I have the great honour of being part of the KConnect consortia, recipients of an EU Horizon 2020 innovation grant. Trip is involved in a number of great projects within KConnect and I plan to blog about them all over the course of the year.  The first to feature is enhancing our PICO interface.

Asking questions may seem straight forward but it can be difficult so by helping users understand the key elements of their questions it typically gives the questions better structure.  PICO stands for:

  • P = Population (eg what condition the user has)
  • I = Intervention (eg a drug, diagnostic test)
  • C = Comparison (eg an alternate drug or test)
  • O = Outcome (eg mortality, QoL)

Take these two, real question:

  • How can you safely treat constipation in pregnancy?
  • In diabetes would an AIIRA benefit over an ACE? 

In the top Q, the P = pregnancy and the O = constipation.  Alternatively the population could be pregnancy and constipation.

The second Q is more complicated but the P = diabetes, I = AIIRA and C = ACE inhibitors

You’ll note that questions don’t need all four elements; it’s a flexible concept!  Irrespective of the number of PICO elements it can be really useful in helping users think about the key elements of the question they may have.

From user feedback I hear time and time again that the PICO interface is great and really helps health professionals think through their questions.

KConnect is helping us improve it still further!  We will simply allow users to type our their question in full and press search.  We will automatically attempt to identify the PICO elements and then pass those elements to our search.  By highlighting the suggested PICO elements it will teach users by experience what the PICO elements are as well as speeding up the question answering process.

A further minor step – which might be really interesting – is to record the full question and the articles the user subsequently clicked on.  It’s not quite the same as a full answer, but a ‘half way house’.

We’ve a good few months of work on this using, various techniques: machine learning, semantic annotation, hard work.

I’ll keep you posted.

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