1. 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

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3. 3 Background Broad 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 Biomarker Expert Translational Medicine Leader Drug Safety Expert Radiologist The complexity of clinical trials is increasing constantly Preliminary analysis for study decisions during conduct Exchange information

4. Many Demands from Science and Others Enabling Innovation 4 Thinking time and space Room for exploration – no guarantee of success Early 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 types Support 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:

5. Enabling Innovation - Facilitated via Standardization Dataflow & ToolsLess tools and system interfaces Cross-functional alignment on standard platforms Study LevelSimplified and standardized data flow Project LevelStandardized data formats and displays SOPs & ProcessesClarified and documented business processes 5 4 Key Topics Driving Innovation Through Standardization 2 1 3 4 Edison's light bulb became a global success story due to its standardized bulb socket.

6. 6 Simplified Data Flow for Clinical Data Developing 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 1

7. 7 Implementing the Roadmap Standards for Data, Systems, Processes 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 1

8. 8 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 2 Study setup ready First data extraction Medical Data Review Study setup ready First data extractionMedical Data Review without standards with standards 80% savings*~50% savings* * Gartner report 2009 Study time Decision point during study conduct Data accumulation / cleaning Time until enrolment start

9. 9 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 3 Medidata Rave Standardize d e-Forms Standardize d Extractions

10. 10 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 Processes A smarter way to manage the “Who is Doing What” 4 The process redesign using a database approach delivered an integrated view of processes and RACI charts. Custom Queries Adobe PDF HTML

11. 11 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

12. 12 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 SetupeCRF and DB build is kept off critical path, and can be reduced to a few weeks if required Fast Data AccessOverall fast availability of study data during conduct, if required, data availability within hours after the assessment Tailored Graphical DisplaysData 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

13. 13 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.

14. Thank you