We are kicking off the new year with a juicy and powerful release – 150 000+ new clinical trials from regional and national clinical trials registries in Asia, Europe and North America!
Thanks to our machine learning framework, the raw data from all registries has been harmonized and translated into searchable, easy-to-digest insights for you. As a result, you can now access:
- New clinical experts and investigator profiles
- Millions of relationships between investigators, companies and medical institutions
- Even more comprehensive and deeper profiles for your already existing clinical experts
All this has been done with the goal to help you better target and engage new international investigators and opinion leaders with the right clinical background, influence and network.
Many more clinical experts from all over the world – better selection of national and local opinion leaders and investigators
As a result of this release, Monocl now features 100 000+ new clinical experts and investigators. This will further help you improve your overall mapping efforts as it gives you access to a wider pool of clinical experts to evaluate for you engagement strategies. In particular, this will help you do an even better job in selecting national and local opinion leaders and investigators across all geographies.
This update has had an extra impact on Asian and Europe regions.
However, the impact of this release lies not primarily in the number of profiles…
Deeper existing profiles and new network insights drives better targeting and engagement
This release increases the depth and comprehensiveness of our already existing investigator profiles. This is especially relevant for all you looking to perform site and/or investigator feasibility studies; if you are looking for opinion leader and rising stars with a particular clinical background or just want updates on your current key customers.
With our machine learning framework, we instantly translate all this raw data into relationships and collaborative insights for you. This means mapping investigator-to-sub-investigator relationships as well as more complex mapping. As an example, we apply a sophisticated linking approach between authors of peer-review trial result publications and investigators of a trial. This is utilized for many registered European trials where the trial investigator is often is missing from. However, with our approach, we can figure out what investigators that conducted the trial, despite having no information on the actual trial in the local or regional trial registry. This is simply impossible to do without a sophisticated machine learning framework.
In total, we have added more than 2 million clinical collaborations with this release. All this allows you to even better assess influence, identify important nodes in collaborative networks as well as map how key influential stakeholders relate to each other.
Happy New Year!