An AI Lab for the NHS

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Should the NHS invest in building artificial intelligence services for healthcare? Or should it instead be a buyer of products and services made by others, in the same way it is for pharmaceuticals and medical devices?

In a time when it’s hard to move beyond worries about balancing the books, it might seem naive to talk about spending more of the NHS’s hard pressed budget on yet another new initiave. But as many others have pointed out, the potential for AI in healthcare is huge, and could help us manage the very challenges that make the financial pressures so acute. The NHS also has a track record of supporting Research and Development, funding the NIHR to the tune of £500 million each year.

If the NHS did decide to set up its own NHS AI Lab for Health (let’s call it NHS AI), what might it look like? I’d suggest that trying to become a competitive player in the bleeding edge of machine learning and AI is nether feasible or desirable. The big commercial sector organisations in this arena (think Google Deepmind, Amazon, Baidu, Facebook) are spending billions in R&D, and employ hundreds, or thousands, of mathematicians, physicicts and computer scientists. Universities and academic groups around the world have whole departments working on AI and related disciplines. So our NHS AI is never going to be a player in pushing the boundaries of computer science or mathematics.

Instead, the real space where NHS AI could shine would be in implementation. By building useful things (algorithms, applications), it’s goal could be to apply AI algorithms and techniques developed in other industries and settings to the big challenges in healthcare. These are developing so quickly that real world uses can barely keep up, and the challenge is increasingly becoming one of applying these amazing algorithms into the design of real world products and services. For example, can the NHS make use of open source image classification algorithms to help radiologists and pathologists diagnose cancer? Can you use chat bots in Facebook Messenger to book appointments or get health information? How can AI help hospitals manage their bed capacity better? The potential applications are myriad, and could help us tackle some really important and difficult challenges in healthcare.

Building useful things and implementing new technologies require different skills than doing cutting edge research in, say, deep neural networks. Being an expert in obscure branches of linear algebra might get you a job at Google, but is not necessarily going to help you much in designing applications that work well for children with asthma. So as well as having people with the technical knowledge to create and train machine learning algorithms, we need people with social knowledge: anthropologists and ethnographers helping us understand the lives and work of patients, clinicians and managers; user experience designers who know how to build things that actually work for users; patient leaders who can help us work through the ethical dilemmas involved in using patient data or turning to algorithms to make decisions. Our NHS Lab needs to bring these people together, and task them with creating new knowledge, products and services. 

To do this well is not going to be cheap. If the NHS spent 0.05% of it’s annual budget on funding AI, this work out as £60 million per year. Not small change by any means, but a drop in the ocean of healthcare spending. Given the potential to help improve the quality and efficiency of the NHS, I think this would be money well spent. This would be small beer in terms of AI projects in other industries, but would be a start.

There are of course many ways this could go wrong. Done badly, the Lab could stifle innovation through bureaucratisation, rule-making and crowding out. Big public sector organisations are hardly beacons of creativity and innovation and often don’t have the risk appetite to take on projects with uncertain returns: there is a reason why the NHS is not a pharmaceutical company. It is easy to see a project like this becoming caught in the fickle political winds that so often change the course of decision making in the NHS. “The Secretary of State has decided ….” are words that could kill a project like this. 

So how might we mitigate these risks? We could start by giving the lab a stable, multi year budget and a clear mandate: “Use AI to build useful things for healthcare. Commit to data transparency and open data. Make everything available open source and under licenses that promote use and creativity. Be responsive to your users. Give patients a leadership role in governance and decision making”.

This may all be hopelessly naive, a futurist day dream blind to the practicalities of making it happen. But I think we should at least imagine what this future could be, and start the conversation.

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Artificial Intelligence & Machine Learning in Healthcare (the first of many posts)

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AI is HOT right now. A lot of money and (so far, mostly human) brains are being put into developing ways of using AI in all sorts of industries: finance, logistics, manufacturing – it’s actually quite hard to find an area of human endeavour which someone, somewhere is not trying to build an AI system for.

One of the areas where AI has been generating the most amount of interest is healthcare. Indeed, Channel 4 News did a nice piece about AI in medicine this week, featuring the likes of Google Deepmind and a lot of visual imagery of glowing numbers cascading down hospital curtains.

Graphic designers: still stuck in the Matrix

There are a lot of people in healthcare who aren’t sure what AI is and what it might mean for their everyday work. I’m not an expert by any means, but I thought that a gentle introduction might be useful for all the doctors, nurses, managers, therapists, pharmacists (and all the many other people working in healthcare) who want to know a bit more about what this means.

Firstly, some terminology. “Artificial intelligence” is a general term for building computer systems to do the things that our human brains are good at: solving complex problems, recognising patterns, communicating through speech, making forecasts about the future and so on.

“Machine learning” is one example of a method used to build AI systems. The central idea here is actually quite intuitive (and the clue is in the name) – it’s all about learning. How are we able to drive a car, speak English, French or Japanese, create a beautiful work of art? These skills are not hard coded into our brains, or activated in an instant when we reach certain ages: we learn them over time though interacting with and sharing data with the world around us.  Machine learning takes the same principle and applies it to computers. Instead of programming computers to do things based on fixed rules (“If yes in English then output oui for French or はい for Japanese”) machine learning involves feeding in data and training the computer to do the thing we want it to do. If we do this well, then we end up with an algorithm (a set of instructions or actions) that does something useful – recognising faces in photos for example, or translating languages. Some of the most exciting recent advances in AI have come about from new techniques in machine learning.

The critical thing about machine learning is that you need data to train these algorithms: often a lot it. Data is the fuel for making useful machine learning algorithms.

So what does this mean for healthcare? Well there are a few reasons why healthcare is fertile ground for AI, and machine learning in particular:

  1. Healthcare is full of complex problems and pattern recognition. The classic example here is the process of making a diagnosis, based on interpeting a rag bag mix of language data (symptoms) and quantitative data (lab tests and imaging). In the past humans were able to solve these types of problems much better than computers – but these are things that AI is getting increasingly good at
  2. Healthcare is full of data – from electronic healthcare records, to lab data, to administrative data, healthcare is heaving with data. Machine learning algorithms will be at home here
  3. Healthcare is expensive. Rich countries spend something like 10% of their GDP on healthcare – globally the amount of money spent on healthcare is probably north of $10 trillion per year. Most of this is spent on people (roughly 60% of the NHS budget is spent on staffing costs for example), since healthcare is mostly a people business. There is therefore a very strong economic drive to automate some of the things in healthcare that people currently do
  4. Healthcare has lots of scope to be, well…better. If we invented no new drugs or medical devices for the next 10 years, and just tried very hard to apply the things we know work, reduce the number of errors and mistakes, close inequalities in access and provision – basically improve the quality of existing healthcare – then we could achieve vast improvements in patient outcomes. Healthcare is a long way from being optimised, meaning that there is lots of room for new ways of doing things (such as AI) to make healthcare better

Making all this happen is however, another matter. From a technical point of view, the role of AI in healthcare is still very limited, but is moving fast. Some areas of healthcare are going to be affected quicker than others. Most of the biggest recent advances in machine learning have been in language processing and image recognition – I suspect the first machine medics are going to be graduating in radiology (…and working 24/7, for no pay). I also think that there are whole swathes of healthcare that are currently off the radar of the likes of Google Deepmind, but where the biggest gains could me made: managing and improving healthcare services for example.

The rise of AI in medicine is also going to raise all sorts of issues. What are the ethical implications of a world where algorithms are making medical decisions? What does this mean for legal liability and regulation? What new skills and knowledge will the people working in healthcare need? What do patients want and who is going to be in control? Who is going to own and profit from this? What are the implications for how we collect and use sensitive healthcare data? If data is so central to all this, do we need to be investing in collecting better data? I hope to explore these topics, and more, in future posts.

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Patients, your data is yours

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I genuinely believe that we can use patient data for good, in ways that are not exploitative and respect people’s right to privacy. In fact, I could not do my job without using this type of data. For example, most of the research studies I work on use data about real patients, to help understand how we can make healthcare services better. I also use this type of data to help hospitals measure and improve the quality of care that patients with stroke receive, and to help plan public health services.

Information about our health is some of the most private and personal information there is, and how this data is used is extremely sensitive. Explaining to people how this data is used in ways that people understand is therefore essential, but something that we have been quite poor at doing in the past. One of the reasons for this is that it is very easy to slip into using technical jargon, using language that we assume that other people understand, but which they do not.

Phrases like “psuedonymised”, “information governance” and “data controller” don’t mean very much to many people. By making it hard for people to understand the language of data sharing, we are locking people out of making meaningful decisions about how patient data is used. This is ethically troubling, and has probably contributed to generating fear and mistrust about how patient data is used.
Understanding Patient Data  is a new initiative by the Wellcome Trust which has recently done some great work to show us how we should be talking when explaining or asking how patient data is used. After carrying out focus groups with experts and patients, they have produced some very useful, and very clear, guidelines:

  • The term “patient data” is well understood by most people
  • Use “patient” and not other terms like citizen, user or consumer
  • Avoid using “personal data” – people often think that this means that the data are identifiable rather than being anonymised
  • Use data in the singular…always (sorry Grammer fans)
  • Use the term “individual care” not “direct care” when talking about data for people’s own treatment
  • The phrase “improving health, care and services through research and planning” is much better than terms like “secondary uses” to describe how patient data can be used for other uses apart from individual care
  • Pictures are powerful ways of explaining what removing identifiable information means

  • Be clear and specific about how much and what kind of data is used, and avoid general terms like “medical records”
  • It’s often better to say “using data” rather than “sharing data”, as this makes it clear that controls are in place to make sure that any data is used responsibly better than saying “data sharing agreement”

    I think that these findings are going to be incredibly helpful. The idea behind this project was to improve communication when discussing using data for research, but I think that these tips are just as valid for other uses of patient data, such as audit, quality improvement and surveillance.

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    Where are the desire paths in healthcare?

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    We have all seen them. Unofficial paths, and grassless trails cutting across almost every park or urban green space, marking the routes that people wanted to go but which planners had not anticipated. They go by the rather delightful name “desire paths” and they tell us something important about how the world around us is designed.

    Flickr: Daniel Neville[/]

    Desire paths are a very physical example of the gap between how people actually use things and what designers intended people to do. As such they are often used as an illustration of why user centred design approaches can help design places that work better for users: laying the tarmac where people actually want to walk, not where we think they do (or should).

    What do muddy tracks in the park have to do with healthcare? Well, desire lines don’t only exist in physical spaces, but also in every service and product. Users find ways of working around, adapting, hacking and remaking things in ways in which the designer never intended. Finding these through observation, and learning how people use things in the real world can help us design and plan better services.

    Where then are the desire lines in healthcare? Some might be obvious – glaring examples where people or patients are breaking the rules of the system en masse. How people access urgent and emergency care in preference to waiting for less convenient appointments might be one example. More subtle desire lines might be found by :

    – using quantitative data to describe how, and why, patients access particular healthcare services

    – using qualitative methods (interviews, focus groups) to explore people’s beliefs and preferences about healthcare services

    – observing how people manage their own health

    Finding desire lines (and, in a virtual sense, laying formal pathways over them) could help us design healthcare services that are more responsive , efficient and provide the things that patients actually want. More radically, we could design services that deliberately encourage people to reshape them through their choices and actions. Citizen hackers, patient pathfinders: healthcare needs you to show the experts the way.

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    Let’s start a blog

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    Blogging is almost an ancient technology by internet standards and so it feels a bit late coming to this party.

    But better late than never. Sometimes (actually almost always) the 140 characters of a tweet are not enough to express exactly what I want to say.

    So this blog is for the longer, slower thoughts, on the subjects that I’m interested in. There isn’t a neat way to describe this, but I’ll be writing about the unnamed overlaps between  :

    – how we can design healthcare to be better, cheaper and fairer. Think QI, design, health services research, public health

    – the role that data (big and small) can have, and could one day play, in helping us live healthier, longer lives.  Think analytics, data visualisation, data science, epidemiology

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