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2015 in numbers

Some statistics from 2015

  • 3,700,000 – page views, up slightly on 2014
  • 900,000 – individual sessions spent on the site
  • 18% – increase in users in November and December compared with 2014. I can only assume this was down to our removal of the requirement to sign-in to use Trip!
  • 650,000 – number of users of the site
  • 4,560,603 – number of minutes spent on the site
  • 76,010 – hours spent on the site
  • 178,000 – number of most recent registered user.  This suggests the actual number of registered users is 142-160,000 registered users.  This represents an increase of nearly 40,000 last year
  • 350 – mentions in articles
  • 83% – users accessing the site via a desktop computer (mobile 11% and tablet 6%)
  • 46% – users accessing the site with Chrome, the most popular browser (IE 20% and Safari 16%)
  • 22% – users from the USA followed by UK 13%, Spain 8%, Canada 5% and Australia 4%

The above figures are based on users actively coming to the website and does not include an increasing large number of people searching Trip via our webservice. So, if we assume an average of 2 searches per session and assume 20% of the searches coming via webservice (obviously the assumptions makes accuracy problematic) that equates to 2,160,000 searches in 2015. From previous analysis we have estimated that 40.8% of searches help improve patient care.  Therefore, probably the most important statistic of the lot:

881,280 – number of times Trip helped improve patient care in 2015.

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Rapid and/or systematic reviews – new post

Historically I have used this blog to write about Trip developments as well as expanding on my own work around rapid reviews.  This can sometimes be appropriate, for instance when we highlight a new tool on Trip to make reviews more rapid.  However, sometimes the articles have nothing to do with Trip and more to do with me (Jon Brassey) as an individual.  It was one of the reasons I started the Rapid Reviews website.

My intention is to post these articles on that site but I will continue to link to these articles if I feel they are substantial and offer value/benefit to Trip users.  One such article, is Why do we do systematic reviews? Part 4, possibly my firmest critique of the current ways we undertake systematic reviews.  This is linked to the need for rapid reviews.

This is probably my last post this side of Christmas, so if you’re ready – and that way inclined – have a wonderful time.

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Site redesign

Every now and then it’s important to take a step back and look at how the site is performing.  I’m not talking about the search results/algorithm, more the user interface. I am broadly happy with the design (logo, colours etc) but I’m less convinced that the site is configured optimally to ensure users can easily find the information they need.

So, we’re exploring that and making some headway.  Don’t expect it to look radically different, but hopefully it’ll be a delightful surprise.

It should be ready early 2016, but that depends on user testing of the new interface.

Feel free to make suggestions as to bits you don’t like…!

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Searching for hypertension

I love clickstream data – the data we collect as people use the site.  We know – anonymously – the search terms used and articles viewed.  We have just developed the ability to ‘mine’ this data; nearly 100 million items it!

Below are two images, both taken from searches for hypertension.  The first is for all the data we have from 2009 (tens of thousands of searches for hypertension) and the second is from 2015 only (still thousands).  What we’ve done is taken these searches, removed the term hypertension and created a wordcloud of the remaining words.  These words being the additional terms users used when searching for hypertension.  So, if a user searched for hypertension pregnancy, we simply chart the pregnancy term.

Searches since 2009
Searches in 2015

They look fairly similar but from a cursory analysis five terms are more prominent in 2015, those being:

  • placebo
  • aspirin
  • patient
  • enalapril
  • obesity

Interesting for sure, but more than that?

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A new analytic ‘toy’

I’m very happy to have been given access to a new analytic tool on Trip.  It analyses the near 100 million bits of clickstream data (sounds like ‘big data’ to me).

I simply enter a search term and it exports every instance of a person using that search term, what date and time, what they clicked on and if they used any additional search terms.  For example, a quick search on an incomplete data set (still only half way through full indexing the 100 million records) for Fingolimod (or the brand name Gilenya) revealed it had been searched over 350 times in the last five years.  The top five articles viewed were:

  1. Fingolimod for the treatment of highly active relapsing-remitting multiple sclerosis. National Institute for Health and Clinical Excellence – Technology Appraisals (47 views)
  2. Fingolimod – a potential new oral treatment for multiple sclerosis? NPC Rapid Reviews (40)
  3. Fingolimod – Multiple Sclerosis. Canadian Agency for Drugs and Technologies in Health – Common Drug Review (35)
  4. Fingolimod (Gilenya) – in highly active relapsing remitting multiple sclerosis. Scottish Medicines Consortium (30)
  5. Fingolimod versus Glatiramer for Adults with Relapsing Remitting Multiple Sclerosis: Clinical and Cost-Effectiveness. Canadian Agency for Drugs and Technologies in Health – Rapid Review (26)

And below is a tag cloud of the additional terms added to the search:

 NOTE: the tag cloud formation software capped the number of words displayed to something like the top fifty.

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Oral tapentadol for cancer pain

I was looking at Twitter when I saw this tweet:

So, why not see what the highly experimental Trip Rapid Review system makes of oral tapentadol for cancer pain.  So, I spent five minutes and came up with this result:

We found three trials and the actual Cochrane Systematic Review found four, the trial difference was an unpublished one (but well done Cochrane for finding that one).  Frustratingly the actual review had no forest plot – so our pseudo-plot (above) will have to do.

Our system gave a score of 0.42 which suggests reasonable, but unspectacular, results for oral tapentadol.  The actual Cochrane conclusion is:

“Information from RCTs on the effectiveness and tolerability of tapentadol was limited. The available studies were of moderate or small size and used different designs, which prevented pooling of data. Pain relief and adverse events were comparable between the tapentadol and morphine and oxycodone groups“

It’s difficult to compare end-points – Cochrane says it’s as good as morphine and oxycodone (which may be good, bad or indifferent – I don’t know) while our system suggests it’s ok/not bad.

Given the highly experimental nature of our system I think we give consistently good results.  The important next steps are:

  • Improve the system – which we’re about to start on, via our Horizon 2020 funded work.
  • Validate the approach so we can understand when it works well and when it doesn’t.

We’ve used this approach before (see the example on SSRIs for the management of hot flashes) with good results and our earliest internal tests found around 85% agreement.

I’m not suggesting this is a replacement system and I’m under no illusion of the potential for harm – if mis-used – but it’s a novel approach which should see further developments.  While much of the developments in machine learning, text mining etc in systematic reviews are about replacing humans in the standard systematic approach I see this approach as being wholly more revolutionary.

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Content, content, content

Broken links are never great but unfortunately they are unavoidable.  They typically happen when a website either removes an article or has a redesign and moves all the old links to new ones.  Users, naturally, get frustrated and Trip has to do better.

We currently have a broken link detector – which sends an alert for each broken link.  In reality it’s crude in that it ‘decides’ a link is broken if it takes longer to load than five seconds.  If a user is on a slow internet connection or a site is running slowly – an alert is sent.  So, we get lots of false positives, making the system poor.

But, recently we spent a great deal of time analysing these and found a large number of sites with problems and have completely revamped these links.  So, the true positives should fall dramatically.  However, moving forward, we’re planning a new broken link system.  If we detect a broken link (or one we suspect is broken) we will re-try it a number of times over the next 24 hours and only after a few tries will it be considered a true positive and an alert created.  The alert will contain additional tools to make it much easier to amend the index.  While no system will be perfect, we hope that this system will help to significantly reduce the problem of broken links.

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Search safety net

The search safety net is a novel feature to help improve searching; helping users not miss important papers.  I wanted to explain it – simply – but have failed on that score.  It’s important so I hope you can make sense of what I’ve written.  If you have any questions, my email is jon.brassey@tripdatabase.com.

After a search you will see a new ‘Search Safety Net’ button

If you click that it’ll bring up a list of related search terms.  It does this by looking at the top 250 search results and analysing the search terms people have previously used when clicking on these results.  This works on the notion that a single document can be clicked on after numerous searches.  For instance, in the example above search terms might have been ‘prostate cancer screening’, ‘MRI screening’ etc.

The next section of the search safety net happens AFTER you’re conducted your search and found a number of documents you like AND looked at (or simply clicked the ‘check box’ to the left of the result).  If you click on the Search Safety Net button again you see three columns of results:

The first column is closely related articles, the second is other related articles and the third is the related search terms.  The latter column is similar to the description of related search terms above, but is based purely on the documents clicked (as opposed to the top 250 results).  However, to understand the process behind the other two columns you need to understand a clickstream data.

Paper 1 ———- Paper 2 ———- Paper 3

In the above there are three papers (1-3).  A user, in the same session, clicks on Paper 1 and Paper 2, therefore we can make a link between the two.  Another user might click on Paper 2 and Paper 3, again making a link.  So, Paper 1 is connected to Paper 2 (a single step, using network language) while Paper 3 is two-steps away from Paper 1.  We have this data for all articles in Trip.

Slightly simplifying things (!) the first column is the most popular related articles based on documents that are one step away from the documents clicked.  So, we look at all the articles clicked by the user and pull back all the documents that are one step away, displaying most ‘popular’ at the top.  The second column are all the documents that are two steps away.  This is likely to find less focused results, but the occasional really interesting study that might have been missed.

Two important issues:

  • For this to work requires clicks, if the documents you’ve looked at has no clicks, then you’ll get no results.
  • This is being released as a ‘beta’ bit of software, as in we’re still developing it.  At present it is available to both free and Premium users of Trip.  However, this is likely to change in the near future.

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Logging in to use Trip

Earlier this month I asked our users their thoughts on logging in to use Trip.  With hundreds of responses the results were pretty evenly split, half didn’t mind logging in and half thought it was a pain and made them less likely to use the site.  There were two main reasons given for not liking logging in (apart from difficulty remembering passwords):

  1. Health professionals, often jumping from computer to computer, in a busy clinic and simply not having the time to login.
  2. Information specialists trying to demonstrate Trip to users and finding it highly problematic to get all the students to register.

As a result of the feedback, and my own disquiet at forcing login, we have rolled back the login screens and users can get unhindered access to the free site, as of now.

As an extension to the easier access we are currently working on two other changes:

  1. Making registration/login not necessary for institutional subscribers who use IP authentication.  Our system will detect the user is from a subscribing institution and they will get seamless access to the Premium Trip.
  2. Trip has the ability to link to your institutions subscription journals full-text (as opposed to the PubMed abstract). Where we have the IP details of the institution we will seamlessly insert the links to these full-text. This feature is open to both free and Premium users. If you belong to an institution who subscribe to journals and would like to take advantage of this feature then let me know.

These two additional features will be ready in the next two weeks.

Thank you all for your feedback, we asked, you replied in your hundreds, we listened and acted pretty quickly!

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