Transcript Why BRIDG - [iis2]

3.1.1. Protocol Author
Process
No formal process to
build the link in an
unmabiguous way
Different translation of
concept into variables by
different data managers
No tools to support
linking (emerging
electronic protocol
generator cannot work
effectively with this)
Technology
People
Limited ressources
Different knowledge &
understanding between scientist
& developper
Additional burden on
protocol authors with
benefits to other people
New/Changing & not
available
Inconsistency ?
External data standards
No structured/unambigous
link between scientific
concepts within protocol
and variables used within
downstream activities (data
collection & analysis)
3.1.2 Data manager/collector
Process
No formal process to share
variable definition across industry
No shared definition of what a variable is
No
unambigous
link between
concepts
within
protocol and
variables
(see section
3.1.1)
No structured metadata
No standardiwed
proprietary extensions
Minimal re-use of content
No tools to store definition
of variables accepted
across the industry (use
caDSR but UI not friendly)
No tool to exchange variable definition
(possibility to use ODM ?)
Technology
People
“inventivity” of people
in different contexts
Not yet enough pressure in need
for increased efficiency in data
collection (changing!)
Mindset that EHR integration is far
away or „the problme to be solved“
by HC actors
No agreement on terminology/code
list (e.g. MEDDRA, SNOMED,
LOINC, CDISC Vocab...)
No „umbrella“ organisation to
decide on variable definition
across pharma and health care
Growing set of
variables (20.000+ for
clinical trial) with
inconsistency,
redundancies
No possibility to share
data across companies
(CROs, in/outlicensing) and with
medical record
CDASH ?????
External data standards
3.1.3 Statistician/reporting
Process
No process to
enforce collection of
meta-data when
collecting data
No process to ensure
consistency in data collection
across studies
No tools to store relationship
between variables at a
conceptual level (e.g. SYSBP
may be collected with site and
position)
Data collection tool
do not support
collection of metadata ???
Technology
People
Additional burden on data collection
team with benefits to other people
Protocol Team focus on ONE
protocol and overlook need for data
integration for submission (ISSE and
ISE) and further data mining
CDISC SDTM does not manage
different groupings in different contexts
(e.g. SYSBP with/without qualifiers)
CDISC SDTM limited to safety
No consistency in
the way variables
are used across
studies
No information on
how variables were
linked together in
data collection
No agreement on terminology/code
list in clinical standards
BRIDG is the conceptual model
linking variables
External data standards
3.1.4 Data curator/miner
Process
No process to
enforce collection of
meta-data when
collecting data
People
Additional burden on data collection
team with benefits to other people
Growing mindest of the need of
secondary use of data
No process to ensure
consistency in data collection
across studies
No tools to store relationship
between variables at a
conceptual level (e.g. SYSBP
may be collected with site and
position)
Data collection tool
do not support
collection of metadata ???
Technology
Protocol Team focus on ONE
protocol and overlook need for data
integration for submission (ISSE and
ISE) and further data mining
CDISC SDTM has limitations
and scope is only clinical safety
Different standards – CDISC,
SEND, HL7 – require mapping
No consistency in
the way variables
are used across
studies/projects
No information on
how variables were
linked together in
data collection
No agreement on terminology/code
list in R&D
BRIDG is the conceptual model
linking variables across standards
External data standards
Data requires significant manipulation in order to
be pooled, or may be difficult to pool consistently.
3.1.6 Application/eCRF developer
Process
No formal process to
hamronize application
variables across
studies/application
People
Lack of experience in
available standards
HL7 very complex and
difficult to learn in pharma
No tools to store definition
of variables accepted
across a company (across
all domains)
BRIDG can be used as the basis of
the „enterprise“ data model with
some adaptation to company
No underlying
“enterprise” data
model, linking all
variables together
with clear semantic
=> inconsistencies
across applications
and across trials
ISO data types should be used
more widely
Technology
External data standards
No common
terminologies across
applications – and
across industry
3.2.1 FDA reviewer
Process
????
People
?????
No possibilities to
compare efficacy and
safety profiles across
companies / products
No tools to store definition
of concept and variables
accepted across a company
(across all domains)
No tools allowing to store
and manage mapping
between HL7 CDISC
content and SDTM view
Technology
SDTM/SAS transport file good only
for safet and for one company
HL7 CDISc content messages
need to rely on a repository of
concept/variables used in the
message
No possibility to
combine clinical trial
data and
pharmacovigilance
data or other data
SNOMED is HSSSP standards,
but not used in the industry
External data standards
3.3.1 Data Standards definition
Process
People
Silo mentality
CDISC standard
developement process
good for ONE standards
- does not enfore
consistency check
ACROSS standards ?
No tools to alowing to have
easy access to all standards
and to make consistency
check (.e.g no easy way to
find how CDAHS define a
variable versus SDTM)
Technology
No clear perception of the
need of fully consistent data
standards
BRIDG being developped to ensure
common semantic across standard
Inconsistencies across
different standards
within CDISC
External data standards
3.3.2 CMDR steward
Process
No FORMAL process
for sharing data
standards content across
industry
No tools to support sharing
of standard content across
organisation
People
No certification authority of
cross industry standard
content
Change in mindset: data are critical
asset, data standards are not a
competitive advantage and should
be shared across in the industry
ODM could be used to support
import/expert within CMDR
Ensure quality of
CDISC MDR in an
environment where
there are inconsistent
and sometime
conflicting definitions
of concepts and
variables
No tools to support storgae
and sharing of standard
content across
organisations
Technology
External data standards