Graphs are used to map relations on unstructured data. Companies’ data are most from database and mined using traditional data mining approach. However, model relational data as a graph can reveal useful insights and discovery relation among data that is ignored by traditional data mining techniques. In this work we used graphs to map physician relations using claim data as a proxy and this approach reveal interesting insights from health insurance company.

1. Ana Paula Appel
Data Scientist & Master Inventor
Discovering the hidden treasure of data using graph
analytic

2. © 2015 IBM Corporation2

3. IBM Research – Brazil
view from Rio de Janeiro Lab
Mission: To be known for our science and technology and vital to IBM, Brazil, our
clients in the region and worldwide

4. Healthcare Data
• Medical attention transactional data
• Large healthcare insurance company in
Brazil
• Nationwide
• Spanning 1.5 years (2013-2014)
• 0.6 Tb (compressed)

5. © 2015 IBM Corporation5
Healthcare Data: Stakeholders
Physicians
Patients
Healthcare providers
Health Services
Claims
Health Insurance
Company

6. © 2015 IBM Corporation6
• Paid Claims
• Total: 109M
• Doctors: 220k (almost half of all doctors In Brazil)
• Patients: 2.2M
• Unique Doctor-Patient pairs: 11.6M
• Other support data:
• Company
• Providers
• Authorizations ~3M
• Claim denials ~13M
• Geolocation
• ...
Over 40 tables,
hundreds of fields
Healthcare Data: Claims
CLAIM
• Physician ID
• Patient ID
• Timestamp
• Service code
• Disease – ICD9
• (80+ extra rows)

7. © 2015 IBM Corporation7
A Complex Network Perspective

8. © 2015 IBM Corporation8
PhysID ICD9 PatientID DATE
SP45962 - 1001 09/04/13
SP45962 Z017 1001 26/04/13
SP47108 Z017 1001 06/12/13
SP47108 Z017 1001 16/12/13
SP45962 - 1002 11/07/13
SP45962 Z017 1002 12/07/13
SP45962 - 1002 19/08/13
SP59938 Z000 1002 24/10/13
… … … …
Bipartite graph
Weighted graph
Directed graph
• Bipartite network of doctors and patients
• |V|=2.4M, |E|=11.6M
• Keep only the largest connected component (92%-99% of all links)
• Remove multiple edges and map to weights
A Network Approach

9. © 2015 IBM Corporation9
Phys - Patient
Nodes = 402
Links = 403
Patient - Patient
Nodes = 377
Links = 5488
Phys - Phys
Nodes = 25
Links = 30
Patient-Sharing networks
Links represent
a shared patient

10. © 2015 IBM Corporation10
One patient with
123 different
physicians
409k patients with
only 1 physician
Patient Histogram Physician Histogram
Physican and Patient Degree Distributions
26 physicians with more
than 5k different
patients, 1 with 30k
(possibly spurious)

11. © 2015 IBM Corporation11
Network-Derived Metrics
• Aim: extend the doctors description with
relevant metrics
• Metrics which, in combination with other
data, will allow to:
• classify
• filter
• reduce
35 0.1 3.2 0 4% 7% ... ...
17 0.2 5.1 1 9% 1% ... ...
Compliant doctors Not-compliant doctors

12. Case: Build Metrics for Describe Physicians using
Complex Network
Mutual Reference CentralityLoyalty

13. Health Insurance: Similarity between Complex
Network
Friendship Physician Network

14. © 2015 IBM Corporation14
Mutual Reference

15. © 2015 IBM Corporation15
a b
w(ab) = 17
Δt = 7 days
w(ba) = 8
Δt = 2 days
time
1 1 2 2
a b b a
visit visit visit visit
Patients
Doctors
Mutual Reference
Same patient visits two doctors
+
Happens in both directions
Δt = 7 days Δt = 2 days
Reciprocal Link
Goal
Identify strong connections between each pair of physicians, in particular, the outliers.

16. © 2015 IBM Corporation16
BA DF SP
Top 50Top 20
PE RJ
Dens.:
Dens.:
0.809 0.4470.8050.845
0.913 0.963 0.834 0.568 0.802
0.576
Mutual Reference

17. Mutual Reference
Alergy Oftalmology

18. © 2015 IBM Corporation18
Mutual Reference
Conclusions and Insights
• Claim data is rich to identify connections among physicians
and how a partnership is done.
• The Mutual Reference is an indicative of physician
relationship and can potentially generate other analyses,
especially in a large volume of data.
• The proposed metric makes possible a frequent
computational analyze of that relationship.
Physician A Physician B rm Rank
MMS028 MMS027 1 1
MSP145 MSP144 0.31 10
Mutual Reference
• Specialties that appear more
• Ophthalmology to ophthalmology
• Gynecologic and obstetrician to Gynecologic and
obstetrician
• DF has most of consultation with irregular interval
• MDF010 and MDF009 with 267 consultations and
average of days equal to 0
• Top pair;
• 205 from MMS028 to MMS027
• 196 from MMS027 to MMS028

19. © 2015 IBM Corporation19
Patient Loyalty

20. © 2015 IBM Corporation20
Patient Loyalty
Goal
Identify (and quantify) doctors that have recurring patients in a systematic way,
suggesting ‘loyalty’
1. Consider patients with many visits to doctors
2. Compute the relative weight for each doctor visited
3. Count the relative number of ‘loyal’ patients for that doctor
Time
Consultations

21. © 2015 IBM Corporation21
Patient Loyalty
São Paulo
1.00
• Weight wij represents the number of visits of patient i to dr. j
• Strength s: sum of the weights attached to links belonging
to a node (i.e., all visits from i)
• Relative weight rw(ij): fraction of weight ij over total
Strength s
Degree k
High rw Low rw

22. © 2015 IBM Corporation22
• The more patients with high rw and high
s, the most likely the doctor is a
candidate to have ‘loyalty’ capacity
• Stability: Many doctors maintain
sustained values of the metric across
time.
• A given doctor is in rank 1 or 2 during
all 5 quarters.
• 20% mean turnover across quarters
• Top 5 specialty among physicians with higher
loyalty (mf > 0.5)
• Orthopedic and traumatology (5 in top 10)
• Ophthalmology (3)
• Gynecologic and obstetrician(2)
• Pediatric (1)
Patient Loyalty
Relativeweight
strength strength
Cardio Cardio
Physician mf RANK
MSP 139 1.54 175
MSP 261 1.18 432
Loyalty

23. © 2015 IBM Corporation23
Centrality

24. © 2015 IBM Corporation24
Goal
Identify physicians role in the network using their relative importance over other
physicians.
• We applied several centrality measures:
• Eigenvalue;
• Degree;
• Betweeness;
• Closeness
• Do the values of these metrics change overtime?
• Is it seasonal?
Physician Centrality
physician eigen Rank Grau
MSP 153 1 1 253
MSP 139 0.55 8 335
2Q 2014
Centrality
Conclusion and insights
• Centrality recommends which physicians are important in the physician
community
• There is a set of physicians with high scores
• This set of physician has a a higher number of patients in common
building a block
• The relative centrality has a positive correlation among close physicians
• This group of physician with high score is stable overtime, with few change
in each quartile.

25. © 2015 IBM Corporation25
Summary & Take Home Messages
• Networks are all about relationships, as most data is.
• Network-derived insights are usually not reachable from other analyses.
• Complex Networks methods are very valuable to data science.
• Large Healthcare claim database from Brazilian insurance company.
• Applied complex network methods to find how physicians build their
network.
• Examples: Temporality, reciprocity and ‘loyalty’.

26. Where find more information..
Introduction basic Advanced

27. Database API’s Visualization
GRAPH ANALYTICS

28. Thanks!
apappel@br.ibm.com