1. Emerging Technologies
Project Prioritization Washington DC, March 18th 2013

2. Agenda 10 – 10: 30 Presentation of 3 projects
Semantic Web
Meta-data
Data visualization
10:30 – 11:00: identification of additional projects i
11:00 – 12:00: prioritization & lead identification

3. Project Prioritization
Priority
Feasibility
Technology
Lead (FDA)
Lead (Industry)
Semantic web
Data visualization
Meta data definition
Meta data E2E process
Meta data: study level standard to BRIDG/JANUS
Meta data: contextual data
Meta data: versioning, upcoding, …
CSV & multi-tenant cloud solutions
Big data: definition & architecture

4. Project X. Title Objective/Scope: …… Approach Deliverable: Timelines:…
Step 1
Deliverable:
document?
Pilot ?
Timelines:…

5. Project Description Template

6. Project A. Semantic web Objective/Scope: Approach Deliverable:
investigate how formal semantic standards can support the clinical trial data life cycle from protocol to submission
Clarify the type of skill set needed around RDF knowledge to develop in the industry
Approach
Investigate Opportunities & challenges (barriers)
Identify use cases (inventory of what can be done)
Area 1: data standards
Area 2: represent clinical trial data in RDF (more experimental – done by W3C)
Area 3: annotated TLFs with information on where the data came from (linked to traceability)
Area 4. Modelling the protocol (try to understand what is feasible with what has already been done)
Linking with other “knowledge providers” (NCI, BRIDG, SNOMED, ..)
Deliverable:
Readiness assessment – is semantic web technology useful or not - and what is the relationship meta data ?
Catalogue & prioritization of uses cases
Identification, execution and experiment on high priority use case
Timelines:
(see project proposal from Fred – deliverable every 3 months)

7. Project B. Data Visualization
Objective/Scope: identify new technologies on how to navigate into lots of information to find the “tree in the forest” (e.g. safety signal) ……
Approach
Identification of audience (reviewers, DMC, …) and related pain points where visualization could help in facilitating decision making
Uses case/requirements: why/how data visualization (in addition to table) Note: make sure to re-use work done already (including work done at FDA/DIA) around visualization
Scientific reviewers (NIH, FDA CDER, FDA CBER, sponsor medical team..)
How does clinical data monitoring/visualization translates into risk based monitoring
Should we include in there a gallery of visualization possibilties
Identify open source technology that could support visualization faster/better/cheaper
Interface/integration between data visualization tools and data standards – what would be the requirements for data visualization
Develop test cases (e.g. NIH) with one
Deliverable:
Stakeholder and Use case document
Catalogue of open source tools that would fit requirements
Requirement for data standards
Pilot with one tool (expanding on NIH ?)
Timelines:…

8. Project 1. Meta Data Definitions
Objective/Scope: common definitions around meta data and related aspects across the industry
Approach
Definition + example / short description of usage
Build/update on existing definition from FDA guidance's, CDISC glossary, check cross industry definition (e.g. Gartner)
To include
Meta data (structural & operational), data elements, attributes
Master data & master reference data
Controlled terminology, code systems, value sets, permissible values
Data pooling, data integration, data aggregation
Interoperability, semantic interoperability
Deliverable: not only a glossary but a practical guide with example to ensure common understanding and usage
Timelines: 6 months

9. Project 2. User guide for E2E use & implementation of meta data
Objective/Scope:
practical guide on how to implement meta-data management (potentially with existing tools) within an organisation for a single trial
Gather set of key requirements for Meta data management tool that would fulfil the needs for an E2E process within an organisation , as well as supporting data consistency for cross trial analysis
Approach
Define study level meta-data versus enterprise wide meta-data (what is needed to ensure automation)
Main user needs (collect user stories) at different phases: protocol definition, system set-up, data visualization & monitoring, integration of collected data, analysis & reporting ..
Main process changes at different phases; definition of key steps in needed new process (basis of WI/SOP)
Link with data traceability project
Identify additional requirements related to
Versioning
Expansion of meta-data structure
Support to upcoding of standards & terminology
Pilot with available tools
Deliverable:
Draft user stories for MDR tool selection
Draft set of Work instruction to support MDR deployment
Pilot ?
Timelines: 12 to 18 months

10. Project 3. Study level standards, define
Project 3. Study level standards, define.xml, Study meta data in JANUS/BRIDG: do they fit ?
Objective/Scope: ensure that the meta-data defined in sponsor and send to FDA through define.xml fit the need of JANUS (and BRIDG based scientific data repository) for cross trial analysis
Approach
Describe how study level meta-data + trial summary in SDTM are stored in BRIDG (DefineActivity) – is this enough for cross trial analysis ?
Need to confirm if this is include visit
How define.xml can be transformed into DefinedActivity – is define complete ?
Deliverable:
Recommendation for update on content/format to define.xml
Specification of transformation function from define.xml to BRIDG DefineActivity
Timelines: 6 to 12 months

11. Project 4. Contextual meta data for cross trial analysis
Objective/Scope: definition of “contextual” metadata needed to support data analysis – in addition to structural meta data such as SDTM - and that should be collected together with the data during clinical trial execution.
Approach
collect and document concrete examples (“user stories”) encountered by staff performing cross trial analysis with different type of contextual information that would be useful to have in the context of data analysis (primary or secondary) of clinical trial data.
Note: would this be specific for a Therapeutic Area or can they be general across TA ? - to be further clarified after few user stories
Check with AdAM team and Integrated Analysis group to ensure we build on work done
extract patterns out of these user stories and define a generic structure that would allow to store and exchange this information between the different stakeholders . The structure should be compliant with ISO21090 and BRIDG
Characterize how we would store & exchange these metadata (as part of define.xml ???)
Pilot the propose exchange standardApproach
Deliverable:
Description of additional – non structural like SDTM – meta data to be collected and store for cross trial analysis
Recommendation for update to defiine.xml or for a new standards to exchange these meta-data
Timelines: 12 months

12. Project C. CSV and multi-tenant cloud solutions
Objective/Scope: lower barriers to adoption and validation of multi-tenant cloud solutions & build understanding and awareness and gaps and technical solutions
Approach
Catalogue of existing efforts (challenges & lessons learned – e.g. OMOP)
Establish definitions: multi-tenant shared infrastructure and ensuring scope, regulatory frameworks (GXP..) that are relevant
Write up and share valuable uses cases – examples of what you would like to do today in a validated cloud environment and how it would be valuable
Write and share the list of real and perceived gaps
Write up best practices, available solutions, technical requirements to address the gaps, meeting specific regulatory requirements and recommend best practice for multi-tenant solutions
Define a use case to pilot/test/leverage FedRamp (set of processes in government to approve cloud) - Inward/Outward facing Scientific Enclave concept ?
Share examples of “uses cases” or successful implementation and explain the value
Deliverable:
Documentation (PhUse Wiki ?) ?
FDA pilot ?
Timelines:…

13. Project X. Big Data definition & architecture
Objective/Scope: define what is big data and how we could/should share this type of information
Approach
Scoping
Definition & level setting
Scope – type of information
challenges & benefits
work that has been done – where this discussion have been taking place already
Collect use cases
Research: propose model or architecture for sharing this type of information
Deliverable:
Definition of Big Data
Use case
Architecture for Big Data sharing
Timelines:…

14. Project X. Title Objective/Scope: …… Approach Deliverable: Timelines:…
Step 1
Deliverable:
document?
Pilot ?
Timelines:…

15. Project X. Title Objective/Scope: …… Approach Deliverable: Timelines:…
Step 1
Deliverable:
document?
Pilot ?
Timelines:…

16. BACKUP from Meta-data Management “table”

17. Background on Meta data discussion
6 recurrent themes
Consistent use across the life-cycle (Protocol, SAP ----> Submission)
Across studies within an organisation
Across studies and across partners
Move away from MS Excel
Version management (versions of standards, terminology, what used when)
Extensible metadata structures
Scope
Structural meta data (SDTM, terminology)
Operational meta data (contextual, implicit info)
Master data

18. Problem and how metadata helps?
Attendee No 1.
Communicate across groups within a company the data needs
To pass data across groups
E.g. Relationships Body Position with Observation value
No 2.
Protocol, data collection, tabulations stds
If one changes, how is rest kept consistent
E.g. Impact analysis for a change
No 3.
Share across silos, need to share the semantics
Unambiguous communication
No 4.
Analyse across different studies
Semantics needs to be consistent
Structured and unstructured metadata

19. Problem and how metadata helps?
No 5.
Standards management, consistent and checking when used in studies, all aligns
Version management
No 6.
Use it to support the lifecycle
More automation
E.g. schedule of events out of protocol
No 7.
Terminology management
Managing the CDISC terminology
No 8.
End to end process, across different collections systems
Away from excel
E.g. Protocol amendments, the unstructured logic behind

20. Problem and how metadata helps?
No 9.
Define concept once and reuse many
e.g. Endpoints and population from protocol into SAP, copied by hand
Version management
No 10.
Tools need to adapt to changing needs and greater capability
No 11.
Clinical observation metadata ok, rapid version change
While still serving the current business needs
What did we use when, what versions used
No 12.
Is XML passed its sell by date, can it meets the need
Traceability, providence
Building on shifting sands of constant version change
Automation, remove silios

21. Problem and how metadata helps?
No 13.
Within org ok, outsourcing causes lack of understanding, communication
E.g. SDTM Domain spec to CRO

22. Themes Consistent use across the life-cycle
Protocol, SAP ----> Submission
Across studies within an organisation
Across studies and across partners
Move away from MS Excel
Version management (versions of standards, terminology, what used when)
Extensible metadata structures

23. Metadata Definition of metadata
Structural
Description of the data, e.g. SDTM, data point attributes and data source, terminologies
Non structural
Protocol amendment, data traceability
Master data
SAP?
Standard v TA v Study v Organisation v Industry