See Through Patterns, Hidden Relationships and Networks to Find Opportunities in Big Data

Graph Search for HealthcareHow algorithms socially disrupt the bad guys while helping socially change health outcomes

Jo Prichard@joprichard

Data Scientist | LexisNexis Risk SolutionsSeptember 2013

See Through Patterns, Hidden Relationships and Networks to Find Opportunities in Big Data.

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http://hpccsystems.com

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Graph Search vs. Page Rank• Real-time search vs. pre-calculated vertex variables.• Ideal is a combination of both.• Measure the whole graph (Page Rank style) AND search the whole

graph (Graph Search style).

HPCC Systems & LexisNexis social graph• Enterprise ready open-source big data high performance

distributed processing platform.• +- 270 million Active Identities, 4 billion people relationships• 24 billion rows in a distributed partitioned graph.

Simple example of a graph calculation Partition a graph. JOIN is your friend (when it is distributed and not on a RDBMS!) LESS CODE, MORE POWER, MORE VALUE!

Case Study Example :Applying graph analysis to measure socialized prescriptions.• Social Graph prescription stats to measure social density.• Case study results.• Transform insights to actionable data.

Graph Search for Healthcare

See through Patterns, Hidden Relationships and Networks to find Opportunities in Big Data.

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Graph Search vs. Page Rank For HealthcareReal-time search vs. pre-calculated vertex variables.


Trusted Relationships

Graph Search Style for a single patient How many of my associates are smokers? Do I have a licensed medical professional in my social network? Are most of my associates and their associates getting the flu shot this year? How many of my associates live near to where I live? I have a prescription for Vicodin, how many of my associates and their associates also have

prescriptions for Vicodin? How far do my associates travel geographically to fill scheduled drug prescriptions relative to their

other prescriptions?

Page Rank Style for all patients Calculate the answer for every vertex!!

Best of both styles. Are health outcomes negatively affected if your associates smoke? Do personal associations with a licensed medical professional impact hospital readmittance rates? Which elderly or disabled patients are more at risk because they do not live near their support

system? Are there dense social clusters with risk factors for obesity? How normal is it for you and 15 of your close friends to all be receiving Vicodin prescriptions at the

same time and are you all catching a plane from Alabama to Tampa to fill them?

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LexisNexis Risk Solutions• A division of Reed Elsevier.• 2012 LexisNexis Risk Solutions Revenue = $1.5 billion• Expanding Healthcare vertical with recent acquisitions in the Healthcare space.

HPCC Systems• High Performance Distributed Processing Platform• Open Source, in Production for more than a decade• Utilizes Commodity Hardware

LexisNexis public data social graph• Relationships inferred from 50TB of Public Records Data.• People connected to people, assets, businesses and more.• +- 270 million Active Identities, 4 billion people relationships, • High Value relationships for Mapping trusted networks.

Examples leveraging LexisNexis social graph.• Healthcare

• Medicaid\Medicare Fraud.• Drug Seeking Behavior.• Disease Management and Wellness Programs.

• Financial Services.• Mortgage Fraud.• “Bust out” Fraud.

• Insurance• Staged Accident Fraud.

About LexisNexis Risk Solutions


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Relationships in a nutshell.


Shared Historical Addresses

Shared Business Ownership

Shared Assets(Property, Vehicles etc.)


No Social Media Data!

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Simple graph



Vertexes (Nodes)From To degree1117906843 1117906843 0.001117906843 1166180939 1.001117906843 71384691 1.001117906843 1572188131 1.001117906843 2182832221 1.301117906843 2280607022 1.251117906843 1773055127 1.201117906843 1541607980 1.801117906843 1531070616 2.00

240422663 240422663 0.00240422663 1166180939 1.00240422663 71384691 1.00240422663 1572188131 1.00240422663 2182832221 1.40240422663 2280607022 1.60240422663 1773055127 1.75240422663 1541607980 1.80240422663 1531070616 2.00

Edges (Links)VertexID f_name l_name age address1117906843 JAMES ANDERSON 34 P.O. BOX 555 MAIN STREET, BROOKLYN, NY1166180939 JANET JACKSON-ANDERSON 36 P.O. BOX 555 MAIN STREET, BROOKLYN, NY

71384691 JAN HUNT 39 SUITE 202, MAIN STREET, BROOKLYN, NY1572188131 GARY JACKSON 45 SUITE 204, MAIN STREET, BROOKLYN, NY2182832221 KEVIN PIETERSON 34 SUITE 143, MAIN STREET, BROOKLYN, NY2280607022 KENNY JACKSON 43 SUITE 322, MAIN STREET, BROOKLYN, NY1773055127 HARRY JAMESON-ANDERSON 41 21 JUMP STREET, BROOKLYN, NY1541607980 JEFF CANAVAN 31 32 WISTERIA LANE, BROOKLYN, NY1531070616 BEVERLY NAGLE 32 32 WISTERIA LANE, BROOKLYN, NY

240422663 MIKE JONES 36 3215 VILLAGE CIR, GREENWICH VILLAGE, NY1166180939 JANET JACKSON-ANDERSON 36 P.O. BOX 555 MAIN STREET, BROOKLYN, NY

71384691 JAN HUNT 39 SUITE 202, MAIN STREET, BROOKLYN, NY1572188131 GARY JACKSON 45 SUITE 204, MAIN STREET, BROOKLYN, NY2182832221 KEVIN PIETERSON 34 SUITE 143, MAIN STREET, BROOKLYN, NY2280607022 KENNY JACKSON 43 SUITE 322, MAIN STREET, BROOKLYN, NY1773055127 HARRY JAMESON-ANDERSON 41 21 JUMP STREET, BROOKLYN, NY1541607980 JEFF CANAVAN 31 32 WISTERIA LANE, BROOKLYN, NY1531070616 BEVERLY NAGLE 32 32 WISTERIA LANE, BROOKLYN, NY

Attributes & VariablesAge, Spend, Claim Velocity…

Degree, Type of Relationship, Date Range..

Now imagine you have 270 Million Vertexes and 24 Billion Edges.

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Simple example in ECL of a graph calculation in scale



import SNA, Person, Healthcare;

Edges := Person.Clusters; // a dataset containing centroid to vertexes within 2 degrees.Transactions := Healthcare.PrescriptionTransactions; // prescriptions for people ids.

// Distribute both datasets across all nodes and do a distributed join (not indexed)ClusterTransactions := JOIN(Edges, Transactions, left.ToId=right.PersonId, HASH);

// Calculate the number of prescription by drug name within 2 degrees of every centroid (person)ClusterStats := TABLE(ClusterTransactions, {FromId, generic_drug_name, prescription_count := COUNT(GROUP); prescription_1degree_count := COUNT(GROUP, degree <= 1); prescription_2degree_count := COUNT(GROUP, degree > 1 and degree <= 2) });OUTPUT(ClusterStats(drug_generic_name='HYDROCODONE'), 200, -prescription_count);

Top 200 patients within a social network with a high volume of patients receiving vicodin prescriptions.

It is just a JOIN and an AGGREGATION

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KEY INDICATORS Tight social group of people who

appear to be well connected to each other.

Multiple family groups receiving HYDROCODONE.

Cluster Stats put this group socially in the 0.0005% of 1million+ HYDROCODONE prescriptions.

One of the Doctors tied to the HYDROCODONE prescriptions in the cluster is also socially a member of this social group.

HYDROCODONE CLUSTER

drug_generic_name drug_countHYDROCODONE BITARTRATE/ACETAMINOPHEN 21SIMVASTATIN 14FLUTICASONE PROPIONATE -Q7PX 12LEVOTHYROXINE SODIUM 8ZOLPIDEM TARTRATE 7CEPHALEXIN MH 7CIPROFLOXACIN HYDROCHLORIDE 7CITALOPRAM HYDROBROMIDE -H2SX 7CYCLOBENZAPRINE HCL 7ALENDRONATE SODIUM 7METOPROLOL TARTRATE 6AZITHROMYCIN 6ALBUTEROL SULFATE 6IBUPROFEN 6LISINOPRIL 6LORAZEPAM 6ATORVASTATIN CALCIUM 5TRAZODONE HCL -H7EX 5BECLOMETHASONE DIPROPIONATE 5FAMOTIDINE 5GUAIFENESIN/CODEINE PHOS -B4SX 5AMOXICILLIN TRIHYDRATE 5METRONIDAZOLE 4NORGESTIMATE-ETHINYL ESTRADIOL 4SULFAMETHOXAZOLE/TMP 4TAMSULOSIN HCL -Q9BX 4VARDENAFIL HCL -F2AX 4WARFARIN SODIUM 4ACYCLOVIR 4

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INTERESTING HYDROCODONE CLUSTER

MIKE JONES MD Is the prescribing doctor who prescribed

Vicodin to patients in the target social cluster (James Anderson)

He is a member of the same social cluster Also personally filled a vicodin

prescription for himself.

Question: Is it normal for you and 15 of your associates to all receive a prescription for vicodin within the same short timespan?

Relationships are from public records (non-obvious in the healthcare data domain)

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SOCIALIZATION OF PRESCRIPTIONS:Social vs Non-Social Drugs

Number of prescriptions by social association.

Highlights which drugs show higher levels of socialization.

Highlights outliers and anomalous social patterns

Provides new insight and context at a social drug level.

Not all drugs are created “socially equal”.

Almost every prescription is in social isolation (> 96%)

Large % of prescriptions show socialization (long tail)

KEY1: Means the number of prescriptions for that drug that are the ONLY prescription of that type within the social group.2: Means the number of prescriptions for that drug that are within a social group where there is one other member receiving a prescription for that drug.3: Means the number of prescriptions for that drug that are within a social group where there are two other members receiving a prescription for that drug.And so on…

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SOCIALIZATION OF PRESCRIPTIONS:Social vs Non-Social Drugs

DIGOXINSPIRONOLACTONEMETHOTREXATE SODIUMISOSORBIDE MONONITRATEEZETIMIBETAMSULOSIN HCLNIFEDIPINEDILTIAZEM HCLFINASTERIDEETANERCEPTENOXAPARIN SODIUMARIPIPRAZOLEFINASTERIDE -Q9BXRISPERIDONEISOSORBIDE DINITRATEDIVALPROEX SODIUMLEVETIRACETAMPROPRANOLOL HCLANASTROZOLEDOXAZOSIN MESYLATEPHENYTOIN SODIUM EXTENDEDESTROGENS, CONJUGATED VAGESTROGENS,CONJUGATED -Q4KXESTRADIOL -Q4KXNIACIN

INSIGHTS INTO SOCIAL SPREAD OF PRESCRIPTION BRANDS

Understand what is normal per drug.

Detect and highlight social outliers

Develop an exclusion list for legitimately social drugs (e.g. Antibiotics & Vaccines)

At a drug name level measure unusual social spread.

More quickly see unusual drugs patterns socially.

Might indicate recruitment or drug seeking behavior.

Strategically focus on problematic prescription types from a social spread perspective not an individual patient volume perspective.

Within all the claims focus on the smaller subset of those that are too social.

HYDROCODONE BITARTRATE/ACETAMINOPHENSIMVASTATINHYDROCODONE-ACETAMINOPHENIBUPROFENLISINOPRILALBUTEROL SULFATEATENOLOLFLUTICASONE PROPIONATE -Q7PXOMEPRAZOLEHYDROCHLOROTHIAZIDEAMLODIPINE BESYLATELEVOTHYROXINE SODIUMGLUCOSE BLOODMETFORMIN HYDROCHLORIDEFLUTICASONE PROPIONATE (NMETFORMIN HCLBLOOD SUGAR DIAGNOSTICLOSARTAN POTASSIUMGLIPIZIDEPREDNISONEAZITHROMYCINAMOXICILLINLANCETSGUAIFENESIN/CODEINE PHOS -B4SXAMOXICILLIN TRIHYDRATECYCLOBENZAPRINE HCLLORAZEPAM

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SOCIALIZATION OF PRESCRIPTIONS:Social Prescription Patterns = Social Health Conditions

Number of prescriptions by social association.

identify social clusters with a prescription pattern associated with same health conditions.

Opportunity for strategic social intervention to influence health outcomes.

If you could identify the specific segment of your population that fit this social model, how would you leverage this opportunity?

KEY1: Means the number of prescriptions for that drug that are the ONLY prescription of that type within the social group.2: Means the number of prescriptions for that drug that are within a social group where there is one other member receiving that prescription drug.3: Means the number of prescriptions of that drug that are within a social group where there are two other members receiving that prescription drug.And so on…

1 2 3 4 5 6 7 8 9 10 11 120

100000

200000

300000

400000

500000

OMEPRAZOLE

Social Distribution

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

200000

400000

600000

800000

SIMVASTATIN

Social Distribution

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

100000200000300000400000500000600000700000

ATENOLOL

Social Distribution

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SOCIALIZATION OF PRESCRIPTIONS:Mapping the spread and density of social prescriptions

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VARIABLES THAT FOCUS ON CROWDSOURCING PRESCRIPTIONS

Identify patient social groups with abnormal prescription densities.

Identify prescribers with unusual social prescription patterns to social groups.

patients see them as an easy source of prescriptions?

Doctors that are more free with prescriptions for family and friends?

Sign of a larger fraud scheme?

Identify specific areas in the graph where there is an opportunity for social disruption.

Dense social clusters with similar health issues e.g. obesity, diabetes

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In Summary

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Benefits of Graph Analysis in Scale for Healthcare Extremely rich source of new perspectives and insight.

Value in cross domain data in scale (you have to have the data, and we do).

Straightforward to roll your own with the new breed of high performance distributed big data processing systems.

Opportunities to disrupt healthcare networks

Change healthcare outcomes.

Tackle organized fraud networks in scale.

30 mins is too short for this topic!

[email protected]

@joprichard

mailto:[email protected]

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See Through Patterns, Hidden Relationships and Networks to Find Opportunities in Big Data