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JointJoint Colloquium of the IACA, PBSS and IAAHS SectionsColloquium of the IACA, PBSS and IAAHS Sections of the International Actuarial Associationof the International Actuarial Association
Westin Copley Place Hotel, Boston, U.S.A. –
4-7 May 2008
Paolo Gaudiano, CTO, Icosystem Corporation
Agent-Based Modeling in Health Care
2Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
OverviewOverview
• Predictive modeling
• Agent-based modeling
• The Bean Machine
• Case Studies
• Closing remarks
3Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Predictive Modeling in HealthcarePredictive Modeling in Healthcare
4Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
What is Predictive Modeling?
• What is a model?
• How are models used to make predictions?
Defining Defining ““Predictive ModelingPredictive Modeling””
Predictive modeling is the process of creating or selecting a model to predict the likelihood of an event or outcome.
5Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
What is a model?What is a model?
6Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Is this a model?Is this a model?
7Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
It takes one to know oneIt takes one to know one
8Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
What is a What is a mathematical mathematical model?model?
A mathematical model is an abstract model that uses
mathematical language to describe the behavior of a system.
From: Wikipedia, the free encyclopedia
9Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Types of mathematical modelsTypes of mathematical models
Mathematical models typically focus on a specific functional form or process, e.g.:
• Linear models
• Generalized linear models
• Hidden Markov models
10Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Using mathematical models for predictionUsing mathematical models for prediction
Steps for mathematical modeling
1. Determine what model type to use
2. Determine what data to use
3. Adjust model parameters to fit the available data
4. Extrapolate to estimate future outcomes
11Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
An exampleAn example
DataModel
Prediction
Accurate fit to historical data does not guarantee predictive accuracy!
Accurate fit to historical data does not guarantee predictive accuracy!
12Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Some (personal) issues with mathematical modelsSome (personal) issues with mathematical models
• The model itself becomes the focus: is it linear? is it exponential? is it logit?
• Unclear connection between statistical accuracy and “true model accuracy”, e.g., r2 =0.5
• The more complex the model, the more data are required to calibrate it
• Models are unable to predict the future in the presence of significant discontinuities (correlation vs. causation)
• Many statistical models were designed prior to the advent of the computer
13Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
IcosystemIcosystem’’s approach to predictions approach to prediction
14Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
A betterA better approachapproach
Develop world-class, custom computer models of complex systems
• Extract domain information about problem
• Identify “correct” elements for simulation
• Blend scientific rigor, experience and intuition
• Take advantage of the power of today’s computers to capture complexity instead of simplifying!
15Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
What is a What is a complex systemcomplex system??
A system consisting of many elements is complex if its overall
behavior emerges from the behavior of the individual elements
and their interactions.
16Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Sample systems with emergent behaviorsSample systems with emergent behaviors
a traffic jam, a financial market, a crowd of people, a
sports event, a termite nest, a flock of birds,
…
a traffic jam, a financial market, a crowd of people, a
sports event, a termite nest, a flock of birds,
…
17Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Emergent behavior: the Icosystem gameEmergent behavior: the Icosystem game
18Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Computer simulation of the Icosystem gameComputer simulation of the Icosystem game
A is the aggressor, B the defender, try to keep B between you and A
A is the aggressor, you are the defender, keep yourself between A and B
See demo at http://www.icosystem.com/game.htm
http://www.icosystem.com/game.htm
19Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Why is this interesting?Why is this interesting?
The Icosystem Game: a deceptively simple complex system
• Knowing the behavior of every individual is not enough to predict system behavior
• Slight changes in rules or interactions can lead to dramatic changes in system behavior
• The traditional reductionist (i.e., divide-and- conquer) does not work!
20Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Controlling emergent behaviorControlling emergent behavior
• How can we control emergence?
• How do we define individual behaviors and interactions to produce desired emergent patterns?
“Here is where we think the
problem is...
21Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
AgentAgent--based modelingbased modeling
• Shift viewpoint from system (centralized) to individual elements (de- centralized)
• Each agent follows local rules
• Behavior depends on interactions with other agents
• Overall system behavior emerges from local interactions
22Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
AgentAgent--based modeling analogybased modeling analogy
• Simulate individual behaviors
• Capture key elements of agents
• Simulate interactions between agents
• Let the simulation unfold over time
• Look for patterns and trends
23Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
The Bean Machine:The Bean Machine:
Statistics vs. AgentStatistics vs. Agent--Based ModelingBased Modeling
24Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
The Bean MachineThe Bean Machine
• Developed by Sir Francis Galton to illustrate the Law of Error and the Normal Distribution
• The machine consists of a vertical board with interleaved rows of pegs
• Marbles are dropped from the top, and bounce randomly left and right as they hit the pegs
• Marbles collect into bins at the bottom
• The height of the marbles in the bins approximates a normal curve
25Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
The Icosystem Bean MachineThe Icosystem Bean Machine
• Pegs can be biased to the left or to the right
• The emergent distribution resulting from peg biases is observed at the bottom
• Two primary uses:
1. Given a certain set of biases, what will be the resulting distribution?
2. Given an observed distribution, what sort of biases might have given rise to it?
26Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Can you Can you explainexplain the resulting distributions?the resulting distributions?
1 2 3 4
Compare to statistical approach: select a function, adjust parameters to fit distribution.
Compare to statistical approach: select a function, adjust parameters to fit distribution.
27Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
How does this help predictive modeling?How does this help predictive modeling?
• The bottom-up approach to modeling explains how behaviors lead to observed outcomes
• The resulting distribution emerges from behaviors, interactions and context
• The model can be used “in reverse” to try to determine behaviors that can explain the observed outcomes
• The model makes it possible to ask natural questions, e.g.: “how much benefit do we get from intervening earlier?” or “how much do I need to influence this stage to get an x% improvement?”
28Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Sample Case StudiesSample Case Studies
PROPRIETARY29
Understanding supermarket shopper behaviorUnderstanding supermarket shopper behavior
Parking lot
Produce
Deli
RegistersEntrance
Fro
zen
foo
d
CS
D
entrance
targetClient: PepsiCo - a Fortune 500 consumer goods company
Challenge: Understand behavior of shoppers moving through a supermarket •How do they navigate?•What do they purchase?•Where to place products?
Complexities:•Correlate cart tracking data with consumer behavior •Predict behavior for novel supermarket configurations
Client: PepsiCo - a Fortune 500 consumer goods company
Challenge: Understand behavior of shoppers moving through a supermarket•How do they navigate?•What do they purchase?•Where to place products?
Complexities:•Correlate cart tracking data with consumer behavior•Predict behavior for novel supermarket configurations
PROPRIETARY30
Predicting Health Insurance enrollmentPredicting Health Insurance enrollment
Client: Humana, Inc. (a Fortune 500 Health Insurance Co.) Challenge:Increase prediction accuracy for health plan enrollment •What factors drive plan selection? •Impact of employer contribution?Complexities:•Many plan options across industries, locations •No historical data for new plans•Slow sales process
Icosystem’s predictive engine now rolled out to sales force, see
press release on Humana & Icosystem web sites
Client: Humana, Inc. (a Fortune 500 Health Insurance Co.)Challenge:Increase prediction accuracy for health plan enrollment•What factors drive plan selection?•Impact of employer contribution?Complexities:•Many plan options across industries, locations•No historical data for new plans•Slow sales process
Icosystem’s predictive engine now rolled out to sales force, see
press release on Humana & Icosystem web sites
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
RiskRisk
CostsCosts BenefitsBenefits
Select PlanSelect Plan
OfferingsOfferings
PROPRIETARY31
The Impact of social nets on product adoptionThe Impact of social nets on product adoption
Client: Fortune 500 drug company puzzled by variability of drug adoption across clinics.
Challenge: Why do some clinics prescribe this emergency room drug much more readily than others, even with similar marketing efforts?
Outcome: Identified different clinic types based on social interactions due to scheduling and patient volume. Clinic social networks correlate well with required marketing effort.
Client: Fortune 500 drug company puzzled by variability of drug adoption across clinics.
Challenge: Why do some clinics prescribe this emergency room drug much more readily than others, even with similar marketing efforts?
Outcome: Identified different clinic types based on social interactions due to scheduling and patient volume. Clinic social networks correlate well with required marketing effort.
Fully connected:
Rapid spread of new ideas.
Sparsely connected:
Repeated efforts required for
spread.
Mostly disconnected:
Each party needs individual attention.
PROPRIETARY32
Medicare Product Launch and DesignMedicare Product Launch and Design
Client: Humana, Senior Products Division
Challenge: Plan marketing strategy for new Medicare products in 2006
Outcome:
• Helped planning for new product launch - Humana now #2 in Medicare space
• Extended the same tool to assist with product design
• Used tool to identify operational issues
Client: Humana, Senior Products Division
Challenge: Plan marketing strategy for new Medicare products in 2006
Outcome:
• Helped planning for new product launch - Humana now #2 in Medicare space
• Extended the same tool to assist with product design
• Used tool to identify operational issues
PROPRIETARY33
Treatment ComplianceTreatment Compliance
Client: Fortune 100 Pharma with an antiviral drug, confused as to why so few patients seek treatment.
Challenge: What makes patients enter a complex treatment regime and comply with it over a long period of time?
Outcome: Discovered unexpected pockets of opportunity in diagnostic procedures and challenged client assumptions about leverage points.
Client: Fortune 100 Pharma with an antiviral drug, confused as to why so few patients seek treatment.
Challenge: What makes patients enter a complex treatment regime and comply with it over a long period of time?
Outcome: Discovered unexpected pockets of opportunity in diagnostic procedures and challenged client assumptions about leverage points.
Prioritization Matrix from Customer ReportPrioritization Matrix from Customer Report
Ability to WinAbility to Win
Size
of O
ppor
tuni
tySi
ze o
f Opp
ortu
nity
22
HighHigh
MedMed
LowLow
11
3*3*
7/87/8
1010
1111
99
1212
1313
11 22 33 44 55
6a6a
--
1414
6b6b
11
223*3* 6a6a6b6b
7/87/8
991111 1212
13131414
1010
Icosystem Results MatrixIcosystem Results Matrix10001000
100100
11.0001.0001 .001.001 .01.01
11
22
6a6a6b6b
7/87/899
111112121414
1010
1010
SensitivitySensitivityPo
tent
ial (
% in
crea
se o
f pat
ient
s)Po
tent
ial (
% in
crea
se o
f pat
ient
s)
11, 12, 1311, 12, 13
3*3*
PROPRIETARY34
Drug development portfolio processDrug development portfolio process
Client: Fortune 100 Pharma
Challenge: Redesign drug development process to reduce complexity, costs and time while improving success.
Approach: Detailed simulation of decision-making and processes and visualization to identify sources of inefficiencies and design/test a more flexible networked, portfolio-centric clinical development organization.
Outcome: New approach tested on small portfolio, recently (‘05) expanded to broader portfolio
Client: Fortune 100 Pharma
Challenge: Redesign drug development process to reduce complexity, costs and time while improving success.
Approach: Detailed simulation of decision-making and processes and visualization to identify sources of inefficiencies and design/test a more flexible networked, portfolio-centric clinical development organization.
Outcome: New approach tested on small portfolio, recently (‘05) expanded to broader portfolio
Asset Asset Group EstimateNPLC
Approval
Implement Work
Interpretable Data
Uninterpretable Data
Yes
NoReplan
DMS Approval Yes
NoReplan
TOXADME Approval Yes
NoReplan
SAFE Approval Yes
NoReplan
TASC/BPC Approval Yes
NoReplan
PCAT II Approval Yes
NoReplan
DDOC Approval Yes
NoReplan
Complex Governance
Inefficient Resourcing
Difficult Planning
Networked Organization
ProblemSet
Definition
ScenarioGeneration Module
Definition
PortfolioScheduling
ModuleImplement
NewData
Analysis
ResourceContract
35Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Detailed CaseDetailed Case StudiesStudies
36Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
AgentAgent--based modeling for healthcarebased modeling for healthcare
Two examples of healthcare applications:
• How physician scheduling in hospitals impacts prescribing behavior
• The impact of social interactions on the perception of healthcare products for seniors
37Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Sample application #1 Sample application #1
How physician scheduling in hospitals How physician scheduling in hospitals impacts prescribing behaviorimpacts prescribing behavior
38Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Problem statementProblem statement
• Large Pharma company observed different prescription rate for ER drug at different hospitals.
• Sales/promotional activities were uniform.
• What drives different adoption rates at different hospitals?
39Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
A simplistic view of the prescribing dynamicA simplistic view of the prescribing dynamic
message
frequency
Sales rep
opinion
Physician
compliance
Patient
40Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
The reality of drugThe reality of drug--based healthcare is complexbased healthcare is complex
message
frequency
Sales rep
Other patients Care
givers
message
frequency
Sales rep
opinion
Physician
compliance
Patient
substitution
Pharmacy
Physician thought leaders
Physician colleagues
Other medical
colleagues
Competing sales reps
formulary
Health insurer
Treatment support teams
Web detailing
41Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Top prescribing clinics, can be
classified by social network type,
through analysis of observable data.
Once classified, the network can be
modeled to predict the response to
proposed marketing efforts.
Social networks to classify clinic typesSocial networks to classify clinic types
42Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
These types of clinics respond differently to marketing activities!
Fully connected: Rapid spread of
new ideas.
Sparsely connected: Repeated efforts
required for spread.
Mostly disconnected: Each party needs
individual attention.
Three general types of clinics observedThree general types of clinics observed
43Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Drivers of social network structure
Shift structures for staff.
Patient volume.
Observability of patient benefit.
Numbers of attending physicians
Socializing opportunities.
Physical layout of building.
VERY STRONG ++++
STRONG +++
MODERATE ++
MODERATE ++
WEAK +
STRONG +++
Social network Social network ““driversdrivers”” can be identifiedcan be identified
44Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Sample application #2 Sample application #2
The impact of social interactions on The impact of social interactions on the perception of healthcare products the perception of healthcare products
for seniorsfor seniors
45Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Predictive modeling for MMA Part DPredictive modeling for MMA Part D
Problem statement:
• The implementation of MMA Part D will cause a profound discontinuity in the Medicare landscape.
• The lack of precedent leads to uncertainty.
• Market research can help -- but how?• What existing data can be used to gain insights?
• What data should be collected after implementation?
• How should data be used: Marketing campaign? Product design? Competitive strategy?
46Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
In reality: many events affect senior opinionIn reality: many events affect senior opinion
Go to seminar Go to
seminar
Contact service & guidance
professionals
Contact service & guidance
professionals
Visit doctorVisit doctor
Evaluate Plan Benefits
Evaluate Plan Benefits Interact
with peers Interact
with peers
Fill prescription
Fill prescription
View information
View information
Go to hospitalGo to hospital
Talk with family members
Talk with family members
47Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
The model: highThe model: high--level summarylevel summary
HMO
FFSPPO
Plans
Selection
Service & Guidance Professionals
SocialInteractions
Marketing
Providers
48Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
1. SimSenior: key factors1. SimSenior: key factors
Personal factors:
• Age
• Income
• Marital status
• Social network
• Physician
Healthcare factors:
• Medical condition
• Current coverage
• Medical expenses
• Prescription expenses
Subjective factors:
• Brand perception
• Sensitivity to plan benefits
• Inertia
49Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Marketing2. The influence of information2. The influence of information
• Brand ads affect brand perception
• Product ads affect the senior’s sensitivity to specific plan benefits
• Seminars affect• Brand perception• Perceived importance of the plan benefits highlighted in
seminar
• Seminars also foster social interactions
• Competitor and government information exerts additional influence on senior choice process
50Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
3. The influence of social interactions3. The influence of social interactions
• When SimSeniors exchange information, they influence each other’s brand and plan perception.
Low brand perception
High brand perception
Raised brand perception
Social Interaction
51Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
4. The influence of customer experience4. The influence of customer experience
Ability to Provide Good Customer Experience
0 1Distributions
0
0.005
0.01
0.015
0.02
0.025
0.03
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.1 0.10
Bad
Med
Good
Probabilistically sample Experience = change in Brand Perception
Service & Guidance Professionals
52Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Simulation tool: screenshotsSimulation tool: screenshots
Main screen
53Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Result Visualization: Baseline exampleResult Visualization: Baseline example
54Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
When drug cost benefits are important When drug cost benefits are important ……
… HMOs are most popular (when available).
Reason: HMOs provide best drug coverage at a very low premiumReason: HMOs provide best drug coverage at a very low premium
55Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
When freedom of choice is important When freedom of choice is important ……
… HMOs capture only 25% of market share.
x
xx
x
xx
56Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
SummarySummary
57Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
Emergent behaviorsEmergent behaviors
Incomplete understanding of interactions can lead to surprising results...
... but predictive modeling can help!
58Joint Colloquium of the IACA, PBSS and IAAHS Sections
Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008
AgentAgent--based simulations for healthcarebased simulations for healthcare
• Not all models are created equal
• Agent-based simulations offer a powerful tool when:• Significant, complex interactions• Insufficient data• Highly dynamic environment• Discontinuities
• Important take-home messages:• Understand the limitations of your model• Find (or build) the right model for problems
Joint Colloquium of the IACA, PBSS and IAAHS Sections�of the International Actuarial Association�Westin Copley Place Hotel, Boston, U.S.A. – 4-7 May 2008OverviewPredictive Modeling in HealthcareDefining “Predictive Modeling”What is a model?Is this a model?It takes one to know oneWhat is a mathematical model?Types of mathematical modelsUsing mathematical models for predictionAn exampleSome (personal) issues with mathematical modelsIcosystem’s approach to predictionA better approachWhat is a complex system?Sample systems with emergent behaviorsEmergent behavior: the Icosystem gameComputer simulation of the Icosystem gameWhy is this interesting?Controlling emergent behaviorAgent-based modelingAgent-based modeling analogyThe Bean Machine:�Statistics vs. Agent-Based ModelingThe Bean MachineThe Icosystem Bean MachineCan you explain the resulting distributions?How does this help predictive modeling?Sample Case StudiesUnderstanding supermarket shopper behaviorPredicting Health Insurance enrollmentThe Impact of social nets on product adoptionMedicare Product Launch and DesignTreatment ComplianceDrug development portfolio processDetailed Case StudiesAgent-based modeling for healthcareSample application #1 Problem statementA simplistic view of the prescribing dynamicThe reality of drug-based healthcare is complexSocial networks to classify clinic typesThree general types of clinics observedSocial network “drivers” can be identifiedSample application #2 Predictive modeling for MMA Part DIn reality: many events affect senior opinionThe model: high-level summary1. SimSenior: key factors2. The influence of information3. The influence of social interactions4. The influence of customer experienceSimulation tool: screenshotsResult Visualization: Baseline exampleWhen drug cost benefits are important …When freedom of choice is important …SummaryEmergent behaviorsAgent-based simulations for healthcare