CSNI: How State Medicaid Agencies Can Use Analytics to Predict Opioid Abuse and Improve Outcomes

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IBM World of Watson 2016 Conference
Session: IDS-2877
How State Medicaid Agencies Can Use Analytics to
Predict Opioid Abuse and Improve Outcomes
Healthcare Payer Analytics –
Accelerating Data Discovery and Operational Efficiencies
Speakers:
John Harding – Senior Director of Operations and Infrastructure
Dev Vijay – Director of Data Services

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Agenda
©2016 CNSI
◼ Introduction
❒ Speaker(s) Profile
❒ About CNSI:
◼ Who We Are
◼ Who We Serve
◼ evoBrix
❒ Cloud Platform for Medicaid
❒ Michigan & Illinois Business Metrics
◼ NASCIO Award for Cloud Innovation
◼ Healthcare Payer Analytics
❒ Business Drivers for Analytics Transformation
❒ Data Analytics Life Cycle
❒ Data Discovery / Analytics Platform
◼ Data Discovery – Our Journey
❒ Use Cases
◼ Future Roadmap
◼ Q & A

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CNSI Disclaimer
©2016 CNSI
This presentation and discussion (including any attachments & other affiliated content) may contain
information that is privileged, confidential, proprietary and/or otherwise protected from disclosure to
anyone other than its intended recipient(s). Any dissemination, copying, retention or use of this
presentation/idea or its contents (including any attachments) by persons other than the intended
recipient(s) is strictly prohibited.

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Introduction

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Speaker(s) Profile
©2016 CNSI
 Brings over 28 years of experience to CNSI
 Leads the Operations, Infrastructure & Data Services Organization
 Key member of the MMIS modernization / implementation for Michigan
 Led Michigan MMIS Operations since 2009
 Leads migration of Applications and Infrastructure to Michigan Medicaid Cloud (evoBrix)
 Previously held senior leadership positions at VisionIT and Management Information Consulting
 Brings over 18 years of experience to CNSI
 Leads the Data Services practice in CNSI
 Manages Data Modeling, Data Conversion and Business Intelligence
 Key contributor to the Data Conversion for the MMIS modernization / implementation for Michigan
 Currently involved in day-to-day Operations and Initiatives
 Previously held Development Lead positions at Alliance Data Systems and at Vertafore
 Experience in Customer Loyalty, Banking, Regulatory Compliance and Healthcare domains

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About CNSI: Who We Are
©2016 CNSI
CERTIFICATIONS
CMMI Level 3
ITIL v3
HISTORY
Founded in 1994
HEADQUARTERS
Rockville, Maryland
5 Offices and 10 Project Sites
FACILITIES
HQ Top Secret Clearance
A LEADING SOLUTIONS PROVIDER
TRANSFORMING THE BUSINESS OF
FEDERAL AND STATE HEALTHCARE
OWNERSHIP
Privately Held
EMPLOYEES
1200+
JOB ROLES
Developers, Business Analysts, Testers,
Project Managers,
Architects, Data Scientists
Infrastructure / Network Specialists
And much more…
WHAT WE DO?
Medicaid Cloud, Mobile,
Analytics, Big Data
C
N
S
I

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Who We Serve
PAYERS
PROVIDERS
REGULATIONS/
POLICY
TECHNOLOGY
CONSUMERS
We connect Consumers, Payers, and Providers with
innovative Healthcare Technology to improve outcomes for
over 28 million Americans
CNSI’s Technology supports
Payers, Providers, and Consumers
CNSI technology
supports Providers in
coordinating care for
those in need
Payers leverage our
solutions to effectively
administer healthcare
programs
Health IT
85% State Clients
Federal Clients15%
92%
7
OUR CLIENTS
©2016 CNSI

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©2016 CNSI
evoBrix CNSI’s Next Generation Modular
Delivery Healthcare Platform

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evoBrix: Cloud Platform for Medicaid
©2016 CNSI
◼ Shared
Infrastructure
◼ Shared
Application
◼ Shared
Implementation
◼ Shared
Operations
◼ Rules-Driven
◼ Service-
Oriented
◼ Web-based
◼ COTS
Integrated

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Michigan & Illinois Business Metrics
©2016 CNSI
49% Higher Member
Enrollment
120% Higher Provider
Enrollment
46% More Programs
85% More Claims &
Encounters Received
70% More Storage
Utilized
30% More User
Accounts
• $34B (FFS and MC)Payments
• 350kActive Providers
• 5.8MActive Members
• 60M Per YearFFS Claims
• 140M Per YearEncounters
• 350Interfaces
• 392Reports
• Providers 250,000
• State Staff 5,000
Active Users
Nearly 9% of US Medicaid population housed in the Michigan cloud

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©2016 CNSI
NASCIO Award for Cloud Innovation

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©2016 CNSI
“What gets measured, gets managed.”
Peter Drucker
(Management Guru)
Healthcare Payer Analytics

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Business Drivers for Analytics Transformation
©2016 CNSI
◼ CMS direction to enhance Medicaid program capabilities with predictive analytics
◼ Improve Medicaid Program Operations to enhance transparency & accountability
❒ Direct correlation in improving health outcomes via better insights
◼ Expansion of existing Medicaid Programs –
❒ Broader geographic areas / demographics with more complex needs
❒ Shift from voluntary to mandatory enrollments in Managed Care Organizations
❒ Transition of long-term services into capitated arrangements
❒ Manage rising costs due to chronic conditions and changing lifestyles
◼ Shift towards pay-for-outcome (treatment effectiveness) rather than pay-for-service
◼ Understand / address existing day-to-day operational challenges and recognize
opportunities

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©2016 CNSI
Structured
Data
Information
Insights
Action
1 2
34
[Reporting]
[Analysis]
[Analytics]
Data Analytics Life Cycle
“Turning data into actionable information & measurable outcomes”

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Data Discovery / Analytics Platform
©2016 CNSI
Operations
Data
Clinical
Data
Financial
Data
External /
Unstructured
Data
Real-time
ODS
Multi-
Dimensional
Data Store
Unstructured /
Big Data Sets
Formatting Cleansing Data Quality Reconciliation
What-If Models
(Retrospective /
Prospective)
Predictive Models
Operational & Clinical
Analytics
DATA
VISUALIZATION /
COLLABORATION
DATA DISCOVERY
AD-HOC
REPORTING
INTEGRATION
DATA SOURCING
CONSOLIDATION*
(Real-time) Health Enterprise Data Store
GoldenGate
DataStage
/Oracle
Oracle/
COGNOS
Watson
Analytics
COGNOS
Analytics
(Real-time) ETL
F
DD
D D
DB/
File

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©2016 CNSI
“We cannot solve our problems with the same
thinking we used when we created them.”
Albert Einstein
(Physicist)
Data Discovery – Our Journey

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Use Cases for Analytics Transformation
©2016 CNSI
evoBrix
Applying
Analytics to
Business
Operations
SUD
Analytics
360˚ Data
View
Solutions Used
 IBM Watson Explorer
 IBM Cognos Analytics
Solutions Used
 IBM BigInsights – Big R
Solutions Used
 IBM Watson Explorer
Content Analytics

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ClaimsSure
©2016 CNSI
Howdoesitwork?
Claims
Submission
Adjudication
System
ClaimsSure
Claim
Claims Adjudication
Engine
Claims
Resolution
Process
P(IB)
Real Time Web Services
Improper Billed Claims Manual Review Process
Supporting
Document
Request
Supporting
Document
Review
Final
Review
Whatisit?
•Operates in Pre-payment mode focusing on Improper Billing
•Cost Avoidance rather than Pay and Chase
A Pre-Payment platform
•Integrated with evoBrix Claims Adjudication Process to provide insights in Real Time
Real Time Determination
•Dynamic probability estimation of anomalous claims using predictive modeling techniques
•Machine learning techniques to enable continuous learning
Probabilistic & Machine Learning Techniques

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Use Case: Applying Analytics to Business Operations
©2016 CNSI
• Clinical forms manually reviewed to validate suspected improperly billed
claims
• Manual review process creates limits on the volume of claims processed
• Reduced efficiency and accuracy in decision making
Challenge / Business Need…
• Use text analytics to extract and identify data in unstructured documents
• Apply business rules to extracted text in order to optimize the review
process
• Evolving solution that expands on contextual search and automated
workflow
Solution / Business Value…

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Use Case: Applying Analytics to Business Operations
©2016 CNSI
◼ Freeform and Faceted Search

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Use Case: Applying Analytics to Business Operations
©2016 CNSI
◼ Create custom annotators to extract text, such as concepts, words, phases,
etc., from unstructured medical documents

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Use Case: Applying Analytics to Business Operations
©2016 CNSI
◼ View the part of speech and other text analytics results using NLP rules

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Substance Use Disorder (SUD) – National Epidemic
©2016 CNSI
On an average day in the U.S.:
◼ More than 650,000 opioid
prescriptions dispensed1
◼ 3,900 people initiate nonmedical use
of prescription opioids1
◼ 580 people initiate heroin use1
◼ 78 people die from an opioid-related
overdose*1
$55 billion in health and social costs
related to prescription opioid abuse
each year1
*Opioid-related overdoses include those involving prescription opioids and illicit opioids such as heroin
Source: 1- U.S. Department of Health & Human Services. (2016) The Opioid Epidemic: By the Numbers.
Available at http://www.hhs.gov/sites/default/files/Factsheet-opioids-061516.pdf

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Use Case: SUD Analytics
©2016 CNSI
• Substance Use Disorder (SUD) negatively affects health, personal, and
financial life
• Opioids contribute to a large number of SUD cases
Challenge / Business Need…
• Build predictive models, leveraging Medicaid data, to analyze the
characteristics of an opioid-influenced individual
• Predicting ‘at risk’ members allow for prevention / intervention strategies
Solution / Business Value…

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Use Case: SUD Analytics – Exploratory Analysis
©2016 CNSI
0
10
20
30
40
50
SubstanceCount
x10000
Substance Name
Top 10 Substances - Class II
Top 10
Substance -
Class II
Unique
Patient
Count
Jan 2016 -
Jun 2016
0
500
1,000
1,500
2,000
2,500
3,000
SubstanceCount
Prescriber ID
Top 10 Prescribers - Same Substance
ALPRAZOLAM
HYDROCODONE
BITARTRATE AND
ACETAMINOPHEN
CLONAZEPAM
Jan 2016 -
Jun 2016
0
2,000
4,000
6,000
8,000
10,000
CII CIV CV CIII
SubstanceCount
DEA Class
Overfilled Prescription by DEA Class
No. of Overfills
No. of Patients
No. of Prescribers
No. of Providers
Jan 2016 –
Jun 2016
CII
3%
CIV
61%
CV
17%
CIII
19%
Overfilled Prescription by DEA Class
Jan 2016 –
Jun 2016
Powered by IBM BigR

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Use Case: 360˚ Data View
©2016 CNSI
• A single entity can have data in many different business areas, in varying
contexts, creating challenges when needing to collate all data available
Challenge / Business Need…
• Consolidated snapshot of pertinent data from relevant business areas
• Gain hitherto unobserved insights supported by trends and distributions
• Adds significant value to day-to-day business operations
Solution / Business Value…

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Use Case: 360˚ Data View
©2016 CNSI
Medicaid Claims
Third Party Insurance
Health Risk Survey
Pharmacy
Claim # Provider $ Paid
321326 1204394 4.80
411227 1308371 325.92
Pharmacy Date Drug Code
Pharmacy A 02/26/2010 60760
Pharmacy B 09/08/2013 63187
Carrier Date Policy
Carrier A 2/19/2015 693367
Medicare 12/31/2999 242117
Year Exercise Flu Shot
2012 1-2 days Yes
2013 Everyday Yes
Demographic
11%
26%
19%
44%
Medicaid
Carrier A
Medicare
Carrier B
Third Party
Insurance
Coverage %
Member #:
73005
DOB: 06/11/1999
Name:
Jane Doe
Gender: F
Street Address:
5529 Walnut
Detroit, MI 48254
Programs:
1. Medicaid
2. CHIP
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Amount($)
Claim Paid Amount
2012 2013

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Future Roadmap
©2016 CNSI
◼ Focus on the three R’s –
◼ Leveraging an ever-expanding ecosystem of data within
evoBrix –
❒ Off-load data-centric business functions to the near real-time Data
Discovery platform
❒ Timely availability of reliable and quality data
◼ Continue adapting to shifting business needs from –
❒ Reactionary (What happened?) to Anticipatory (What will happen?)
◼ Harness the data to provide analytic insights as part of
day-to-day Operations
◼ Facilitate an evolutionary rather than a revolutionary
transformation -
❒ Leveraging our strengths in Foundational (Hindsight) analytics
❒ Refining and building on Tactical (Insight) analytics
❒ Expanding the journey to Cognitive (Foresight) analytics
Right Data Right Audience Right Time

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Questions?
Email: John.Harding@cns-inc.com

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Thank You! Persistence, Perseverance and Passion
as always remains our credo.