By ROBERT C. MILLER, JR. and MARIELLE S. GROSS, MD, MBE
The problem with porridge
Today, we regularly hear stories of research teams using artificial intelligence to detect and diagnose diseases earlier with more accuracy and speed than a human would have ever dreamed of. Increasingly, we are called to contribute to these efforts by sharing our data with the teams crafting these algorithms, sometimes by healthcare organizations relying on altruistic motivations. A crop of startups have even appeared to let you monetize your data to that end. But given the sensitivity of your health data, you might be skeptical of this—doubly so when you take into account tech’s privacy track record. We have begun to recognize the flaws in our current privacy-protecting paradigm which relies on thin notions of “notice and consent” that inappropriately places the responsibility data stewardship on individuals who remain extremely limited in their ability to exercise meaningful control over their own data.
Emblematic of a broader trend, the “Health Data Goldilocks Dilemma” series calls attention to the tension and necessary tradeoffs between privacy and the goals of our modern healthcare technology systems. Not sharing our data at all would be “too cold,” but sharing freely would be “too hot.” We have been looking for policies “just right” to strike the balance between protecting individuals’ rights and interests while making it easier to learn from data to advance the rights and interests of society at large.
What if there was a way for you to allow others
to learn from your data without compromising your privacy?
To date, a major strategy for striking this balance has involved the practice of sharing and learning from deidentified data—by virtue of the belief that individuals’ only risks from sharing their data are a direct consequence of that data’s ability to identify them. However, artificial intelligence is rendering genuine deidentification obsolete, and we are increasingly recognizing a problematic lack of accountability to individuals whose deidentified data is being used for learning across various academic and commercial settings. In its present form, deidentification is little more than a sleight of hand to make us feel more comfortable about the unrestricted use of our data without truly protecting our interests. More of a wolf in sheep’s clothing, deidentification is not solving the Goldilocks dilemma.
Tech to the rescue!
Fortunately, there are a handful of exciting new technologies that may let us escape the Goldilocks Dilemma entirely by enabling us to gain the benefits of our collective data without giving up our privacy. This sounds too good to be true, so let me explain the three most revolutionary ones: zero knowledge proofs, federated learning, and blockchain technology.
On Episode 3 of HardCore Health, Jess & I start off by discussing all of the health tech companies IPOing (Livongo, Phreesia, Health Catalyst) and talk about what that means for the industry as a whole. Zoya Khan discusses the newest series on THCB called, “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?”, which follows & discuss the legislation being passed on data privacy and protection in Congress today. We also have a great interview with Paul Johnson, CEO of Lemonaid Health, an up-and-coming telehealth platform that works as a one-stop-shop for a virtual doctor’s office, a virtual pharmacy, and lab testing for patients accessing their platform. In her WTF Health segment, Jess speaks to Jen Horonjeff, Founder & CEO of Savvy Cooperative, the first patient-owned public benefit co-op that provides an online marketplace for patient insights. And last but not least, Dr. Saurabh Jha directly address AI vendors in health care, stating that their predictive tools are useless and they will not replace doctors just yet- Matthew Holt
Matthew Holt is the founder and publisher of The Health Care Blog and still writes regularly for the site.
The year is 2019 and Imaging By Machines have fulfilled their prophesy and control all Radiology Departments, making their organic predecessors obsolete.
One such lost soul tries to decide how he might reprovision the diagnostic equipment he has set up on his narrow boat on the Manchester Ship Canal, musing at the extent of the digital take over during his supper (cod of course).
What I seek to do in this short paper is not to revisit the well-trodden road of what Artificial Intelligence, deep learning, machine learning or natural language processing might be, the data-science that underpins them nor limit myself to what specific products or algorithms are currently available or pending. Instead I look to share my views on what and where in the patient journey I perceive there may be uses for “AI” in the pathway.
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^.
With the application deadline for Bayer’s G4A Partnerships program coming up on Friday, I thought I’d throw out a little inspiration to would-be applicants by featuring an interview I did with one of last year’s program participants at the grand-finale Launch Event.
Not only was this a great party, but a microcosm of the G4A program experience itself: a way to meet Bayer execs en-masse, an opportunity to sell directly to key decision-makers across Bayer’s various global business units, and a chance to feed off the energy of like-minded innovators eager to see ‘big health care’ change for the better.
While the G4A program itself has changed a bit this year to be more streamlined and to allow for bespoke deal-making that may or may not involve giving up equity (my favorite new feature), startups questioning whether or not they have what it takes should take a look at some alums.
There’s a playlist with nearly two dozen interviews waiting for you here if you’re REALLY up for some procrastinating, or you can click through and just check out my chat with Joe Curcio, CEO of KinAptic. A healthtech startup taking wearables to the bleeding edge, Joe shows us a mock-up of the KinAptic ‘smart shirt’ which features their real innovation: printed ink electronics that look and feel like screenprinting ink, but work bi-directionally to both collect data from the body AND apply signals back to it. Is it AI-enabled? Did you have to ask? Listen in for a mindblowing chat about how this tech can change diagnostic analysis and treatment and completely redefine our current limitations when it comes to healthcare wearables.Once you’re inspired, don’t forget to head over to www.g4a.health and fill out your own application for this year’s partnership program.
Jessica DaMassa is the host of the WTF Health show & stars in Health in 2 Point 00 with Matthew Holt
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.
Catalyst @ Health 2.0 is proud to have worked with the Robert Wood Johnson Foundation to address issues in substance misuse and artificial intelligence through two exciting innovation challenges. Following the finalists’ live pitches at the Health 2.0 Annual Conference, Matthew Holt and Indu Subaiya had the pleasure to interview leaders from the six companies that placed in the top spots across both competitions.
First Place Winners
RWJF Opioid Challenge: the Grand Prize award went to Sober Grid, a social network designed to support, assist, and educate those suffering from addiction and substance misuse. The Sober Grid platform incorporates a suite of geolocated support, a “burning desire” distress beacon, and coaching tools. For those looking to get help and support, the Sober Grid platform is a fantastic free utility.
RWJF AI Challenge: the Grand Prize award went toBuoy, a virtual triage chatbot designed to work on any browser. All too often we rely on quick online searches for health information and sometimes receive inaccurate or unreliable results. The Buoy system takes a more conversational approach and emulates similar techniques a doctor would use when diagnosing symptoms and speaking with a patient.
Second and Third place prizes were also awarded to the following organizations:
If your heart throbs with desire for the new Apple Watch, the Series 4 itself can track that pitter-pat through its much-publicized ability to provide continuous heart rate readings.
On the other hand, if you’re depressed that you didn’t buy Apple stock years ago, your iPhone’s Face ID might be able to discover your dismay and connect you to a therapist.
In its recent rollout of the Apple Watch, company chief operating officer Jeff Williams enthused that the device could become “an intelligent guardian for your health.” Apple watching over your health, however, might involve much more than a watch.