UK’s health data guardian sets a firm line for app development using patient data – gpgmail


The UK’s health data watchdog, the National Data Guardian (NDG), has published correspondence between her office and the national privacy watchdog which informed the ICO’s finding in 2017 that a data-sharing arrangement between an NHS Trust and Google-owned DeepMind broke the law.

The exchange was published following a Freedom of Information request by gpgmail.

In fall 2015 the Royal Free NHS Trust and DeepMind signed a data-sharing agreement which saw the medical records of 1.6 million people quietly passed to the AI company without patients being asked for their consent.

The scope of the data-sharing arrangement — ostensibly to develop a clinical task management app — was only brought to light by investigative journalism. That then triggered regulatory scrutiny — and the eventual finding by the ICO that there was no legal basis for the data to have been transferred in the first place.

Despite that, the app in question, Streams — which does not (currently) contain any AI but uses an NHS algorithm for detecting acute kidney injury — has continued being used in NHS hospitals.

DeepMind has also since announced it plans to transfer its health division to Google. Although — to our knowledge — no NHS trusts have yet signed new contracts for Streams with the ad giant.

In parallel with releasing her historical correspondence with the ICO, Dame Fiona Caldicott, the NDG, has written a blog post in which she articulates a clear regulatory position that the “reasonable expectations” of patients must govern non-direct care uses for people’s health data — rather than healthcare providers relying on whether doctors think developing such and such an app is a great idea.

The ICO had asked for guidance from the NDG on how to apply the common law duty of confidentiality, as part of its investigation into the Royal Free NHS Trust’s data-sharing arrangement with DeepMind for Streams.

In a subsequent audit of Streams that was a required by the regulator, the trust’s law firm, Linklaters, argued that a call on whether a duty of confidentiality has been breached should be judged from the point of view of the clinician’s conscience, rather than the patient’s reasonable expectations.

Caldicott writes that she firmly disagrees with that “key argument”.

“It is my firm view that it is the patient’s perspective that is most important when judgements are being made about the use of their confidential information. My letter to the Information Commissioner sets out my thoughts on this matter in some detail,” she says, impressing the need for healthcare innovation to respect the trust and confidence of patients and the public.

“I do champion innovative technologies and new treatments that are powered by data. The mainstreaming of emerging fields such as genomics and artificial intelligence offer much promise and will change the face of medicine for patients and health professionals immeasurably… But my belief in innovation is coupled with an equally strong belief that these advancements must be introduced in a way that respects people’s confidentiality and delivers no surprises about how their data is used. In other words, the public’s reasonable expectations must be met.”

“Patients’ reasonable expectations are the touchstone of the common law duty of confidence,” she adds. “Providers who are introducing new, data-driven technologies, or partnering with third parties to help develop and test them, have called for clearer guidance about respecting data protection and confidentiality. I intend to work with the Information Commissioner and others to improve the advice available so that innovation can be undertaken safely: in compliance with the common law and the reasonable expectations of patients.

“The National Data Guardian is currently supporting the Health Research Authority in clarifying and updating guidance on the lawful use of patient data in the development of healthcare technologies.”

We reached out to the Royal Free NHS Trust and DeepMind for comment on the NDG’s opinion. At the time of writing neither had responded.

In parallel, Bloomberg reported this week that DeepMind co-founder, Mustafa Suleyman, is currently on leave from the company. (Suleyman has since tweeted that the break is temporary and for “personal” reasons, to “recharge”, and that he’s “looking forward to being back in the saddle at DeepMind soon”.)

The AI research company recently touted what it couched as a ‘breakthrough’ in predictive healthcare — saying it had developed an AI model for predicting the same condition that the Streams app is intended to alert for. Although the model was built using US data from the Department of Veterans Affairs which skews overwhelmingly male.

As we wrote at the time, the episode underscores the potential value locked up in NHS data — which offers population-level clinical data that the NHS could use to develop AI models of its own. Indeed, a 2017 government-commissioned review of the life sciences sector called for a strategy to “capture for the UK the value in algorithms generated using NHS data”.

The UK government is also now pushing a ‘tech-first’ approach to NHS service delivery.

Earlier this month the government announced it’s rerouting £250M in public funds for the NHS to set up an artificial intelligence lab that will work to expand the use of AI technologies within the service.

Last fall health secretary, Matt Hancock, set out his tech-first vision of future healthcare provision — saying he wanted “healthtech” apps and services to support “preventative, predictive and personalised care”.

So there are certainly growing opportunities for developing digital healthcare solutions to support the UK’s National Health Service.

As well as — now — clearer regulatory guidance that app development that wants to be informed by patient data must first win the trust and confidence of the people it hopes to serve.




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Biotech researchers venture into the wild to start their own business – gpgmail


Much of Silicon Valley mythology is centered on the founder-as-hero narrative. But historically, scientific founders leading the charge for bio companies have been far less common.

Developing new drugs is slow, risky and expensive. Big clinical failures are all too common. As such, bio requires incredibly specialized knowledge and experience. But at the same time, the potential for value creation is enormous today more than ever with breakthrough new medicines like engineered cell, gene and digital therapies.

What these breakthroughs are bringing along with them are entirely new models — of founders, of company creation, of the businesses themselves — that will require scientists, entrepreneurs and investors to reimagine and reinvent how they create bio companies.

In the past, biotech VC firms handled this combination of specialized knowledge + binary risk + outsized opportunity with a unique “company creation” model. In this model, there are scientific founders, yes; but the VC firm essentially founded and built the company itself — all the way from matching a scientific advance with an unmet medical need, to licensing IP, to having partners take on key roles such as CEO in the early stages, to then recruiting a seasoned management team to execute on the vision.

Image: PASIEKA/SCIENCE PHOTO LIBRARY/Getty Images

You could call this the startup equivalent of being born and bred in captivity — where great care and feeding early in life helps ensure that the company is able to thrive. Here the scientific founders tend to play more of an advisory role (usually keeping day jobs in academia to create new knowledge and frontiers), while experienced “drug hunters” operate the machinery of bringing new discoveries to the patient’s bedside. This model’s core purpose is to bring the right expertise to the table to de-risk these incredibly challenging enterprises — nobody is born knowing how to make a medicine.

But the ecosystem this model evolved from is evolving itself. Emerging fields like computational biology and biological engineering have created a new breed of founder, native to biology, engineering and computer science, that are already, by definition, the leading experts in their fledgling fields. Their advances are helping change the industry, shifting drug discovery away from a highly bespoke process — where little knowledge carries over from the success or failure of one drug to the next — to a more iterative, building-block approach like engineering.

Take gene therapy: once we learn how to deliver a gene to a specific cell in a given disease, it is significantly more likely we will be able to deliver a different gene to a different cell for another disease. Which means there’s an opportunity not only for novel therapies but also the potential for new business models. Imagine a company that provides gene delivery capability to an entire industry — GaaS: gene-delivery as a service!

Once a founder has an idea, the costs of testing it out have changed too. The days of having to set up an entire lab before you could run your first experiments are gone. In the same way that AWS made starting a tech company vastly faster and easier, innovations like shared lab spaces and wetlab accelerators have dramatically reduced the cost and speed required to get a bio startup off the ground. Today it costs thousands, not millions, for a “killer experiment” that will give a founding team (and investors) early conviction.

What all this amounts to is scientific founders now have the option of launching bio companies without relying on VCs to create them on their behalf. And many are. The new generation of bio companies being launched by these founders are more akin to being born in the wild. It isn’t easy; in fact, it’s a jungle out there, so you need to make mistakes, learn quickly, hone your instincts, and be well-equipped for survival. On the other hand, given the transformative potential of engineering-based bio platforms, the cubs that do survive can grow into lions.

Image via Getty Images / KTSDESIGN/SCIENCE PHOTO LIBRARY

So, which is better for a bio startup today: to be born in the wild — with all the risk and reward that entails — or to be raised in captivity

The “bred in captivity” model promises sureness, safety, security. A VC-created bio company has cache and credibility right off the bat. Launch capital is essentially guaranteed. It attracts all-star scientists, executives and advisors — drawn by the balance of an innovative, agile environment and a well-funded, well-connected support network. I was fortunate enough to be an early executive in one of these companies, giving me the opportunity to work alongside industry luminaries and benefit from their well-versed knowledge of how to build a world-class bio company with all its complex component parts: basic, translational, clinical research, from scratch. But this all comes at a price.

Because it’s a heavy lift for the VCs, scientific founders are usually left with a relatively small slug of equity — even founding CEOs can end up with ~5% ownership. While these companies often launch with headline-grabbing funding rounds of $50m or above, the capital is tranched — meaning money is doled out as planned milestones are achieved. But the problem is, things rarely go according to plan. Tranched capital can be a safety net, but you can get tangled in that net if you miss a milestone.

Being born in the wild, on the other hand, trades safety for freedom. No one is building the company on your behalf; you’re in charge, and you bear the risk. As a recent graduate, I co-founded a company with Harvard geneticist George Church. The company was bootstrapped — a funding strategy that was more famine than feast — but we were at liberty to try new things and run (un)controlled experiments like sequencing heavy metal wildman Ozzy Osbourne.

It was the early, Wild West days of the genomics revolution and many of the earliest biotech companies mirrored that experience — they weren’t incepted by VCs; they were created by scrappy entrepreneurs and scientists-turned-CEO. Take Joshua Boger, organic chemist and founder of Vertex Pharmaceuticals: starting in 1989 his efforts to will into existence a new way to develop drugs, thrillingly captured in Barry Werth’s The Billion-Dollar Molecule and its sequel The Antidote in all its warts and nail-biting glory, ultimately transformed how we treat HIV, hepatitis C and cystic fibrosis.

Today we’re in a back-to-the-future moment and the industry is being increasingly pushed forward by this new breed of scientist-entrepreneur. Students-turned-founder like Diego Rey of in vitro diagnostics company GeneWEAVE and Ramji Srinivasan of clinical laboratory Counsyl helped transform how we diagnose disease and each led their companies to successful acquisitions by larger rivals.

Popular accelerators like Y Combinator and IndieBio are filled with bio companies driven by this founder phenotype. Ginkgo Bioworks, the first bio company in Y Combinator and today a unicorn, was founded by Jason Kelly and three of his MIT biological engineering classmates, along with former MIT professor and synthetic biology legend Tom Knight. The company is not only innovating new ways to program biology in order to disrupt a broad range of industries, but it’s also pioneering an innovative conglomerate business model it has dubbed the “Berkshire for biotech.”

Like the Ginkgo founders, Alec Nielsen and Raja Srinivas launched their startup Asimov, an ambitious effort to program cells using genetic circuits, shortly after receiving their PhDs in biological engineering from MIT. And, like Boger, renowned machine learning Stanford professor Daphne Koller is working to once again transform drug discovery as the founder and CEO of Instiro.

Just like making a medicine, no one is born knowing how to build a company. But in this new world, these technical founders with deep domain expertise may even be more capable of traversing the idea maze than seasoned operators. Engineering-based platforms have the potential to create entirely new applications with unprecedented productivity, creating opportunities for new breakthroughs, novel business models, and new ways to build bio companies. The well-worn playbooks may be out of date.

Founders that choose to create their own companies still need investors to scrub in and contribute to the arduous labor of company-building — but via support, guidance, and with access to networks instead. And like this new generation of founders, bio investors today need to rethink (and re-value) the promise of the new, and still appreciate the hard-earned wisdom of the old. In other words, bio investors also need to be multidisciplinary. And they need to be comfortable with a different kind of risk: backing an unproven founder in a new, emerging space. As a founder, if you’re willing to take your chances in the wild, you should have an investor that understands you, believes in you, can support you and, importantly, is willing to dream big with you.


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The UK’s National Health Service is launching an AI lab – gpgmail


The UK government has announced it’s rerouting £250M (~$300M) in public funds for the country’s National Health Service (NHS) to set up an artificial intelligence lab that will work to expand the use of AI technologies within the service.

The Lab, which will sit within a new NHS unit tasked with overseeing the digitisation of the health and care system (aka: NHSX), will act as an interface for academic and industry experts, including potentially startups, encouraging research and collaboration with NHS entities (and data) — to drive health-related AI innovation and the uptake of AI-driven healthcare within the NHS. 

Last fall the then new in post health secretary, Matt Hancock, set out a tech-first vision of future healthcare provision — saying he wanted to transform NHS IT so it can accommodate “healthtech” to support “preventative, predictive and personalised care”.

In a press release announcing the AI lab, the Department of Health and Social Care suggested it would seek to tackle “some of the biggest challenges in health and care, including earlier cancer detection, new dementia treatments and more personalised care”.

Other suggested areas of focus include:

  • improving cancer screening by speeding up the results of tests, including mammograms, brain scans, eye scans and heart monitoring
  • using predictive models to better estimate future needs of beds, drugs, devices or surgeries
  • identifying which patients could be more easily treated in the community, reducing the pressure on the NHS and helping patients receive treatment closer to home
  • identifying patients most at risk of diseases such as heart disease or dementia, allowing for earlier diagnosis and cheaper, more focused, personalised prevention
  • building systems to detect people at risk of post-operative complications, infections or requiring follow-up from clinicians, improving patient safety and reducing readmission rates
  • upskilling the NHS workforce so they can use AI systems for day-to-day tasks
  • inspecting algorithms already used by the NHS to increase the standards of AI safety, making systems fairer, more robust and ensuring patient confidentiality is protected
  • automating routine admin tasks to free up clinicians so more time can be spent with patients

Google-owned UK AI specialist DeepMind has been an early mover in some of these areas — inking a partnership with a London-based NHS trust in 2015 to develop a clinical task management app called Streams that’s been rolled out to a number of NHS hospitals.

UK startup, Babylon Health, is another early mover in AI and app-based healthcare, developing a chatbot-style app for triaging primary care which it sells to the NHS. (Hancock himself is a user.)

In the case of DeepMind, the company also hoped to use the same cache of NHS data it obtained for Streams to develop an AI algorithm for earlier detection of a condition called acute kidney injury (AKI).

However the data-sharing partnership ran into trouble when concerns were raised about the legal basis for reusing patient data to develop AI. And in 2017 the UK’s data watchdog found DeepMind’s partner NHS trust had failed to obtain proper consents for the use of patients’ data.

DeepMind subsequently announced its own AI model for predicting AKI — trained on heavily skewed US patient data. It has also inked some AI research partnerships involving NHS patient data — such as this one with Moorfields Eye Hospital, aiming to build AIs to speed up predictions of degenerative eye conditions.

But an independent panel of reviewers engaged to interrogate DeepMind’s health app business raised early concerns about monopoly risks attached to NHS contracts that lock trusts to using its infrastructure for delivering digital healthcare.

Where healthcare AIs are concerned, representative clinical data is the real goldmine — and it’s the NHS that owns that.

So, provided NHSX properly manages the delivery infrastructure for future digital healthcare — to ensure systems adhere to open standards, and no single platform giant is allowed to lock others out — Hancock’s plan to open up NHS IT to the next wave of health-tech could deliver a transformative and healthy market for AI innovative that benefits startups and patients alike.

Commenting on the launch of NHSX in a statement, Hancock said: “We are on the cusp of a huge health tech revolution that could transform patient experience by making the NHS a truly predictive, preventive and personalised health and care service.

“I am determined to bring the benefits of technology to patients and staff, so the impact of our NHS Long Term Plan and this immediate, multimillion pound cash injection are felt by all. It’s part of our mission to make the NHS the best it can be.

“The experts tell us that because of our NHS and our tech talent, the UK could be the world leader in these advances in healthcare, so I’m determined to give the NHS the chance to be the world leader in saving lives through artificial intelligence and genomics.”

Simon Stevens, CEO of NHS England, added: “Carefully targeted AI is now ready for practical application in health services, and the investment announced today is another step in the right direction to help the NHS become a world leader in using these important technologies.

“In the first instance it should help personalise NHS screening and treatments for cancer, eye disease and a range of other conditions, as well as freeing up staff time, and our new NHS AI Lab will ensure the benefits of NHS data and innovation are fully harnessed for patients in this country.”


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What tech gets right about healthcare – gpgmail


We often hear how tech just doesn’t get the healthcare industry but how is it helping where government and big pharma can’t?

Why is tech still aiming for the healthcare industry? It seems full of endless regulatory hurdles or stories of misguided founders with no knowledge of the space, running headlong into it, only to fall on their faces.

Theranos is a prime example of a founder with zero health background or understanding of the industry — and just look what happened there! The company folded not long after founder Elizabeth Holmes came under criminal investigation and was barred from operating in her own labs for carelessly handling sensitive health data and test results.

But sometimes tech figures it out. It took years for 23andMe to breakthrough FDA regulations — it’s since more than tripled its business and moved into drug discovery.

And then there’s Oscar Health, which first made a mint on Obamacare and has since ventured into Medicare. Combined with Bright, the two health insurance startups have pulled in a whopping $3 billion so far.

It’s easy to shake our fists at fool-hardy founders hoping to cash in on an industry that cannot rely on the old motto “move fast and break things.” But it doesn’t have to be the code tech lives or dies by.

So which startups have the mojo to keep at it and rise to the top? Venture capitalists often get to see a lot before deciding to invest. So we asked a few of our favorite health VC’s to share their insights.

Phin Barnes – First Round Capital




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