By ROBERT C. MILLER, JR. and MARIELLE S. GROSS, MD, MBE
This piece is part of the series “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?” which explores whether it’s possible to advance interoperability while maintaining privacy. Check out other pieces in the series here.
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.
Leave your bias aside and take a look into the healthcare future with me. No, artificial intelligence, augmented intelligence and machine learning will not replace the radiologist. It will allow clinicians to.
The year is 2035 (plus or minus 5 years), the world is waking up after a few years of economic hardship and maybe even some dreaded stagflation. This is an important accelerant to where we are going, economic hardship, because it will destroy most radiology AI startups that have thrived on quantitative easing polices and excessive liquidity of the last decade creating a bubble in this space. When the bubble pops, few small to midsize AI companies will survive but the ones who remain will consolidate and reap the rewards. This will almost certainly be big tech who can purchase assets/algorithms across a wide breadth of radiology and integrate/standardize them better than anyone. When the burst happens some of the best algorithms for pulmonary embolism, stroke, knee MRI, intracranial hemorrhage etc. etc. will become available to consolidate, on the “cheap”.
Hospitals can now purchase AI equipment that is highly effective both in cost and function, and its only getting better for them. It doesn’t make sense to do so now but soon it will. Consolidation in healthcare has led to greater purchasing power from groups and hospitals. The “roads and bridges” that would be needed to connect such systems are being built and deals will soon be struck with GE, Google, IBM etc., powerhouse hundred-billion-dollar companies, that will provide AI cloud-based services. RadPartners is already starting to provide natural language processing and imaging data to partners; that’s right, you speak into the Dictaphone and it is recorded, synced with the image you dictated, processed with everyone else to find all the commonalities in descriptors to eventually replace you. It is like the transcriptionists ghost of the past has come back to haunt us and no one cried for them. Prices will be competitive, and adoption will be fast, much faster than most believe.
Now we have some patients who arrive for imaging, as outpatients, ER visits, inpatients; it does not matter the premise is the same. Ms. Jones has chest pain, elevated d-dimer, history of Lupus anti-coagulant and left femoral DVT. Likely her chart has already been analyzed by a cloud-based AI (merlonintelligence.com/intelligent-screening/) and the probability of her having a PE is high, this is relayed to the clinician (PA, NP, MD, DO) and the study is ordered. She’s sent for a CT angiogram PE protocol imaging study. This is important to understand because there will be no role for the radiologist at this level. The recommendation for imaging will be a machine learning algorithm based off more data and papers than any one radiologist could ever read; and it will be instantaneous and fluid. Correct studies will be recommended and “incorrectly” ordered studies will need justifications without radiologist validation.
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^.
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.