People-Powered Knowledge Generation

Transcription

1 What s Next? People-Powered Knowledge Generation Harlan M. Krumholz, MD SM Harold H. Hines, Jr. Professor of Medicine June 1, 2016

2 The Problem The medical research enterprise cannot keep pace with the information needs of patients, clinicians, administrators, policy makers.

3 The Problem Too slow Too inadequate Too expensive

4 Clinical Studies of High-Risk Therapeutic Medical Devices Receiving FDA Premarket Approval in 2010 and 2011 Total Product Life Cycle Evidence Generation Vinay Rathi Nick Downing Fred Masoudi Harlan Krumholz Joseph Ross

5 Study Objective To characterize the clinical evidence generated for newly marketed high-risk therapeutic devices over the total product life cycle

6 Summary Wide variation in amount and quality of evidence for devices Few postmarket studies completed 3-5 years after approval

7 The Goal

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11 Importantly Advanced analytics cannot substitute for the absence of high quality data.

12 Data Data generated every day, for a variety of practical purposes, could serve as an inexhaustible source of knowledge to fuel a learning health care system.

13 What is possible Real-time research Dynamic decision support

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16 US Hospital Adoption View Download

17 Impediment Business models that depend on data as proprietary assets.

18 Also Privacy Access

19 Fragmented and disconnected healthcare data

20 Data Access Consent Alignment

21 Data Access Consent Alignment Data-Holder

22 Data Access Consent Alignment Data-Holder Legislated/Regulated

23 Access is to poor quality data Distributed data bases Incomplete information Incompatible data Better data expensive

24 What are the properties of what we want? Longitudinal Comprehensive Data Timely Affordable

25 Data Access Consent Alignment Data-Holder Legislated/Regulated

26 Data Access Consent Alignment Data-Holder People Powered Legislated/Regulated

27 This is the big idea People Powered

28 People-Powered Research

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31 Right to access your health information

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36 Regs People Power Tech New Era

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39 Blue Button Blue Button gives consumers ability to obtain their health care records in both humanreadable and machine-readable format, and to send them where they choose.

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42 FHIR Fast Healthcare Interoperability Resources is a draft standard describing data formats and elements (known as "resources") and an Application Programming Interface (API) for exchanging Electronic health records. Make it easier for third-party application developers to provide medical applications which can be easily integrated into existing systems.

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44 Fragmented and disconnected healthcare data

45 What should be

46 Sync for Science Sync for Science (S4S) is a platform and standard that empowers participants to enable the acquisition and harmonization of their health data between personal, clinical, and research databases.

47 1906 Pure Food and Drug Act

48 Key Principles of the Precision Medicine Initiative Make it easier for patients to access, understand, and share their own digital health data, including donating them for research Engage participants as partners in research, including returning results to them in dynamic, user-centered ways

49 Precision Medicine Task Force Leslie Kelly Hall, co-chair Andy Wiesenthal, co-chair May 17, 2016

50 Three Interoperability Pathways Critical to PMI as Discussed by the Task Force 1 Focus on EHR data first (e.g., labs, meds) HPO s EHR (labs, meds, etc.) Individual s EHR Patient Portal (e.g., labs, meds) and PGHD Individual s App NIH PMI Cohort

51 Three Interoperability Pathways Critical to PMI as Discussed by the Task Force 1 Focus on EHR data first (e.g., labs, meds) Individual s EHR Patient Portal (e.g., labs, meds) and PGHD Individual s App NIH PMI Cohort

52 Three Interoperability Pathways Critical to PMI as Discussed by the Task Force 1 Focus on EHR data first (e.g., labs, meds) HPO s EHR (labs, meds, etc.) Individual s EHR Patient Portal (e.g., labs, meds) and PGHD Individual s App NIH PMI Cohort 2 Enable data gathering from other independent non-provider sources NIH PMI Cohort Labs, PBMs, Claims Retail Pharmacies 3 Accelerate ability to return an individual participant s aggregated data from multiple sources, and eventually research results Provider Patient NIH PMI Cohort HPO, Patients, Other Sources

53 Sync for Science

54 1906 Pure Food and Drug Act

55 CDRH Strategic Priorities

56 CDRH Strategic Priorities

57 Properties of Tools Highly secure Cloud-based User-activated Secure Messaging Automated Encrypted at rest and in motion Easy

58 Properties of Tools User Benefit

59 Putting your health information to work

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65 From where? Images Labs Clinicians Person Devices Data Drugs

66 Privacy No data moves without your permission.

67 Foundational Principles for Responsible Sharing Respect individuals, families and communities Advance research and scientific knowledge Promote health, wellbeing and the fair distribution of benefits Foster trust, integrity and reciprocity

68 Time is now

69 The Goal

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73 Thank you.