Breaking into the NHS

No, not burglary. Digital Catapult had a half-day event on “NHS: The Procurement Minefield” last Monday. The first speaker was Mahiben Maruthappu from NHS England, who listed six big challenges for the NHS, or things that are needed more of: prevention, innovation, self-care, breaking silos and scaling, IT interoperability, and making the financial case. (Most of these sound like they would fit any major organisation really…)

He then listed three focus areas: organisational change to handle new kinds of services and local innovations (no surprise there!), combining innovations to achieve synergies, and achieving national scale. In terms of medical issues, diabetes, cancer and mental health are the three big priorities for the next ten years.

The other speakers weren’t as interesting to my ears, but the panel discussion towards the end had some good nuggets. For example, in answer to a question about how best to get into the NHS as a new service provider, the answers included having inside knowledge, “talking clinical” (i.e. not just business and tech), having a global view, and being adaptable and having perseverance (expect that anything will take years…). Someone even called the NHS “the hardest market to crack”, and recommended going direct to consumers, even if you then have to go to the US and Australia.

Some food for thought there, though mostly confirming the impression I’ve already got from other health and medical startups about the difficulties involved in working (or trying to work, to be more precise?) with the UK national health care system.


Two approaches to innovation in health

Two events I went to the other week, both dealing with innovation in health (care): one a meeting for the government’s Accelerated Access Review (AAR), the other a Nesta day on “People Powered Health”. And two rather different approaches to innovation.

The Government first. AAR is a big thing, on how new treatments, medicines, and devices could more easily be brought into the NHS. I attended this final stakeholder session as a “health startup” representative, mostly just listening to the discussions, and quietly shaking my head. Just one example of the many issues needing attention that someone raised is the Cancer Drugs Fund – how is that existing monster going to fit in with any new processes and organisations? A government minister gave a pep talk, said how excited he was, and concluded that he is looking forward to significant progress in the next six month. Someone sat next to me quietly noted that with the time it takes to complete the NHS budget processes, not much can possibly change in the next 18 months at least. Not exactly agile…

The Nesta thing then. A whole day of presentations, most pretty interesting. One of my highlights was a talk titled “From South Sudan to West Essex”, or how the Rapid Results Institute’s methods born in the developing world have made a big difference for local NHS services in the UK. Basically it was all about empowering people to do the best in their jobs, setting ambitious targets, and being willing to ask the patients what they would prefer to happen. And there was more on topics like peer support (e.g. Peers for Progress are doing interesting work).

And interestingly, several people said at different times and in different ways that randomised controlled trials are not the only – and not always the best – method of finding things out. Which should be obvious, but clearly it isn’t, so good that it gets said. I will have much more to say on this topic, but on another day!

Thoughts on finally reading Bad Pharma

I finally got round to reading Ben Goldacre’s book Bad Pharma. I’m not going to go into too much detail about the book, but if you have even a passing interest in medicine, public health and the costs of providing it, or your own health, I would highly recommend that you read it too. These are some of the random thoughts it raised in me.

First, you only had to know medical students at university, and see what freebies they got from drug companies, to know that something was up. And I’ve long been interested in publication bias. So I was already at least aware of most of the issues, but the book is still quite a catalogue of all kinds of rogue behaviours by many actors. Pharma companies misbehave of course, but so do drugs regulators around the world (I was probably most surprised by just how useless – and even worse – they are), professional bodies, journals and their editors, patient groups, doctors and academics. There is a lot of money going around, and therefore corruption both big and small, explicit and implicit. Nobody comes out too well in this story.

Second, since my backgroud is in banking, I couldn’t help making comparisons. Bankers misbehave too, no doubt about it. Both industries are heavily regulated, but the regulators have in both instances been fairly comprehensively captured by industry interests. In bankers’ defense, when they fiddle LIBOR rates, some other financial company may lose a few million dollars, but when drug developers intentionally hide adverse data about their products, thousands of people will die. So why is there so much less outcry about pharma? It is probably more complex to understand publication bias than lying about benchmark rates. And the deaths are isolated and hidden from view, whereas the financial crisis was very much visible on every high street.

And finally. In passing, Goldacre says something along the lines of “just because there are issues with medicine, it doesn’t mean that alternative medicine works”. Sure. But the opposite works as well: just because homeopathy doesn’t work, it doesn’t mean that (“traditional” or whatever you want to call it) medicine necessarily works any better. To be clear, I don’t think there’s any physical way that homeopathy works. But you can also be prescribed medicines by your GP that are not much better than placebo, if at all. So when a lazy skeptic rants about homeopathy and “the scientific method”, they should always be reminded that there is science and then there’s cargo cult science. Medicine is beginning to look more and more like a cargo cult than the real thing – there are journals, trials, complex statistics etc, but if it’s all based on smoke and mirrors then what do you really have that you can rely on? My attitude is to pay more attention to studies of how science is actually done: its history and sociology, and not just what scientists say in after-dinner speeches. The reality is always much more messy than “hypothesis, test, replication”.

big health data

Last week I attended the Operational Research Society’s Data Science: The Final Frontier – Health Analytics event (hashtag: #bighealth) at Westminster Uni. Two of the six presentations were worth noting.

Cono Ariti from The Nuffield Trust spoke about predictive risk modelling in health care. He mentioned the “Kaiser pyramid”, which is the old 20/80 rule, slightly expanded, saying that 3% of patients make up 45% of health care costs. The next 13% are responsible for another 33%; added up, these are approximately 20/80!

And he made two important points to keep in mind with health analytics. First, just building a model is useless without corresponding interventions in place. In other words, if you identify patient segments, say, you also need to have suitable treatments available for them. And secondly, that regression to the mean is a major issue in this area: many people get better by themselves, without any treatment at all. This will complicate evaluations between treatments (and no-treatments), since a large number of patients in all groups, whether treatment or control, may improve significantly. And any differences between control and treatment groups may be very small and difficult to identify.

The second interesting talk was more of a blue sky horizon scan, from Rob Smith at IBM. He talked about the future of health analytics, noting the differences in people of different ages when it comes to tech, gadgets, and privacy, and consequent health behaviours. He also talked a bit about the data issues around genomics, and more about what IBM is doing with Watson. For example, it gets fed as much medical literature as possible, so that it can propose not only treatments to match symptoms, but also suggest new research avenues. Very impressive stuff, and potentially useful in things like cancer treatment which is getting very complex. So much so in fact, that my conclusion was to ask whether artificial intelligence is now the only thing clever enough to handle modern medicine?