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How can we mobilise knowledge that has been generated by AIM?

Authors: AIM Community;

How can we mobilise knowledge that has been generated by AIM?

Abstract

The workshop was designed to provide members of the AIM community with an opportunity to discuss key topics shaping the future of AI and MLTC research, aiming to improve lives for those living with or caring for people with Multiple Long-Term Conditions. In collaboration with NIHR, six discussion topics with guiding questions were prepared to explore challenges and potential solutions. The event welcomed 80-100 registered attendees, with 12-16 participants engaging in discussions on each topic and eight joining online. A one-hour session included 50 minutes for discussion, focusing on capturing attendees' insights and identifying three key takeaways for feedback. There were 7 topics discussed in the workshop, this summary includes the discussion and takeaway from the 1st topic. Topic 1: How can we mobilise knowledge that has been generated by AIM? Topic 2: What are the strategies & good practices for engaging and sustaining patient communities? Topic 3: How can we champion early career researchers in MLTC research? Topic 4: What technical support is needed for the next stage of translation/impact? Topic 5: Who do we need to engage to ensure broad collaboration for translating research? Topic 6: How would we move towards system thinking and transdisciplinary research in MLTC? Topic 7: What are the strategies & good practices for engaging and sustaining patient communities? Playlist of the conference recordings from "Pioneering AI in MLTC: Bridging Research and Practice Conference 2024" that took place on 9-10 Sept 2024 can be accessed hereThe AI for Multiple Long-term Conditions Research Support Facility (AIM RSF) is based at The Alan Turing Institute, in partnership with Swansea University and the University of Edinburgh. The RSF offers AI and advanced data science expertise and support to the eight research consortia, as part of a broader £23M investment in multiple long-term conditions research (the AIM Programme).

Keywords

AIM Conference 2024

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average