How Google’s DeepMind is using blockchain-like technology to make patient health data transparent and secure
Google's DeepMind has lifted the lid on its development of "blockchain related" technology that it's planning to use to make patient health data both more transparent and secure.
The British artificial intelligence company which is working on trial projects with the NHS in London, is seeking to allay fears over access to patient's personal health data, and late last year exclusively told City PM it was developing technology that was related to blockchain for greater transparency.
Co-founder Mustafa Suleyman and head of security Ben Laurie have now revealed more details about its efforts to create a digital ledger which records how data is accessed, by whom and where, but not necassirly access to the records themselves.
"Given the sensitivity of health data, we’ve always believed that we should aim to be as innovative with governance as we are with the technology itself," they said.
Read more: Google's DeepMind is using "blockchain related" technology
"Like blockchain, the ledger will be append-only, so once a record of data use is added, it can’t later be erased. And like blockchain, the ledger will make it possible for third parties to verify that nobody has tampered with any of the entries," they wrote in a blog post.
However, the digital ledger will not be decentralised like blockchain "because we already have trusted institutions like hospitals or national bodies who can be relied on to verify the integrity of ledgers".
It will also differ in being in a tree-like structure rather than a chain, but entries on the ledger will still generate what's known as a cryptographic hash. This hash is a key element in keeping track of the footprint of the data and makes it unchangeable.
Read more: Google's DeepMind wants to make doctor's paperwork obsolete in new NHS deal
"In simple terms, you can think of it as a bit like the last move of a game of Jenga. You might try to gently take or move one of the pieces – but due to the overall structure, that’s going to end up making a big noise," they said.
"So, now we have an improved version of the humble audit log: a fully trustworthy, efficient ledger that we know captures all interactions with data, and which can be validated by a reputable third party in the healthcare community."
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Hospitals that DeepMind has partnered with will be able to examine the audit trail of data in real-time, and it will also work on creating alerts for any unusual activity.
And it also indicated that it may let others such as individual patients or patient groups check the ledger.
DeepMind admitted building it will be "really hard" and that the "toughest challenges are by no means the technical ones", but hopes to start rolling the first parts of the project known as "Verifiable Data Audit" later this year.
While DeepMind is known for its artificial intelligence technology, famously beating a human player of the chess-like game Go last year, its work with the NHS does not involve AI.
It is working with the Royal Free London NHS Trust to develop an app called Stream, which collects clinical information on patients that are usually kept as paper notes. Digitising the records means information is all in one place and that notifications and alerts can be pinged to doctors about patients if any anomolies or changes are identified.
WATCH: How DeepMind and Royal Free London NHS Trust are working together
Privacy experts have raised concerns about the private Google-owned company being given access to data, despite third party access being allowed under NHS data sharing agreements since 2014.
In addition to the latest efforts to allay these concerns, it has created an independent review board made up of science, data and technology experts.
"Data can only benefit society if it has society’s trust and confidence, and here we all face a challenge," the company said in the blog post.
"We see Verifiable Data Audit as a powerful complement to this [independent review board] scrutiny, giving our partner hospitals an additional real-time and fully proven mechanism to check how we’re processing data. We think this approach will be particularly useful in health, given the sensitivity of personal medical data and the need for each interaction with data to be appropriately authorised and consistent with rules around patient consent."