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Artificial intelligence set to revolutionise future occupational health delivery

“Technology will replace many doctors, and other professions.” This headline caught my eye in October 2016 when I was preparing a presentation for the Medgate UK annual conference.

The article, from the Harvard Business Review (Susskind R and Susskind D, 2016), has become my wake-up call. It states that faced with the claim that artificial intelligence (AI) and robots are poised to replace most of today’s workforce, most mainstream professionals — doctors, lawyers, accountants, and so on — believe they will emerge largely unscathed.

But the research and analysis challenges the idea that these professionals will be spared. The researchers expect that within decades, the traditional professions will be dismantled, with most professionals replaced by fewer expert people, new types of experts, and high-performing systems.

My initial reaction was “not on my watch”. Having worked within the field of OH since 1993, I have seen, and been part of, many paradigm shifts.

I began my career as a factory nurse and studied at the Royal College of Nursing when the Occupational Health Certificate was a non-English National Board approved course (the ENB was subsequently replaced by the Nursing and Midwifery Council). I then acquired an occupational health Master’s degree and a Master of Business Administration (MBA).

The key factor behind all the change I have seen and innovations I have implemented has been the use of technology to reduce the effort required to complete

high-volume tasks. This has involved deconstructing some of the traditional ways of working, and challenging statements such as: “Only qualified occupational health advisers [OHAs]/physicians can perform/undertake that task.”

What is artificial intelligence?

AI is an umbrella term for a group of technologies – including machine learning – that enable computers to learn new skills and capabilities based on the data they are exposed to.

Machine learning started making its way into industry in the early 1990s. It began with relatively simple tasks, such as assessing credit risk from loan applications, and sorting the mail by reading handwritten characters from post codes. In recent years there have been many dramatic breakthroughs. Machine learning is now capable of far more complex tasks.

In 2012, the Kaggle data science platform challenged its community to build an algorithm that could grade high-school essays (Goldbloom, 2016). The winning algorithms were able to match the grades given by real teachers.

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