🤖 AI can generate essays, pictures, and, it turns out, huge healthcare savings

Also: Did Washington fix US infrastructure? Are we good?

In This Issue

Short Essay: AI can generate essays, pictures, and, it turns out, huge healthcare savings

Long Essay: Did Washington fix US infrastructure? Are we good?

Micro Reads: immigration rebound, taxes and investment, China and robots, and much more

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Quote of the Issue

“So, yes, this nation remains fully committed to America's space program. We're going forward with our shuttle flights. We're going forward to build our space station. And we are going forward with research on a new Orient Express that could, by the end of the next decade, take off from Dulles Airport, accelerate up to 25 times the speed of sound, attaining low Earth orbit or flying to Tokyo within 2 hours.” - President Ronald Reagan, 1986 State of the Union Address.

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Short Essay

🤖 AI can generate essays, pictures, and, it turns out, huge healthcare savings

I was only going to write about infrastructure (see the essay below), but I just couldn’t resist a fresh working paper about AI and healthcare costs. In “The Potential Impact of Artificial Intelligence on Healthcare Spending” by Nikhil Sahni, George Stein, Rodney Zemmel, and David M. Cutler. (The first three researchers are from McKinsey & Company, while Cutler is the Harvard economist best known as one of the chief architects of the Affordable Care Act.) Whether or not America spends “too much” on healthcare given how wealthy we are — currently the United States devotes 18 percent of its economy to healthcare spending — is a separate issue from whether those resources could be used more efficiently. Sahni, Stein, Zemmel, and Cutler, or SSZC from here on out, suggest greater productivity is possible:

• A quarter of all national healthcare spending is gobbled up by administrative costs.

• The rapid increase in medical knowledge could help with diagnosis given that “only 6 percent of what the average new physician is taught at medical school today will be relevant in ten years.”

• Operating rooms could be used more efficiently leading to more effective use of building space, better patient access, and increased financial margins.

The results of their analysis:

We find that AI adoption within the next five years using today’s technologies could result in savings of 5 to 10 percent of healthcare spending, or $200 billion to $360 billion annually in 2019 dollars, without sacrificing quality and access. For hospitals, the savings come largely from use cases that improve clinical operations (for example, operating room optimization) and quality and safety (for example, condition deterioration management or adverse event detection). For physician groups, the savings also mostly come from use cases that improve clinical operations (for example, capacity management) and continuity of care (for example, referral management). For private payers, the savings come largely from use cases that improve claims management (for example, autoadjudication or prior authorization), healthcare management (for example, tailored care management or avoidable readmissions), and provider relationship management (for example, network design or provider directory management). While we only quantify cost savings in this paper, there are additional non-financial benefits from the adoption of AI, including improved healthcare quality, increased access, better patient experience, and greater clinician satisfaction.

Keep in mind that these considerable savings are the result of AI technology we currently possess and could deploy, not HealthGPT or some such. And by AI, the researchers are referring to machine learning (“computational techniques that learn from examples rather than operating from predefined rules”) and natural language processing (“a computer’s ability to understand human language and transform unstructured text into machine-readable structured data”). What could ML do?

ML examples include predicting whether a patient is likely to be readmitted to a hospital; using remote patient monitoring to predict whether a patient’s condition may deteriorate; optimizing clinician staffing levels in a hospital to match patient demand; and assisting in interpreting images and scans.

And NLP?

NLP examples include extracting words from clinician notes to complete a chart or assign codes; translating a clinician’s spoken words into notes; filling the role of a virtual assistant to communicate with a patient, help them check their symptoms, and direct them to the right channel such as a telemedicine visit or a phone call; and analyzing calls to route members to the right resource and to identify the most common call inquiries. Sometimes combining ML and NLP can create greater value; for example, using NLP to extract clinician notes and then using ML to predict whether a prior authorization is needed.

A few thoughts here: