how to Change Practice
23
Technical Issues That Can
Compromise Validity of QI’s
•Reliability & Validity of the data
•Multi-dimensionality of Quality & Indicators
•Stability of Estimates Sensitive to Sample Size
•Ranks can Overestimate Differences
•Patient Level Risk Adjustment Complex
•Differences in Assessment Practices Influence
QI Scores & Comparisons
24
Reliability Studies: NH
219 of 462 (47.4%) facilities approached chose to participate
in full study (52.4% for HB and 45.6% for non-HB);
Non-participants were more likely to be for-profit, less well
staffed and with more regulatory deficiencies
5758 patients (ave. 27.5/facility) included in reliability
analyses;
119 patients assessed twice by research nurses
Patients resemble traditional US nursing home patient
25
Reliability of “Gold Standard” Nurses
Of 100 items, only 3
didn’t reach Kappa>.4
50%+ items had Kappa
>.75
Pct. Agreement high even
for ordinal items with
variance
Item
% Agree
Kappa
DNR
91%
.83
Memory
88%
.63
Decisions
97%
.89
Understood
96%
.82
Understand
96%
.80
Fears
97%
.76
Wander
99%
.85
Walk
95%
.86
Pain Fx.
93%
.78
26
Reliability of Facility RNs to “Gold
Standard”
Of the 100 data items 28 had Kappa <.4 and 15
had Kappa >.75
Worst Kappa items were rare binary items like
“end stage”, didn’t use toilet, recurrent lung
aspirations, etc.
ADLs and other Functioning items had Kappa
values above .75
27
Reliability of Constructed
Quality Indicators: NH
Quality Indicators are composites of several RAI
items; a definition of the denominator and of the
conditions required to meet the QI definition
The inter-rater reliability of a QI is a function of
the reliability of all the component items defining
the algorithm
28
Prevalence and Inter-Rater Agreement and Reliability of
Selected Facility Quality Indicators [N=209 homes]
Avg. QI
Prev
rate
Facility
Ave
SD of
QI Prev
rate
Ave
Kappa for
Items
used in
QI
% Agree
Resch &
facility
RNs on
QI
QI
Kappa
Behavior Problems High &
Low Risk Combined
.20
.10
.71
89.8
.61
Little no activities
.12
.12
.28
65.3
.23
Catheterized
.07
.05
.71
92.5
.67
Incontinence
.62
.13
.88
91.4
.78
Urinary Tract Infection
.08
.05
.53
89.1
.45
Tube Feeding
.08
.05
.73
98.1
.83
Inadequate Pain
Management
.11
.08
.85
86.5
.87
29
Facility QI Reliability Variation:
Bladder/Bowel Incontinence
70
60
50
40
30
20
Std. Dev = .21
10
Mean = .78
N = 209.00
0
0.00
.06
.13
.25
.19
.38
.31
.50
.44
.63
.56
.75
.69
.88
.81
1.00
.94
kappa Bladder/Bowel Incontinence (High and Low Risk)
30
Facility QI Reliability Variation:
Inadequate Pain Management
30
20
10
Std. Dev = .30
Mean = .50
N = 209.00
0
-.13
0.00
-.06
.13
.06
.25
.19
.38
.31
.50
.44
.56
.63
.69
.75
.88
.81
1.00
.94
kappa Inadequate Pain Management
31
Reliability Studies: Home Health
Fewer inter-rater reliability studies of OASIS
More expensive to send two nurses at separate
times on the same day to do the same assessment
Largest Reliability Study done as part of research
to develop case-mix reimbursement system
ADL and other function items yield high levels
of reliability; symptoms achieve “ok” reliability
32
Selected Inter-Rater Reliability Results
from OASIS test
Signs & Symptoms
1. Diarrhea
Sampl Percent
e Size Agreement
304
93.4%
Kappa
0.44
2. Difficulty urinating or >=3x/night
304
91.5%
0.45
3. Fever
304
96.7%
0.63
4. Vomiting
304
97.4%
0.49
5. Chest Pain
304
95.4%
0.51
6. Constipation in 4 of last 7 days
304
92.1%
0.53
7. Dizziness or lightheadedness
304
89.1%
0.46
8. Edema
304
81.3%
0.50
9. Delusions
304
99.0%
0.66
10. Hallucinations
304
98.4%
0.44
33
OASIS Reliability Results: Function
Variable
Sample
Percent
Size
Agreement
Kappa
Grooming: Current ability to tend to personal
hygiene needs
304
74.7%
0.83
Dressing: Current ability to dress upper body with
or without dressing aids
304
71.1%
0.83
Dressing: Current ability to dress lower body with
or without dressing aids
304
77.0%
0.85
Bathing: Current ability to wash entire body
Toileting: Current ability to get to and from the toilet
or bedside commode
304
304
64.8%
82.6%
0.80
0.86
Transferring: Current ability to move from bed to
chair, on/off toilet or commode, tub, …
304
74.3%
0.88
Ambulation/Locomotion: Current ability to safely
walk, use a wheelchair…
304
77.6%
0.87
34
Validity of the Data & Measures
Validity of the data shown by the extent to which
items and measures behave as expected relative
to “gold standard” variables or “hard” outcomes
Compared MDS diagnoses to Hospital discharge
diagnoses
Looked at MDS predictors of survival
Related to MDS measures to research scales
35
MDS vs. CMS Hospital diagnoses
Neurological
Cerebrovascular disorders (ICD-9: 432, 434, 436, 437)
• PPV
= 0.73
Parkinson’s disease (ICD-9: 332)
• PPV
= 0.86
Alzheimer’s disease (ICD-9: 331)
• PPV
= 0.68
Brain degeneration (ICD-9: 331.0, 331.2, 331.7, 331.9)
• PPV
= 0.84
36
One Year Survival by Gender & Cognition Level
Women
(CPS 2-4)
Men
(CPS 0-1)
Months
37
Survival Time by CHESS Score and Age
0.70
0.60
Percent Died
0.50
0.40
0.30
0.20
0.10
0.00
CHESS Score/Age Group
<1 year
1-2 years
2-3 years
38
Construct Validity: Cognitive
Performance Scale & Correlates
Cognitive Performance Scale (CPS) Derived from 5 MDS
Items
Strong (>.85) Correlation with MMSE
High Kappa with Global Deterioration Scale (.76)
Percent Patients with Dementia Increases as CPS Declines
MDS Communication Correlated (.85) with MMSE
ADL, CPS Symptoms & Select Diagnoses Related to
Survival
39
Sample Size and QI Stability
Providers and Consumers want QI to reflect not
just what WAS but what WILL BE; SO
QI stability is desired
QI must be based upon minimum # observations
Correlation between quarters among QIs varies
Correlation among prevalence based QIs is high
because same individuals assessed each quarter
Correlation between quarters among incidence
and change based QIs lower and VERY sensitive
to sample size
40
Residents’ Expected Rates of
Change on Quality Indicators
Over 90 day period 77.1% of residents still in
facility do not change on ADL, 14.7% decline
and 8.2% improve.
Over 12 months 58% of residents in home don’t
change and 30.2% decline.
Similar pattern for Communication, Cognition
and individual ADL items
Means that rates of decline are low and many
residents are needed to estimate a home’s rate of
ADL decline with confidence.
41
Estimated Sample Size for Change
Number
Residents
Decline
Estimate
Residents
Expected
to Decline
20th Pctile
Expected
Residents
Declining
80th Pctile
Expected
Residents
Declining
20 Beds
12
1
<1
1
30 Beds
16
1
<1
3
50 Beds
28
2
1
4
80 Beds
45
4
100 Beds
56
5
2
7
150 Beds
83
7
4
11
200 Beds
117
9
Facility
Size
2
5
6
14
42
Long Term Predictability of Quality
Facility QI Trend: Incidence of Late-Loss ADL Worsening
(Stratified by Quality at Baseline)
0.250
0.200
0.150
0.100
0.050
Best-quality at baseline
Mixed-quality at baseline
Good-quality at baseline
Worst-quality at baseline
43
2004Q4
2004Q3
2004Q2
2004Q1
2003Q4
2003Q3
2003Q2
2003Q1
2002Q4
2002Q3
2002Q2
2002Q1
2001Q4
2001Q3
2001Q2
2001Q1
2000Q4
2000Q3
2000Q2
2000Q1
1999Q4
1999Q3
0.000
Quality Fluctuation: Seasonality
Figure 2. Quarterly ADL Decline in Nursing Home Residents &
Flu Mortality Rates in 122 CDC Monitored Cities: 2000-2005
.12
.115
.11
.105
Flu Deaths/100,000 Population
.125
3.5
3
2.5
2
1.5
2000Q1
2000Q2
2000Q3
2000Q4
2001Q1
2001Q2
2001Q3
2001Q4
2002Q1
2002Q2
2002Q3
2002Q4
2003Q1
2003Q2
2003Q3
2003Q4
2004Q1
2004Q2
2004Q3
2004Q4
2005Q1
2005Q2
2005Q3
2005Q4
.13
ADL Decline
Flu Mortality
44
Transforming QI Scores into Ranks
Many QI score distributions are skewed; many
facilities with little or no problem and few
facilities with many residents experiencing the
problem.
Median facility might be very similar to the
“best” (the one with fewest problems)
Transforming to ranks means saying there is a
difference between the 10th and 40th percentile
when there is little difference
45
Pressure Ulcer Prevalence Facility
Distribution: Meaning of Ranks
46
Variability in Ranking Distributions
Anti-psychotics: Median Ranks
Persistent Pain: Median Ranks
600
600
500
500
400
Median Ranks
400
300
300
200
200
100
80% Confidenc e
100
80% Confidence
Intervals
Interv als
0
Median
598 Facilities
0
Median
598 Facilities
47
Complexity of Determining
Appropriate Risk Adjustment
•Risk Factors May not be Measured
Independent of the Provider (tx) Effect
•Potential for Over Adjustment as Great as
Under Adjustment
•How to Adjust for Socio-Economic
Differences Known to Affect Health
Behavior or Clinical Characteristics (e.g. PU
not “seen” on African American NH pts until at Stage 2 OR Pain
Harder to “see” in Cognitively Impaired & Oldest pts)
48
Risk Adjustment Complexity
49
Why Adjust QIs
Facilities should be compared on ‘level playing field’,
acknowledging differences in
 Types of residents admitted
 Ability to ameliorate clinical characteristics thought
to predispose to poor outcomes irrespective of care
quality
 Variability in measurement acumen of assessors
50
Average Admission Prevalence of
Pressure Ulcers Across All States, 1999
Louisiana
Districtof Columbia
New Jersey
Mississippi
California
Georgia
New York
WestVirginia
Maryland
Alabama
Tennessee
South Carolina
Oklahoma
Pennsylvania
Nevada
Illinois
Texas
Florida
Virginia
Kentucky
Michigan
Rhode Island
North Carolina
Arkansas
Ohio
Arizona
Delaware
Indiana
Haw aii
New Mexico
Alaska
Massachusetts
Missouri
Washington
New Hampshire
Colorado
Connecticut
Kansas
Utah
Oregon
Wisconsin
Iow a
Maine
Idaho
South Dakota
Montana
Nebraska
Vermont
North Dakota
Minnesota
Wyoming
0.0
.1
.2
Admission Prevalence of Pressure Ulcers in 1999
.3
51
9.1
9.2
Source: MDS 2000; Medicare inpatient claims 2000.
52
8.3
8.5
8.7
8.9
10.8
10.4
9.5
12.3
16.1
16.2
15.6
15.5
14.2
13.7
12.6
16.3
16.3
14.9
14.0
16.6
16.0
14.6
13.4
12.3
11.3
10.1
10.0
14.9
15.0
16.5
19.4
18.7
17.3
16.9
20.1
18.8
17.7
20.9
20.7
19.7
18.3
21.3
21.5
21.2
20.0
20.0
23.2
23.9
24.9
25.0
LA
MS
NJ
OK
TX
KY
AR
WV
GA
IL
FL
MO
AL
TN
MD
OH
PA
IN
MI
NY
IA
SD
CA
VA
MA
NC
SC
RI
NV
DE
KS
NE
MN
WY
CT
WI
AZ
CO
WA
ND
MT
VT
ID
OR
NH
ME
NM
UT
% Re-Hospitalized
Hospitalization Rate in a 6-Month Period in 2000 Among Long-Stay NH
Residents (Who Spent 90+ Days in the Facility)
30.0
5.0
0.0
53
Multi-dimensionality of QIs
Consumers want to know “Best” nursing home &
Regulators want to know where to focus their
survey energies & Purchasers want to buy best.
If Quality is multi-dimensional no such thing as
the “best”; most valuable dimension is a
preference and will be individualized
Combining QIs that aren’t highly correlated may
mask differences between facilities on important
individual QIs
54
Does Poor Performance on One
Measure Mean NF is Poor?
•Average Correlation Among QIs is Low;
•Anti-Psychotics and Restraints Correlated .04
•What is a “Good” Home if QIs not Related?
•Can Performance Measures Help Pick Good
Homes?
• Are Some Measures More Meaningful?
•Should Users of Performance Measures Select
the Measures they Value Most?
55
Summary Results of Factoring
Functional
Decline
Mood/
Behavior
Pressure
Ulcers
Treatment &
Condition
[No Factor]
Worsening Bladder
Poor Mood State
[Prevalence]
Worsening Pressure
Ulcers
Prevalent Catheter
Worsening Bowel
Worsening Mood
Prevalent Pressure
Ulcer (Hi Risk)
Prevalent Restraint
ADL Decline
Poor Mood w/o Anti- Prevalent Pressure
depressants
Ulcer (Lo Risk)
Prevalent AntiHypnotic Use
Mobility Decline
Behavior Problems
[Prevalence]
Prevalent AntiPsychotic Use
Cognitive Decline
Worsening Behavior
Weight Loss
Communication
Decline
Worsening
Relationships
Falls
Worsening Pain
56
Regression Modeling Results
 Results of relating each QI to all others revealed
very low R2 for all Treatment & Conditions
 While R2 higher for QIs within other factors, many
conceptually unrelated QIs found to weakly predict
other QI
 Many QIs “load” (related to) on multiple factors
 QI “type” (e.g. prevalence, longitudinal, change) as
influential as QI content in factor
 Factor structure sensitive to which QIs included
 Many QIs totally uncorrelated with others
57
Provisional Test of Combining
Unlike Quality Indicators
Use 1999 MDS 2.0 from OH, NY & CA
Create Risk and Admission adjusted QIs for
Pressure Ulcers, Anti-Psychotic Use and Pain
Correlate Measures: PU & Pain<.05; PU & AntiPsych = -.15; Pain & Anti-Psych = .16
Only 13% of facilities in bottom half on all 3
QI’s; 5% if use bottom third on all measures
58
Public Reporting of Quality
NURSING HOME COMPARE allows
consumers and advocates to identify facilities in
their geographic area and to select using a “Five
Star” global rating OR based upon global
domains OR specific measures.
HOME HEALTH COMPARE allows consumers
and advocates to identify agencies in geographic
area and presents detail of many different Quality
Indicators
59
60
61
62
63
64
65
Effect of Public Reporting
Research to date all done on nursing homes
Broken down by market served; long stay
residential vs. short stay, post-acute
First wave of studies surveyed administrators to
find out how they were responding
More recent studies use MDS data to examine
changes in outcomes and admission patterns
66
Facility Response to Reporting
Castle (2005) initially found Administrators were
skeptical and unconvinced that reporting mattered
Zinn & colleagues (2005) surveyed leaders and
found they were aware of their scores and those of
closest competitors; concluded spurred quality
improvement
Castle (2007) concurred in a separate survey that
more competitive markets affected response
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Reporting Improve Quality?
Werner & colleagues (2009) found significant
improvement in BOTH measured and unmeasured
quality measures following public reporting –
BUT general improvement trend
Mukamel et al (2007) looked carefully at initial
response relative to prior quality patterns and also
found improvement on most but not all measures
Werner, et al, 2010 also found improvement in
post-acute quality scores
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Reporting Alter Admissions?
Werner & Colleagues have examined whether
facilities with worse quality scores in competitive
markets manifest reductions in admissions
Very complicated; must infer from the data why
someone entering a facility; should affect those
entering to stay more, but hard to know who
However, evidence suggest small but significant
changes in referral patterns favoring better quality
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Summary
Public Reporting of long term care providers’
quality performance is possible;
All measures are flawed, but no more than acute
and ambulatory care
Pre-requisite is to have uniform data collected
with relevant clinical detail AND should be able
to be audited with penalties to minimize bad data
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Summary (cont.)
Constructing quality measures can be complex
Sample size, seasonality, risk adjustment are all
important to assure the “fairness” of the system
Like case mix reimbursement, don’t want
incentives for providers to limit access to sickest
Still at the infancy of understanding how
consumers & advocates use the data
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Issues for the Future
Preferable to have common items, measures and
metrics across different types of long term care
options, technically AND for consumers
Challenge of Creating Composite Scores
consumers want that are technically less sensitive
than domain specific measures
However, Movement to “Pay for Performance”
requires we develop a solution
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