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Managing Cost and Outcome of Managing Cost and Outcome of Post Hospital CarePost Hospital Care
Reg Warren PhD,Rick Glanz and Amy Leibensberger
The National Predictive Modeling SummitSeptember 13, 2007
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The dilemma for post hospital care?The dilemma for post hospital care?
MCO’s spent $7.9B on SNF and Home Health in 2005 (MedPac 2007)
1/3 of hospitalized seniors will receive post acute care
1/3 of those will not go to the most appropriate setting
Misalignment of incentives: per diem High degree of practice variation Over-utilization unnecessarily exposes members
to institutional risks
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Managing Cost and Outcome of Managing Cost and Outcome of Post Hospital CarePost Hospital Care
Diagnosis vs Function The Predictive Model
Regression Severity adjustment
Application Real-time decision support Retrospective comparison
Influence on cost and outcome
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People get postacute care because they are frail and Care Dependent:
Function Driven
People go to the Hospital because they are Sick:
Disease Driven Treat the Illness
Restore the Ability
Acu
teP
osta
cute
Key PredictorKey Predictor
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Functional MeasurementFunctional Measurement
EatingGroomingBathingDressing Upper BodyDressing Lower BodyToiletingBladder ManagementBowel ManagementBed, Chair, WC TransferToilet TransferTub/Shower TransferWalk/WCStairs
• Expression• Comprehension• Social Interaction• Problem Solving• Memory
Functional Independence Measure - FIM (18-126)
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Function Burden of Care Function Burden of Care
Functional needs Drives >90% of skilled utilization Admit function- predict outcome Discharge setting : 5 points FIM equate
to one hour caregiver burden/day
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Calibrate the ContinuumCalibrate the Continuum
Home
Post-acute
Other
HH
SNF
20% of SNF patients would get the same result at home
SNF LOS can be reduced by 40% without impacting functional result
HH Cost can remain stable even with increased referrals
Most Acute Rehab cases would get the same result in SNF
Acute DischargesSr. Population
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Managing Cost and Outcome of Managing Cost and Outcome of Post Hospital CarePost Hospital Care
Diagnosis vs Function The Predictive Model
Regression Severity adjustment
Application Real-time decision support Retrospective comparison
Influence on cost and outcome
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• Leader in post-acute outcome measurement since 1995
• Manage over 900,000 Senior lives in SNF, Acute Discharge, Acute Rehab, Home Health
• Database of 250,000+ post-acute cases• Over 30,000 new records added each year
• California, Colorado, Washington, Maryland, District of Columbia, Virginia, Arizona, Pennsylvania and Tennessee
• Kaiser Permanente, PacifiCare, Health Net, Group Health Coop, and AmeriGroup
• MHS Participant
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Improvement in Function in SNF: Improvement in Function in SNF: PredictablePredictable
ADMFIM
120100806040200
DIS
FIM
140
120
100
80
60
40
20
0
Correlations
1.000 .818**. .000
500 500.818** 1.000.000 .500 500
Pearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)N
ADMFIM
DISFIM
ADMFIM DISFIM
Correlation is significant at the 0.01 level(2-tailed).
**.
80 yr old female with CHF, cellulitis, UTI
and prior stroke
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Discharge Site and Functional LevelDischarge Site and Functional Level(FIM 18-126)(FIM 18-126)
Discharge Site Considerations FIM Score
Home AloneOP Therapy/Home Safety >108
Home with AssistOP (Outpatient) Therapy >90
Home w/Assist or ALFHome Health Services >80
Home, SNF, Custodial, B&C w/24-hour Assistance <79
Diagnosis, medical complexity or other social, caregiver or medical issues may influence the functional level at which the patient is discharged.
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ADMFIM
120100806040200
EPIS
OD
E
120
100
80
60
40
20
0
Length of SNF Stay: Length of SNF Stay: Less Predictable Less Predictable
Correlations
1.000 -.265**. .000
500 500-.265** 1.000.000 .500 500
Pearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)N
ADMFIM
EPISODE
ADMFIM EPISODE
Correlation is significant at the 0.01 level (2-tailed).**.
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Medical Complexity ScaleMedical Complexity Scale
Level 0: No systemic disease other than primary diagnosis
Level 1: Pre-morbid, inactive and/or irrelevant Level 2: Active, relevant. Not limiting function Level 3: Active, relevant. Limiting function Level 4: Active, relevant. Severely limiting function Level 5: Moribund/Terminal
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Relevant ConditionsRelevant Conditions
Restricted Weight BearingRestricted Weight Bearing Pressure Wound: II, III or IVPressure Wound: II, III or IV Vascular (non-pressure) woundVascular (non-pressure) wound IVIV Vent Vent
Hospital: not currently dependent Hospital: not currently dependent Currently DependentCurrently Dependent
Severe ObesitySevere Obesity HemodialysisHemodialysis
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Regression: Length of Skilled StayRegression: Length of Skilled StayIndependent
VariableCoefficient Patient’s
Actual Value
Result
Random Error(constant)
24.45
Admission FIM -.234 65 -15.21
Age .034 82 2.78
Days Post Onset
.565 4 2.26
Condition(IV/Obese)
13.1
Predicted Episode LOS =
27.38
X
X
=
=
=
+
+
+
X =
=+
82 y/o femaleUTIAcute: 6 days
(9.24)
(12.02)
(14.28)
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How powerful is the model?How powerful is the model?
60%
38%
2%
SMTX Model Treatment Unknown
TherapyIntensity
DisabilityCo-morbidityDPODx (stroke)AgeCondition Grouper
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EACH patient is case adjusted:
Impairment Group
Age
DPO
Adm FIM
Med Complex
Pt. #30: CVA ,75 yrs, DPO 12, Adm FIM 30, and Med Complex 4
Pt. # 1: UTI, 82 yrs, DPO 31, Adm FIM 57, and Med Complex 3
Query
LOS: 15 days DC FIM: 50
LOS: 11 days DC FIM: 81
Best Practice Calculation
LOS: 13 days DC FIM: 62.5
LOS: 21 days DC FIM: 50
LOS: 14 days DC FIM: 81
Actual Calculation
LOS: 18 days DC FIM: 62
SMTX National Comparison
(60% most efficient facilities)Actual Values
Pts #2-30
VARIANCELOS: 28%DC FIM: 1%
REGRESSION
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Managing Cost and Outcome of Managing Cost and Outcome of Post Hospital CarePost Hospital Care
Diagnosis vs Function The Predictive Model
Regression Severity adjustment
Application Real-time decision support Retrospective comparison
Influence on cost and outcome
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Expe
ctat
ions
High Practice VariationHigh Practice Variation
Actual Result
Ass
essm
ent
Treatment
Dis
char
ge
Plan
ning
Admission Discharge
Postacute Episode
Full
Rec
over
yN
o R
ecov
ery
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Real-Time Decision Support
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Reduced Practice VariationReduced Practice VariationA
sses
smen
t
Mos
t Lik
ely
Res
ult
Treatment
Expe
ctat
ions
Dis
char
ge
Plan
ning
Actual Result
Admission Discharge
Traditional Timeframe
Value Added
Postacute Episode
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12
Retrospective ComparisonRetrospective ComparisonSeverity-Adjusted ComparisonSeverity-Adjusted Comparison
Case
s in
SM
TX
Reha
b St
art L
ag
Opt
imal
Reh
ab C
ycle
Acut
al R
ehab
Cyc
le
Reha
b Cy
cle
Varia
nce
Disc
harg
e La
g
Reha
b AL
OS
Expe
cted
Dis
char
ge F
IM
Actu
al D
isch
arge
FIM
FIM
Dis
char
ge V
aria
nce
% D
isch
arge
d to
Com
mun
ity
Ther
apy
Hrs
per D
ay
Jefferson Ave 45 1.4 7.9 10.2 28% 1.1 12.7 101.2 96.0 -5% 0.83 0.97
Mercy Court 67 1.3 11.7 12.1 3% 0.8 14.2 77.0 76.1 -1% 0.83 1.43St AllensGarden RidgeSan AngeloPacific CrestCareBrook
Total/Average
Efficiency Quality
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Managing Cost and Outcome of Managing Cost and Outcome of Post Hospital CarePost Hospital Care
Diagnosis vs Function The Predictive Model
Regression Severity adjustment
Application Real-time decision support Retrospective comparison
Influence on cost and outcome
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SNF LOS Variance Trend
-10
0
10
20
30
40
50
July
Aug
Sep Oct
Nov
Dec Ja
n
Feb
Mar
Apr
May
June July
Aug
Sep
t
Month
Perc
ent V
aria
nce/
Que
ry
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Ther
apy
Hour
s/Da
y
cycle dcFIM Hours/Day
Client Q3, 2006 Source: SMTX
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73
96.7 93.2
105.8115.5
5060708090
100110120130140
SNF Admit SNF DC HH Admit HH DC Follow-up
Assessment
Tota
l FIM
Sco
reFunctional recovery Across Settings:
SNF thru HH to Follow-up*11-01 to 9-05
Source: SMTX
SNF: all dc homeHH: all admitted from SNFFollow –up: all records (N=1259)
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Improving Acute DC PlacementImproving Acute DC Placement
49%
58% 58%61% 63%
60%
67%64%
57%
40%
34%37%
32%28% 30% 28% 30% 31%
11%8%
5% 7% 9% 10%5% 6%
12%
0%
10%
20%
30%
40%
50%
60%
70%
80%
J an(N=291)
Feb(N=264)
Mar(N=260)
April(N=247)
May(N=284)
Jun(N=321)
July(N=334)
Aug(N=317)
Sep(N=195)
Home Skilled Other
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Influence on UtilizationInfluence on Utilization
Average Medicare PlanLOS: 22 daysSNF Admits/k: 50-65SNF Days/k: 900-1100PMPM: $33
Predictive Model ResultsLOS: 16 daysSNF admits/k: 40-50SNF days/k: 600- 800PMPM: $22.50
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Reducing Practice Variation Using Reducing Practice Variation Using Predictive ModelsPredictive Models
11
SNF Days of Treatment SNF Days of Treatment
Patie
nt H
ealth
Impr
ovem
ent
(Fun
ction
al Im
prov
emen
t Mea
sure
men
t)
Pre-Implementation Post-Implementation
SNF Days of Treatment SNF Days of Treatment
Patie
nt H
ealth
Impr
ovem
ent
(Fun
ction
al Im
prov
emen
t Mea
sure
men
t)
Pre-Implementation Post-Implementation
$5,655 average cost per case
23 point average gain in Functional Improvement Measurement (“FIM”), an internationally recognized scale of disability
$4,485 average cost per case (20.7% decrease)
23 point average gain in FIM (unchanged)
One Year