Step forward AI, the solution to the impending healthcare problem….

Artificial Intelligence is still seen as the digital bogeyman in some quarters, even though it has been around for several decades. Maybe people are scared because every film that has Artificial Intelligence always shows the dark rise of the machine side (e.g. Terminator and Skynet, iRobot) which has etched into our minds of how horribly wrong it can go when we give in and let computers take full unregulated control of our lives.

But that is only an extreme and fictional example of when AI goes rogue. What about the good? Technology, like most things’ humans create, has good and bad sides, depending how you use it. For example, a first aid kit can be a weapon in the wrong hands.

I firmly believe that AI can support healthcare professionals to do their jobs better and more efficiently, picking up things that would normally be overlooked, meaning we can live healthier longer lives.

How can AI help healthcare professionals?

Well for starters;

• Support and train staff to understand how AI can be applied to what they are doing in order to create the relevant benefit
• Improve the accuracy of patient diagnosis, AI software can rapidly cross reference patient data far greater than humans to spot signs of disease earlier, which means that a doctor can better prescribe preventative measures.
• Speed up treatment times. The quicker, and more accurate doctors can diagnose a problem, the quicker they can treat it.
• A major issue in the NHS at the moment is that there is an increase in demand and a decrease in supply *e.g. Junior Doctors, hospital beds. This can lead to delays in patients being treated and increase the chances of potentially fatal conditions being overlooked.
• Save the NHS a lot of money, AI can also be programmed to carry out time consuming administrative tasks which can cost the NHS a considerable amount of a year to carryout.
• With more accurate diagnoses being made and AI support or spotting any admin errors as a result of AI adoption, the amount of money that is set aside for negligence and malpractice claims would naturally come down. The NHS paid out more than £1.63 billion in damages to claimants in 2017/18, an increase from £1.08 billion in 2016/17.
• Data could be used to understand when best and how best to carry out operations and treatments meaning creating efficiencies which would reduce overall waiting times.
• Allow an AI to define the workforces needed (staff roles and competences required) and carryout quality control to make sure this is in place.
• An AI driven system would be able to understand the needs and automate the change (AI for recruitment, AI for training, AI analysing the needs of a population, AI for people management and resource distribution, AI predicting future needs)

The population as a whole is ageing, which will mean a further increase in demand of the NHS resources.

Implementing AI software in the right places can help improve the overall state of the NHS making it easier to deal with the rise in demand for its services. This fear of the unknown is understandable, as AI is still a new concept, it was only in 1997 that AI first came to the wider public’s attention when chess grandmaster Gary Kasparov was beaten by IMB’s deep blue computer. This is an example of what AI is capable of, and that was only 21 years ago. But that fear needs to be quelled soon through rigorous research, logical thinking and a more pro-active mindset.

AI has a new talent. You can teach a computer to play video games, understand language, and even how to identify people or things. This tip of the iceberg new skill comes from an old concept that only recently got the processing power to exist outside of theory. I’m talking about Machine Learning.

You don’t need to come up with advanced algorithms anymore. You just have to teach a computer to come up with its own advanced algorithm.

Axela Innovations has been busy working on our Artificial Intelligence driven Patient Health Record (PHR) system. cAir:ID stores and analyses all your health and medical data in one place. This gives real-time depiction of your health to forecast future health issues before they become a problem. This is only possible by the power of data and AI. We hope this will allow for the right level of care to be delivered when it’s precisely needed.

I don’t believe Artificial Intelligence will replace traditional doctors in all instances however, I feel what they can offer should augment the work that doctors and healthcare professionals are already doing. Allowing the AI to analyse the different data points on an individual or the population will allow for a clearer picture of an individual’s health to be presented to the healthcare professional. This is something we are striving to achieve with cAir:ID, we believe the mundane work and tasks should be left to the AI freeing up time to deliver what the machine can’t do, the human touch.

Maybe we aren’t ready for AI to support our medical needs as a society and are happy with Siri to help pick music and Tesla auto drive to steer our cars. But when it comes to supporting something as personal as health, we still want a face to speak to or just someone that can emphasise and hold our hand through it. However, we need embrace how technology and AI can deliver real change. We just need a pioneer to do this, we all need to work together to create innovation that really makes a change.

Though we have only started our journey we are excited to see what the future holds for us.

#WeLoveCAIR

NHS in numbers, could AI driven healthcare help reduce some of the below numbers could cAIr:ID be the solution, we will only know once we try…

• In January 2019, 15.6% of people attending A&E spent more than 4 hours from arrival to admission, transfer or discharge. This is the highest it has been since the data set began.
• 83,519 patients spent more than 4 hours waiting on a trolley from decision to admit to admission in January 2019.
• January 2019 there was an average of 15,513 hospital beds occupied by long-stay patients
• The proportion of people waiting over 18 weeks to start elective treatment reached 13.4% in December 2018, the worst level of performance since January 2009.
• 2,237 people had been waiting over 52 weeks to start elective treatment in December 2018, which is 28% higher than in December 2017
• In December 2018, 3.3% of patients had been waiting over 6 weeks for a diagnostic test
• In 10 years’ time the NHS will have a shortfall of 108,000 full-time equivalent nurses

[stats from Combined Performance Summary: December 2018 — January 2019 Nuffield trust]