Investigative report on FDA enforcement under Trump from Science’snews department shows a steep decline in enforcement actions.
I’ve noticed this, but it is good to see actual data behind it.
I’ll be frank, it would take a lot of data that does not exist to make me feel the companies under the FDA’s oversight have gotten better as a whole. Anecdotally, well there are a lot of less than sterling players out there.
I have mostly questions:
Have we seen this trend in previous Republican administrations, and is it more pronounced here?
Is there any evidence that the increase under Obama was a reaction to the previous Republican administration? Are we in a cycle of lax and then tougher enforcement that maybe evens out? That sort of variance is not healthy.
What, if any data, will we be able to see about impact? There are certainly concerns that the FDA has not done enough. Will this be exacerbated?
What will it take for this to start affecting the mutual recognition agreements with the EU and other major bodies?
In a presentation on practical applications of data integrity for laboratories at the March 2019 MHRA Laboratories Symposium held in London, UK, MHRA Lead GCP and GLP Inspector Jason Wakelin-Smith highlighted the important role data process mapping plays in understanding these challenges and moving down the DI pathway.
He pointed out that understanding of processes and systems, which data maps facilitate, is a key theme in MHRA’s GxP data integrity guidance, finalized in March of 2018. The guidance is intended to be broadly applicable across the regulated practices, but excluding the medical device arena, which is regulated in Europe by third-party notified bodies.
Data process maps look at the entire data life-cycle from creation through storage (covering key components of create, modify and delete) and include all operations with both paper and electronic records. Data maps are cross-functional diagrams (swim-lanes) and have the following sections:
While focused on medical devices, this proposal is interesting read for folks interested in applying machine learning and artificial intelligence to other regulated areas, such as manufacturing.
We are seeing is the early stages of consensus building around the concept of Good Machine Learning Practices (GMLP), the idea of applying quality system practices to the unique challenges of machine learning.
Solid focus on both external and internal signifiers of quality culture. A little basic but very worth reinforcing.
And then I left, skipping the last keynote to get to the airport.
Good conference this year. Overall I felt that many of my choices for sessions ended up being more basic than I thought, but there is a lot of value in that. I will hopefully make the time to turn my thoughts into better blog posts.
Future of work thought leadership….People First, Digital Second
Digital second is an interesting keynote theme (2 out of 4) and I appreciate the discussion on equitable futures and moving companies away from autocracy. Not sure anyone who speaks at large corporations is really all that committed to the concept. And I didn’t feel much more than lip service to the concept in this keynote.
Stressing reverse mentoring is good, something that all of us need to be building the tools to do better. Building it into technology integration is good.
Basic sum-up is that Change Leadership Traits are:
Relational vs transactional
Focus on ‘people’ first
Highly adaptable to people
In short, any talk that thinks having a clip from “In Good Company” is a good idea for teaching agile thinking is problematic.
“Storytelling: The Forgotten Change Management Tool ” by Keith Houser
Storytelling is one of the critical jobs of a quality professional, and this was a great presentation. Another flip session with pre-work that a lot of folks didn’t do.
This was marked basic. And unlike a lot of stuff marked intermediate this felt like truly a best practice, pushing the envelope in many ways. Sure I apply these principles, but the discipline here is impressive.