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Health IT and Personalized Medicine Policy Implications John Glaser, PhD Senior Advisor, ONC/HHS Vice President and CIO Partners HealthCare October 26,

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Presentation on theme: "Health IT and Personalized Medicine Policy Implications John Glaser, PhD Senior Advisor, ONC/HHS Vice President and CIO Partners HealthCare October 26,"— Presentation transcript:

1 Health IT and Personalized Medicine Policy Implications John Glaser, PhD Senior Advisor, ONC/HHS Vice President and CIO Partners HealthCare October 26, 2009

2 % 50% Size of Practice > 50 physicians Percentage physicians DesRoches CM et al., N Engl J Med 2008;359: % 13% Level of EHR Function Fully Functional Basic System Percentage EHR Adoption: Where are we now in Office Practices?

3 An Overview of the National Interoperable EHR Strategy AdoptionMeaningful UseOutcomes Meaningful Use definition and incentives EHR certification criteria and process Data, exchange, and quality measure standards and process Privacy and security standards, practices and policies Provider implementation support (extension centers) Exchange implementation support (State HIE/NHIN) Workforce development Structure Implement

4 The Partners Vision for Personalized Medicine EHR with clinical decision support Genomic research with high capacity IT Integrated genomic and phenotypic data repository Facilitated translational research leading to Diagnostic discovery Drug development Improved individualized medicine & pre / post symptomatic disease management

5 Costs of High Throughput Clinical Research

6 Survival in Patients with HER1/EGFR Mutations Bruce Johnson, Dan Farber Cancer Institute

7 Viewing Genetic Test Results

8 Genetics Clinical Decision Support

9 Broad Policy Implications Encourage the use of integrated phenotype (EHR-based) and genotype data to further clinical research and surveillance Encourage the incorporation of genetic data, genetic-based clinical decision support and family history into EHRs

10 Policy Implications Develop an understanding of the analytical boundaries of using EHR data as the source of phenotype in association studies Devise methods (heuristics, algorithms, data standards) to improve the yield of EHR data Explore models of multi-site analyses of federated data Further the development of standards for genetics-oriented data capture and exchange

11 Policy Implications Develop strategies to address genetics-based clinical decision support algorithms and results reporting Define approaches for delivering curated genetic test results, their meanings and decision support logic Examine role of family history data Develop strategies for providing patients with information about their genetic data and associated health risks Further methods to protect privacy of patient data


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