I’d filed away to read later a Fast Company article whose headline proclaimed Google will be applying artificial intelligence to healthcare problems. Upon returning to read it, I was a bit disappointed to see how speculative the article was. Basically, Google acquired a company with a messaging app for hospital staff that streamlines communication. It’s been hinted that Google might apply artificial intelligence tools to help identify patients at risk of kidney failure whom a clinician might not deem at risk.

Joining forces?

Even if machine learning were applied to predict which patients might be at risk of kidney failure,  that’s not really the end of the story. Once patients are identified, there have to be effective interventions to help them, and perhaps moreover, the entire system where this is occurring needs to allow for these predictions and interventions to take place. Looking at the big picture, is identification of at-risk patients and communication among clinicians the true ‘problem’ that needs to be solved? Or is there a different systemic problem or bottleneck that is truly responsible for delaying care? While reading the article, I found myself nodding in agreement to this excerpt:

[S]ome health experts fear that this kind of technology is just putting a Band-Aid on a broken system… “Some people have this utopian plan that you can sprinkle some AI on a broken health system and make things better,” says Jordan Shlain, a Bay Area-based doctor and entrepreneur who has advised the NHS.

Overall, I’m really excited about the idea of using data and machine learning to improve care, but it’s important to be realistic about where these tools can help. I think the promise to fix “broken systems” is overinflated. Artificial intelligence might help us identify at-risk patients, make better diagnoses, or select specific treatment plans, but at the end of the day, healthcare systems are built by and made up of people– and I’m not sure machines can fix those systems.