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The current DocGraph social graph was built in Neo4J. With new enhancements in Neo4J 2.0, now was a good time to rebuild the social graph. The goal of this session is to show participants how simple it is to perform basic graph analysis of a healthcare dataset.
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Graphs Opening Medical Care Information
@davefauthwww.intelliwareness.org
2
About Me
• My Blog: http://www.intelliwareness.org• Find me on Twitter: @davefauth• Email me: [email protected]• GitHub: http://github.com/davidfauth
Not talking about this….
Or this….
But we want to talk about this:
Ryan Weald – isurfsoftware.com
And this:
I’ll try not to do this…
Or this….
Where we are today
Healthcare Data
• Recommend watching Fred Trotter speak at GraphConnect – SF
• Moving from no data -> bad data -> better data -> good data
• Claims Data– Hard to accurately describe what a doctor is
doing and how they are getting paid without claims data
– Limited and not a good data set by any standard
Examples of Bad Data
• Not enough data – More transparency without having to FOIA
• State level data is hard to get
Better Data Sets
• DocGraph Data– One of the “best” available– “Best” does not mean “good”
• DocGraph Rx– Prescribing patterns for Medicare Part D patients
• NPPES• NUCC
DocGraph Dataset
• DocGraph by the numbers – Directed graph – Average total degree 52.8 – 940,492 providers (graph nodes/vertices) – 49,685,810 shared edges
DocGraph Data
Doctor Detail (docNPI.com)
Doctor Detail
NPPES
• National Plan and Provider Enumeration System • Source of NPI (National Provider Identifier) • No cost download • Information is entered and updated by provider
Data quality is good to poor
• CSV file with 314 columns
NUCC
• National Uniform Claim Committee– Healthcare Provider Taxonomy– No cost download
• CSV file with 5 columns and 830 rows– Link taxonomy to NPPES reported taxonomy
DocGraph DataNodesOrganizationsSpecialtiesProvidersLocationsCountiesZipCensus
Relationships* Organizations -[:PARENT_OF] – Providers -[:SPECIALTY]- Specialties* Lcations-[:LOCATED_FOR]-Providers* Providers -[:REFERRED]-Providers* Counties -[:INCOME_IN]- CountiesZip* Locations – [:LOCATED_IN]-CountiesZip
DocGraph Data
Provider refers
DocGraph Data
Provider refers
Specialty
Specializes_in
DocGraph Data
Provider refers
Specialty
Specializes_in
Parent Org
Parent_Of
Location
Location_In
DocGraph Data
Provider refers
Specialty
Specializes_in
Parent Org
Parent_Of
Location
Location_In
DocGraph Data
Provider refers
Specialty
Specializes_in
Parent Org
Parent_Of
Location
Location_For
CountiesZip
Located_In
Income
Income_In
DocGraph RX Data
• Reinforcing Jonathan Freeman’s talk on Hadoop and Neo4J
Time for Analysis
Fraud Referrals
April 2013 - The owner and another senior executive of Sacred Heart Hospital and four physicians affiliated with the west side facility were arrested today for allegedly conspiring to pay and receive illegal kickbacks, including more than $225,000 in cash, along with other forms of payment, in exchange for the referral of patients insured by Medicare and Medicaid to the hospital, announced U.S. Attorney for the Northern District of Illinois Gary S. Shapiro.
Hadoop Page Rank
DocGraph RX Data
• Originally obtained by ProPublica• Prescribing pattern for all physicians for
Medicare Part D – 2011• Largest public released prescribing database• 2 sets of data - 30M edges each• Related to business name and NDC-9 code– NDC 9 code allows for aggregation of drugs
DocGraph RX Data
DocGraph RX Data
DocGraph RX Data
DocGraphRx Data
Provider refers
Specialty
Specializes_in
Parent Org
Parent_Of
Location
Location_For
CountiesZip
Located_In
Income
Income_In
Drugs
prescribes
DocGraph RX Data
• http://whnt.com/2013/03/27/follow-up-decatur-family-claims-prescription-drugs-from-dr-shelinder-aggarwal-killed-their-son/
• http://www.palmbeachpost.com/news/news/state-regional/doctors-booted-fom-medicaid-for-massive-oxy-doses-/nPpMf/
DocGraph RX Data
• Back to “bad data”• http://www.albme.org/actions.html
Combine additional datasets
• Medical data– Doctor referral data– Medicare doctor prescription practices– “Dollars for Doctors” – Drug company promotional
payments• Census Data– Income data– Poverty data
Recommendation Engine?
• Build a graph model of the data• Build a recommender model from the graph
model• Graphs can be visualized, explained, discussed
and debugged collaboratively