1. The Data Driven Healthcare Enterprise The Transformation to Value Based, Personalized Healthcare Brett J. Davis Senior Director, Personalized Healthcare Oracle Health Sciences Global Business Unit
2. The following is intended to outline our general product direction. It is intended for information purposes only, and may be incorporated into any contract. It is not a commitment to deliver any material code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle's products remains at the sole discretion of Oracle. Roadmaps previously communicated by Phase Forward are subject to Oracle review and amendment.
6. Life Sciences and Healthcare are converging Predictive, Preventive, Personalized and Participatory Healthcare HEALTHCARE LIFE SCIENCES “ Trial and Error” Healthcare “ Evidence Based” Healthcare “ Precision” Healthcare Blockbusters and mass-production of novel drugs Targeted Therapies Increased regulation and efficacy standards Analytics LIFE SCIENCES HEALTHCARE DNA chemistry and advanced technology “ Managed” Healthcare Paper based Records Electronic Data Capture Pharmacovigilance and Risk Mgmt Safety at Point of Care Electronic Medical Records Paper based Systems Patient Care and Disease Mgmt Translational Med Personalized Healthcare
10. The new drug development paradigm A networked healthcare and life science model Basic Research Discovery & Development Point of Care Source: adapted from DataMonitor CRO Academia, CRO & Sponsor Sponsor Healthcare
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12. Patient-centered, collaborative care “ Care that is safe, effective, patient-centered, timely, efficient, and equitable.” -- Institute of Medicine, 2001 Reid, Compton, Grossman, and Fanjiang, Editors, Committee on Engineering and the Health Care System, National Academy of Engineering and the Institute of Medicine, National Academies Press, 2005, pg. 20. Adapted from Ferlie and Shortell, 2001, Improving the quality of health care in the United Kingdom and the United States: A framework for change, Milbank Quarterly 79(2): 281-315
14. Page The Learning Healthcare Organization Accountable Care Organizations Enterprise Quality Standards Clinical Effectiveness Comparative Effectiveness Automating “Today’s” Healthcare Enterprise Need for Secure, Interoperable Healthcare Data and Analytics Impact on HC Transformation / Value to Healthcare System Today Performance Management Implications for healthcare providers This evolution has both clinical and operational implications Today’s “Transactional” Systems Were Not Designed to Enable this Transformation Evidence Based Medicine Value-based, Personalized Healthcare Trial and Error Medicine Core Transactional Systems Clinical & Enterprise Integration Enterprise Data Warehouse Context Specific Analytics and Applications Core Systems Requirements:
15. Framework for PHC Enabled by HIT Achieving this will create a “learning healthcare” paradigm -Basic / Translational Research -Clinical Research/Trials -CER -Deep Analytics / Informatics “ Learning Healthcare” Paradigm Supported by Robust, Interoperable Informatics -Trustworthy data from EHRs -Longitudinal Biobank data -Imaging Translate guidelines and empirical results into specific process steps Leverage workflow driven informatics processes to drive to point of care with decision support analytics Trustworthy data to measure protocol with analytics to track outcomes or deviations Employ analytics to measure results and teach people, activate patients and transform care
16. Framework for PHC Enabled by HIT Achieving this will create a “learning healthcare” paradigm -Basic / Translational Research -Adaptive Clinical Research/Trials -CER -Deep Analytics / Informatics “ Learning Healthcare” Paradigm Supported by Robust, Interoperable Informatics -Trustworthy data from EHRs -Longitudinal Biobank data -Imaging Translate guidelines and empirical results into specific process steps Leverage workflow driven informatics processes to drive to point of care with decision support analytics Trustworthy data to measure protocol with analytics to track outcomes or deviations Employ analytics to measure results and teach people, activate patients and transform care Health Management Platform Enterprise Health Analytics Context Specific Analytics Existing Clinical Systems Novel Research Methods for Enabling Rapid Learning Networks (e.g. adaptive trial design, signal detection) Real-time Feedback
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18. Personalized Healthcare Requires An Integrated View Insights From Combining Clinical, Biomedical, Operational and Financial Data “ How is our nurse overtime policy affecting our ICU quality measures? Patient satisfaction?” FINANCIAL HR SUPPLY CHAIN CLINICAL “ Do our cardiac care reimbursements reflect our improved quality measures?” “ How many patients with a particular molecular profile and clinical attributes are in our system?” “ INTEGRATED VIEW ANALYTICS” “ GENOTYPE” “ SILO ANALYTICS” “ What’s our overall performance? Our quality performance and cost to deliver that quality?”
19. What does this ultimately mean for a health system? Data and applications must be decoupled for robust use of the data and for applications to draw upon multiple data sources Fulfill Health Information Request Health Information Services Agreement For Secondary Use Health Information Request Authenticate Requester Transform and Normalize Health Informatio n Patient Health Information Aggregated for Normalization Transformed into Information Service Format Patient Health Information Gathered Clinical Innovations Clinical & Operational Benefit
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21. Effective Care Cycle Management Has a Positive Impact on Both Cost and Quality Source: Intel Corp 2006 Cost of Care/Day 0% 100% $1 $10 $100 $1,000 $10,000 Acute Care Residential Care Home Care ICU Community Hospital Specialty Clinic Assisted Living Skilled Nursing Facility Doctor’s Office Community Clinic Chronic Disease Management Healthy Independent Living Quality of Life
22. These platforms also create opportunities for biopharma industry innovation and novel collaboration Real-time Clinical Information Exchange Across the System Patient Recruitment Clinical Data PROVIDERS Hospital Clinics Bio-banks CRO Pharma/ Biotech Life Sciences Clinical Trials Safety & Pharmacovigilance HEALTHCARE LIFE SCIENCES
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Notes de l'éditeur
The intersection of the 2 industries starts with PV on LS side and Safety at Point of Care on HC side. So it is not the end result, it is first and most active step in move toward personalized health. Future & Oracle vision: multiple data sources from LS & HC co-exist and one can apply all the traditional reactive engines and predictive event-based engines for real-time information on impact of the product. Feed knowledge back into drug development lifecycle mgmt. Patient safety is immediate benefit, l/t benefit is better understanding of patient population.
Evolution of industry business model where parties move from separate & isolated entities to towards a “2020 network model”, where Sponsor is focused on core activities and IP.
Evolution of industry business model where parties move from separate & isolated entities to towards a “2020 network model”, where Sponsor is focused on core activities and IP. Merck External Basic Research (EBR) expect will deliver 25 per cent of Merck’s early pipeline from external partnerships by 2013. Manage partnerships in more than 20 countries— eight of those countries are in Asia. http://www.pharmafocusasia.com/strategy/rethinking.htm PPD http://www.fiercebiotech.com/press-releases/ppd-enters-strategic-collaboration-merck-ppd-acquires-mercks-vaccine-testing-lab-expa Moffitt http://www.flbog.org/documents_meetings/0024_0064_0438_14.pdf Patheon http://www.patheon.com/Services/DevelopmentServicesPDS/tabid/96/Default.aspx
