ChatGPT Won’t Fix Healthcare, But It Might Save Doctors Some Time

In a healthcare industry still burdened with 1960s technology, generative AI may offer a little relief — but companies are still working to overhaul a broken system that’s keeping doctors and nurses more focused on paperwork than patients.

By Katie Jennings and Rashi Shrivastava, Forbes Staff

Every week, Eli Gelfand, chief of general cardiology at Beth Israel Deaconess Medical Center in Boston, wastes a lot of time on letters he doesn’t want to write — all of them to insurers disputing his recommendations. A new drug for a heart failure patient. A CAT scan for a patient with chest pain. A new drug for a patient with stiff heart syndrome. “We’re talking about appeal letters for things that are life-saving,” says Gelfand, who is also an assistant professor at Harvard Medical School.

So when OpenAI’s ChatGPT began making headlines for generally coherent artificial intelligence-generated text, Gelfand saw an opportunity to save some time. He fed the bot some basic information about a diagnosis and the medications he’d prescribed (leaving out the patient’s name) and asked it to write an appeal letter with references to scientific papers.

ChatGPT gave him a viable letter — the first of many. And while the references may sometimes be wrong, Gelfand told Forbes the letters require “minimal editing.” Crucially, they have cut the time he spends writing them down to a minute on average. And they work.

Gelfand has used ChatGPT for some 30 appeal letters, most of which have been approved by insurers, he says. But he’s under no illusion that ChatGPT or the AI that powers it is going to save the U.S. healthcare system anytime soon. “It’s basically making my life a little easier and hopefully getting the patients the medications they need at a higher rate,” Gelfand says. “This is a workaround solution for a problem that shouldn’t really exist.”

That problem: The U.S. spends more money on healthcare administration than any other country. In 2019, around a quarter of the $3.8 trillion spent on healthcare went to administrative issues like the ones bemoaned by Gelfand. It’s estimated around $265 billion of that was “wasteful” — unnecessary expenditures necessitated by the antiquated technology that undergirds the U.S. healthcare system. Gelfand can use a chatbot to electronically generate an appeal letter. But he has to fax it to the insurer. And that encapsulates the challenge facing companies hoping to build time-saving AI back-office tools for a healthcare system stuck in the 1960s.

Cut The “Scut”

The fax machine isn’t going away anytime soon, says Nate Gross, cofounder and chief strategy officer of Doximity, a San Francisco-based social networking platform used by two million doctors and other healthcare professionals in the U.S. That’s why Doximity’s new workflow tool, DocsGPT, a chatbot that helps doctors write a wide range of letters and certificates, is connected to its online faxing tool.

“Our design thesis is to make it as easy as possible for doctors to interface with

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5 Strategies ChatGPT Will Improve Healthcare Permanently, For Better

About the previous ten years, I’ve retained a near eye on the emergence of artificial intelligence in health care. Through, a person reality remained continuous: Irrespective of all the hoopla, AI-centered startups and founded tech organizations alike have unsuccessful to transfer the needle on the nation’s over-all health and health-related prices.

At last, immediately after a decade of underperformance in AI-driven drugs, results is approaching quicker than physicians and people at present understand.

The motive is ChatGPT, the generative AI chatbot from OpenAI that’s using the electronic planet by storm. Since its launch in late November, ChatGPT has completed remarkable feats—passing graduate-level tests for business enterprise, regulation and professional medical college (the solutions to which just cannot simply just be Googled).

The upcoming version, ChatGPT4, is scheduled for release afterwards this 12 months, as is Google’s rival AI products. And, very last 7 days, Microsoft unveiled an AI-run research motor and web browser in partnership with OpenAI, with other tech-field opponents slated to sign up for the fray.

It remains to be noticed which corporation will finally earn the generative-AI arms race. But no matter of who will come out on top, we have reached a tipping stage.

Generative AI will renovate medicine as we know it

In the similar way the Apple iphone became an critical aspect of our lives in what seemed like no time, ChatGPT (or whichever generative AI tool prospects the way) will alter medical observe in beforehand unimaginable techniques.

Here’s how:

1. By starting to be exponentially a lot quicker and far more potent

The human mind can conveniently forecast the level of arithmetic growth (whereby figures increase at a consistent level: 1, 2, 3, 4). And it does reasonably effectively at comprehending geometric expansion (a pattern that increases at a continual ratio: 1, 3, 9, 27), as very well.

But the implications of steady, exponential growth show tougher for the human mind to grasp. When it comes to generative AI, that is the fee of advancement to target on.

Let’s think that the ability and pace of this new technological innovation have been to stick to Moore’s Law, a posit that computational development doubles approximately every two decades. In that situation, ChatGPT will be 32 situations more powerful in a ten years and in excess of 1,000 situations far more potent in two many years.

That is like buying and selling in your bicycle for a motor vehicle and then, soon soon after, a rocket ship.

So, as a substitute of dwelling on what today’s ChatGPT can (or can not) do, look forward a 10 years. With vastly much more computing power, together with far more knowledge and facts to attract from, upcoming generations of ChatGPT will possess analytical and issue-resolving powers that considerably exceed existing anticipations. This revolution will empower tomorrow’s technological innovation to match the diagnostic abilities of clinicians right now.

2. By emulating how

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