From Data Capture to Decisions Making Innovation through Standardization How Can Standardization Help Innovation Michaela Jahn, Stephan Laage-Witt PHUSE 2010, DH04 October 19 th ,2010
Dec 20, 2015
From Data Capture to Decisions Making Innovation through Standardization
How Can Standardization Help Innovation
Michaela Jahn, Stephan Laage-WittPHUSE 2010, DH04October 19th,2010
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BackgroundBroad Range of Responsibilities for Clinical Science
Ongoing work of the study management
team
Medical data review during study conduct
Signal detection on study/project
level
Publications & presentations at congresses
Data base closure preparation and
clinical study report writing
Communication to project team and
management
Innovate!
Clinical Pharmacologist
BiomarkerExpert
TranslationalMedicine Leader
Drug Safety Expert
Radiologist
The complexity of clinical trials is increasing constantly
Preliminary analysis for study decisions during
conduct
Exchange information
Many Demands from Science and OthersEnabling Innovation
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Thinking time and space
Room for exploration – no guarantee of successEarly and speedy access to quality data
Integrated data displays
Further improved operational efficiency
High quality and regulatory compliance
Flexibility for different study designs and new data typesSupport for study amendments before and after enrolment
Clinical Data Flow & Tools
Processes and Data on Study Level
Processes and data on Project Level
Cross-functional SOPs& Business Processes
Standards for:
Enabling Innovation - Facilitated via Standardization
Dataflow & Tools • Less tools and system interfaces• Cross-functional alignment on standard platforms
Study Level • Simplified and standardized data flow
Project Level • Standardized data formats and displays
SOPs & Processes • Clarified and documented business processes
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4 Key TopicsDriving Innovation Through Standardization
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Edison's light bulb became a global successstory due to its standardized bulb socket .
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Simplified Data Flow for Clinical DataDeveloping a 2 years roadmap
In 2007, a detailed analysis of the existing data flow revealed a fairly complex system environment with a number of gray areas.
A cross-functional team designed a new data flow and a target system environment which we implemented over the recent 2 years. Key elements are:
• Streamlined data flow• Less systems and fewer interfaces• Minimize redundant data storage• EDC for all studies
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Implementing the Roadmap Standards for Data, Systems, Processes
Key Decisions for clinical data withinRoche Exploratory Development (pRED)
– Use of Medidata Rave as the standard data capture tool
– Use of SAS for data extraction and reformatting across all involved functions
– Implementation of CDISC/CDASH as data capture standard
– Implementation of CDISC/SDTM as data extraction standard
– Single, cross-functional repository for clinical data
– The same standardized data flow for preliminary data during study conduct and final data after study closure
– Grant scientists access to the data during study conduct
– Allow state of the art tool for medical data review and early decision making
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Clinical Science requires early access to quality data
Addressed by• Studies are handled in the same way• Reduce study start up times• First data extraction within study are done earlier• Clinical Science gets data earlier
Providing Speedy Access To Study Data
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Study setup ready First data extraction Medical Data Review
Study setup ready First data extraction Medical Data Review
without standards
with standards80% savings* ~50% savings*
* Gartner report 2009
Study time
Decision point during study conduct
Data accumulation / cleaningTime until enrolment start
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Clinical Science requires easy access to interpretable data
Addressed by• Standardized e-Forms are used to capture data (CDASH)• Extraction of data into a standardized data model (SDTM)• Standardized data model is translated into language beyond variable
names (data model repository)
Standardizing Data Formats and Displays
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Medidata Rave
Standardized e-Forms
Standardized
Extractions
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•Clear distinction between mandatory steps and deliverables versus flexible ways of working
•Clear identification of roles and responsibilities•Consistent and integrated graphical
representation of the business processes
Clarifying Business ProcessesA smarter way to manage the “Who is Doing What”
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The process redesign using a database approach delivered an integrated view of processes and RACI charts.
CustomQueries
AdobePDF
HTML
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Receiving data early
New Responsibilities for Clinical Science
Accept unclean data
Accessing study data
More responsibility to protect the integrity of the study
Reading study data directly
Learn and understand the concept of data models and standards
Managing flexibility via protocol amendments
Moving away from standards costs time and resources
Exploring study data Understand the concept of exploration and noise
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Summary of Success
The implementation of the changes to systems, data flow and process began in 2008 and finished in 2010.
Experience to date
Fast Study Setup eCRF and DB build is kept off critical path, and can be reduced to a few weeks if required
Fast Data Access Overall fast availability of study data during conduct, if required, data availability within hours after the assessment
Tailored Graphical Displays
Data displays in Spotfire showing up-to-date study data, receiving very positive feedback from clinical science
Flexibility for changes to running studies
Very fast implementation of changes to studies during conduct as required for many exploratory studies.
Strong partnership between Data Management, Biostatistics, Programming and Clinical Science
Collaboration on the development of standardized data extraction and cross-functional business processes. Enabling pragmatic solutions where needed.
Speed
Flexibility
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Conclusions & Learning
• The key elements for enabling scientific innovation are:
• Access to data in a usable format
• Time for the clinical scientists to work with it
• The clinical data flow relies on a complex machinery of systems and processes across multiple disciplines.
• Changing one single component will not deliver the expected benefits
• Innovation does not necessarily come with sophistication. Key critical factors are rather the opposite:
• Simplification and standardization across all components of the data flow
• Access to timely data during the entire lifecycle of a study comes with responsibilities
• Use it wisely!
… and it still uses the same standardized bulb socket.
Thank you