interoperability solution - hybrid update -- from pahe ii and iii to post market medical record...
DESCRIPTION
TRUE INTEROPERABILITY MEDICAL RECORD SYSTEMS-- A hybrid between a central warehouse and distributed query system.TRANSCRIPT
2/2014
MedDATA FOUNDATION © 2013 –ORIGINAL Distributed August 2013
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"For an idea that does not at first seem insane, there is no hope." - Albert Einstein
The views expressed herein are solely those of Stephen A. Weitzman, J.D. LL.M. Executive Director of MedDATA Foundation.
Leaflets and PPIs (Physician Package Insert ) cannot provide full prescribing information given what we know today about the response of patients by phenotype, genotype and other omics.
Current labeling therefore cannot meet FDA or other legal standards of “Adequate Directions for Use” or absence of False and Misleading Information (including omissions).
In the age of personalized precision medicine are blanket warnings or precautions adequate now that we know that individual patients, because of “omics,” respond differently in terms of adverse events (in degree) and effectiveness (degree)?
In that case is there sufficient information about the patients who participated in the clinical studies for the prescriber to make the "risk benefit decision" for their patients?
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How do we make maximum use of the data down to the granular level for the benefit of patients? (PCAST 2010)
Given the legal framework noted above embraced in the FD&C ACT and the comparable statutes of all nations, what is the baseline for sharing dossier or NDA submitted information?
How do we achieve maximum use of data without harming incentives for research and discovery?
How do we overcome obstacles to exchanging electronic clinical data, bothe HIT & Governance.
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A platform and methods for sharing data in a way that it can be analyzed for all the good purposes
Standards and Common Data Models for all disease areas
Incentives for the Pharma and Healthcare Systems Silos to share data (The Silos include holders of post market patient medical records and pharma companies that hold the pre-market data that shows safety and efficacy or lack thereof)
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2012
20251956
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Federal Aid Highway Act of 1956
I Guess Not!
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POST MARKET MEDICAL RECORD DATA
PREMARKET CLINICAL DATA SHOWING SAFETY AND EFFECTIVENESS OF THERAPIES
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It is argued that we do not have data standards. That is not true. We do have medical record formats in current use by pharma companies by which data is collected in clinical studies and submitted to FDA or EMA for evaluation of safety and efficacy. If we use these data structures then we can collect and merge post market data with premarket data in the same way that FDA evaluates data.
It is time to create incentives for pharma to make disclosure – full transparency – of protocols and clinical data of approved therapies available to advance creation of the next generation of therapies.
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Central Database
Distributed Database
Hybrid Database
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Data in medical records are collected into a Central Database for Querying and Analysis
The database is the GPRD/CPRD with millions of patients and over 60 million records
The database is to be expanded to 55 million patients
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1. Data is kept in the hands of the original data holders
2. Decrease proprietary and liability concerns
3. Decrease risk and severity of data breaches
4. Data holders know their data; improve value and better interpretation of findings
5. Minimize data transfer; minimum necessary
6. Voluntary – data partner autonomy
7. Reciprocity – value for participation
8. Partnership
9. Well-defined purpose
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1- User creates and submits query (a computer program)
2- Data partners retrieve query
3- Data partners review and run query against their local data
4- Data partners review results
5- Data partners return results via secure network
6 Results are aggregated
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1. Data must be kept in the hands of the original data holders –In the U.S. we will never get a central database – but we can get close!
2. Decrease proprietary and liability concerns – Can be handled3. Decrease risk and severity of data breaches – Disagree4. Data holders know their data; improve value and better
Interpretation of findings – DisagreeData in distributed system is not uniformly indexed or coded
5. Minimize data transfer; minimum necessary – Security Issue6. Voluntary – Data partner autonomy - Same as 17. Reciprocity – Value for Participating: Access more data8. Partnership 9. Well-defined purpose
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1 – Mirror Data and 2 Index
1. Data held by partners is
mirrored at their location
(Silo)
2. Mirrored data is "reindexed"
24/7 in a uniform manner
using NLP and
Auto-Coding
3. Indexes (inverted files) of
partners are aggregated in
central computer 24/7
4. User selects data sources
and creates and submits
query to "central" portal
5. Query locates data in the
partner sites through the
central index
6. Data relevant to the query is
aggregated in a cloud
7. Analytics is applied to
generate the report
8. Results are obtained and
published with reference to
sources of data (trail)
9. Data is erased
Data Partner4 – Select
Data Sources; Run Query
8- Obtain Results
Mirrored Data and
Index
Mirrored Data and
Index
Mirrored Data and
Index
Mirrored Data and
Index
5 - CentralCatalog - Index
Data Partner
Data Partner
Data Partner
Data Partner
Data Partner
7-Aggregate Data, Analyze,
and
Index Path
Data Path
9- Erase Data
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Mirrored Data and
Index
1. Researcher formulates logical query
2. Researchers system translates query using
3. Metadata services
4. Researcher identifies data sources and submits query to "central index" portal
5. Data in the partner sites is located
6. Data relevant to the query is aggregated in a cloud
7. Common data elements are matched (ala SHARP) and analytics applied to generate the report (User can use its own analytics engine)
8. Obtain results and publish with reference to sources of data (trail) - and log query
9. 9. Erase data
Researcher 15- Run Query
8- Obtain results
Central Catalog Index
9- Erase Data
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2. Researcher Formulates logical query
Translates logical query to physical query
3. Metadata services
Mirrored and Enhanced Data
Data Partner 1
Researcher Formulates Logical Query
Translates logical query to physical query
3. Metadata services
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5
Data Partner (2)
Mirrored and Enhanced Data
7-tAggregate data for answer and
analysis
5Researcher (n)
4
Data Partner (n)
Mirrored and Enhanced Data
Researcher selects databases, uses the chosen query system, uses the chosen analytics.
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The System is Data Agnostic, and Query System Agnostic Can access all available data for that user based upon data use agreements Data is kept in the hands of the original data holders (Same as distributed) Hybrid system is more efficient - Scalable (New Silos add Pointers to Index, “Catalog”) Hybrid system can obtain results faster Hybrid system can be multi-purpose
Outcomes Research (CER) Drug Safety Signaling (surveillance) Personalized medicine Make Clinical Research More Efficient
Rapidly design and implement observational trials Quickly and affordably conduct randomized studies Significantly reduce usual expenses associated with start-up and shut-down of
clinical research studies Identify patients for clinical studies
Data is uniform – NLP and Coded to Snomed-CT Reciprocity – value for participation (Same as distributed) Partnership (Same as distributed) Well-defined purpose (Same as distributed)
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1. Recognition that pharma is global and a solution needs to be adopted globally with EU and U.S. pharma taking the first steps on trial data and owners of medical record systems agreeing to share their data. (Or is it not really the patients who own the data?)
2. Adoption and expansion of CDISC standards for all disease areas based on BRIDG and finish SHARE (Shared Health and Research Electronic Library).
3. Capitalize on the SAS platform for clinical trial data.
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4. Designate a trusted 3rd NGO party to run a global entity to administer data sharing in an efficient sustainable model.
5. The 3rd party coordinates data sharing so that qualified researchers can pose questions and do analytics without release of personal information to provide research papers based upon information.
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