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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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