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All Payer Claims Datasets:Big Data is Coming to Public Health
Officials, Providers and Patients Near You
StrataRxJohn Freedman MD MBA
October 16, 2012
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
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
Steps in the Transformation
• IT infrastructure
• Payment reform
• Transparency
• Workforce education & training
• Evidence-based medicine
• Access, analysis and distribution of health information
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
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What Do Claims Tell Us?
• What was done?
• When?
• For whom?
• By whom?
• Then what happened?
• What did it actually cost?
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
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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
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National Map of State APCDs
Source: APCD Council www.apcdcouncil.org 10/10/20129
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
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Antidepressant Use in Utah
Utah Atlas of Health Care, Sept. 2010
Distribution of Antidepressant Use
Utah Atlas of Health Care, Sept. 2010
Source: VT Healthcare Claims Uniform Reporting & Evaluation System
30-Day Readmission Rates
Source: VT Healthcare Claims Uniform Reporting & Evaluation System
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NHHealthCost.org
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APCD Data Sources
• Commercial (private) carriers
• Medicaid
• Medicare
• Uninsured
• Dental
• Pharmacy
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Privacy
• Patients– HIPAA as minimum
• Providers– Reputation – Proprietary information
• Payer protections– Reputation– Proprietary information
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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
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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
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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
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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
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