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

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

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

  1. 1. innovation@work 1 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
  2. 2. innovation@work 2 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
  3. 3. innovation@work 3 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.
  4. 4. innovation@work 4 Introduction
  5. 5. innovation@work 5 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
  6. 6. innovation@work 6 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
  7. 7. innovation@work 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
  8. 8. innovation@work 8 ©2016 CNSI evoBrix CNSI’s Next Generation Modular Delivery Healthcare Platform
  9. 9. innovation@work 9 evoBrix: Cloud Platform for Medicaid ©2016 CNSI ◼ Shared Infrastructure ◼ Shared Application ◼ Shared Implementation ◼ Shared Operations ◼ Rules-Driven ◼ Service- Oriented ◼ Web-based ◼ COTS Integrated
  10. 10. innovation@work 10 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
  11. 11. innovation@work 11 ©2016 CNSI NASCIO Award for Cloud Innovation
  12. 12. innovation@work 12 ©2016 CNSI “What gets measured, gets managed.” Peter Drucker (Management Guru) Healthcare Payer Analytics
  13. 13. innovation@work 13 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
  14. 14. innovation@work 14 ©2016 CNSI Structured Data Information Insights Action 1 2 34 [Reporting] [Analysis] [Analytics] Data Analytics Life Cycle “Turning data into actionable information & measurable outcomes”
  15. 15. innovation@work 15 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
  16. 16. innovation@work 16 ©2016 CNSI “We cannot solve our problems with the same thinking we used when we created them.” Albert Einstein (Physicist) Data Discovery – Our Journey
  17. 17. innovation@work 17 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
  18. 18. innovation@work 18 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
  19. 19. innovation@work 19 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…
  20. 20. innovation@work 20 Use Case: Applying Analytics to Business Operations ©2016 CNSI ◼ Freeform and Faceted Search
  21. 21. innovation@work 21 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
  22. 22. innovation@work 22 Use Case: Applying Analytics to Business Operations ©2016 CNSI ◼ View the part of speech and other text analytics results using NLP rules
  23. 23. innovation@work 23 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
  24. 24. innovation@work 24 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…
  25. 25. innovation@work 25 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
  26. 26. innovation@work 26 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…
  27. 27. innovation@work 27 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
  28. 28. innovation@work 28 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
  29. 29. innovation@work 29 Questions? Email: John.Harding@cns-inc.com
  30. 30. innovation@work 30 Thank You! Persistence, Perseverance and Passion as always remains our credo.

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