What’s in a name?

With respect to the Bard, the question doesn’t arise concerning the smell of a rose, but rather concerning the best way to understand the distinction between the technologies that are increasingly impacting healthcare. 

Writing recently at Health Europa, Ian Jackson, Medical Director and Clinical Safety Officer for Refero, a technology platform focused on providing access to public and private health and social services, dubbed himself one of the growing number of healthcare technologists concerned by the frequency and misuse of the term “Artificial Intelligence.”

In his view, “AI has become misunderstood. It tickles up a feeling of unease in many households as something to be feared when applied to medicine. It’s turning the public opinion of everything it promises into a dead end of mistrust.”

The answer, he argues, is to switch to “machine learning” to refer to the technological ability to “connect the knowledge and present the statistical information” that no clinician will ever be able to compute on his or her own.

“The intelligence from machine learning,” he continues, “can 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. 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.”

In his view, the immediate societal value of machine learning is clear. “Let the technology in, to analyze data and risk, and link health and social care intelligence so that bed management becomes simpler and less emotionally charged. Let the technology analyze 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.”

Life and death, and everything in between, he concludes, “becomes easier if medical data becomes intelligent, and there’s nothing artificial about that.”