How big data could revolutionise the healthcare industry

By William Smith
We generate data about ourselves all the time, the acquisition and interpretation of which is big business. In many cases, this data may be fairly trivi...

We generate data about ourselves all the time, the acquisition and interpretation of which is big business. In many cases, this data may be fairly trivial or inconsequential. In others, this may be the most private and confidential data of all - about our health, for example. 

Regardless of what type it falls under, there are businesses out there that will want it.

The question of data in healthcare is timely considering the UK’s exit from the European Union, and the potential for healthcare data to be used as a bargaining chip in future trade deals with the United States and others. 

The UK’s National Health Service (NHS) is one of the oldest and most established public healthcare systems in the world. Its practice of tying data to an individual NHS number means its data provides a broader account of patients’ health, and with the NHS increasingly digitising old records, a chronologically longer one too. EY has estimated the data to be worth almost £10bn.

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The potential value of that data led to raised eyebrows when, in December, the UK government gave Amazon unfettered access to non-patient data for free, with Amazon’s Alexa voice assistant in turn providing health information via voice search. 

Elsewhere, other big tech firms are interested in acquiring medical data, with Google doing a deal with Ascension, a company that runs 2,600 hospitals in the US.

Of course, such data is not purely useful to American tech giants, with the NHS and other health bodies themselves able to capitalise upon it. 

One possible avenue of exploration lies in opening up such data sets to AI. The CEO of NHSX, the NHS’s digital transformation arm, Matthew Gould wrote in a recent blog post: “Artificial Intelligence (AI) holds enormous potential for the NHS, if we can use it right. It can reduce the burden on the system by taking on the tasks that can be converted into an algorithm. Many of these are in areas of greatest pressure, like radiography and pathology. It could improve patient outcomes, and increase productivity across the system, freeing up clinicians’ time so they can focus on the parts of the job where they add the most value”

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