Healthcare Executives Weigh in on AI

Healthcare Executives Weigh in on AI

We’ve written in the past about artificial intelligence and how its use in a London hospital to assess and fast-track treatment for the most serious cases has turned some heads. There’s great excitement for artificial intelligence. The technology is in its infancy and its potential seems near limitless.

But that excitement is somewhat tempered by runaway-train, doomsday scenarios, a la The Terminator, The Matrix, etc. People are understandably hesitant to fully endorse something that may render them redundant.

That being said, in the healthcare space there’s hope that the technology could be used to solve two of the industry’s most persistent challenges: rising costs and patient care.

So how do healthcare executives feel about AI?

A recent survey of 200 U.S. healthcare decision-makers conducted by Intel and Convergys Analytics found that most healthcare executives don’t see AI as something that will impact their businesses — for better or worse — any time soon. Most surveyed (70%) said it would be widespread in 3 to 10 years.

Fatal error was the number one fear reported by healthcare executives when discussing AI, and it ties in closely with the number one perceived obstacle in implementing the technology: lack of trust. Clinicians and healthcare execs alike simply don’t trust the technology sufficiently to have it playing an active role in healthcare.

Getting the message, those building the technology are focusing on not just what it can accomplish, but also making it accountable, transparent, and interpretable to the clinicians deploying it.

Where AI will make the most impact

The survey respondents were divided on what specific areas of healthcare they thought AI would make the most impact. Here are the percentages that agreed or strongly agreed that AI would:

– Provide predictive analytics for intervention — 91

– Used in clinical decision support — 91

– Enable multiple specialists to interact and improve patient care — 88

– Improve accuracy of medical diagnosis — 83

– Improve efficiency and reduce costs — 81

– Allow clinicians to spend more time with each patient by automating routine tasks — 75

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