Reward Health Sciences, LLC Business Plan · Intervention Design. Cause ‐ Effect Model. includes....

Using Intervention Models and Predictive Models to Optimize Patient Selection for Care

Management in ACOs The Fifth National Predictive Modeling Summit

Washington, DC

Richard E. Ward, MD, MBA Reward Health Sciences, Inc.

November 9, 2011

Health

SciencesHealthHealth

SciencesSciencesREWARDREREWWARDARD

Copyrighted 2011, Reward Health Sciences, Inc.

1

Outline

• How can ACOs reduce cost• IT investment priorities

• Using analytic models – Population Management

– Provider– ACO Financial Models

Copyrighted 2011, Reward Health Sciences, Inc.2

Whatever we call it….

• Accountable Care Organization

• Patient‐Centered Medical Home

• Clinical Integration

• Population Management

• Value Based Health Care

• Managed Care 2.0


Providers

•Taking responsibility for cost and

quality of care for a defined

population of patients

•Working as a team

•Sharing some gains and bearing

some risk

ENABLING INVESTMENTSNEW STRUCTURES

NEW INFORMATION TECHNOLOGYNEW ANALYTIC CAPABILITIES

3

Clinician Workstation‐

Results‐

Profiles‐

To Do List‐

Guidelines

Sources of Cost Savings for ACOs


Cost Impact

Reduce Use of Low

Value Services of

Specialists and

Facilities

PCP Referral Influence

Reduce Rate of

Avoidable Clinical

Events

Patient Self‐Management Support

Care Coordination

Reduce Resources

Per Clinical ServiceLean

Reduce Duplication

of Services

Clinical Decision Support

Health Information Exchange

Provider Consolidation

increasing Market Power

Increase Price per

Clinical Service or

Episode

Delivery System Delivery System

TransformationTransformation

PatientPatient‐‐Centered Centered

Medical HomeMedical Home

andand

Accountable Care Accountable Care

OrganizationOrganization

andand

Meaningful Use of Meaningful Use of

Health Information Health Information

TechnologyTechnology

4

Health Information Technology


Accountable CareAccountable CarePatient CenteredPatient Centered

PopulationPopulationProcessProcess

Guidelines & ProtocolsGuidelines & ProtocolsMeasuresMeasures

Going PaperlessGoing PaperlessClinical Data Accessibility, Efficiency, SecurityClinical Data Accessibility, Efficiency, SecurityOld Vision

New Vision

5

Health Information Technology


ProcessProcess

DataDataOld Vision

New Vision

6

Benchmarks

Goals

Quality & Cost

PerformanceAnalysisLiterature

Expert Opinion

BestPractices

Data

Outcomes

Process

Feedback

Incentives

&Protocols

&

Guide

lines Implementation

HealthCare

Care‐Delivery

Care‐Planning


Systems to Enable Process Transformation

HealthCare

Care‐Delivery

Care‐Planning


Leverage Workflow Automation / Leverage Workflow Automation /

Business Process Mgmt Business Process Mgmt

Technology used in other Technology used in other

industriesindustries

TightlyTightlyIntegratedIntegrated

Care Planning ToolsCare Planning ToolsPatient CenteredPatient CenteredProblem OrientedProblem Oriented

SmartSmartPopulationPopulation

Care ProcessCare ProcessManagement ToolsManagement Tools

Physician controlledPhysician controlledMeasurableMeasurableCoordinationCoordination

8

UnstructuredUnstructured Passively StructuredPassively Structured

• Free text• Dictated and Transcribed• Dictated and voice‐

recognized• Document Images• Optical Character

Recognition

• Drawings• Clinical Images• Sounds

• Text‐to‐code logic• Commands to include text

blocks in notes

• Loose XML messages

Actively StructuredActively Structured

• Registry• Questionnaire• Form‐based Template

Charting

• Problem‐oriented clinical

documentation templates

• Rigorous XML messages

Enables:•Reminders and alerts•Performance measures•Comparative effectiveness

Health Information


Analytic Data RepositoryAnalytic Data Repository

RawRawVersionedVersioned

DataData

Source SystemsSource Systems

Reports &Reports &Reporting Reporting

ApplicationsApplications

out

Analytic Data Repository Framework to Support ACOs

Scheduling

Admit, Discharge,Transfer (ADT)

Billing

MedicationAdministration

Operating Room

Credentialing

Etc.

in

Data Derivation Engines & ServicesData Derivation Engines & Services

Disease ID Risk ScoresGaps in CareEpisodes of

Care

Clinical Data

Repository

Cubes & Other Cubes & Other

SummarySummary

Data StructuresData Structures

Care

Relationships

Specialty /

Peers

ReferralRelationships

Etc.

Derived data

Analyzable Analyzable

DataData

••NormalizedNormalized••DocumentedDocumented••With derived With derived

entities and entities and

attributesattributes



MODELSREPORTS &MEASURES

vs.

Looking back Looking ahead

11

12

Intervention Design

Cause‐Effect Model

includes

Process Model

includes

Intervention Model

informsinformsEvaluation

Plan informs

ProcessMgmtSystem

Configuration

informs

Clinical ProgramOperations

orchestrates

Activity Datacreates

enablesextrapolation of

Calculated ActualOutcomes

to

Projected OutcomesFor Alternative

Intervention Designs

enablescalculation of

supports assumptions

of

confirms plausibility

of

Effect Measurement

informs

informs


Chronic

Conditions

Chronic

ConditionsWellnessWellness Concerns

& Symptoms

Concerns

& SymptomsAcute

Conditions

Acute

Conditions

ElectiveSurgical Conditions

ElectiveSurgical Conditions

Complex

Catastrophic

Conditions

Complex

Catastrophic

Conditions

Continuum of Patient Needs

Using Models for Care Management


14

Is Care Management Effective?

• Are drugs effective?• Is a scalpel effective?

• Which population?• What point in time?• What intervention?• What outcomes of interest?• What time horizon?• What evidence threshold?

It depends


15

TARGETEDHOLISTICCompeting Intervention Design Philosophies

•

Easier to design•

Respects professionalism•

Addresses patient complexity•

Difficult to evaluate

Many “triggers”

General Assessment

Multi‐Issue Care Plan

Intervention Periodas Coach Evolves Goalsand Revised Care Plan

•

Consistent intervention

process enables process

improvement•

Targeting protocol can be

applied to comparison

population for evaluation

Targeting of PatientsBased on Objective CriteriaBased on Opportunity to

Benefit from aparticular intervention

Outreach Protocol

Intervention Protocol


16

Using Intervention Models to Explore Alternative Interventions

Care Transition

Nurse On Site

Care Transition

Nurse On Phone

Identified Population/Spend $100 $100

Patients Identified in when still in hospital

$100 $48

Target Rate$100 $41

Reach and Engagement Rate

Effectiveness Rate in avoiding need for readmission

$65 $13

Total Gross Savings $20 $2

100% 48%

100% 86%

65% 32%

30% 15%

IllustrativeCopyrighted 2011, Reward Health Sciences, Inc.

17

Intervention ModelsAssumptions

Epidemiology‐‐‐‐‐‐‐‐Effectiveness‐‐‐‐‐‐‐‐Economic‐‐‐‐‐‐‐‐Preferences‐‐‐‐‐‐‐‐‐‐

Optimistic Best Pessimistic‐‐‐‐‐‐‐‐‐‐‐‐‐‐

‐‐‐‐‐‐

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

Calculations Results

Can be used to determine:• Optimal targeting threshold• Program dynamics (ramp up)• Uncertainty (ranges)• Geographic critical massCopyrighted 2011, Reward Health Sciences, Inc.

18

Illustrative


19

Illustrative


20

Number of IP admissions per 1000 members identified with CHF, by percentile of risk score

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

0102030405060708090100Percentile of Symmetry risk score

IP A

dmit

Rate

per

100

0

Predicted rate per 1000

Overall IP Rate

Illustrative

Threshold

Y N

0% 10% 20% 30% 40% 50% 100%

Target RateCopyrighted 2011, Reward Health Sciences, Inc.

21

Diabetes Disease Management

(1,000,000)

-

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

0% 5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

Finding Target Penetration that Yields Max Net Savings:

Maximizing Beneficial Impact for Members for the Amount Spent

Gross Savings

Cost

Net Savings

Dollars

41%

Fixed Cost

Illustrative

Target Penetration Rate (as % of Diabetes population)


22


(1,000,000)

-

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

0% 5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%



Gross Savings

Cost

Net Savings

Dollars


41%

Fixed Cost

Threshold

Y N

Illustrative


23


(1,000,000)

-

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

0% 5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%



Gross Savings

Cost

Net Savings

Dollars

41%

Fixed Cost

Illustrative



24

Highest ROI Does Not Yield Maximum Net Savings or

Maximum Penetration Rate for Member Impact


(1,000,000)

-

1,000,000

2,000,000

3,000,000

4,000,000

5,000,0000% 5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

-

0.50

1.00

1.50

2.00

2.50

Gross Savings

Cost

Net Savings

ROIDollars

41%18%

Fixed Cost

ROI*

Increasing the target penetration

rate from 18% to 41% leads to a

lower ROI, but the net savings

increases by 24%.

Illustrative



25

Chronic Disease Management

(0.15)

(0.10)

(0.05)

-

0.05

0.10

0.15

0.20

0.25

- 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

Variable Cost PMPM

Net

Sav

ings

PM

PM IHDCHFDiabetesCOPDAsthma

47% of Ischemic Heart Disease

87% of CongestiveHeart Failure

41% of Diabetes

34% ofCOPD 20% of Asthma

Max Net Savings Signature Illustrative


26

Does global opportunity score / stratification make sense

with targeted interventions?

Intervention Proxy for Return

Care Transition ProgramFor Patients Admitted to Hospital

Probability of Being Re‐AdmittedWithin 30 days of Discharge to Home

Nurse Advice about Pros and ConsOf Spine Surgery

Probability of Getting Back SurgeryIn Next Year

Nurse Coaching to IncreaseChronic Condition Self‐ManagementMotivation and Effectiveness

Probability of Being Admitted toHospital in Next Yearfor Chronic Disease


27

Dynamic Models

• Thinking like an accountant analyzing accounts receivable


28

Model of One Engagement Cohort for a Program Component

Total Savings

$ per 100 Members

Intervention

Engage

Outreach

121110987654321

Months Since Member was Targeted

Capital/Operating Cost, Benefit Savings

Total $ Impact

$ per 100 Members

Targeting Volume

Target

Development Cost

121110987654321

Months Since Started Program Development


Model of Program Component, Rolled‐Out “Go”

Decision

Total Portfolio $

Etc.

Program B

Program A

AprMarFebJanDecNovOctSepAugJulyJunMayAprMarFebJan

20122011


Model of Overall Portfolio of Clinical Programs, in Calendar Time


29

-$5M

$0M

$5M

$10M

$15M

$20M

Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Benefit Cost SavingsOperational CostsInvestment CostsQuarterly Economic Impact

Dynamic Models

Quarterly Economic Impact

2009 2010 2011 2012 2013 2014

Case Management

Illustrative


30

Dynamic Models


2009 2010 2011 2012 2013 2014

ILLUSTRATION

Chronic Condition Management

‐$1.0M

‐$0.5M

$0.0M

$0.5M

$1.0M

$1.5M

$2.0M

$2.5M

$3.0M

$3.5M

Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Benefit Cost Savings

Operational Costs

Investment Costs



31

Dynamic Model of Entire WCM Solution


2009 2010 2011 2012 2013 2014

ILLUSTRATION

‐$3M

‐$2M

‐$1M

$0M

$1M

$2M

$3M

$4M

$5M

$6M

Benefit Cost Savings

Operational Costs

Investment Costs & Core Costs

Net Quarterly Impact


32

Analyzing Uncertainty Using Monte Carlo Simulation

Assumptions

Calculations

90% Interval of Uncertainty


33

Chronic Condition Management—Sensitivity Analysis

2014 Cumulative Net Savings Frequency Distribution 2014 Cumulative Net Savings Variable Sensitivity

Contribution to Variance

Illustrative

1%

1%

1%

1%

2%

17%

71%

7%

0% 20% 40% 60% 80%

Other

Average Length of RegularEngagement Phone Calls (min)

MA PPO Annual Inflation (ProgramCosts) Growth Rate

MA PPO Annual Medical SpendGrowth Rate Above Inflation

Double Counting Assumption

Engagement Rate (% of reachedmembers engaged)

Member Reach Rate (% of targetedmembers reached)

Total Spend Reduction for EngagedMembers

0

200

400

600

800

1,000

1,200

‐$10M $4M $18M $32M $46M $60M

Freq

uency


34

Example of “Hurricane Diagram”

WCM Solution Cumulative Net Savings

90%

Confidence

Note: Based on a Monte Carlo analysis with 10,000 trials, and triangular distributions on 72 input variables for entire portfolio

Range of Outcomes—Cumulative Portfolio Net Savings

ILLUSTRATION

‐$20M

$M

$20M

$40M

$60M

$80M

$100M

$120M

$140M

Jun‐10 Dec‐10 Jun‐11 Dec‐11 Jun‐12 Dec‐12 Jun‐13 Dec‐13 Jun‐14 Dec‐14


35

InghamInghamInghamInghamInghamInghamInghamInghamIngham

KalamazooKalamazooKalamazooKalamazooKalamazooKalamazooKalamazooKalamazooKalamazoo

KentKentKentKentKentKentKentKentKent

OaklandOaklandOaklandOaklandOaklandOaklandOaklandOaklandOakland

WashtenawWashtenawWashtenawWashtenawWashtenawWashtenawWashtenawWashtenawWashtenaw WayneWayneWayneWayneWayneWayneWayneWayneWayne

Modeling Geographically‐Sensitive Interventions

County # Facilities

# Nurse Case Mgrs

Annual Net Savings

Engaged LocallyOakland County 85 10 $ xWayne County 91 8 $ xKent County 22 4 $ xWashtenaw

County 19 3 $ x

Ingham County 7 2 $ xKalamazoo

County 12 2 $ x

Engaged TelephonicallyAll Other

Counties 364 $ x

= Counties targeted locally

ILLUSTRATIVEIn‐HospitalDischarge Planning


36

WashtenawWashtenawWashtenawWashtenawWashtenawWashtenawWashtenawWashtenawWashtenaw

KentKentKentKentKentKentKentKentKentOttawaOttawaOttawaOttawaOttawaOttawaOttawaOttawaOttawa

MacombMacombMacombMacombMacombMacombMacombMacombMacombOaklandOaklandOaklandOaklandOaklandOaklandOaklandOaklandOakland

WayneWayneWayneWayneWayneWayneWayneWayneWayne

Modeling Geographically‐Sensitive Interventions

Top Counties –ILLUSTRATIVE SNF/LTC Spend

County Members SNF-LTC Spend % of SNF Spend

Wayne County 1,281 $ x 10-15%

Oakland County 1,470 $ x 10-15%

Kent County 1,292 $ x 5-10%

Macomb County 778 $ x 5-10%

Washtenaw County 546 $ x 5-10%

Ottawa County 478 $ x 2-5%

Kalamazoo County 330 $ x 2-5%

Ingham County 298 $ x 2-5%

Genesee County 183 $ x 1-2%

Muskegon County 195 $ x 1-2%

Livingston County 166 $ x 1-2%

Jackson County 246 $ x 1-2%

St. Clair County 196 $ x 1-2%

Calhoun County 122 $ x 1-2%

Grand Traverse County 164 $ x 1-2%

Berrien County 153 $ x 1-2%

Saginaw County 185 $ x 1-2%

Eaton County 167 $ x 1-2%

Bay County 97 $ x 1-2%

Allegan County 139 $ x 1-2%

Monroe County 117 $ x 1-2%

Wexford County 52 $ x 1-2%

$ x

$ x

$ x

$ x

$ x

$ x

Total SNF/LTC Spend

Nursing HomeCare Coordination

Illustrative


37

Projected benefit cost savingsAnnual savings by initiative category

$5,006

$6,281

$7,530

$8,472

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

$9,000

2010 2011 2012 2013

Ben

efit

cost

sav

ings

($k)

Service utilization ConditionClinical IT Core clinical processNew group Planned

Projected benefit cost savingsAnnual savings by initiative category as % of

total benefit cost

0.23% 0.24% 0.24% 0.24%

0.18%

0.23% 0.25% 0.25%

0.25% 0.25% 0.25% 0.25%

0.35%

0.40%0.43% 0.44%

0.00% 0.00% 0.00% 0.00%0.0%

0.1%

0.1%

0.2%

0.2%

0.3%

0.3%

0.4%

0.4%

0.5%

0.5%

2010 2011 2012 2013

Ben

efit

cost

sav

ings

(%)

Service utilization ConditionClinical IT Core clinical processNew group Planned

Modeling for Provider‐facing Clinical Programs Savings for Customer X for 41 Initiatives in the BCBSM Physician

Group Incentive

Program

ILLUSTRATIVECopyrighted 2011, Reward Health Sciences, Inc.


MODELSREPORTS &MEASURES vs.

Looking back Looking ahead

38

CareOffered

CareReceived

PhysiologicEffect

Mortality

Reduced,FunctionImproved

CostsSaved orIncurred

PopulationHealthier

PremiumLowered

Process Outcome

• Simpler to define, concrete • Less expensive• Fewer confounding variables• Less measurement variation• Faster improvement cycle• Less problem with turnover

• Measures based on ultimate goals• More intuitive to consumers• Avoids “micro‐management”• Promotes innovation

RE Ward (9/96)

Types of Measures

ReminderSystem

DocumentedStandards

QACommittee

Structure


CareOffered

CareReceived

PhysiologicEffect

Mortality

Reduced,FunctionImproved

CostsSaved orIncurred

PopulationHealthier

PremiumLowered

Process Outcome

Meaningful

Use of

Certified HIT

componentsRE Ward (9/96)

Types of Incentives

ReminderSystem

DocumentedStandards

QACommittee

Structure

Quality ofCare Metrics

ACOMedicare Gain

Sharing


41

Measurement of Outcomes

• Can only measure events that did not happen by comparison

• Two basic types of comparison groups:–Pre‐Post

–Concurrent


42

•

Formal analysis uses more

rigorous methods to deal with

potential confounding

variables and assess

confidence interval.

•

Iterative process requires

methods expertise; impractical

to do over and over for

monthly reporting.

•

Outcome measure defined so as to

be able to define the denominator

population symmetrically for

intervention and comparison

group.

•

Comparison could be historical or

concurrent.

•

Objective is to track actual results

to determine if expected results

are achieved.

Periodic RetrospectiveProgram Evaluation

ConcurrentOutcomes Monitoring

The Levels of Effect Measurement


43

•

Comparison group is not truly

comparable

•

Noise > Signal

•

Noise = “common cause”

or

“random”

variation in people

and their response to disease

and treatment

BIASVARIATION

The Two Key Challenges to Measurement


44

• Risk adjustment does not help.

TightEligibilityCriteria

More ConsistentIntervention(“Lab Conditions”)

Reduce Variation Increase sample size

(“Power”)

Methods to Address Variation


45

Confounding Variables Create Bias

Voluntary

ParticipationIn Disease

Management Program

Personality trait:Tendency to be

proactive and engaged

Example of a CandidateConfounding Variable

People who are

proactive and

engaged are more

likely to say “yes”

when invited to

participate in

voluntary program.

LowerBenefitCost

HypothesizedCause‐EffectRelationship

People who are

proactive and

engaged are more

likely to comply with

medications and take

steps to avoid disease

exacerbations

46

• These only work if you have data for all important candidate confounders.

• Increasing sample size does not help.

Disprove

No assoc withRisk Factor

or Intervention

No CausalAssoc wOutcome

Control

StratificationStatisticalModels

(Regression)

Avoid Confounders w/Randomization

Deal withConfounders

Methods to Address Bias

47

Addressing Bias

Top Five ROI Bias Issues

1.

Regression to the Mean

2.

Biased Secular Trend Adjustment

3.

Once‐chronic‐always‐chronic “migration bias”

4.

Risk Factor Switcharoo

5.

Volunteer Bias with “I did my best”

control for confounding


48

6,533

3,450

-1,0002,0003,0004,0005,0006,0007,0008,0009,000

10,000

Pre Intervention (3 months) Post Intervention (3 month)

Ave

rage

Cos

t Per

Cas

e (P

MP

M)


47.1%Reduction!

$3,083SavingsPer Case!

Medicare Advantage Cases referred between April 2007 ‐

Dec 2008. n=11,768

Case Management – Cost per Case before and after referral

Illustrative


49



Dec 2008. n=11,768


*Post date ranges in relation to 5‐days after targeting.

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Pre61-90

Pre31-60

Pre0-30

Post'0-30

Post31-60

Post61-90

Post91-120

Post121-150

Post151-180

Post181-210

Days in Relation to Targeting for Case Management*

Cos

t Per

Mem

ber P

er M

onth

Engaged

Illustrative


50



Dec 2008. n=11,768


*Post date ranges in relation to 5‐days after targeting.

Engaged

Not Engaged-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Pre61-90

Pre31-60

Pre0-30

Post'0-30

Post31-60

Post61-90

Post91-120

Post121-150

Post151-180

Post181-210

Days in Relation to Targeting for Case Management*

Cos

t Per

Mem

ber P

er M

onth

Illustrative


51

Solution = Outcomes Monitoring with “Re‐qualification”Regression to the Mean

0

50

100

150

200

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350M

on 1

Mon

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Mon

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Pre-Intervention Actual

Pre-Intervention Trend

Illustrative


52

0

50

100

150

200

250

300

350M

on 1

Mon

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Expected Post-Intervention Trend


Ramp‐Up Intervention Steady State

Illustrative


53

0

50

100

150

200

250

300

350M

on 1

Mon

2

Mon

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Post-Intevention Actual



Illustrative


54

0

50

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150

200

250

300

350M

on 1

Mon

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Post-Intevention Actual



Illustrative


55

Applying Outcomes Monitoring to a

Vendor‐delivered Disease Mgmt Program


56

Using Statistical Models


57

Key Conclusions

• Plans and Providers must prepare to

share risk

• Systems should capture Actively Structured Data• Cause‐Effect models

should be developed to support

intervention design, prospective outcomes estimates and

evaluation plan• Intervention Models

should be used to prospectively

estimate outcomes of clinical programs and to determine

optimal targeting• Engagement Cohort method

should be used to model the

dynamics of program ramp‐up and ramp‐down.• Monte Carlo analysis

should be used to assess

uncertainty • Pre‐Post Analysis

without “requalification”

is analytic

malpractice


Thank You!


QuestionsContact Info:

Richard E. Ward, MD, [email protected]

519‐817‐8300

58

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