While I'm eager to digest his speculations across the board (as I draft this I'm on Chapter 2, "Cognifying"), his take on "AI" in health care is of particular interest, given what I've posted on the topic before -- e.g., see "AI vs IA: At the cutting edge of IT R&D."
Like many parents of a bright mind, IBM would like Watson to pursue a medical career, so it should come as no surprise that the primary application under development is a medical diagnosis tool. Most of the previous attempts to make a diagnostic AI have been pathetic failures, but Watson really works. When, in plain English, I give it the symptoms of a disease I once contracted in India, it gives me a list of hunches, ranked from most to least probable. The most likely cause, it declares, is giardia— the correct answer. This expertise isn’t yet available to patients directly; IBM provides Watson’s medical intelligence to partners like CVS, the retail pharmacy chain, helping it develop personalized health advice for customers with chronic diseases based on the data CVS collects. “I believe something like Watson will soon be the world’s best diagnostician— whether machine or human,” says Alan Greene, chief medical officer of Scanadu, a startup that is building a diagnostic device inspired by the Star Trek medical tricorder and powered by a medical AI. “At the rate AI technology is improving, a kid born today will rarely need to see a doctor to get a diagnosis by the time they are an adult.”
Medicine is only the beginning...Yeah. Interesting, in the wake of my most recent book, which I finished while back east attending my grandson's college graduation in Minnesota followed by a family wedding in Alabama:
Kelly, Kevin (2016-06-07). The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future (pp. 31-32). Penguin Publishing Group. Kindle Edition.
It may be unsettling to a reader thus far unaccustomed to these concepts to be told that uncertainty is central to modern medicine. A sense of despair can set in when discussions of probability and statistics take center stage in the doctor-patient interaction. Frank admissions of uncertainty can often be met with irritation, because the idea that a test doesn’t provide an unassailable answer that describes a crystal-clear reality is so foreign to many people. Some may have the emotional urge to conclude, after reading thus far, that these tests are pretty much worthless and that, in the immortal words of screenwriter William Goldman, “nobody knows anything.”An excellent read (though I found his discussion of applied stats in the text and in the appendix a bit wanting. More about that later).
But this book is not a jeremiad. The nihilism of “nobody knows anything,” although emotionally satisfying on a certain level, is just that: an emotional response, a spasm of frustration with a health-care system that is mightily complicated enough, to say nothing of expensive, bureaucratic, and frequently impersonal. Only by stripping away the layers of misunderstanding about what medicine is and how it works can patients and families begin to be their own best advocates. Uncertainty is far from the only area in which misconceptions exist, but I would argue it is a critical area, and grasping it might just help people avoid some of the more unpleasant shocks that medicine is capable of delivering.
Indeed, the point of highlighting all these various instances of the limits of our medical knowledge is to demonstrate that these can be teaching moments— occasions where we can illustrate what’s at stake in a medical decision and how we think about a problem. Are the stakes high or low? Are the repercussions of a decision significant or trivial? And is the evidence supporting a given decision overwhelming, minimal, or somewhere in between? By opening up about uncertainty, we are championing patient autonomy, rather than arrogantly flicking it away as an irritating feel-good ideal...
Hatch, Steven (2016-02-23). Snowball in a Blizzard: A Physician's Notes on Uncertainty in Medicine (pp. 18-19). Basic Books. Kindle Edition.
His discussion of the PSA and prostate cancer was of particular interest to me, given what I went through last year.
See again the review over at Science Based Medicine.
I have an ongoing concern that simply throwing more data at clinicians will continue to prove problematic, given the myriad contending, countervailing system forces (beyond intractable issues of epistemic/clinical "uncertainty"). Efficient, effective diagnostic UX will be ever-more important. Jerome Carter, MD continues to do great work in this area over at his EHR Science. "IA" ("Intelligence Augmentation") will also be significantly important here (e.g., "Down in the Weeds'").
Keyword/phrase searches ("health care," "medical," "medicine") of "The Inevitable" don't yield just a whole lot beyond that already cited above.
As the old joke goes: “Software, free. User manual, $ 10,000.” But it’s no joke. A couple of high-profile companies, like Red Hat, Apache, and others make their living selling instruction and paid support for free software. The copy of code, being mere bits, is free. The lines of free code become valuable to you only through support and guidance. A lot of medical and genetic information will go this route in the coming decades. Right now getting a full copy of all your DNA is very expensive ($ 10,000), but soon it won’t be. The price is dropping so fast, it will be $ 100 soon, and then the next year insurance companies will offer to sequence you for free. When a copy of your sequence costs nothing, the interpretation of what it means, what you can do about it, and how to use it— the manual for your genes, so to speak— will be expensive. This generative can be applied to many other complex services, such as travel and health care [pg 69].
What has happened to music, books, and movies is now happening to games, newspapers, and education. The pattern will spread to transportation, agriculture, health care. Fixities such as vehicles, land, and medicines will become flows. Tractors will become fast computers outfitted with treads, land will become a substrate for a network of sensors, and medicines will become molecular information capsules flowing from patient to doctor and back [pg 80].
Every public health care expert declared confidently that sharing was fine for photos, but no one would share their medical records. But PatientsLikeMe, where patients pool results of treatments to better their own care, proves that collective action can trump both doctors and privacy scares. The increasingly common habit of sharing what you’re thinking (Twitter), what you’re reading (StumbleUpon), your finances (Motley Fool Caps), your everything (Facebook) is becoming a foundation of our culture. Doing it while collaboratively building encyclopedias, news agencies, video archives, and software in groups that span continents, with people you don’t know and whose class is irrelevant— that makes political socialism seem like the logical next step.
A similar thing happened with free markets over the past century. Every day someone asked: What can markets do better? We took a long list of problems that seemed to require rational planning or paternal government and instead applied marketplace logic. For instance, governments traditionally managed communications, particularly scarce radio airways. But auctioning off the communication spectrum in a marketplace radically increased the optimization of bandwidth and accelerated innovation and new businesses. Instead of a government monopoly distributing mail, let market players like DHL, FedEx, and UPS try it as well. In many cases, a modified market solution worked significantly better. Much of the prosperity in recent decades was gained by unleashing market forces on social problems.
Now we’re trying the same trick with collaborative social technology: applying digital socialism to a growing list of desires— and occasionally to problems that the free market couldn’t solve— to see if it works. So far, the results have been startling. We’ve had success in using collaborative technology in bringing health care to the poorest, developing free college textbooks, and funding drugs for uncommon diseases. At nearly every turn, the power of sharing, cooperation, collaboration, openness, free pricing, and transparency has proven to be more practical than we capitalists thought possible. Each time we try it, we find that the power of the sharing is bigger than we imagined [pp 145-146].
Routine robosurgery will necessitate the new medical skills of keeping complex machines sterile. When automatic self-tracking of all your activities becomes the normal thing to do, a new breed of professional analysts will arise to help you make sense of the data [pg 58].
Now in the third age, we’ve moved from daily mode to real time. If we message someone, we expect them to reply instantly. If we spend money, we expect the balance in our account to adjust in real time. Why should medical diagnostics take days to return results instead of immediately? [pg 64]
Digital magic has shrunk devices such as thermometers, heart rate monitors, motion trackers, brain wave detectors, and hundreds of other complex medical appliances to the size of words on this page. A few are shrinking to the size of the period following this sentence [pg 237].
Computer scientist Larry Smarr tracks about a hundred health parameters on a daily basis, including his skin temperature and galvanic skin response. Every month he sequences the microbial makeup of his excrement, which mirrors the makeup of his gut microfauna, which is fast becoming one of the most promising frontiers in medicine. Equipped with this flow of data, and with a massive amount of amateur medical sleuthing, Smarr self-diagnosed the onset of Crohn’s disease, or ulcerative colitis, in his own body, before he or his doctors noticed any symptoms. Surgery later confirmed his self-tracking [pg 239].
The standard way of doing medical research today is to run experiments on as many subjects as one possibly can. The higher the number (N) of subjects, the better. An N of 100,000 random people would be the most accurate way to extrapolate results to the entire population of the country because the inevitable oddballs within the test population would average out and disappear from the results. In fact, the majority of medical trials are conducted with 500 or fewer participants for economic reasons. But a scientific study where N = 500, if done with care, can be good enough for an FDA drug approval.
A quantified-self experiment, on the other hand, is just N = 1. The subject is yourself. At first it may seem that an N = 1 experiment is not scientifically valid, but it turns out that it is extremely valid to you. In many ways it is the ideal experiment because you are testing the variable X against the very particular subject that is your body and mind at one point in time. Who cares whether the treatment works on anyone else? What you want to know is, How does it affect me? An N = 1 provides that laser-focused result.
The problem with an N = 1 experiment (which was once standard procedure for all medicine before the age of science) is not that the results aren’t useful (they are), but that it is very easy to fool yourself. We all have hunches and expectations about our bodies, or about things we eat, or ideas of how the world works (such as the theory of vapors, or vibrations, or germs), that can seriously blind us to what is really happening... [pg 241]
An embrace of an expanded version of lifelogging would offer these four categories of benefits:
- A constant 24/ 7/ 365 monitoring of vital body measurements. Imagine how public health would change if we continuously monitored blood glucose in real time. Imagine how your behavior would change if you could, in near real time, detect the presence or absence of biochemicals or toxins in your blood picked up from your environment. (You might conclude: “I’m not going back there!”) This data could serve both as a warning system and also as a personal base upon which to diagnose illness and prescribe medicines.
- An interactive, extended memory of people you met, conversations you had, places you visited, and events you participated in. This memory would be searchable, retrievable, and shareable.
- A complete passive archive of everything that you have ever produced, wrote, or said. Deep comparative analysis of your activities could assist your productivity and creativity.
- A way of organizing, shaping, and “reading” your own life.
To the degree this lifelog is shared, this archive of information could be leveraged to help others work and to amplify social interactions. In the health realm, shared medical logs could rapidly advance medical discoveries... [pp 249-250]
My day in the near future will entail routines like this: I have a pill-making machine in my kitchen, a bit smaller than a toaster. It stores dozens of tiny bottles inside, each containing a prescribed medicine or supplement in powdered form. Every day the machine mixes the right doses of all the powders and stuffs them all into a single personalized pill (or two), which I take. During the day my biological vitals are tracked with wearable sensors so that the effect of the medicine is measured hourly and then sent to the cloud for analysis. The next day the dosage of the medicines is adjusted based on the past 24-hour results and a new personalized pill produced. Repeat every day thereafter. This appliance, manufactured in the millions, produces mass personalized medicine [pg 173].That's pretty much it.
apropos, see my prior post on Kevin Kelly, "Anything that CAN be tracked WILL be tracked." Inevitable Tech Forces That Will Shape Our Future."
See also "Technology, particularly the technology of knowledge, shapes our thought." And, The future of health care? "Flawlessly run by AI-enabled robots, and 'essentially' free?"
Again, I'm only two chapters into Kevin's new book (while still plowing through Siddhartha Mukherjee's amazing opus "The Gene: an intimate history"), and have supplanted my initial reading with a bit of topical searching. It's a fun read overall thus far. I'm really liking Kelly's thoughtful, broad speculations on "cognition," a fundamental issue of concern as we move toward widespread "AI."
I will be tying all of this stuff back to prior postings on "evolution" shortly.
BTW: You might want to see my September 2015 cite of "The Guide to the Future of Medicine: Technology AND the Human Touch" in my post "The future of Healthcare Futurism."
I'm flying to Miami Tuesday to cover this year's event. Last year's Summit in Dallas was off-the-hook fine. The 2016 agenda:
WednesdayThe Lean Summits are comprised of people and organizations who are doing it. No mere theorizing and other abstract talk (and whining). I will be all eyes and ears.
- John Toussaint, ThedaCare Center for Healthcare Value
- Patrick Conway, MD
- Kathryn Correia
- Leader Standard Work
- How to Lead by Asking Effective Questions
- How Government, Healthcare, and Lean Come Together
- Lean Transformation Across Cultures: The lean journey of a disability hospital & newborn healthcare programme in East Africa
- Population Health: A journey to deploying real time decision support
- Lean Dentist
- Business Intellligence is no longer an Option!
- Experiments Around the Network AM
- Experiments Around the Network PM
- Elizabeth Mitchell
- John Shook, Lean Enterprise Institute
- Engaging Physicians: Lean as Preventive Medicine for Burnout
- Results Focused, Process Driven Ambulatory Clinic Redesign
- Payment Reform: The Employers' Perspective
- Improving Patient Experience, Patient Safety and Patient Progression through a Lean Management System
- Doing the Splits in the ED: Emerging Models in Academic Medicine
- Preparing Senior Leadership and The Lean Office for Organization Transformation
- Applying Lean to Federal Healthcare Policy, the Story of a Strategic Design Event
- Experiments Around the Network AM
- Experiments Around the Network PM
A bunch of the presentation decks have been made available to us. I'm reviewing them now and will post more shortly.
In addition to any Lean HIT - workflow integration, I'll be particularly interested in the "Leadership" presentations, e.g.,
More to come...