In the previous post (Clickstream data and results reordering) I highlighted how the clickstream data could be used to easily surface articles that are not picked up by usual keyword searches. That post highlighted how it could be used to improve search results. In my mind I was thinking this could help surface documents to improve a clinician trying to answer their clinical questions.
But what about in systematic reviews (or similar comprehensive searches)? A couple of scenarios spring to mind:
- A user conducts a search and find, say 15, controlled trials. We could create a system that highlights the most connected clinical trials that have not been selected already. So, possibly an in-built safety check to ensure that no trials are missed.
- Related concepts. You see some spectacularly complex search terms, no doubt human generated. There may be other systems but we could surface related concepts. A simple example was shown in the early post (Clickstream data and results reordering) where it highlighted that obesity is related to diet. OK, we all know that – but the computer didn’t, it spontaneously highlighted it. Doing this on a large scale using Trip’s ‘big data’ will generate more obscure relationships – potentially very useful in generating a comprehensive search strategy!
If there are any systematic reviewers/searchers I’d love to hear what you think!
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