Similar to CISummit 2013: Pete DeWarn, Brigham Hyde, Mark Degatano, Breakthrough KOLs Panel: Quantifying Network Structure and Contextual Expertise (20)
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CISummit 2013: Pete DeWarn, Brigham Hyde, Mark Degatano, Breakthrough KOLs Panel: Quantifying Network Structure and Contextual Expertise
1. Adoption in a network
September 2010
November 2011
December 2009
February
October
January
August
March
April
June
May
July
Workshop: Physician Network Analysis
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2. Finding networks with surveys
• Survey physician population
• Find “Thought Leaders”
• Sample can be incomplete as long as it
is reasonably representative
Workshop: Physician Network Analysis
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3. Finding networks with claims data
• Use commercially available claims data
• Link through shared patients
• Much more complete network
Workshop: Physician Network Analysis
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7. Bladder control in Boston
Urologist– 98 total, 3% of all ties
Generalist – 1434 total, 56.7% of all ties
Other Specialist – 1364 total, 40.3%
2896 Physicians – 4706 Ties
Workshop: Physician Network Analysis
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8. Smoking cessation in Boston
Psychiatrist – 251 total, 7% of all ties
Cardiologist – 165 total, 5.4% of ties
Generalist – 1364 total, 58.8% of ties
Other Specialist – 875 total, 28.7% of all ties
2655 Physicians – 4264 Ties
Workshop: Physician Network Analysis
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9. Passing information through a network
High Betweenness Centrality
• Examples of simple contagion –
Transmission of disease, ideas, or
physical objects/materials
• Effect can spread with a single
contact
• Centrality becomes analytically
important
High Closeness Centrality
Workshop: Physician Network Analysis
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10. This is how we do it here
• Examples of complex contagion –
Changes in health habits, social
behaviors, cultural behaviors
Unclustered
• Spread of complex contagion usually
requires sustained interaction with
multiple carriers
Maintain
Behavior
• Clustering becomes analytically
important
Workshop: Physician Network Analysis
Clustered
10
Change
Behavior
11. Finding communities of practice
Community A
• 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
Community B
Community C
Workshop: Physician Network Analysis
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13. Examining diabetes in Raleigh-Durham
Selective Targeting
•Multiple practices highly
interconnected
Cluster of non-users
•Why target all these high
prescribers?
•Family Practice & Internal
Medicine
•Not group practice
•Most likely target central to
cluster
High Influence
Locations
•Cynthia M. Goodwin,
medium prescriber
Pediatrics Cluster
Not group practice
Bridge
Dr. Debra Baskett
Connects cluster of 10
with 50% users to
cluster of 9 non-users
Green – Adopters
Red – Non-Users
Pediatrics Cluster
Group practice
Workshop: Physician Network Analysis
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14. Network predictive power in a launch
Januvia in Raleigh-Durham
Impact of having alters at geodesic distance one who have previously prescribed
Instantaneous Hazard
Cumulative Hazard
Unconnected
Unconnected
Connected
Connected
0.25
0.008
0.2
% Adoption
0.3
.01
% Adoption
0.012
0.006
0.15
0.004
0.1
0.002
0.05
0
0
1
3
5
7
9
11
13
15
17
19
21
23
25
1
Months
Workshop: Physician Network Analysis
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5
7
9
11
13
15
Months
14
17
19
21
23
25
15. Lipitor use in Raleigh-Durham
Resistant Clusters
Top – Group of Family
Practitioners
Bottom – Cardiologists,
Family Practice, Internal
Medicine
Change Cluster
Multiple Specialties:
Cardiology, Family
Medicine
Mostly on different
floors of same building
Red – Decreasing use
Yellow – Stable use
Green – Increasing use
Key Relationship
Change Cluster
Dr. Thomas Nelson – Family
Practitioner increasing use
Group practice – Family
Medicine
Dr. Soon Kwark – Family
Practitioner decreasing use
Workshop: Physician Network Analysis
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16. Predictive power for an inline product
Maintain
Prescribing Level
Difference model
Decrease
Prescribing Level
Predict change in proportion Lipitor of Lipitor &
Simvastatin prescriptions
Unclustered
Control for cash, Medicaid, Medicare prescribing
Control for secular decline in Lipitor usage
Control for number of dyslipidemia initiations
Clustered
Mean switching among alters
at 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)
Workshop: Physician Network Analysis
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17. Introducing a priority score
Similar Positions
James Brown decile 7
Key Bridge
Paula Smith , decile 2
Chris Cole
Connects five
generalists to key
high prescribing
cardiologists
Seth Murphy &
Colin Jones
Highly Influential Position
Jean Mills
Cardiologist
232 Physicians – 320 Ties
Endocrinologist
Nephrologist
Generalist
Other Specialist
Workshop: Physician Network Analysis
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Node size represents
physician decile
Large: 8-10
Medium: 2-7
Small: 0-1
18. Components of the influence index
• 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
Community
1st
Degree
Physician
of Interest
2nd
Degree
Workshop: Physician Network Analysis
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3rd
Degree
19. Bladder control – community in Boston
Urologist
Generalist
Other Specialist
Node size represents
Physician decile
Large: 8-10
Medium: 2-7
Small: 0-1
1024 Physicians – 255 Ties
Workshop: Physician Network Analysis
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20. Are we really measuring influence?
0.2
• Influence – the answer we are all looking for
• Other factors
Similar patient mix
Similar managed care environment
Similar promotional environment
• Let’s assume half influence/half other factors
• We have 0.1 correlation to work with
Workshop: Physician Network Analysis
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21. Adoption in a network
High Prescribers
Influence Index
Influence exists, but is
unmeasured
Relationships measured
Potential value measured
Target
Value
Prescribing
Workshop: Physician Network Analysis
Prescribing + Influence
21
22. What can we do with the other half?
Promotion Priority
=
0.2
Potential Value
Prescriptions Written
Total Prescribing Correlation
0.1
Workshop: Physician Network Analysis
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X
Receptiveness
Access
Behavioral Attributes
Market Segmentation
0.1
23. Adoption in a network
Dr. A
Dr. B
When Dr. A adopts Januvia, what do we know about Dr. B?
Likely to be influenced
Likely to have similar individual characteristics that led Dr. A to adopt
Likely to be subject to similar confounding variables
Workshop: Physician Network Analysis
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24. Adoption in a network
Initially targeted High Influence/ High Prescriber/ Early Adopter
Second wave More receptive
Third wave Increasing acceptance
Workshop: Physician Network Analysis
Measuring susceptibility
Promotion is more effective
24
25. A simulation using contagion marketing
• 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
Workshop: Physician Network Analysis
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27. A controlled trial
Choose targets based
on network influence
Choose targets based
on prescribing volume
Both methods have
the same:
Marketing message
Sales reps.
Number of targets
Time period
Targeted Physicians are those who
occupy influential network positions
Workshop: Physician Network Analysis
Targeted Physicians are those who
have a high prescribing volume
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28. The results
3 Months
5 Months
3 Months
Pre-test measurement
Detailing
Post-test measurement
Market share capture is 50% greater in city A due to network targeting
Workshop: Physician Network Analysis
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31. Adoption in a network
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 three
Community measure
Lagged and current time period
Controlled for diabetes prescribing (strongly significant effect, some combination of
opportunity to prescribe and detailing effort)
Workshop: Physician Network Analysis
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32. Details from survival model
Relative to baseline –
Increase in probability of Januvia adoption during month
(All coefficients significant at 0.01 level)
2%
Probability of Adoption
Being an endocrinologist
Any new adopter at distance one *
Ten percent more adopters at distance one *
Ten percent increase in community adoption
Endocrinologist as network neighbor
Endocrinologist adopting at distance one * 47%
* Also significant at distances two and three
1%
Cox Proportional Hazards Model
Estimation of Baseline Hazard
10
20
Notes
All social variables are time-lagged
Diabetes initiations, other products, payment method controlled for
30
Months since Launch
Workshop: Physician Network Analysis
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40
65%
27%
4%
5%
7%
34. Simulation by volume
• Assumed that prescribers would match average Januvia
proportion of patients
• Sum of approximately 70K scripts over 18 months
Prescriptions per Month
(based on claims data)
60000
Scripts/Month
50000
40000
No Intervention
High Prescribers
30000
Network Based
20000
10000
0
1
2
3
4
5
6
7
8
9
10
11
12
Months since launch
Workshop: Physician Network Analysis
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13
14
15
16
17
18
36. Inline targeting by percentage
Increase in Abilify prescribing with network targeting
0.16
0.14
% Increase in Scrips
0.12
0.1
0.08
0.06
0.04
0.02
0
1
2
3
4
5
Quarter
Workshop: Physician Network Analysis
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6
7
8
Editor's Notes
Serrano, Boguñá & Vespignani, 2009 wrote a paper which Nicholas introduced me too. A representation of the problem they are trying to solve is to show a meaningful map of the US air traffic system. Counting flights will not give a useful map, because a few airports, O’Hare, DFW, etc., will take over the map. Other thresholding methods will not keep all the airports or show their most important ties. They figured a method, called the “backbone” method, which takes only the most important relationships for each node. In other words, there is a separate threshold for each airport which takes account of its particular air traffic, rather than a universal threshold applied across all of them. I implemented this method early on.
Community boundaries are not the same as geographic boundaries.
This one slide now covers two points-In mode one before the animation build we are showing for the first time in this deck, a social network map and can discuss value of understanding structure, influence, etc then using the animation , we can discuss specific examples of how to leverage knowing doctors, community, location and influence
Initially, we can provide a lot of value in thinking better about the potential value of a target. Once the project is underway and we can see initial patterns of adoption, we can also provide information about receptiveness of a target.
We have some studies in the field, but do not yet have results that we can show. However, we can show a simulation. The social network is the actual one from Chicago, and the correlations used are the actual ones from the first eighteen months of Januvia adoption in Chicago. The primary assumption is the effect of promotion, which we cannot adequately measure retrospectively, especially since promotion was actually done in Chicago. However, because each scenario uses the exact same promotional resources, the relative ranking of the approaches should be robust under a wide variety of conditions. The exact difference would depend on the actual promotion resources used and their effectiveness. To give a baseline for the assumptions we used, we also included a simulation without any promotion. To the extent that the difference between “No Promotion” and “High Prescriber” seems realistic, the improvement from “Network-Based” targeting should follow.
This chart shows the total percentage of prescribers who have adopted Januvia at each month since launch in our simulation. The physicians included are all those who had at least five diabetes prescriptions in a three year lookback. This does not show the adoption rates for the targeted physicians – those would be substantially higher – but instead shows what happens in the entire population of diabetes prescribers when different promotional strategies are used. Again, because the promotional effectiveness is the same in both scenarios, the relative ranking of these curves would not change, although the absolute distance between them likely would.
This slide shows some of the coefficients from a hazard model predicting Januvia adoption in Chicago. The baseline hazard shows the adoption rate through time with all variables controlled for. During the first month, approximately one percent of physicians will use Januvia for the first time. This percentage rises and then enters a long decline as time passes since the introduction. The coefficients for each variable show how much more likely a physician is to prescribe than the baseline when the characteristic is true. For example, an endocrinologist is 65% more likely than a non-endocrinologist, while having a network neighbor adopt is associated with a 27% increase in probability.
This simulation shows the total number of prescriptions made in each scenario. As with the adoption rates, the actual numbers are subject to assumptions about promotional resources, but the relative rankings are robust to this. When measuring in total prescriptions, the difference between promotion and non-promotion is more pronounced, because failure to target high prescribers leads to a disproportionate loss of total market share. We had to assume that each physician would use Januvia on the average proportion of their patients as seen in the real data, which is likely a conservative assumption. As well, since this is base on claims data, it does not represent the entire market. Although we are unable to tell exactly, previous experience suggests that commercial claims data represents about 20 – 40 % of the entire market, so the absolute numbers should be multiplied by some 3-5x to better reflect the real world.