I’ve been talking in recent posts about how our typical methods of testing AI systems are inadequate and potentially unsafe. In particular, I’ve complainedthat all of the headline-grabbing papers so far only do controlled experiments, so we don’t how the AI systems will perform on real patients.
Today I am going to highlight a piece of work that has not received much attention, but actually went “all the way” and tested an AI system in clinical practice, assessing clinical outcomes. They did an actual clinical trial!
Big news … so why haven’t you heard about it?
The Great Wall of the West
Tragically, this paper has been mostly ignored. 89 tweets*, which when you compare it to many other papers with hundreds or thousands of tweets and news articles is pretty sad. There is an obvious reason why though; the article I will be talking about today comes from China (there are a few US co-authors too, not sure what the relative contributions were, but the study was performed in China).
China is interesting. They appear to be rapidly becoming the world leader in applied AI, including in medicine, but we rarely hear anything about what is happening there in the media. When I go to conferences and talk to people working in China, they always tell me about numerous companies applying mature AI products to patients, but in the media we mostly see headline grabbing news stories about Western research projects that are still years away from clinical practice.
This shouldn’t be unexpected. Western journalists have very little access to China**, and Chinese medical AI companies have no need to solicit Western media coverage. They already have access to a large market, expertise, data, funding, and strong support both from medical governance and from the government more broadly. They don’t need us. But for us in the West, this means that our view of medical AI is narrow, like a frog looking at the sky from the bottom of a well^.
This is interesting for a bunch of reasons. First it’s a good example of how technology is now being applied to help with the almost absurd complexity of modern medicine–complexity that technology has both added to and may yet cure. Secondly, Surveyor Health has been building its technology for several years and (FD) I’ve been advising them off and on since 2009 and know the principals well. Thirdly, and this is mostly for grins, it represents some of the absurd language used to describe our crazy health care system.
What does the tech do? Surveyor Health’s technology is very
complex optimization technology that examines the incredible number of symptoms
and interactions undergone by patients taking multiple medications. As you know
most chronically ill patients are on upwards of half a dozen medications and some
are on many more. The more medications, the more the potential for serious and
sometimes fatal drug-drug interactions, side effects and more. You only have to
think of the litany of celebrity drug deaths (Michael Jackson, Prince, Anna Nicole
Smith, Health Ledger, Tom Petty, to name a few) to understand the seriousness
of the issue. Erick von Schweber, a real theoretical physicist and CEO of
Surveyor Health tells me that when you get above 11 drugs the calculations
involved are more complex than what Google has to do to index the web. (And yes,
he now is allowing me to call it AI!)
Two years ago we wouldn’t have believed it — the U.S. Congress is considering broad privacy and data protection legislation in 2019. There is some bipartisan support and a strong possibility that legislation will be passed. Two recent articles in The Washington Post and AP News will help you get up to speed.
Federal privacy legislation would have a huge impact on all healthcare stakeholders, including patients. Here’s an overview of the ground we’ll cover in this post:
Six Key Issues for Healthcare
We are aware of at least 5 proposed Congressional bills and 16 Privacy Frameworks/Principles. These are listed in the Appendix below; please feel free to update these lists in your comments. In this post we’ll focus on providing background and describing issues. In a future post we will compare and contrast specific legislative proposals.
What’s received little attention from physicians or the public is the company’s quiet metamorphosis into a powerhouse focused on the actual practice of medicine.
If “data is the new oil,” as the internet meme has it, Google and its Big Tech brethren could become the new OPEC. Search is only the start for Google and its parent company, Alphabet. Their involvement in health care can continue through a doctor’s diagnosis and even into monitoring a patient’s chronic condition for, essentially, forever. (From here on, I’ll use the term Google to include the confusing intertwining of Google and Alphabet units.)
Today, we are featuring Dr. Jesse Ehrenfeld from the American Medical Association (AMA) on THCB Spotlight. Matthew Holt interviews Dr. Ehrenfeld, Chair-elect of the AMA Board of Trustees and an anesthesiologist with the Vanderbilt University School of Medicine. The AMA has recently released their Digital Health Implementation Playbook, which is a guide to adopting digital health solutions. They also launched a new online platform called the Physician Innovation Network to help connect physicians with entrepreneurs and developers. Watch the interview to find out more about how the AMA is supporting health innovation, as well as why the AMA thinks the CVS-Aetna merger is not a good idea and how the AMA views the role of AI in the future of health care.
Zoya Khan is the Editor-in-Chief of THCB as well as an Associate at SMACK.health, a health-tech advisory services for early-stage startups.
I have seen the light. I now, finally, see a clear role for artificial intelligence in health care. And, no, I don’t want it to replace me. I want it to complement me.
I want AI to take over the mandated, mundane tasks of what I call Metamedicine, so I can concentrate on the healing.
In primary care visits in the U.S., doctors and clinics are buried in government mandates. We have to screen for depression and alcohol use, document weight counseling for every overweight patient (the vast majority of Americans), make sure we probe about gender at birth and current gender identification, offer screening and/or immunizations for a host of diseases, and on and on and on. All this in 15 minutes most of the time.
Never mind reconciling medications (or at least double checking the work of medical assistants without pharmacology training), connecting with the patient, taking a history, doing an examination, arriving at a diagnosis, and formulating and explaining a patient-focused treatment plan.
At long last, we seem to be on the threshold of departing the earliest phases of AI, defined by the always tedious “will AI replace doctors/drug developers/occupation X?” discussion, and are poised to enter the more considered conversation of “Where will AI be useful?” and “What are the key barriers to implementation?”
As I’ve watched this evolution in both drug discovery and medicine, I’ve come to appreciate that in addition to the many technical barriers often considered, there’s a critical conceptual barrier as well – the threat some AI-based approaches can pose to our “explanatory models” (a construct developed by physician-anthropologist Arthur Kleinman, and nicely explained by Dr. Namratha Kandulahere): our need to ground so much of our thinking in models that mechanistically connect tangible observation and outcome. In contrast, AI relates often imperceptible observations to outcome in a fashion that’s unapologetically oblivious to mechanism, which challenges physicians and drug developers by explicitly severing utility from foundational scientific understanding.
Today on Episode 58 of Health in 2 Point 00, Jess and I have more to share from Exponential Medicine, but this time we’re at the Health Innovation Lab checking out all of the startups. In this episode, Jess and I talk to Meghan Conroy from CaptureProof about decoupling medical care from time and location, Care Angel‘s Wolf Shlagman about the world’s first AI and voice powered virtual nursing assistant, and highlight Humm’s brain band which improves working memory, concentration, and visual attention. We leave you with some parting words from Godfrey Nazareth: “Let’s set the world on fire. Let’s change the world, with love.” -Matthew Holt
On Episode 57 of Health in 2 Point 00, Jess and I report from Exponential Medicine. In this episode, Jess and I talk about digital surgery and how Shafi Ahmed and Stefano Bini are transforming surgical training. She also asks me about my favorite session, one by Anita Ravi on health care for those who have been sex trafficked. Other highlights include ePatient Dave’s talk about access to data for patients and letting patients help, and Leerom Segal’s overview of why voice matters- Matthew Holt
WTF Health – ‘What’s the Future’ Health? is a new interview series about the future of the health industry and how we love to hate WTF is wrong with it right now. Can’t get enough? Check out more interviews at www.wtf.health.
What can you find diving into the black hole of healthcare’s unstructured data? Natural Language Processing (NLP) seems to be the ‘tech du jour’ this year, so I spoke to early-entrant Simon Beaulah of Linguamatics about the big picture of NLP-plus-AI and the tech’s evolving role in improving care by putting together a more complete ‘patient narrative’ in the EMR.
Wanna hear his thoughts on what’s next for NLP in terms of scaling? Jump in at 2:15 mark.