Re-Engineering Clinical Trials

<p>Section 1: Why Does the Industry Need a Change?<br>1. Why is our industry struggling?<br>2. What are the current main obstacles to reach drug approval?<br>3. Japan: An opportunity to learn?<br>4. The "Clinical Trial App"</p> <p>Section 2: What Does Our Industry and What Do Others Do<br>5. What does "re-engineering" mean in our industry?<br>6. How can the Innovative Medicines Initiative help to make drug development more efficient?<br>7. Experiences with Lean and Shopfloor Management in R&D in other branches<br>8. Well-known methodologies, but not in our world: FMEA</p> <p>Section 3: Where to Start: The Protocol<br>9. No patients, no data: Patient recruitment in the 21st century<br>10. The impact of bad protocols<br>11. Data mining for better protocols<br>12. It is all in the literature<br>13. What makes a good protocol better?<br>14. The Clinical Trial Site</p> <p>Section 4: Alternative Study Designs<br>15. Do we need new endpoints? Surrogate and bio-marker<br>16. On the measurement of the disease status in clinical trials: lessons from MS<br>17. Generating evidence from historical data using “robust prognostic matching”: Experience from Multiple Sclerosis<br>18. Studies with fewer patients involved: the Adaptive Trial<br>19. Studies with less site involvement: the Hyper Trial<br>20. Studies without sites: the Virtual Trial</p> <p>Section 5: From Data to Decisions<br>21. Data standards against data overload<br>22. Data management 2.0<br>23. What do Sites Want?<br>24. From data to information and decision: ICONIK<br>25. Knowledge Management<br>26. Taking Control of Ever Increasing Volumes of Unstructured Data<br>27. Share the Knowledge based on quality data</p> <p>Section 6: You Need Processes, Systems and People<br>28. It's all about the people (and their competencies)<br>29. Manage the Change<br>30. How Key Performance Indicators help to manage the change<br></p> <p>Conclusions</p>