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StrataRx John Freedman MD MBA October 16, 2012

All Payer Claims Datasets: Big Data is Coming to Public Health Officials, Providers and Patients Near You. StrataRx John Freedman MD MBA October 16, 2012. Health Care Transformation - Before. Focus on the individual patient in front of you Physician autonomy is paramount

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StrataRx John Freedman MD MBA October 16, 2012

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  1. All Payer Claims Datasets:Big Data is Coming to Public Health Officials, Providers and Patients Near You StrataRx John Freedman MD MBA October 16, 2012

  2. Health Care Transformation - Before • Focus on the individual patient in front of you • Physician autonomy is paramount • All else being equal, more is better • Physicians make the decisions • Money has no place in the conversation • Valued tools: • Patient chart • Physician knowledge and experience • Well-equipped facilities

  3. Health Care Transformation - After • Population health management • Patient autonomy is paramount • All else being equal, less is better • Physicians guide patients to their decisions • Money has a limited place in the conversation • Valued tools: • Electronic health data • Learning systems • Physician analytic and interpersonal skills • Well-equipped facilities

  4. Steps in the Transformation • IT infrastructure • Payment reform • Transparency • Workforce education & training • Evidence-based medicine • Access, analysis and distribution of health information

  5. All Payer Claims Dataset • An aggregation of data files – including eligibility records plus medical and pharmacy claims – compiled from multiple health benefits payers • First statewide APCD created in Maine in 2003 5

  6. What Do Claims Tell Us? • What was done? • When? • For whom? • By whom? • Then what happened? • What did it actually cost?

  7. Why an APCD? • Rich information for health policy • How does spending differ by location? Patient mix? • What are the trends in disease prevalence? • What are the trends in treatment choices? • How do disease, treatments, outcomes, etc. vary from region to region? By gender? By type of insurance coverage? By provider? • Which providers are better/worse in quality and cost? • Support for performance improvement • Transparent reporting of provider and payer results • Data set can be used by providers to drive their QI efforts 7

  8. Why an APCD (Cont’d) • Support for informed consumer choice • Where should I be treated? • What will it cost? • Powerful data for researchers • Policy research and clinical research 8

  9. National Map of State APCDs 9 Source: APCD Council www.apcdcouncil.org 10/10/2012

  10. Examples • Leading causes of illness and hospitalization • Rates of accidents, infections and cancer • Geographic differences in incidence of diseases, such as diabetes or heart disease • Ethnic, gender or socioeconomic variations in illness • Most expensive diagnoses and procedures • Role of prevention on illness and costs 10

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  12. Antidepressant Use in Utah Utah Atlas of Health Care, Sept. 2010

  13. Distribution of Antidepressant Use Utah Atlas of Health Care, Sept. 2010

  14. Source: VT Healthcare Claims Uniform Reporting & Evaluation System

  15. 30-Day Readmission Rates Source: VT Healthcare Claims Uniform Reporting & Evaluation System

  16. 16 NHHealthCost.org

  17. 17

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  19. APCD Data Sources • Commercial (private) carriers • Medicaid • Medicare • Uninsured • Dental • Pharmacy 19

  20. Privacy • Patients • HIPAA as minimum • Providers • Reputation • Proprietary information • Payer protections • Reputation • Proprietary information 20

  21. Links to Other Data and Initiatives • Quality – CMS, state reports, regional collaboratives • Vital statistics – to assess mortality rates • Hospital Discharge Datasets – for additional data detail and measures • Health Information Exchanges – integrate claims and clinical (EMR) data • Health Insurance Exchanges 21

  22. National Collaboration • APCD Council (state and national data users), America’s Health Insurance Plans, and national data standards organizations (ANSI X12, NCPDP) • Supported by the Commonwealth Fund and AHRQ • “Harmonization” to reduce work involved • Allow data sharing across states • Long term goal of creating a national standard 22

  23. Limitations of APCDs • Based on claims data • Not real-time • Completeness and accuracy • Alternative payment arrangements • Cost • Implementation and ongoing operating expenses • Still lacks a clear business model • Access • Variable limits on access to data • Comparability between states • Harmonization will improve comparability 23

  24. Trends and Future Directions • Power and complexity are about to explode • Better understanding what we do and the effects that it has will make a bigger difference to health than more data about specific individuals 24

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