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2018 Predictive Analytics Symposium
Session 12: Social Network Analytics for Health Care
SOA Antitrust Compliance Guidelines SOA Presentation Disclaimer
Social Network Analytics for HealthcareModerator:Randy Olson, PhDLead Data Scientist, Life Epigenetics Inc.
Speakers:Joe LongAssistant Actuary and Data Scientist, Milliman Inc.
Ben Copeland, ASA, MAASenior Data Scientist, Milliman Inc.
September 20, 2018
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SOA Antitrust Compliance GuidelinesActive participation in the Society of Actuaries is an important aspect of membership. While the positive contributions of professional societies and associations are well-recognized and encouraged, association activities are vulnerable to close antitrust scrutiny. By their very nature, associations bring together industry competitors and other market participants.
The United States antitrust laws aim to protect consumers by preserving the free economy and prohibiting anti-competitive business practices; they promote competition. There are both state and federal antitrust laws, although state antitrust laws closely follow federal law. The Sherman Act, is the primary U.S. antitrust law pertaining to association activities. The Sherman Act prohibits every contract, combination or conspiracy that places an unreasonable restraint on trade. There are, however, some activities that are illegal under all circumstances, such as price fixing, market allocation and collusive bidding.
There is no safe harbor under the antitrust law for professional association activities. Therefore, association meeting participants should refrain from discussing any activity that could potentially be construed as having an anti-competitive effect. Discussions relating to product or service pricing, market allocations, membership restrictions, product standardization or other conditions on trade could arguably be perceived as a restraint on trade and may expose the SOA and its members to antitrust enforcement procedures.
While participating in all SOA in person meetings, webinars, teleconferences or side discussions, you should avoid discussing competitively sensitive information with competitors and follow these guidelines:
• -Do not discuss prices for services or products or anything else that might affect prices• -Do not discuss what you or other entities plan to do in a particular geographic or product markets or with particular customers.• -Do not speak on behalf of the SOA or any of its committees unless specifically authorized to do so.• -Do leave a meeting where any anticompetitive pricing or market allocation discussion occurs.• -Do alert SOA staff and/or legal counsel to any concerning discussions• -Do consult with legal counsel before raising any matter or making a statement that may involve competitively sensitive information.
Adherence to these guidelines involves not only avoidance of antitrust violations, but avoidance of behavior which might be so construed. These guidelines only provide an overview of prohibited activities. SOA legal counsel reviews meeting agenda and materials as deemed appropriate and any discussion that departs from the formal agenda should be scrutinized carefully. Antitrust compliance is everyone’s responsibility; however, please seek legal counsel if you have any questions or concerns.
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Presentation DisclaimerPresentations are intended for educational purposes only and do not replace independent professional judgment. Statements of fact and opinions expressed are those of the participants individually and, unless expressly stated to the contrary, are not the opinion or position of the Society of Actuaries, its cosponsors or its committees. The Society of Actuaries does not endorse or approve, and assumes no responsibility for, the content, accuracy or completeness of the information presented. Attendees should note that the sessions are audio-recorded and may be published in various media, including print, audio and video formats without further notice.
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What is Social Network Analytics?
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It’s all about cool graphs…
but not these kinds of graphs…
Rise of Social Network Analytics
Example Social Media Websites Twitter Instagram LinkedIn Facebook
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Visualize connections between members
Example Business Questions Who is an influential member? What group of people mention topic X?
Google’s PageRank Algorithm
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Determines importance of a website
Page B is the most important page C is more important than E E is more accessible than C C is accessible by B
Example from Medical Device Manufacturing
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Identifying causes of failure in medical devices
Introduction to Graph Theory
Nodes (or vertices) are entities Edges link nodes
Edges can be in unidirectional, or bidirectional Both edges and nodes can have properties A node can have a relationship with itself
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Basis of graph theory is composed of nodes and edges
Node
Edge
Relational Database vs Graph Database
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Graph DBs allow for quicker searches than Relational DBs via nodes and edges
Graphs Stores data in nodes Connected with edges Search by traversing
edges
Relational DBs Stores in records Search with joins
Source: https://www.slideshare.net/lyonwj/natural-language-processing-with-graph-databases-and-neo4j
eBay ShopBot
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How eBay ShopBot uses Graphs and Predictive Analytics
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Shoes
Men
Women
Color
Nike
Brand
White
Black
0.5
0.5 0.3
0.7
0.6
0.40.5
0.5
0.5ShopBot helps decide what question to ask next.https://shopbot.ebay.com/
Example:Input: “I want men’s sneakers”ShopBot: “Okay, what color?”
Input: “I want women’s sneakers”ShopBot: “Okay, what brand?”
How?:Deep Learning + Natural Language Understanding + GraphsAdidas
0.5
Graphs in Healthcare
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Possible Structures
Determine the structure of the healthcare graph that best answers the question being researched A graph that connects healthcare providers or provider groups A graph that connects provider characteristics, such as specialty A graph that connects patients A graph that connects patient characteristics, such as diagnoses or
treatments
Healthcare Graph Visualization Example
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Visualizing organization referrals among providers
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• Transfer of inpatients can possibly play a role in resistance
• Can networks be used to identify this issue?
Source: https://doi.org/10.1038/s41598-017-02245-7
Case Study: Patient Transfer NetworksAntibiotic resistant bacterial infections play a role within morbidity and mortality of patients
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• 1.0% increase in C. diff incidence is associated with a 0.5% increase with average neighbor C. diffincidence.
• Connected hospitals had a more similar C. diff incidence compared to non-connected hospitals
Case Study: Patient Transfer NetworksMonitor the relationship of Clostridium difficile (C. diff) incidence for hospitals
It’s great graphs have all of these uses
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but how are they constructed?
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Constructing Healthcare GraphsData Sources
Primary data sources Referral information Prior authorizations
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Secondary data sources Electronic medical records Claims data
Constructing Healthcare GraphsData Sources
Graph Inference Logic
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Constructing Healthcare GraphsMethodology for Mining Secondary Data Sources
Co-occurrence on records
Sequential occurrences on chronological records
Source and target from transmission records
Example Construction of Healthcare GraphPatient Flow
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Constructing Patient Flow GraphData Sources
Medical Claims Pharmacy Claims Provider Information
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Constructing Patient Flow GraphStructure and Methodology
Structure Methodology
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Making Connections in Patient Flow
Sally has an Office Visit with her PCP on Monday
Sally has an ER Visit leading to an Inpatient Admission on Friday
Sally is discharged next Tuesday
Sally sees a cardiologist the following Friday
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Making Connections in Patient Flow
Joe has an Office Visit with a podiatrist on Monday
Joe sees a dermatologist two months later
Joe gets X-rays on Wednesday
Joe sees a neurologist on Thursday
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Use Cases
Out-of-Network Hospital
$1mil Total Costs
(Traditional Leakage)
$250k w/o Inbound Links
$250k w/o In-Network Inbound Links
$300k linked from City Hospital (In Network)
$200k linked from Dr Smith (In Network)
LinkedLeakage
Linked Leakage
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Use Cases
Identify and visualize readmissions that occur at other hospitals
Identify treatment gaps that are causing patients to be transferred
Determine which post-acute care providers are being recommended by a hospital’s discharge planners
Post-acute Care
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Use Cases
Displays geocoded provider data Quickly identify rural isolation Observe the impact of natural obstacles
(e.g. rivers) Find geographic coverage gaps (perhaps
by specialty)
Geographic Exploration
