Post on 15-Jan-2016
1
Public Reporting of Long Public Reporting of Long Term Care Quality:Term Care Quality:The US ExperienceThe US Experience
Vincent Mor, Ph.D. Brown University
2
BackgroundBackgroundLong 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
3
BackgroundBackgroundInstitute 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
4
Background (cont.)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
5
PurposePurposeSummarize 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
6
The Nursing Home Resident The Nursing Home Resident Assessment Instrument Assessment Instrument (RAI)(RAI)1986 Institute of Medicine Report on Nursing Home Quality Recommended a Uniform RAI to Guide Care Planning --MDSOBRA ‘87 Contained Nursing Home Reform Act Including RAI RequirementA 300 Item, Multi-Dimensional RAI Tested for 2 Years Mandated Implementation in 1991
7
Clinical Planning Basis of Clinical Planning Basis of the MDSthe 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
8
MDS BackgroundMDS BackgroundMDS 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
9
10
11
MDS: Putting Practice into MDS: Putting Practice into ResearchResearch
12
CMS Quality MeasuresCMS 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
13
CMS Quality Measures - Long CMS Quality Measures - Long TermTerm
Percent of Long-Stay Residents Given Influenza Vaccination During the Flu Season Percent of Long-Stay Residents Given Pneumococcal VaccinationPercent 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 BladderPercent 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 WorsePercent of Residents with a Urinary Tract Infection (Looks back 30 days)Percent of Residents Who Lose Too Much Weight (Looks back 30 days)
14
Physical Functioning- Physical Functioning- October/December 2009October/December 2009
State ADL Worse
Bed Bound
Move Worse
Decline in ROM
National 14.9% 4.7% 14.7% 6.6% AK 17.4% 5.3% 14.6% 10.9% AL 11.6% 6.5% 11.7% 5.4% AR 14.2% 4.2% 12.6% 5.5% AZ 13.9% 4.9% 14.6% 5.5% CA 10.4% 7.1% 11.6% 6.2% CO 15.3% 3.0% 14.9% 6.4% CT 15.4% 2.5% 16.3% 4.8% DC 13.4% 1.7% 13.2% 5.4% DE 13.8% 4.1% 15.5% 8.1% FL 12.7% 4.6% 12.5% 5.2%
15
Psychotropic Drug Use- Psychotropic Drug Use- October/December 2009October/December 2009
State Anti-Psychotics Overall
Anti-Psychotics LOW Risk
Anti-Anxiety Agents
National 18.6% 15.6% 23.1%
AK 11.2% 4.7% 21.5%
AL 15.9% 14.0% 27.2%
AR 17.9% 15.6% 21.1%
AZ 19.2% 15.8% 21.5%
CA 16.8% 14.0% 20.4%
CO 18.6% 15.1% 18.1%
CT 23.7% 21.2% 22.7%
DC 13.6% 12.4% 13.4%
DE 20.2% 17.8% 23.3%
FL 12.2% 10.1% 27.5%
16
CMS Quality Measures – CMS Quality Measures – Short stayShort stayPercent of Short-Stay Residents Given Influenza Vaccination During the Flu Season Percent of Short-Stay Residents Who Were Assessed and Given Pneumococcal VaccinationPercent of Short-Stay Residents With Delirium Percent of Short-Stay Residents in Moderate to Severe Pain Percent of Short-Stay Residents With Pressure Sores
17
Home Health Quality Home Health Quality MeasurementMeasurementOASIS 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
18
Outcome Based Quality Outcome Based Quality ImprovementImprovement
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
19
Risk-adjusted Home Health Outcome Risk-adjusted Home Health Outcome Report for Improvement of Activities of Report for Improvement of Activities of Daily LivingDaily LivingEXAMPLE:
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
20
CMS OASIS Report – 2009CMS OASIS Report – 2009Rates of Improvement in ADLRates of Improvement in ADL
State Grooming Upper Dressing
Lower Dressing
Bathing
Alabama 71.0% 73.0% 75.0% 68.0% Alaska 67.0% 66.0% 56.0% 59.0% Arizona 67.0% 69.0% 67.0% 64.0% Arkansas 68.0% 69.0% 70.0% 65.0% California 71.0% 72.0% 70.0% 67.0% Colorado 70.0% 71.0% 70.0% 64.0% Connecticut 69.0% 69.0% 68.0% 62.0% Delaware 68.0% 69.0% 70.0% 62.0% District of Columbia 75.0% 79.0% 79.0% 72.0% Florida 69.0% 69.0% 68.0% 66.0% Georgia 71.0% 73.0% 74.0% 67.0%
21
Risk-adjusted Home Health Risk-adjusted Home Health Outcome Report for Utilization Outcome Report for Utilization OutcomesOutcomes
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.
22
Risk-adjusted Home Health Risk-adjusted Home Health Outcome ReportOutcome Report
State Any Emergent Care
Discharged to Community
Acute Care Hospital
Alabama 23.0% 64.0% 33.0% Alaska 20.0% 71.0% 25.0% Arizona 25.0% 67.0% 29.0% Arkansas 24.0% 64.0% 32.0% California 18.0% 72.0% 25.0% Colorado 23.0% 69.0% 26.0% Connecticut 27.0% 65.0% 32.0% Delaware 20.0% 70.0% 26.0% District of Columbia 21.0% 71.0% 26.0% Florida 18.0% 70.0% 26.0% Georgia 22.0% 68.0% 29.0%
23
Conceptual Issues Inherent Conceptual Issues Inherent in Applying Quality in Applying Quality IndicatorsIndicatorsRequires “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
24
Technical Issues That Can Technical Issues That Can Compromise Validity of QI’sCompromise 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
25
Reliability Studies: NHReliability 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 nursesPatients resemble traditional US nursing home patient
26
Reliability of “Gold Standard” Reliability of “Gold Standard” NursesNurses
Of 100 items, only 3 didn’t reach Kappa>.450%+ 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
27
Reliability of Facility RNs to Reliability of Facility RNs to “Gold Standard”“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
28
Reliability of Constructed Reliability of Constructed Quality Indicators: NHQuality 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
29
Prevalence and Inter-Rater Agreement and Prevalence and Inter-Rater Agreement and Reliability of Selected Facility Quality Reliability of Selected Facility Quality
Indicators [N=209 homes]Indicators [N=209 homes] Avg. Avg. QI QI
Prev Prev raterate
FacilitFacility Avey Ave
SD of SD of QI QI
Prev Prev raterate
Ave Ave Kappa Kappa
for for Items Items
used in used in QIQI
% % Agree Agree Resch Resch
& & facility facility RNs on RNs on
QIQI
QI QI KappKapp
aa
Behavior Problems Behavior Problems High & Low Risk High & Low Risk Combined Combined
.20.20 .10.10 .71.71 89.889.8 .61.61
Little no activitiesLittle no activities .12.12 .12.12 .28.28 65.365.3 .23.23
Catheterized Catheterized .07.07 .05.05 .71.71 92.592.5 .67.67
Incontinence Incontinence .62.62 .13.13 .88.88 91.491.4 .78.78
Urinary Tract Urinary Tract InfectionInfection
.08.08 .05.05 .53.53 89.189.1 .45.45
Tube FeedingTube Feeding .08.08 .05.05 .73.73 98.198.1 .83.83
Inadequate Pain Inadequate Pain ManagementManagement
.11.11 .08.08 .85.85 86.586.5 .87.87
30
Facility QI Reliability Facility QI Reliability Variation: Bladder/Bowel Variation: Bladder/Bowel IncontinenceIncontinence
kappa Bladder/Bowel Incontinence (High and Low Risk)
1.00
.94
.88
.81
.75
.69
.63
.56
.50
.44
.38
.31
.25
.19
.13
.06
0.00
Nu
mb
er
of
Fa
cilit
ies
70
60
50
40
30
20
10
0
Std. Dev = .21
Mean = .78
N = 209.00
31
Facility QI Reliability Facility QI Reliability Variation: Inadequate Pain Variation: Inadequate Pain ManagementManagement
kappa Inadequate Pain Management
1.00
.94
.88
.81
.75
.69
.63
.56
.50
.44
.38
.31
.25
.19
.13
.06
0.00
-.06
-.13
Nu
mb
er
of
Fa
cilit
ies
30
20
10
0
Std. Dev = .30
Mean = .50
N = 209.00
32
Reliability Studies: Home Reliability Studies: Home HealthHealth
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
33
Selected Inter-Rater Reliability Selected Inter-Rater Reliability Results from OASIS testResults from OASIS test
Signs & Symptoms Sample Size
Percent Agreement
Kappa
1. Diarrhea 304 93.4% 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
34
OASIS Reliability Results: OASIS Reliability Results: FunctionFunction
Variable Sample Size
Percent 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 304 64.8% 0.80
Toileting: Current ability to get to and from the toilet or bedside commode
304 82.6% 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
35
Validity of the Data & Validity of the Data & MeasuresMeasuresValidity 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
36
MDS vs. CMS Hospital MDS vs. CMS Hospital diagnosesdiagnosesNeurological
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
37
Men (CPS 0-1)
Women (CPS 2-4)
Months
One Year Survival by Gender & Cognition Level
38
39
Construct Validity: Cognitive Construct Validity: Cognitive Performance Scale & Performance Scale & CorrelatesCorrelates
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
40
Sample Size and QI StabilitySample Size and QI Stability
Providers and Consumers want QI to reflect not just what WAS but what WILL BE; SOQI stability is desiredQI must be based upon minimum # observationsCorrelation between quarters among QIs variesCorrelation among prevalence based QIs is high because same individuals assessed each quarterCorrelation between quarters among incidence and change based QIs lower and VERY sensitive to sample size
41
Residents’ Expected Rates Residents’ Expected Rates of Change on Quality of Change on Quality IndicatorsIndicatorsOver 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 itemsMeans that rates of decline are low and many residents are needed to estimate a home’s rate of ADL decline with confidence.
42
Estimated Sample Size for Estimated Sample Size for ChangeChange
Facility Size
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
2
6
100 Beds
56
5
2
7
150 Beds
83
7
4
11
200 Beds
117
9
5
14
43
Facility QI Trend: Incidence of Late-Loss ADL Worsening(Stratified by Quality at Baseline)
0.000
0.050
0.100
0.150
0.200
0.250
199
9Q
3
199
9Q
4
200
0Q
1
200
0Q
2
200
0Q
3
200
0Q
4
200
1Q
1
200
1Q
2
200
1Q
3
200
1Q
4
200
2Q
1
200
2Q
2
200
2Q
3
200
2Q
4
200
3Q
1
200
3Q
2
200
3Q
3
200
3Q
4
200
4Q
1
200
4Q
2
200
4Q
3
200
4Q
4
Best-quality at baseline Good-quality at baseline
Mixed-quality at baseline Worst-quality at baseline
Long Term Predictability of Long Term Predictability of QualityQuality
44
1.5
2
2.5
3
3.5
Flu
De
aths
/10
0,00
0 P
opul
atio
n
.105
.11
.115
.12
.125
.13
AD
L D
eclin
e R
ate
2000
Q1
2000
Q2
2000
Q3
2000
Q4
2001
Q1
2001
Q2
2001
Q3
2001
Q4
2002
Q1
2002
Q2
2002
Q3
2002
Q4
2003
Q1
2003
Q2
2003
Q3
2003
Q4
2004
Q1
2004
Q2
2004
Q3
2004
Q4
2005
Q1
2005
Q2
2005
Q3
2005
Q4
ADL Decline Flu Mortality
Figure 2. Quarterly ADL Decline in Nursing Home Residents &Flu Mortality Rates in 122 CDC Monitored Cities: 2000-2005
Quality Fluctuation: Quality Fluctuation: SeasonalitySeasonality
45
Transforming QI Scores into Transforming QI Scores into RanksRanks
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
46
Pressure Ulcer Prevalence Pressure Ulcer Prevalence Facility Distribution: Facility Distribution: Meaning of RanksMeaning of Ranks
47
598 Facilities
Me
dia
n R
an
ks
600
500
400
300
200
100
0
80% Confidence
Intervals
Median
598 Facilities
Me
dia
n R
an
ks
600
500
400
300
200
100
0
80% Confidence
Intervals
Median
Persistent Pain: Median RanksPersistent Pain: Median Ranks Anti-psychotics: Median RanksAnti-psychotics: Median Ranks
Variability in Ranking DistributionsVariability in Ranking Distributions
48
Complexity of Determining Complexity of Determining Appropriate Risk Appropriate Risk AdjustmentAdjustment•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)
49
Risk Adjustment ComplexityRisk Adjustment Complexity
50
Why Adjust QIsWhy Adjust QIsFacilities 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
51
Average Admission Prevalence Average Admission Prevalence of Pressure Ulcers Across All of Pressure Ulcers Across All States, 1999States, 1999
LouisianaDistrict of Columbia
New JerseyMississippi
CaliforniaGeorgia
New YorkWest Virginia
MarylandAlabama
TennesseeSouth Carolina
OklahomaPennsylvania
NevadaIllinoisTexas
FloridaVirginia
KentuckyMichigan
Rhode IslandNorth Carolina
ArkansasOhio
ArizonaDelaware
IndianaHawaii
New MexicoAlaska
MassachusettsMissouri
WashingtonNew Hampshire
ColoradoConnecticut
KansasUtah
OregonWisconsin
IowaMaineIdaho
South DakotaMontana
NebraskaVermont
North DakotaMinnesotaWyoming
Admission Prevalence of Pressure Ulcers in 1999
.3.2.10.0
52
Hospitalization Rate in a 6-Month Period in 2000 Among Long-Stay NH Residents (Who Spent 90+ Days in the Facility)
24.9
23.9
23.2
21.5
21.3
21.2
20.9
20.7
20.1
20.0
19.7
19.4
18.8
18.7
18.3
17.7
17.3
16.9
16.6
16.5
16.3
16.3
16.2
16.1
16.0
15.6
15.5
14.9
14.9
14.6
14.2
14.0
13.7
13.4
12.6
12.3
12.3
11.3
10.8
10.4
10.1
9.5
9.2
9.1
8.9
8.7
8.5
8.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
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
% R
e-H
osp
italiz
ed
Source: MDS 2000; Medicare inpatient claims 2000.
53
54
Multi-dimensionality of QIsMulti-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
55
Does Poor Performance on Does Poor Performance on One Measure Mean NF is One Measure Mean NF is Poor?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?
56
Summary Results of Summary Results of FactoringFactoring
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-depressants
Prevalent Pressure Ulcer (Lo Risk)
Prevalent Anti-Hypnotic Use
Mobility Decline Behavior Problems [Prevalence]
Prevalent Anti-Psychotic Use
Cognitive Decline Worsening Behavior Weight Loss
Communication Decline
Worsening Relationships
Falls
Worsening Pain
57
Regression Modeling ResultsRegression 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
58
Provisional Test of Provisional Test of Combining Unlike Quality Combining Unlike Quality IndicatorsIndicators
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 & Anti-Psych = -.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
59
Public Reporting of QualityPublic Reporting of QualityNURSING 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
60
61
62
63
64
65
66
Effect of Public ReportingEffect of Public ReportingResearch 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
67
Facility Response to Facility Response to ReportingReportingCastle (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
68
Reporting Improve Quality?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
69
Reporting Alter Admissions?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
70
SummarySummary
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
71
Summary (cont.)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
72
Issues for the FutureIssues 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