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
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Outline
• How can ACOs reduce cost• IT investment priorities
• Using analytic models – Population Management
– Provider– ACO Financial Models
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Whatever we call it….
• Accountable Care Organization
• Patient‐Centered Medical Home
• Clinical Integration
• Population Management
• Value Based Health Care
• Managed Care 2.0
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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
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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
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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
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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
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Systems to Enable Process Transformation
HealthCare
Care‐Delivery
Care‐Planning
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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
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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
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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
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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
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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
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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
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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.
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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.
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Illustrative
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Illustrative
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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.
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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)
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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
Target Penetration Rate (as % of Diabetes population)
41%
Fixed Cost
Threshold
Y N
Illustrative
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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)
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Highest ROI Does Not Yield Maximum Net Savings or
Maximum Penetration Rate for Member Impact
Diabetes Disease Management
(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
Target Penetration Rate (as % of Diabetes population)
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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
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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
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Dynamic Models
• Thinking like an accountant analyzing accounts receivable
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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
Capital/Operating Cost, Benefit Savings
Model of Program Component, Rolled‐Out “Go”
Decision
Total Portfolio $
Etc.
Program B
Program A
AprMarFebJanDecNovOctSepAugJulyJunMayAprMarFebJan
20122011
Capital/Operating Cost, Benefit Savings
Model of Overall Portfolio of Clinical Programs, in Calendar Time
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-$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
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Dynamic Models
Quarterly Economic Impact
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
Quarterly Economic Impact
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Dynamic Model of Entire WCM Solution
Quarterly Economic Impact
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
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Analyzing Uncertainty Using Monte Carlo Simulation
Assumptions
Calculations
90% Interval of Uncertainty
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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
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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
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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
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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
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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.
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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
Copyrighted 2011, Reward Health Sciences, Inc.39
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
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41
Measurement of Outcomes
• Can only measure events that did not happen by comparison
• Two basic types of comparison groups:–Pre‐Post
–Concurrent
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•
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
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•
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
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• Risk adjustment does not help.
TightEligibilityCriteria
More ConsistentIntervention(“Lab Conditions”)
Reduce Variation Increase sample size
(“Power”)
Methods to Address Variation
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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
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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)
Regression to the Mean
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
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Regression to the Mean
Medicare Advantage Cases referred between April 2007 ‐
Dec 2008. n=11,768
Case Management – Cost per Case before and after referral
*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
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Regression to the Mean
Medicare Advantage Cases referred between April 2007 ‐
Dec 2008. n=11,768
Case Management – Cost per Case before and after referral
*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
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Solution = Outcomes Monitoring with “Re‐qualification”Regression to the Mean
0
50
100
150
200
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300
350M
on 1
Mon
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Pre-Intervention Actual
Pre-Intervention Trend
Illustrative
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0
50
100
150
200
250
300
350M
on 1
Mon
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Pre-Intervention Actual
Pre-Intervention Trend
Expected Post-Intervention Trend
Solution = Outcomes Monitoring with “Re‐qualification”Regression to the Mean
Ramp‐Up Intervention Steady State
Illustrative
Copyrighted 2011, Reward Health Sciences, Inc.
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Solution = Outcomes Monitoring with “Re‐qualification”Regression to the Mean
Ramp‐Up Intervention Steady State
Illustrative
Copyrighted 2011, Reward Health Sciences, Inc.
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Solution = Outcomes Monitoring with “Re‐qualification”Regression to the Mean
Ramp‐Up Intervention Steady State
Illustrative
Copyrighted 2011, Reward Health Sciences, Inc.
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Applying Outcomes Monitoring to a
Vendor‐delivered Disease Mgmt Program
Copyrighted 2011, Reward Health Sciences, Inc.
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Using Statistical Models
Copyrighted 2011, Reward Health Sciences, Inc.
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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
Copyrighted 2011, Reward Health Sciences, Inc.
Thank You!
Copyrighted 2011, Reward Health Sciences, Inc.
QuestionsContact Info:
Richard E. Ward, MD, [email protected]
519‐817‐8300
58