Workshop: Physician Network Analysis 1Workshop: Physician Network Analysis

Adoption in a network

October 2009November 2009December 2009January 2010February 2010March 2010April 2010May 2010June 2010July 2010August 2010September 2010October 2010November 2010December 2010January 2011February 2011March 2011April 2011May 2011June 2011July 2011August 2011September 2011

Workshop: Physician Network Analysis 2Workshop: Physician Network Analysis

• Survey physician population

• Find “Thought Leaders”

• Sample can be incomplete as long as it is reasonably representative

Finding networks with surveys

Workshop: Physician Network Analysis 3Workshop: Physician Network Analysis

• Use commercially available claims data

• Link through shared patients

• Much more complete network

Finding networks with claims data

Workshop: Physician Network Analysis 4

Generalist

Specialist

Edge thickness representsnumber of patients

Patient flow network

Workshop: Physician Network Analysis 5

One Physician’s RelationshipsOne Physician’s Relationships

Finding key relationships

Workshop: Physician Network Analysis 6

Backboning method

Serrano, Boguna & Vespignani, 2009.

Workshop: Physician Network Analysis 7

Generalist – 1434 total, 56.7% of all ties

Other Specialist – 1364 total, 40.3%

2896 Physicians – 4706 Ties

Urologist– 98 total, 3% of all ties

Bladder control in Boston

Workshop: Physician Network Analysis 8

Psychiatrist – 251 total, 7% of all ties

Generalist – 1364 total, 58.8% of ties

Other Specialist – 875 total, 28.7% of all ties

Cardiologist – 165 total, 5.4% of ties

Smoking cessation in Boston

2655 Physicians – 4264 Ties

Workshop: Physician Network Analysis 9Workshop: Physician Network Analysis

• Examples of simple contagion – Transmission of disease, ideas, or physical objects/materials

• Effect can spread with a single contact

• Centrality becomes analytically important

High Betweenness Centrality

High Closeness Centrality

Passing information through a network

Workshop: Physician Network Analysis 10Workshop: Physician Network Analysis

Unclustered

Clustered

Maintain Behavior

Change Behavior

• Examples of complex contagion – Changes in health habits, social behaviors, cultural behaviors

• Spread of complex contagion usually

requires sustained interaction with multiple carriers

• Clustering becomes analytically important

This is how we do it here

Workshop: Physician Network Analysis 11Workshop: Physician Network Analysis

Community A

Community B

Community C

• Community members are more likely to tie with each other than with outsiders

• Our methods employ new iterative maximizing algorithms which dramatically increase efficiency

• Porter, Onnela & Mucha, 2009

Finding communities of practice

Workshop: Physician Network Analysis 12Workshop: Physician Network Analysis

Geographic layout of communities

Workshop: Physician Network Analysis 13

Cluster of non-users• Family Practice & Internal

Medicine

• Not group practice

• Most likely target central to cluster

• Cynthia M. Goodwin, medium prescriber

BridgeDr. Debra Baskett

Connects cluster of 10 with 50% users to

cluster of 9 non-users

Pediatrics ClusterGroup practice

Pediatrics ClusterNot group practice

Selective Targeting• Multiple practices highly

interconnected

• Why target all these high prescribers?

High Influence Locations

Examining diabetes in Raleigh-Durham

Green – AdoptersRed – Non-Users

Workshop: Physician Network Analysis 14

Impact of having alters at geodesic distance one who have previously prescribed

Instantaneous Hazard

0

0.002

0.004

0.006

0.008

.01

0.012

1 3 5 7 9 11 13 15 17 19 21 23 25

Months

% A

dopti

on

Unconnected

Connected

Cumulative Hazard

0

0.05

0.1

0.15

0.2

0.25

0.3

1 3 5 7 9 11 13 15 17 19 21 23 25

Months

% A

dopti

on

Unconnected

Connected

Januvia in Raleigh-Durham

Network predictive power in a launch

Workshop: Physician Network Analysis 15

Key RelationshipDr. Thomas Nelson – Family Practitioner increasing use

Dr. Soon Kwark – Family Practitioner decreasing use

Change ClusterGroup practice – Family

Medicine

Change ClusterMultiple Specialties:

Cardiology, Family Medicine

Mostly on different floors of same building

Resistant ClustersTop – Group of Family

Practitioners

Bottom – Cardiologists, Family Practice, Internal

Medicine

Lipitor use in Raleigh-Durham

Red – Decreasing useYellow – Stable useGreen – Increasing use

Workshop: Physician Network Analysis 16Workshop: Physician Network Analysis

Unclustered

Clustered

Maintain Prescribing Level

Decrease Prescribing Level

Difference model

Predict change in proportion Lipitor of Lipitor & Simvastatin prescriptions

Control for cash, Medicaid, Medicare prescribing

Control for secular decline in Lipitor usage

Control for number of dyslipidemia initiations

Mean switching among altersat distance 1. . . . 0.08 (4.4)at distance 2 . . . . 0.04 (12.3) at distance 3 . . . 0.02 (0.8)

Community switching . . . 0.15 (24.2)

Predictive power for an inline product

Workshop: Physician Network Analysis 17

Cardiologist

Endocrinologist

Generalist

Other Specialist

Nephrologist

Node size representsphysician decile

Large: 8-10Medium: 2-7Small: 0-1

Similar Positions

James Brown decile 7

Paula Smith , decile 2Key BridgeChris Cole

Connects five generalists to key high prescribing cardiologistsSeth Murphy & Colin Jones

Highly Influential PositionJean Mills

Introducing a priority score

232 Physicians – 320 Ties

Workshop: Physician Network Analysis 18

• Influence Index is a weighted measure which uses:• The physician’s own target value• The target values of the physician’s distance one ties• The target values of the physician’s distance two ties• The target values of the physician’s distance three ties• The target values of the physician’s community• Uniqueness of influence position• Treatment directionality

1st

Degree

2nd

Degree

Community

Physician of Interest

3rdDegree

Components of the influence index

Workshop: Physician Network Analysis 19

1024 Physicians – 255 Ties

Generalist

Other Specialist

Urologist Node size representsPhysician decileLarge: 8-10Medium: 2-7Small: 0-1

Bladder control – community in Boston

Workshop: Physician Network Analysis 20Workshop: Physician Network Analysis

• Influence – the answer we are all looking for

• Other factorsSimilar patient mixSimilar managed care environmentSimilar promotional environment

• Let’s assume half influence/half other factors

• We have 0.1 correlation to work with

0.2

Are we really measuring influence?

Workshop: Physician Network Analysis 21Workshop: Physician Network Analysis

Prescribing

Target Value

Prescribing + Influence

High PrescribersInfluence exists, but is

unmeasured

Influence IndexRelationships measured

Potential value measured

Adoption in a network

Workshop: Physician Network Analysis 22Workshop: Physician Network Analysis

Promotion Priority

Receptiveness

AccessBehavioral AttributesMarket Segmentation

XPotential Value

Prescriptions Written

=

0.1 0.1

0.2

Total Prescribing Correlation

What can we do with the other half?

Workshop: Physician Network Analysis 23Workshop: Physician Network Analysis

Dr. A Dr. B

When Dr. A adopts Januvia, what do we know about Dr. B?

Likely to be influencedLikely to have similar individual characteristics that led Dr. A to adoptLikely to be subject to similar confounding variables

Adoption in a network

Workshop: Physician Network Analysis 24Workshop: Physician Network Analysis

Initially targeted High Influence/ High Prescriber/ Early Adopter

Second wave More receptive

Third wave Increasing acceptance

Measuring susceptibilityPromotion is more effective

Adoption in a network

Workshop: Physician Network Analysis 25Workshop: Physician Network Analysis

• Actual network from Chicago, actual network derived correlations

• Target 5% of diabetes prescribers for promotion

• Apply same promotional resources to both strategies

Three strategies

No Promotion – What would have happened absent any effort

High Prescriber – Promote to the highest prescriber not yet adopting

Network-based – Promote to highest promotion priority Potential (with influence) times receptiveness

A simulation using contagion marketing

Workshop: Physician Network Analysis 26Workshop: Physician Network Analysis

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180%

5%

10%

15%

20%

25%

30%

No Intervention; 17%

High Prescribers; 21%

Network-Based; 25%

Simulated Januvia Adoption in Chicago

Months since launch

Perc

ent A

dopti

on

Network-based significantly outperforms

Workshop: Physician Network Analysis 27

Choose targets basedon network influence

Choose targets basedon prescribing volume

Both methods havethe same:

Marketing messageSales reps.

Number of targetsTime period

Targeted Physicians are those whooccupy influential network positions

Targeted Physicians are those whohave a high prescribing volume

A controlled trial

Workshop: Physician Network Analysis 28

3 Months Pre-test measurement5 Months Detailing3 Months Post-test measurement

Market share capture is 50% greater in city A due to network targeting

The results

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Appendix

Workshop: Physician Network Analysis 30

Januvia Details

30

Workshop: Physician Network Analysis 31Workshop: Physician Network Analysis

Januvia 2nd or 3rd line prescription in diabetes therapeutic area

Cox Proportional Hazards Model (first use, time in months)

Typical – Washington, D.C., tested in 9 other regions

Every variable set up as Individual measure, Alter mean at distances one to threeCommunity measureLagged and current time period

Controlled for diabetes prescribing (strongly significant effect, some combination of opportunity to prescribe and detailing effort)

Adoption in a network

Workshop: Physician Network Analysis 32Workshop: Physician Network Analysis

Prob

abili

ty o

f Ado

ption

1 %

2%

Months since Launch

10 20 30 40

Relative to baseline – Increase in probability of Januvia adoption during month

(All coefficients significant at 0.01 level)

Being an endocrinologist 65% Any new adopter at distance one * 27% Ten percent more adopters at distance one * 4%Ten percent increase in community adoption 5% Endocrinologist as network neighbor 7% Endocrinologist adopting at distance one * 47%

* Also significant at distances two and three

NotesAll social variables are time-laggedDiabetes initiations, other products, payment method controlled forCox Proportional Hazards Model

Estimation of Baseline Hazard

Details from survival model

Workshop: Physician Network Analysis 33

Simulated Results

Workshop: Physician Network Analysis 34Workshop: Physician Network Analysis

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180

10000

20000

30000

40000

50000

60000

Prescriptions per Month(based on claims data)

No InterventionHigh PrescribersNetwork Based

Months since launch

Scrip

ts/M

onth

• Assumed that prescribers would match average Januvia proportion of patients

• Sum of approximately 70K scripts over 18 months

Simulation by volume

Workshop: Physician Network Analysis 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180

0.05

0.1

0.15

0.2

0.25

0.3

Simulated Pristiq Adoption

High Prescriber MedNetworks Targeting

Months since launch

% A

dopti

on

Network targeting

Another launch

Workshop: Physician Network Analysis 36

1 2 3 4 5 6 7 80

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Increase in Abilify prescribing with network targeting

Quarter

% In

crea

se in

Scr

ips

Inline targeting by percentage