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Maximizing “Meaningful Use” of Data Across the Healthcare Ecosystem

Maximizing “Meaningful Use” of Data Across the Healthcare Ecosystem. Niall Brennan Policy & Data Analysis Group, CMS March 16, 2012. What is Meaningful Use?. The American Recovery and Reinvestment Act of 2009 specifies three main components of Meaningful Use:

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Maximizing “Meaningful Use” of Data Across the Healthcare Ecosystem

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  1. Maximizing “Meaningful Use” of Data Across the Healthcare Ecosystem Niall Brennan Policy & Data Analysis Group, CMS March 16, 2012

  2. What is Meaningful Use? • The American Recovery and Reinvestment Act of 2009 specifies three main components of Meaningful Use: • The use of a certified EHR in a meaningful manner, such as e-prescribing. • The use of certified EHR technology for electronic exchange of health information to improve quality of health care. • The use of certified EHR technology to submit clinical quality and other measures. EHR Meaningful Use still evolving, BUT lots of other healthcare data sources that can be “meaningfully used” now!

  3. What Data Can We Meaningfully Use? • CMS is the largest single payer for health care services in the US • 1.5 billion claims submitted annually • Receive billions of other “non-claim” data points • Eligibility queries • 1-800 MEDICARE calls • Website hits • Quality reporting • Survey and certification • Significant additional data sources on the way • EHRs • Medicare Advantage plan encounter data • Health Insurance Exchange/Medicaid expansion data

  4. Why Do We Need Meaningful Use Across All Data Sources? • As we transition to value-based purchasing, data will be central to our efforts to: • Establish baselines • Identify problems • Promote accountability • Evaluate progress • Select and spread innovations that work to improve quality while lowering costs We are not maximizing meaningful use of health system data  Need to do a better job in order to achieve ACA goals

  5. Recent Progress in Meaningful Use of CMS Data • Providing data to ACOs • Monthly assigned beneficiary claims data • Quarterly summary reports • Medicare Data Sharing for Performance Measurement • 100% extracts of Parts A, B and D data to “qualified entities” with other private/ public claims data • Creation of additional non-beneficiary identifiable data sets • Health Indicators Warehouse HRR level data • More public use files for comparative effectiveness research

  6. Meaningful Use Will Require New Approaches to Data Release and Dissemination • Future data release processes will need to: • Permit routine 100 percent extracts of data across multiple years • Enable analysis across multiple care settings • Allow for routine creation of customized analytic files • Improve data architecture and structure (e.g. combine claims and EHR data) • Accommodate large increases in number of data users or volume of data

  7. How Does CMS Transition to More Meaningful Use of its Data? • Employ advanced analytics to create actionable information products • Establish new policies to support more flexible use and reuse of CMS data • Expand pool of CMS data users while maintaining appropriate beneficiary protections • E.g., establish data enclave/portal to expand secure access to different levels of CMS data for a wider range of users • Establish dedicated data and information products “line of business” at CMS

  8. How will this Transformation Impact CMS Data Users? We will strive to ensure that CMS Data becomes… • More Timely: Timely enough for real program management and action (e.g., ACOs, QRUR, and program management). • More Accessible: Structured to anticipate questions ahead of time (e.g., race/ethnicity breakdowns available across programs). • More Intelligent: Optimized to easily answer complex questions (e.g., not only providers with excessive utilization, but also providers they refer to and beneficiaries they see). • More Flexible: Individual data extracts can be used for multiple purposes.

  9. What will be the Health System Impact of Greater Access to CMS Data? By making CMS data more timely, accessible, intelligent and flexible for external users we will… • Support CMS in becoming a data driven value-based purchaser • Make the health care marketplace more transparent to help beneficiaries make the right health care decisions • Help providers move from “maximizing volume of services delivered” to “maximizing health and value delivered” • Support community and state efforts to identify variations in care delivery and take action that supports care and health improvement • Help researchers of all kinds advance knowledge about how to improve health and care

  10. Data + Analytics = INFORMATION • Greater access to CMS data for users is a key goal, but is not enough: • Some users want “raw” data • CMS data files and layouts can be intimidating and expensive • Can we provide users with the information they need without releasing beneficiary level data? • How does CMS “unlock” our data to develop insights and information for internal and external users? • Can we create an “information marketplace” based on our data? • Without data and analytics we cannot establish baselines, identify interventions or evaluate progress relative to our goals

  11. Turning Data into Information:Hospital Readmissions 2007-2010

  12. Turning Data Into Information:Hospital All-Cause Readmission Rate (2010)

  13. Turning Data Into Information:Change in All-Cause Readmission Rate (2007-2010) Harlingen Miami

  14. OH Readmission Rates Constant 2007-2010 • Total counts of admissions and readmissions both decreased by about 20% from 2007-2010, but readmission rate stayed constant • Ohio rates are consistently about 5% greater than the national average • States with similar risk scores have fewer readmissions: • Michigan: 19.6% in 2010 • Pennsylvania: 19.2% in 2010

  15. Readmission Rate Varies by County Hancock, rate= 13.5% Washington, rate= 23.2% Wyandot, rate= 14.0% Jackson, rate= 24.3%

  16. OH And PA Have Similar Spending Patterns • Despite similar levels of risk (HCC = 1.05) in PA and OH, spending is higher in OH • Service type spending similar except for inpatient, PAC and outpatient

  17. OH and PA have similar spending patterns by service type, except for inpatient, PAC and outpatient Total per capita spending: OH = $9,906 ($562 above natlavg) WI = $9,618 ($274 above natlavg)

  18. Publicly Available Geographic Variation Data • Institute of Medicine (IOM): Response to IOM request (geographic variation data: cost, utilization and quality) • http://iom.edu/Activities/HealthServices/GeographicVariation/Data-Resources.aspx • Health Indicators Warehouse: Geographic variation data (utilization and quality) • http://healthindicators.gov/Indicators/Initiative_CMS-Community-Indicators_5/Selection • CMS Public Use Files: Geographic variation data from health indicator warehouse • http://www.cms.gov/Medicare-Geographic-Variations/

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