2. 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
premarket data that shows safety and efficacy
or lack thereof)
6/18/2013
2
7. POST MARKET MEDICAL RECORD DATA
PREMARKET CLINICAL DATA SHOWING SAFETY
AND EFFECTIVENESS OF THERAPIES
6/18/2013 MedDATA FOUNDATION 7
8. 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.
6/18/2013 MedDATA FOUNDATION 8
10. Data in Silos are collected into a Central
Database for Querying and Analysis
The database is the GPRD/CPRD with 12
million patients and over 60.0 million records
The database is to be expanded to 55 million
patients
6/14/2014 MedDATA FOUNDATION 10
11. “In other sectors, universal exchange standards have
resulted in new products that knit together
fragmented systems into a unified infrastructure.”
“The resulting ‘ network effect’ then increases the
value of the infrastructure for all, and spurs rapid
adoption.”
“By contrast, health IT has not made this transition.”
“The market for new products and services based on
health IT remains relatively small and undeveloped
compared with corresponding markets in most other
sectors of the economy, and there is little or no network
effect to spur adoption.”
6/18/2013
12
12. 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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13. 6/14/2014 MedDATA FOUNDATION 14
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
14
Jeffrey Brown, PhD and
Richard Platt, MD
Harvard Pilgrim Health Care
Institute
/ Harvard Medical School
15. 1. Data must be kept in the hands of the original data holders –
(In the U.S. we will never get a central database)
2. Decrease proprietary and liability concerns – Can Be Handled
3. Decrease risk and severity of data breaches – Disagree
4. Data holders know their data; improve value and better
interpretation of findings – Disagree
Data in distributed system is not uniformly indexed or coded
5. Minimize data transfer; minimum necessary –(Security Issue)
6. Voluntary – Data partner autonomy (Same as 1)
7. Reciprocity – Value for Participating: Access more data
8. Partnership
9. Well-defined purpose
6/14/2014 MedDATA FOUNDATION 16
17. 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. Obtain results and publish
with reference to sources of
data (trail)
9. Erase data
Data Partner
4 – Select
Data Sources;
Run Query
Obtain
Results
Mirrored
Data and
Index
Mirrored
Data and
Index
Mirrored
Data and
Index
Mirrored
Data and
Index
Mirrored
Data and
Index
5 - Central
Catalog - Index
Data Partner
Data Partner
Data Partner
Data Partner
Data Partner
Alternative: Hybrid/Library/Query /Response
7-Aggregate
Data; Analyze;
and
Index Path
Data Path
9-Erase Data
3
6
6/14/2014 MedDATA FOUNDATION 18
18. 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. Obtain results and publish
with reference to sources of
data (trail)
9. Erase data
Data Partner
4 – Select
Data Sources;
Run Query
Obtain
Results
Mirrored
Data and
Index
Mirrored
Data and
Index
Mirrored
Data and
Index
Mirrored
Data and
Index
Mirrored
Data and
Index
5 - Central
Catalog - Index
Data Partner
Data Partner
Data Partner
Data Partner
Data Partner
Alternative: Hybrid/Library/Query /Response
7-Aggregate
Data; Analyze;
and
Index Path
Data Path
9-Erase Data
3
6
6/14/2014 MedDATA FOUNDATION 19
19. 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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