From Data Capture to Decisions Making
Innovation through Standardization
How Can Standardization Help Innovation
Michaela Jahn, Stephan Laage-Witt
PHUSE 2010, DH04
October 19th,2010
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Background
Broad Range of Responsibilities for Clinical Science
Innovate!
Ongoing work of the study
management team
Exchange information
Clinical Pharmacologist
Data base closure preparation
and clinical study report writing
Biomarker
Expert
Communication to project team
and management
Radiologist
Medical data review during
study conduct
Publications &
presentations at
congresses
Drug Safety Expert
Translational
Medicine Leader
Preliminary analysis for study
decisions during conduct
Signal detection on
study/project level
The complexity of clinical trials is increasing constantly
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Many Demands from Science and Others
Enabling Innovation
Thinking time and space
Room for exploration – no guarantee of
success
Standards for:
Early and speedy access to quality data
Clinical Data Flow
& Tools
Integrated data displays
Processes and Data
on Study Level
Flexibility for different study designs and
new data types
Support for study amendments before and
after enrolment
High quality and regulatory compliance
Processes and data
on Project Level
Cross-functional SOPs
& Business Processes
Further improved operational
efficiency
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4 Key Topics
Driving Innovation Through Standardization
Edison's light bulb became a global success
story due to its standardized bulb socket .
Enabling Innovation - Facilitated via Standardization
Dataflow & Tools
1
• Less tools and system interfaces
• Cross-functional alignment on standard platforms
Study Level
2
• Simplified and standardized data flow
Project Level
3
• Standardized data formats and displays
SOPs & Processes
4
• Clarified and documented business processes
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Simplified Data Flow for Clinical Data
Developing a 2 years roadmap
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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
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Key Decisions for clinical data within
Roche 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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Providing Speedy Access To Study Data
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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
Decision point during
study conduct
without standards
Study setup ready
First data extraction
80% savings*
Study setup ready
Medical Data Review
~50% savings*
First data extraction
Time until enrolment start
with standards
Medical Data Review
Data accumulation / cleaning
Study time
* Gartner report 2009
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Standardizing Data Formats and Displays
3
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)
Medidata Rave
Standardized eForms
Standardized
Extractions
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Clarifying Business Processes
A smarter way to manage the “Who is Doing What”
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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
The process redesign using a database approach delivered an integrated view of
processes and RACI charts.
Adobe
PDF
Custom
Queries
HTML
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New Responsibilities for Clinical Science
Accessing study data
More responsibility to protect the integrity of the
study
Receiving data early
Accept unclean data
Reading study data directly
Learn and understand the concept of data models
and standards
Exploring study data
Understand the concept of exploration and noise
Managing flexibility via protocol
amendments
Moving away from standards costs time and
resources
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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.
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.
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Thank you