Natural Language Processing (NLP) is a hot topic in healthcare.
At this year's AMIA Annual Symposium, in Washington DC, we brought the discussion on clinical NLP to a roundtable held on Monday lunchtime and were also invited to the AMIA NLP workgroup to present some real-life use cases in clinical NLP.
However, as much as we like sharing what we're doing, we were keen to know what other people think when it comes to how NLP can transform patient care, today and in the future.
So that’s what we did – we asked peers at the AMIA conference that question (How can NLP transform patient care?) as part of a contest with an incentive of an iPad Mini and $50 Starbucks voucher for 1st and 2nd place respectively.
More than a third of entries identified mining the unstructured, free text narrative of a medical record to be crucial to the transformation of patient care. Unsurprising really, if you consider that around 80% of data in an electronic health record is unstructured and the only real way to get this information into a useable format is using NLP.
But what was interesting was the difference in how to use this data. Ideas included; for better patient information, for using the extracted coded concepts in clinical decision support and to retrieve full patient cohorts.
It was a tough contest to judge but the winning entry came from Edgar Chou at Drexel University College of Medicine. He had a few ideas but the one we thought was most interesting, with the potential to have the greatest impact on patient care was to around the payer care mix.