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A Payer’s Perspective: Business Intelligence and Analytics

A Payer’s Perspective: Business Intelligence and Analytics. AmeriHealth Mercy. Overview Started as Mercy Health Plan in early 1980’s Managed care solutions for physical health, behavioral health, and pharmacy services Predominant focus is on Medicaid populations

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A Payer’s Perspective: Business Intelligence and Analytics

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  1. A Payer’s Perspective: Business Intelligence and Analytics

  2. AmeriHealth Mercy Overview • Started as Mercy Health Plan in early 1980’s • Managed care solutions for physical health, behavioral health, and pharmacy services • Predominant focus is on Medicaid populations • Physical Health plans in 6 States, 2 more going live in 2012 Challenges • Limited funding • Characteristics of population

  3. Underlying Goals of Payer Analytics • Understand utilization and cost trends • Improve clinical outcomes • Prevent unnecessary services • Improve HEDIS scores • Maximize revenue • Influence policy • Align incentives • Identify trends early – appropriate interventions

  4. Critical Functions • Add value to existing data • Getting data into the right hands at the right time • Continually seek out new data sources

  5. Key Data Domains • Member • Provider • Claims – PH/BH/Rx • Care Management • Pharmacy • External Data Sources

  6. Data Schematic

  7. General Management

  8. Management Dashboards

  9. “Make Every Member Contact Count” • “360o View of the Member”

  10. Member Data • Demographics • Claims data (Medical, Dental, Vision) – including historical data • Pharmacy data • Race/Ethnicity/Language • Coverage Category • Lab Results • Risk Scores – prospective, concurrent • PCP History • Clinical Conditions • Maternity History • Etc….

  11. Clinical Care Gaps

  12. Early Intervention • Early Identification and Stratification of High Risk Maternity Cases • Prenatal Vitamins • Lab Codes • Lab Test Results • Member Risk Score • Medication History • Diagnosis codes (e.g., SMI) • Age • Health Risk Assessment Reponses • Prior Delivery History

  13. Patient Stratification Algorithms • Likelihood of Hospitalization

  14. Align Incentives with Providers

  15. Shared Savings: Potentially Preventable Readmits

  16. PQI Reporting

  17. PCP Specific Statistics

  18. Strategic Analytic Tools • Today: • Verisk Groupers • DxCG Risk Scoring • Likelihood of Hospitalization • Treo Services • MedAssurant – Catalyst • Internal Algorithms • Access Databases • Soon: • Sybase IQ • WEB Intelligence (WEBi) • User Maintained Production Schemas • Data Quality/Profiling

  19. Looking Ahead • Future Directions: • Innovative algorithms • “Logical” phone queues • Infrastructure strategies • Reform implications • HIE • Social media

  20. Innovative Member Algorithms • Ability to “Impact” Member • Success in contacting Member • Ratio of PCP to ER visits • Medication compliance • Rate of historical “preventable” events • Participation in prior programs • Overall family “compliance” score

  21. Health Information Exchange

  22. Thank You!! • Questions?

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