From Data Capture to Decisions Making Innovation through Standardization

1. 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

3. BackgroundBroad 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 Publications & presentations at congresses Drug Safety Expert BiomarkerExpert Communication to project team and management TranslationalMedicine Leader Preliminary analysis for study decisions during conduct Radiologist Medical data review during study conduct Signal detection on study/project level The complexity of clinical trials is increasing constantly

4. Many Demands from Science and OthersEnabling Innovation Thinking time and space Room for exploration – no guarantee of success Standards for: Clinical Data Flow & Tools Early and speedy access to quality data Processes and Data on Study Level Integrated data displays Flexibility for different study designs and new data types Processes and data on Project Level Support for study amendments before and after enrolment Cross-functional SOPs& Business Processes High quality and regulatory compliance Further improved operational efficiency

5. 4 Key TopicsDriving Innovation Through Standardization Edison's light bulb became a global successstory due to its standardized bulb socket . 1 2 3 4

6. Simplified Data Flow for Clinical DataDeveloping a 2 years roadmap 1 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

7. Implementing the RoadmapStandards for Data, Systems, Processes 1 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

8. Providing Speedy Access To Study Data 2 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 Medical Data Review 80% savings* ~50% savings* with standards Medical Data Review Study setup ready First data extraction Data accumulation / cleaning Time until enrolment start Study time * Gartner report 2009

9. 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 e-Forms Standardized Extractions

10. Clarifying Business ProcessesA smarter way to manage the “Who is Doing What” 4 • 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. AdobePDF CustomQueries HTML

11. New Responsibilities for Clinical Science Receiving data early Accessing study data More responsibility to protect the integrity of the study Accept unclean data Learn and understand the concept of data models and standards Reading study data directly Understand the concept of exploration and noise Exploring study data Moving away from standards costs time and resources Managing flexibility via protocol amendments

12. Summary of Success The implementation of the changes to systems, data flow and process began in 2008 and finished in 2010. Speed Flexibility

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