Managing Cost and Outcome of Post Hospital Care Reg Warren, PhD The National Predictive Modeling Summit September 22-23, 2008

Managing Cost and Outcome of Managing Cost and Outcome of Post Hospital CarePost Hospital Care

Reg Warren, PhDReg Warren, PhD

The National Predictive Modeling SummitThe National Predictive Modeling SummitSeptember 22-23, 2008September 22-23, 2008

The dilemma for post hospital care?The dilemma for post hospital care?

2008 Medicare Advantage enrollment: 8.5 million* 2.2 million hospital admissions in 2008 (260 admits/k) 750,000 of hospitalized seniors will receive post acute care 250,000 of those will not go to the most appropriate setting MCO’s will spend $12.4B on SNF and Home Health in 2008* Challenges

Misalignment of incentives: per diem High degree of practice variation Lack of common measurement across care settings Over-utilization unnecessarily exposes members to

institutional risks and over-burdens taxpayers

*Kaiser Foundation 2008

2


Diagnosis vs. Function A Predictive Model

Regression Severity adjustment

Application Real-time decision support Retrospective comparison

Influence on cost and outcome

3

People get postacute care because they are frail and Care Dependent:

Function Driven

People get postacute care because they are frail and Care Dependent:

Function Driven

People go to the Hospital because they are Sick:

Disease Driven

People go to the Hospital because they are Sick:

Disease DrivenTreat the

Illness

Treat the Illness

Restore the Ability

Restore the Ability

Acu

teA

cute

Pos

tacu

teP

osta

cute

Model ConstructsModel Constructs

4

Functional MeasurementFunctional Measurement

Functional Independence Measure (FIM)(18 – 126)

Expression Comprehension Social Interaction Problem Solving Memory

Eating Grooming Bathing Dressing Upper Body Dressing Lower Body Toileting Bladder Management Bowel Management Bed, Chair, WC Transfer Toilet Transfer Tub/Shower Transfer Walk/WC Stairs

5

Function Burden of Care Function Burden of Care

Functional needs

Drives >90% of skilled utilization Admit function- predict outcome 5 points FIM equate to one hour

caregiver burden/day SNF admission: FIM 65 (6 hrs care/day) HH admission: FIM 85 (2 hrs care /day)

6

Calibrate the ContinuumCalibrate the Continuum

Home

Post-acute

Other

HH

SNF

20% of SNF patients would get the same result at home

20% of SNF patients would get the same result at home

SNF LOS can be reduced by 30% without impacting functional result

SNF LOS can be reduced by 30% without impacting functional result

HH Cost can remain stable even with increased referrals

HH Cost can remain stable even with increased referrals

Many Acute Rehab cases would get the same result in SNF

Many Acute Rehab cases would get the same result in SNF

Acute Discharges

Sr. Population

Acute Discharges

Sr. Population

7

Discharge Site and Functional LevelDischarge Site and Functional Level(FIM 18-126)(FIM 18-126)

Discharge Site Considerations FIM Score

Home Alone

OP Therapy/Home Safety >108

Home with Assist

OP (Outpatient) Therapy >90

Home w/Assist or ALF

Home 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.

8


Diagnosis vs. Function The Predictive Model




9

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 50,000 new records added each year

California, Colorado, Washington, Maryland, District of Columbia, Virginia and Tennessee

Kaiser Permanente, PacifiCare, Health Net, Group Health Coop, and AmeriGroup

MHS Participant

10

““Not everything that counts can Not everything that counts can be counted, and not everything be counted, and not everything that can be counted counts”that can be counted counts”

Albert Einstein

11

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 Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (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

12

ADMFIM

120100806040200

EP

ISO

DE

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 Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

ADMFIM

EPISODE

ADMFIM EPISODE

Correlation is significant at the 0.01 level (2-tailed).**.

13

Specific Relevant ConditionsSpecific Relevant Conditions(Groupers)(Groupers)

Feeding tube (Peg/J vs. NG) Restricted Weight Bearing Pressure Wound: II, III or IV Vascular/ surgical wound IV Vent Severe Obesity (BMI>50) Hemodialysis

14

Regression: Length of Skilled StayRegression: Length of Skilled StayIndependent

Variable*Coefficient Patient’s

Actual Value

Result

Random Error

(constant)

24.08

Admission FIM -.196 65 -12.74

Age .034 82 2.78

Days Post Onset

.074 6 .444

Condition(IV/Stage II)

10.87 1 10.87

Predicted Episode LOS =

25.43

X

X

=

=

=

+

+

+

X =

=

+

82 y/o femaleHip fractureAcute: 6 days

11.34

14.12

14.56

X

*sig p<.05

15

How powerful is the model?How powerful is the model?

60%

38%

2%

SMTX Model Treatment Unknown

TherapyIntensity

DisabilityDPOAgeCondition Grouper

16

EACH patient is case adjusted:

Impairment Group

Age

DPO

Adm FIM

Grouper Condition

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


Best Practice Calculation

LOS: 13 days DC FIM: 62.5

Best Practice Calculation

LOS: 13 days DC FIM: 62.5



Actual Calculation


Actual Calculation


SMTX Best Practice

(25% most efficient facilities) Actual Practice

Pts #2-30

VARIANCELOS: 28%DC FIM: 1%

REGRESSION

17






18

Ex

pe

cta

tio

ns

High Practice VariationHigh Practice Variation

Actual Result

As

se

ss

me

nt

Treatment

Dis

ch

arg

e

Pla

nn

ing

Admission Discharge

Postacute EpisodePostacute Episode

Ful

l R

ecov

ery

No

Rec

over

y

19

Real-Time Decision Support

20

Reduced Practice VariationReduced Practice VariationA

sses

smen

t

Mo

st

Lik

ely

R

es

ult

Treatment

Exp

ecta

tio

ns

Dis

ch

arg

e

Pla

nn

ing

Actual Result

Admission Discharge

Traditional Timeframe

Value Added

Postacute EpisodePostacute Episode

21

22

Retrospective ComparisonRetrospective ComparisonSeverity-Adjusted ComparisonSeverity-Adjusted Comparison

Cas

es i

n S

MT

X

Reh

ab S

tart

Lag

Op

tim

al R

ehab

Cyc

le

Acu

tal

Reh

ab C

ycle

Reh

ab C

ycle

Var

ian

ce

Dis

char

ge

Lag

Reh

ab A

LO

S

Exp

ecte

d D

isch

arg

e F

IM

Act

ual

Dis

char

ge

FIM

FIM

Dis

char

ge

Var

ian

ce

% D

isch

arg

ed t

o C

om

mu

nit

y

Th

erap

y H

rs p

er 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






23

SNF LOS Variance TrendSNF LOS Variance Trend

-10

0

10

20

30

40

50

July

Aug

Sep Oct

Nov

Dec Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

t

Month

Per

cen

t V

aria

nce

/Qu

ery

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Th

erap

y H

ou

rs/D

ay

cycle dcFIM Hours/Day

Client Q3, 2006 Source: SMTX

25% reduction in skilled days

24

73

96.793.2

105.8

115.5

50

60

70

80

90

100

110

120

130

140

SNF Admit SNF DC HH Admit HH DC Follow-up

Assessment

To

tal F

IM S

co

reFunctional recovery Across Settings:Functional recovery Across Settings:

SNF thru HH to Follow-up*SNF thru HH to Follow-up*11-01 to 9-0711-01 to 9-07

Source: SMTX

SNF: all dc homeHH: all admitted from SNFFollow –up: all records (N=1967)

25

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

No difference in HH and SNF outcome or acute readmit

26

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

27

Reducing Practice Variation Using Reducing Practice Variation Using Predictive ModelsPredictive Models

28

SNF Days of Treatment SNF Days of Treatment

Pat

ient H

ealth Im

pro

vem

ent

(Funct

ional

Im

pro

vem

ent M

easu

rem

ent)

Pre-Implementation Post-Implementation

SNF Days of Treatment SNF Days of Treatment

Pat

ient H

ealth Im

pro

vem

ent

(Funct

ional

Im

pro

vem

ent M

easu

rem

ent)

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

Cost efficiency …….Cost efficiency…….Cost efficiency

Documents

Managing Cost and Outcome of Post Hospital Care Reg Warren, PhD The National Predictive Modeling Summit September 22-23, 2008