What we learned at the phuse SDE
The theme of PHUSE SDE Netherlands was on data processing solutions and integration and was focusing on systems, applications and processing to make progress in this area. The topics ranged from statistical computing environments to metadata repositories and down to the tools that help to map SDTM data or manage programming lifecycles.
The spirit of the day was very much around sharing and delved into the reaches of how companies were accomplishing some of their complex tasks and gaining efficiency through implementation of tools or solutions.
We could see from the discussions that Cloud based technologies and the use of open source, particularly R, are becoming more common in projects within the industry. Those projects are typically in their infancy, but they are now out there rather than a dream just a few years or months ago.
Real world data and other non-crf data is more prevalent now than ever before, and industry needs to find ways to integrate that with the classical eCRF data that one thinks of in terms of clinical trials. As that shift continues to gain momentum, will it create a shift in thinking on how to efficiently run clinical trials? For label extensions, there are proven cases where real world evidence has successfully been used to achieve that desired outcome.
Beyond those more ground-breaking items, getting into the detail of industry processes, which is the beauty of the willingness to share at these single day events, some companies are finding ways to extend LSAF capabilities to manage workflow, others writing java tools for when other classic functionality won’t work – for example working with PDF outputs.
When it comes to solving repeatable problems or tasks, such as mapping SDTM datasets, there are some good ideas in companies to do that. They have created tools to automate and thought outside the box to fix the pain they were feeling, taking care of the 80% and freeing up their time for the remaining 20%.
The range of presentations at this single day event really showed the quality in the industry, from the thought leadership, to the detailed focus; the fat versus thin dataset structure analogy covers the range of topics well and summed up the day.