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A Payer’s Perspective: Business Intelligence and Analytics.

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Presentation on theme: "A Payer’s Perspective: Business Intelligence and Analytics."— Presentation transcript:

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” “360 o 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 Risk Driver Conditions RankAgeGender LOH Score DiabetesCADCOPDCHFAsthma Decubitus Ulcer Cardio- Respiratory Arrest Total 138F0.99--X-X--2 111M0.99----X--2 162F0.99XX-----3 143F0.99--XX--X3 13F ----X-X2 137F0.99XX-XX-X7 14F -------3 162F0.99-------2 262F0.99XX-----3 325F0.99----X--1 457F0.98X-XXX--7 552F0.97--XX---2 649F0.95--X-X--3 751M0.94-XX-X--5 836M0.93-------1 928F0.91X---X--3 101M0.91------X2 1143F0.88X--XX--4 1261F0.86XXX-X--6 1347F0.83XXXXX-X9

14 Align Incentives with Providers

15 Shared Savings: Potentially Preventable Readmits

16 PQI Reporting Top 20 PCP Groups Drilldown Admissions Between 1/2010 and 12/2010 Paid Through March 2011 PQI3 - Diabetes Long-term Complication Admission Rate NOGroup IDGroup Name Total Admits for Facility Inclusion Admits* Avoidable AdmitsAvoidable % Paid Amount for Avoidable Admits Average Cost/ Avoidable Admit 120008298 11850816.0%$56,662$7,083 220003456 8547612.8%$147,113$24,519 320000049 7828517.9%$78,667$15,733 420015716 13033515.2%$37,270$7,454 520050838 21239410.3%$29,752$7,438 620004619 1965036.0%$35,211$11,737 720004307 2194037.5%$17,372$5,791

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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