By Sy Mukherjee
April 1, 2019

Happy Monday, readers.

I’m on my way to the Fortune Brainstorm Health conference in San Diego, where our ridiculously stacked speaker set will address, well, a good chunk of things worth addressing in the digital health, biopharma, and public health spheres.

A study published in the journal Nature Digital Medicine last week just happens to happily coincide with our Southern California get together. It raises one the most important questions in this nascent era of algorithmic medicine—when are AI and big data analytics most effective in real world medicine?

The authors propose some theories following an extensive, and unique, collaboration between AI researchers and medical professionals at Stanford and Utah’s Intermountain LDS Hospital system, the MIT Technology Review reports. In short, sensors were placed into patients’ rooms to keep tabs on when they moved around (a predictor of faster healing in many cases); while these proved accurate for patient movements, the researchers realized there could be problems with more people, such as medical staff, in the room. Ultimately, the hope is such technology can assist doctors and nurses by predicting problems with a patient before they actually happen.

What may be most interesting about the findings, though, is the practical problem researchers faced with multiple people in the room. That’s the kind of real-world troubleshooting that will determine whether these technologies transform health care or remain the stuff of pipe dreams. And those are the exact kinds of issues we’ll be chewing over in the coming days at Brainstorm Health (if you’ll be there, I hope you’ll say hello).

Read on for the day’s news.

Sy Mukherjee


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