A payer s perspective business intelligence and analytics
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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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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

  • Physical Health plans in 6 States, 2 more going live in 2012

    Challenges

  • Limited funding

  • Characteristics of population


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


Critical Functions

  • Add value to existing data

  • Getting data into the right hands at the right time

  • Continually seek out new data sources


Key Data Domains

  • Member

  • Provider

  • Claims – PH/BH/Rx

  • Care Management

  • Pharmacy

  • External Data Sources


Data Schematic


General Management


Management Dashboards


  • “Make Every Member Contact Count”

  • “360o View of the Member”


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….


Clinical Care Gaps


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


Patient Stratification Algorithms

  • Likelihood of Hospitalization


  • Align Incentives with Providers


Shared Savings: Potentially Preventable Readmits


PQI Reporting


PCP Specific Statistics


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


Looking Ahead

  • Future Directions:

  • Innovative algorithms

  • “Logical” phone queues

  • Infrastructure strategies

  • Reform implications

  • HIE

  • Social media


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


Health Information Exchange


  • Thank You!!

  • Questions?


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