1. 3.1.1. Protocol Author Process People Technology
Limited ressources
No formal process to build the link in an unmabiguous way
Different knowledge & understanding between scientist & developper
Different translation of concept into variables by different data managers
No structured/unambigous link between scientific concepts within protocol and variables used within downstream activities (data collection & analysis)
Additional burden on protocol authors with benefits to other people
No tools to support linking (emerging electronic protocol generator cannot work effectively with this)
New/Changing & not available
Inconsistency ?
Technology
External data standards

2. 3.1.2 Data manager/collector
Process
People
Technology
External data standards
Growing set of variables ( for clinical trial) with inconsistency, redundancies
No possibility to share data across companies (CROs, in/out-licensing) and with medical record
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
No tools to store definition of variables accepted across the industry (use caDSR but UI not friendly)
No formal process to share variable definition across industry
“inventivity” of people in different contexts
Mindset that EHR integration is far away or „the problme to be solved“ by HC actors
Not yet enough pressure in need for increased efficiency in data collection (changing!)
No shared definition of what a variable is
No unambigous link between concepts within protocol and variables (see section 3.1.1)
No tool to exchange variable definition (possibility to use ODM ?)
CDASH ?????
Minimal re-use of content
No standardiwed proprietary extensions
No structured metadata

3. 3.1.3 Statistician/reporting
Process
People
No process to enforce collection of meta-data when collecting data
Additional burden on data collection team with benefits to other people
No consistency in the way variables are used across studies
No information on how variables were linked together in data collection
No process to ensure consistency in data collection across studies
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)
No tools to store relationship between variables at a conceptual level (e.g. SYSBP may be collected with site and position)
CDISC SDTM limited to safety
No agreement on terminology/code list in clinical standards
Data collection tool do not support collection of meta-data ???
BRIDG is the conceptual model linking variables
Technology
External data standards

4. 3.1.4 Data curator/miner Process People Technology
No process to enforce collection of meta-data when collecting data
Additional burden on data collection team with benefits to other people
Growing mindest of the need of secondary use of data
Same as statistician/reporting with scope across R&D
No consistency in the way variables are used across studies/projects
No information on how variables were linked together in data collection
No process to ensure consistency in data collection across studies
Protocol Team focus on ONE protocol and overlook need for data integration for submission (ISSE and ISE) and further data mining
No tools to store relationship between variables at a conceptual level (e.g. SYSBP may be collected with site and position)
CDISC SDTM has limitations and scope is only clinical safety
Different standards – CDISC, SEND, HL7 – require mapping
No agreement on terminology/code list in R&D
Data collection tool do not support collection of meta-data ???
BRIDG is the conceptual model linking variables across standards
Technology
External data standards
Data requires significant manipulation in order to be pooled, or may be difficult to pool consistently.

5. 3.1.6 Application/eCRF developer
Process
People
No formal process to hamronize application variables across studies/application
Lack of experience in available standards
HL7 very complex and difficult to learn in pharma
No underlying “enterprise” data model, linking all variables together with clear semantic => inconsistencies across applications and across trials
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
ISO data types should be used more widely
Technology
External data standards
No common terminologies across applications – and across industry

6. 3.2.1 FDA reviewer Process People Technology External data standards
????
?????
No possibilities to compare efficacy and safety profiles across companies / products
No possibility to combine clinical trial data and pharmacovigilance data or other data
No tools to store definition of concept and variables accepted across a company (across all domains)
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 tools allowing to store and manage mapping between HL7 CDISC content and SDTM view
SNOMED is HSSSP standards, but not used in the industry
Technology
External data standards

7. 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 clear perception of the need of fully consistent data standards
Inconsistencies across different standards within CDISC
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)
BRIDG being developped to ensure common semantic across standard
Technology
External data standards

8. 3.3.2 CMDR steward Process People Technology External data standards
No certification authority of cross industry standard content
No FORMAL process for sharing data standards content across industry
Change in mindset: data are critical asset, data standards are not a competitive advantage and should be shared across in the industry
Ensure quality of CDISC MDR in an environment where there are inconsistent and sometime conflicting definitions of concepts and variables
No tools to support sharing of standard content across organisation
ODM could be used to support import/expert within CMDR
No tools to support storgae and sharing of standard content across organisations
Technology
External data standards