A pediatrician, a cardiologist and a biomedical informaticist walk into a pharmacy. They all look as if they could use some strong medicine. “We want a Green Button,” they tell the pharmacist in unison.
“Green Button? Hmmm. I can’t say I know how to compound that prescription,” the puzzled pharmacist replies. “But if all three of you are ordering it, maybe I should. Can you tell me what, specifically, goes into a Green Button?”
“A lot of patients,” reply the three thirsty health experts.
“OK, I’ll play along,” says the pharmacist, beginning to lose his patience. “What comes out?”
“If we knew the answer to that, we wouldn’t need a Green Button.”
Actually, that punch line is no joke. The “Green Button” signifies a profound, potentially pervasive approach that could revolutionize medical practice. In a just-published feature in Inside Stanford Medicine, I report on a futuristic (but not too futuristic) vision of a “learning health-care system” outlined in a 2014 Health Affairs paper by three Stanford experts: pediatric specialist Chris Longhurst, MD, cardiologist Bob Harrington, MD, and biomedical informaticist Nigam Shah, MBBS, PhD.
As I noted in that feature:
The randomized clinical trial is considered the gold standard of medical research. In a randomized clinical trial… participants are randomly assigned to one of two – or sometimes more – groups. One group gets the drug or the procedure being tested; the other is given a placebo or undergoes a sham procedure. … Once the trial’s active phase ends, rigorous statistical analysis determines whether the hypothesis, spelled out in advance of the trial, was fulfilled.
There’s one problem: Clinical trials select only a small, artificial subset of the real population. The rest of us are kind of out of luck.
“Clinical trials are designed to prove one thing,” Shah told me. “And you’re testing it on people with just one thing: type 2 diabetes, eczema, whatever. But most real-life people don’t have just one thing. They have three or four or five things.”
Enter the Green Button. Suppose you’re a clinician facing a patient for whom no clear clinical guidelines exist. Instead, according to the scheme depicted by Longhurst, Harrington and Shah, you press a virtual “green button” on a computer screen displaying your patient’s electronic medical record. This triggers a real-time search of millions, or tens or millions, of other electronic records. In a matter of minutes, up pops a succinct composite summary of the outcomes of 25 or 100 or perhaps 1,000 patients very similar to the one in front of you – same race, same height, same age, same symptoms, similar medical histories, lookalike lab-test results – who were given various medications or procedures for the condition you’re hoping to treat. Those “lookalikes,” it turns out, respond much better to one treatment than to the others – something you’d have been hard put to guess on your own.
That’s all very nice, you say. Now I get your “artisanal faux-joke” lead. But, you ask, why does the button have to be green? And I answer: It doesn’t. But the other good colors were already taken.
Previously: Widely prescribed heartburn drugs may heighten heart-attack risk, New research scrutinizes off-label drug use and A new view of patient data: Using electronic medical records to guide treatment
Photo by Green Mamba :)–<