Trip Database Blog

Liberating the literature


October 2018

Trip mobile app. It’s getting there!

We’ve been working on a mobile app for Trip and the trial version I’ve been playing with has been really impressive.  You might say that mine is a biased opinion, but believe me I’m our harshest critic as I always want to exceed expectations. With the app I think we’ve got a great chance!  An annotated screenshot is below.

Not 100% sure when it’ll be out, possibly the biggest hurdle will be app store bureaucracy, but we’ll see.

What we’re up to

We’re currently lovely and busy and working on the following:

  • Evidence Maps. I’m very close to finishing the paper describing our evidence maps. I don’t write many papers so no idea if it’ll get published – hopefully the skills of my co-authors will see it get accepted!
  • MeSH. We’re currently working to add MeSH to Trip. Grabbing the MeSH terms from the articles we grab from PubMed is really easy, auto-annotating non-PubMed articles is more of a challenge – but we think it’s in hand!
  • Algorithm – Search is at the heart of Trip and we’re working with a specialist third-party search company to improve our search results. This could/should be massively important.
  • App – We’re making very good progress on developing our first mobile app.

A lot of work which will hopefully all be finished by the end of the year

Using Trip to support research prioritisation

Research prioritisation is really important – it ensures the research that’s undertaken (be it primary research or evidence reviewing) answers important topics that have – as yet – not been answered.

So, how might Trip help?

We’ve been involved in some research which should be published shortly that explored transforming the search patterns of Trip into clinical questions.  For example, if a search was for acne and minocyline we can infer the clinical question was ‘Is minocycline useful in the management of acne?‘.  Our work focused on a small clinical area and was able to isolate and chart the distribution of searches/questions in a chart – with the condition on one axis and intervention on the other. Interestingly, the majority of questions occurred in a small number of areas.  Using a ficitious example here is how a chart might look:

So, in the above you can see that for condition A there have been eight searches relating to intervention 6 and one search for condition A and intervention 3.

Now, using the automated PICO annotation system we have labelled all the RCTs and systematic reviews (SR) and these can be charted in a similar way:

In the above for condition A and intervention 1 there are five RCTs and zero SRs and for condition A and intervention 6 there are three RCTs and one SRs.

So, how does that help us?

Example one: We can say that there is a lot of interest in condition D and intervention 1 and we can see that it is well served with RCTs (18) and SRs (4).  We could go further and report on how up to date the SRs are and if there are many new RCTs that might not be included in the SRs.  We could even see if users are clicking on the individual RCTs and SRs to see if they are meeting the needs of the user.  In other words, of those 18+4 studies users may click on some and not others and this can give us a further clue as to the users intentions and therefore improve potential procurement of new research.

Example two: Condition B and intervention 9 is popular. We can see there are 4 RCTs but no SRs. Surely a candidate for an SR?

Example three: Condition B and intervention 4 is popular but there are no RCTs and SRs which indicates a potential area for research procurement. This could be verified by exploring what articles the user clicked on when doing that search – again useful insight.

Sounds plausible to me but I’d welcome some insight from others!

Trip and the Evidence Ecosystem

I’ve been trying to see how Trip fits in to the evidence ecosystem and have attempted to draw it.  However, my imagination has let me down so I need some help!  This is what I’ve got so far:


So, what am I trying to say?

Funders give money to academics to produce research which is then published.  The published material can be viewed directly by health professionals but it can also be accessed via Trip.

The dotted grey lines, from Trip to those on the right-hand side are relationships that could be developed with regard the needs of the health professionals.  So, we could help funders understand the type of material users are asking for (we have a paper on that due soon) and that should be of use to academics.  And, for publishers, we could give comparative information as to how their content performs relative to other publishers.

We tend to focus our relationships on the left-hand side, but we could – for influence and business purposes – look increasingly to the right-hand side.

So, the help needed is to perfect the image!  Have I missed relationships?  Can you articulate how Trip might support those on the right-hand side?  Anything else obvious that might help me visualise Trip’s role in the evidence ecosystem??

Thank you in advance 🙂


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