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3.1.1. Protocol Author

3.1.1. Protocol Author. Process. People. 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.

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3.1.1. Protocol Author

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  1. 3.1.1. Protocol Author Process People 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. Process People No formal process to share variable definition across industry “inventivity” of people in different contexts No shared definition of what a variable is Not yet enough pressure in need for increased efficiency in data collection (changing!) Growing set of variables (20.000+ for clinical trial) with inconsistency, redundancies No possibility to share data across companies (CROs, in/out-licensing) and with medical record No unambigous link between concepts within protocol and variables (see section 3.1.1) No structured metadata Mindset that EHR integration is far away or „the problme to be solved“ by HC actors No standardiwed proprietary extensions Minimal re-use of content No agreement on terminology/code list (e.g. MEDDRA, SNOMED, LOINC, CDISC Vocab...) No tools to store definition of variables accepted across the industry (use caDSR but UI not friendly) No „umbrella“ organisation to decide on variable definition across pharma and health care No tool to exchange variable definition (possibility to use ODM ?) CDASH ????? Technology External data standards 3.1.2 Data manager/collector

  3. 3.1.3 Statistician/reporting Process People Additional burden on data collection team with benefits to other people No process to enforce collection of meta-data when collecting data 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 Additional burden on data collection team with benefits to other people No process to enforce collection of meta-data when collecting data Same as statistician/reporting with scope across R&D Growing mindest of the need of secondary use of data 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 CDISC SDTM has limitations and scope is only clinical safety No tools to store relationship between variables at a conceptual level (e.g. SYSBP may be collected with site and position) 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 BRIDG can be used as the basis of the „enterprise“ data model with some adaptation to company No tools to store definition of variables accepted across a company (across all domains) 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 ???? ????? No possibilities to compare efficacy and safety profiles across companies / products No possibility to combine clinical trial data and pharmacovigilance data or other data SDTM/SAS transport file good only for safet and for one company No tools to store definition of concept and variables accepted across a company (across all domains) 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 BRIDG being developped to ensure common semantic across standard 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 External data standards

  8. 3.3.2 CMDR steward Process People 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 ODM could be used to support import/expert within CMDR No tools to support sharing of standard content across organisation No tools to support storgae and sharing of standard content across organisations Technology External data standards

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