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.