Dr Ian Jackson of Refero discusses how the only way to make the medical data useful to doctors and nurses is to let the machines turn intelligent – and why there’s nothing ‘artificial’ about that
The Department of Health and Social care has refreshed its code of conduct for the use of Artificial Intelligence (AI) within the NHS.
AI has become misunderstood. It tickles up a feeling of unease in many households as something to be feared when applied to medicine. And it’s turning the public opinion of everything it promises into a dead end of mistrust
Standards and regulations are good, but I’m one of the growing number of healthcare technologists concerned by the frequency and misuse of the term.
AI has become misunderstood. It tickles up a feeling of unease in many households as something to be feared when applied to medicine. And it’s turning the public opinion of everything it promises into a dead end of mistrust.
I think it’s all in the nomenclature.
We should discount the word ‘artificial’ when we talk about how Artificial Intelligence can save the lives of sick people. Let’s, instead, use the more-useful and accurate term ‘machine learning’.
The impact that the word ‘artificial’ is having on machine learning and on the public’s perception of its potential to bring positive changes to healthcare is too great.
Let’s set aside NHS progress on paper records and communications for a moment, as machine learning can’t harness handwritten knowledge.
Your local hospital probably has about 150 distinct software applications running right now. Each contains important data that could save or change a life, so let’s call it the hospital’s total ‘intelligence’, which largely remains dormant unless called on.
The potential in that latency for saving present and future lives is huge – enter machine learning.
There’s no doctor, nurse or midwife in that hospital that can access all 150, all at once, and use what the hospital really ‘knows’ about an illness, a crisis, or a patient.
Machine learning could connect the knowledge and present the statistical information that no clinician will ever be able to compute themselves.
The hospital’s intelligence is no longer simply artificial, it becomes real. Crucially, it also becomes widely available too, and that’s where the learning part of machine learning is important.
That real, not artificial intelligence, drawn together by machine learning technology, can now be treated as another string to the clinician’s bow, rather than a technology that is destined to supersede them.
Machine learning has changed banking, customer service and retail, making commercial products out of business processes. This can be done within healthcare
The intelligence from machine learning can then be used to build bridges between healthcare and other life-changing public services; social care, policing or mental health services.
Potentially it could solve delayed transfer of care with its ability to connect systems and master the patterns of crisis periods.
Long-term conditions such as cystic fibrosis can be treated at home, and recurring illness could be triaged there too.
Clinicians can be connected with patients at their university hall of residence, their retirement community or their hospice. And mental illnesses can be more-accurately integrated and available to a patient’s treatment pathway across all sources of care, from the maternity unit to student welfare officers.
Health Secretary, Matt Hancock’s support for AI/machine learning in healthcare is valuable, but it’s unlocking the value that the technology can bring which will make the difference.
Clinicians, nurses, social workers themselves have no ability to unlock the value of information without technology giving them access to it. We must secure their support, their enthusiasm, and pass them the key to commercialising their specialities in the same way that the private sector has been able to.
Machine learning has changed banking, customer service and retail, making commercial products out of business processes. This can be done within healthcare.
Machine learning could bridge the commissioner/provider split, accelerating STPs to success. It could also transform the services commercialised by Global Digital Exemplars within the NHS, at the highest level.
Immediate societal value of machine learning is clear. Let the technology in to analyse data and risk, and link health and social care intelligence so that bed management becomes simpler and less emotionally charged.
Life and death, and everything in between, becomes easier if medical data becomes intelligent, and there’s nothing artificial about that
Let the technology analyse data associated with long-term conditions and patient appointments and it can determine when appointments are necessary, and where, and how they should take place – something no clinician has time to map for each case they are responsible for.
The technology is the way forward, just not the term for it.
The Department for Health and Social Care, unified and renamed to build better care pathways, has recognised the need for clarity around AI, or machine learning. The code of conduct, particularly around development by technology firms, is essential.
Life and death, and everything in between, becomes easier if medical data becomes intelligent, and there’s nothing artificial about that.