Demystify and transform raw data into insights

In short, NLP is a combination of linguistics, artificial intelligence and computer science. Think of it as a type of technology designed to build a communication bridge between humans and machines – by using NLP, computers are able to pick up on certain words used in online conversation through speech recognition.

For example, let’s pretend that you’re discussing a new type of migraine medication in an online forum such as Reddit or Facebook. NLP will be able to quickly highlight the name of the drug, alongside any identifying adjectives or relevant expressions that might be able to give an insight into patient opinion and how frequently they appear. Maybe the words “bad side effects”, “nausea” or “fatigue” show up regularly, whilst more positive descriptors such as “affordable” or “no side effects” aren’t picked up by the computer as often. By using NLP, the pharmaceutical companies who made this drug are able to understand both the positive and negative aspects of their medication by “listening” to patient voices, and taking their opinions into account when tweaking the recipe for the drug.

Get to grips with data-driven solutions

If you want to get technical, there are a bunch of different types of NLP – with the main three being symbolic, statistical, and neural – but it’s only the latter that is really used in modern AI, with the former two no longer able to provide the type of analysis AI is capable of. As you can probably tell from its name, neural NLP mimics the human brain more closely than the other two types of NLP, leading to more accurate and reliable results. As data analysts are often dealing with enormous amounts of unstructured data, it can be difficult for them (and their human brains) to make sense of all of this – which is where NLP and AI join forces.

By transforming free text and random strings of words into usable, understandable data, information can be transferred quickly, easily and efficiently to the people who can make a difference. Of course, NLP can be used in a variety of other ways across the healthcare spectrum – voice-activated digital health companions, for example – but there’s particularly exciting potential surrounding the extraction and interpretation of patient opinions online.

Better understand the realities of the patient journey

Understandably, there’s some concern around data security and the use of personal information when it comes to data analytics. However, it’s important to make a clear distinction between more common uses of personal data (for example, social media sites tracking your activity in order to sell products) and the use of anonymised data for analytics purposes, which has no referral back to individual patients. Sensitive information relevant to a particular person is not used in healthcare-centric data analytics – because it’s simply not necessary or needed.

What this all means is that biotech and pharma companies are increasingly considering the opinions of patients when sending a drug through the clinical trial process. Patients are no longer seen as one homogenous, unfeeling hivemind – they are individuals with unique needs and preferences, and their voices matter. Over the course of the past few years, there’s been a steady increase in the use of NLP in the healthcare sector – and for good reason. The use of NLP and data analytics is a gamechanger in terms of how individual medical needs are understood and met, and might be a real solution in tackling the world’s trickier medical conditions.