7 ways to use AI to massively reduce costs in the NHS

I once met Tony Blair and asked him “Why are you not using technology in learning and health to free it up for everyone, anyplace, anytime?” He replied with an anecdote, “I was in a training centre for the unemployed and did an online module – which I failed. The guy next to me also failed, so I said ‘Don’t worry, it’s OK to fail, you always get another chance…. To which the unemployed man said 'I’m not worried about me failing, I’m unemployed – you’re the Prime Minister!” It was his way of fobbing me off.

Nevertheless, 25 years later, he publishes this solid document on the use of technology in policy, especially education and health. It’s full of sound ideas around raising our game through the current wave of AI technology. It forms the basis for a rethink around policy, even the way policy is formulated, through increased engagement with those who are disaffected and direct democracy. Above all, it offers concrete ideas in education, health and a new social contract with the tech giants to move the UK forward.

In healthcare, given the challenges of a rising and ageing population, the focus should be on increasing productivity in the NHS. To see all solutions in terms of increasing spend is to stumble  blindly onto a never-ending escalator of increasing costs. Increasing spend does not necessarily increase productivity, it can, in some cases, decrease productivity. The one thing that can fit the bill, without inflating the bill, is technology, AI in particular. So how can AI can increase productivity in healthcare:

1. Prevention

2. Presentation

3. Investigation

4. Diagnosis

5. Treatment

6. Care

7. Training

1. Prevention

Personal devices have taken data gathering down to the level of the individual. It wasn’t long ago that we knew far more about our car than our own bodies. Now we can measure signs, critically, across time. Lifestyle changes can have a significant effect on the big killers, heart disease, cancer and diabetes. Nudge devices, providing the individual with data on lifestyle – especially exercise and diet, is now possible. Linked to personal accounts online, personalised prevention could do exactly what Amazon and Netflix do by nudging patients towards desired outcomes. In addition targeted AI-driven advertising campaigns could also have an effect. Public health initiatives should be digital by default.

2. Presentation

Accident and Emergency can quickly turn in to a war zone, especially when General Practice becomes difficult to access. This pushes up costs. The trick is to lower demand and costs at the front end, in General Practice. First, GPs must adopt technology such as email, texting and Skype for selected

patients. There is a double dividend here, as this increases productivity at work, as millions need not take time off work to travel to a clinic, sit in a waiting room and get back home or to work. This is a particular problem for the disabled, mentally ill and those that live far from a surgery. Remote consultation also means less need for expensive real estate – especially in cities. Several components of presentation are now possible online; talking to the patient, visual examination, even high definition images from mobile for dermatological investigation. As personal medical kits become available, more data can be gathered on symptoms and signs. Trials show patients love it and successful services are already being offered in the private sector.

Beyond the simple GP visit, lies a much bigger prize. I worked with Alan Langlands, the CEO of the NHS, the man who implemented NHS Direct. He was adamant that a massive expansion of NHS Direct was needed but commented that they were too risk averse to make that expansion possible. He was right and now that these risks have fallen, and the automation of diagnostic techniques has risen, the time is right for such an expansion. Chatbots, driven by precise, discovery techniques, can start to do what even Doctors can’t, do preliminary diagnosis at any time 24/7, efficiently and in some areas, more accurately, than most Doctors. Progress is being made here, AI already has successes under its belt and progress will accelerate.

3. Investigation

Technology is what speeds up the bulk of investigative techniques; blood tests, urine tests, tissue pathology, reading of scans and other standars tests, have all benefited from technology. In pathology, looking at tissues under a microscope is how most cancer diagnosis takes place. Observer variability will always be a problem but image analysis algorithms are already doing a good job here. Digitising slides, and scans also means the death of distance. Faster and more accurate investigation is now possible. Digital pathology and radiology, using data and machine learning, is the future. If you need convincing further look at this famous test for radiologists.

4. Diagnosis

AI already outperforms Doctors in some areas, matches them in others and it is clear that progress will be rapid in others. Esteva et al. in Nature (2017) describes an AI system trained on a data set of 129,450 clinical images of 2,032 different diseases compared its diagnostic performance to 21 board-certified dermatologists. The AI system classified skin cancer at a level of competence comparable to the dermatologists. This does not means that Doctors will disappear but it does mean they, and other health professionals, will have less workload and be able to focus more on the emotional needs of their patients. Lots of symptoms are relatively undifferentiated, some conditions rare and probability-based reasoning is often beyond that of the brain of the clinician. AI technology, and machine learning, offers a way forward from this natural, rate-limiting step. We must accept that this is the way forward.

5. Treatment

Robot pharmacies already select and package prescriptions. They are safer and more accurate than humans. Wearable technology can provide treatment for many conditions, as can technology provided for the patient at home. Repeat prescriptions and on-going treatment could certainly be better managed by GPs and pharmacists online, further reducing workload and pressure on patients time. Above all patient data could be used for more effective treatment and a vast reduction in waste through over-prescribing.

Treatment in hospitals through automated robots, such as TUG, are already delivering medication, food and test samples, reducing the humdrum tasks that health professionals have to do, day in, day out. Really a self-driving car, it negotiates hospital corridors, even lifts, using lasers and internally built AI maps. The online management of treatement regimes would increase complaince to those regimes and save costs.

6. Care

Health and social care are intertwined. Much attention has been given to robots in social care but it is  AI-driven personalized care plans and decision support for care workers along with more self-care that holds most promise and is already being trialed. AI will help the elderly stay at home longer by providing detailed support. AI also gives support to carers. It may also, through VR and AR, provide some interesting applications in autism, ADHD, PTSD, phobias, frailty and dementia.

7. Medical education

Huge sums are spent on largely inefficient medical training. There are immense amounts of duplication in the design and delivery of courses. AI created content can create high quality, high-retention content in minutes not months (WildFire). Adaptive, personalized learning gets us out of the trap of batched, one size fits all courses. On-demand courses can be delivered and online assessments, now possible with AI-driven digital identification, keystroke tests and automated marking make assessment easier. Healthcare must get out of the ‘hire a room with round tables, a flipchart and PowerPoint (often awful)’ approach to training. The one body that is trying here is HEE with their E-learing For Health initiative. Online learning can truly reduce costs, increase knowledge and skills at a much lower cost.

Conclusion

It is now clear that AI can alleviate clinical workload, speed up doctor-patient interaction, speed up investigation, improve diagnosis and provide cheaper treatment options, as well as lower the cost of medical training. We have a single, public institution, the NHS, where, with some political foresight, a policy around the accelerated research and application of AI in healthcare could help alleviate the growing burden of healthcare. Europe has 7% of the world’s population, 25% of its wealth and 50% of its welfare spending, so simply spending more on labour is not the solution. We need to give more support to healthcare professionals to make them more effective by taking away the mundane sides of their jobs through AI, automation and data analysis.