In the Future, You'll Need Your Doctor Less

What If Healthcare Just… Fades?

Most discussions of disruption in healthcare assume the healthcare system remains essential — just reshaped. Virtual visits replace office visits. AI handles documentation. Automation reduces friction. But the assumption is always that the care is still needed.

What happens when it isn’t?

Some of the most significant disruptions now emerging aren’t aimed at healthcare, but may make large parts of it unnecessary.

Prevention That Sidesteps the System

For example:

• GLP-1 drugs may reduce obesity, diabetes, and hypertension across entire populations. In fact, in 2023 the US population obesity rate declined for the first time in a decade — and one leading theory attributes it to the effect of GLP drugs.

• Self-driving cars could cut motor vehicle trauma by 30% or more. RAND estimated back in 2017 that even a 10% decrease in accident rate could save “hundreds of thousands of lives”. Meanwhile, even current technology promises a much greater reduction, with Waymo reporting a 57% decrease in police-reported accidents compared to human drivers.

• AI therapy chatbots are already showing promise for mental health treatment, and could dramatically lower the cost of therapy — and might prevent mental health crises before they begin. And they may just be the thin edge of the wedge, presaging the automation of all medical conversations where only information is exchanged.

What happens to healthcare when obesity goes away?

Endocrine Disruptors

Each of these innovations reduces the need for healthcare, rather than streamlining its delivery or improving its process. And yet few health systems — actually, none that I am aware of — are modeling what happens when demand drops dramatically, not for elective procedures but for entire categories of care.

Food companies have run scenarios to prepare for GLP-1-driven shifts in consumption, and are already rolling out products tailored to GLP users. Where are the equivalent conversations inside healthcare?

If the number of diabetic patients drops by 20% (a conservative estimate, given the effectiveness of GLP-1 agonists like Ozempic), what happens to the business model of the average hospital? Will we need fewer endocrinologists — or none at all? What happens to primary care visits (already drastically declining) if hypertension declines by a similar percentage? What happens to orthopedics and emergency medicine if autonomous vehicles mean car crashes plummet by 50? Who owns prevention — and what happens to the institutions built on managing failure to prevent?

Failing to Plan is…

Healthcare leaders need to consider, in a world where health problems are increasingly prevented, not treated — what’s left for the system to do? I’ve spoken with hundreds of healthcare leaders in the past five years — including dozens of CEOs — and not one has mentioned the kind of “best case / worst case” modeling now common in the snack food industry.

If healthcare is going to meet this moment, it needs to do better.

And yes, there’s something ironic about hospitals taking planning cues from potato chip companies. But maybe that’s exactly what needs to happen.

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Much of this post has focused on technologies that reduce the need for healthcare by preventing illness or injury. But the most widely discussed disruption today — generative AI — may reduce the need by eroding healthcare’s monopoly on information and expertise.

From diagnostic chatbots to at-home guidance systems, AI is already starting to displace parts of the care experience.

I’ve written about that in previous posts — and I’ll return to it soon.

Disruption for Doctors 3: the Rise of Selfcare

This is the third post of a three part series on “Disruption for Doctors” (which is nice alliteration, but really this is for anyone working in, or thinking about, healthcare):

• Part I, introduced the idea of “disruption” as first defined by the late Clayton Christensen in his 1997 best-seller The Innovator’s Dilemma: the process whereby a new technology creates a new business model, allowing new companies to successfully overturn incumbents.

• Part II provided specific examples of disruption within the healthcare system: from Walmart Health to IDx-DR, the first AI (artificial intelligence) system approved by the FDA to make a medical diagnosis (of diabetic retinopathy) without a doctor’s input, to Chestlink , an AI system designed to read chest x-rays.

This post focuses on even bigger disruptions to the whole healthcare system, on innovations that move things not just from a specialist to a generalist (like IDx-DR), or from a hospital clinic to a Walmart clinic, but away from providers and clinics entirely.

I refer to this as “selfcare”: the system whereby untrained consumers can perform former healthcare tasks without interacting with a “provider” like a doctor or nurse.

I’ll start with over-the-counter (OTC) medicines, a great example of disruptive selfcare that is completely under the radar of most in healthcare. Then I’ll move on to more directly IT-intensive disruptors like AI.

The IT revolution goes consumer

Computing has gone through three major stages over the last 100+ years, when viewed from the eye of the consumer. In the first, which reached all the way up into the 1970s, computers were so expensive and difficult to purchase and maintain that only large institutions (government, industry, universities) could be customers.

The second stage began in the 1970s with the early “personal” computers, and ran all the way through the beginning of the 21st century. In this stage, the cost (and size) of components dropped sufficiently that it was possible to begin selling computers (i.e. PCs) to much smaller institutions, including small businesses or even individuals.

Consumer computer

The third stage, which has characterized computing in the 21st century, is the consumer stage: the biggest chunk of the worldwide market for information technology is for consumer technology, with smartphones taking the biggest piece of that chunk.

From: IDC State of the Market IT Spending by Industry Outlook - 2021 Sep

Compared to the roughly $1.5 trillion projected for consumer IT spending, the portion expected for healthcare, roughly $200 billion, is small potatoes. And these numbers reflect a substantial decrease in consumer spending projected for 2022 after the huge spike in lockdown-related sales of laptops and other computers to consumers during the pandemic.

The augmented consumer

These IT spending numbers matter, because in our lifetimes the most important way that we all gain new capabilities is via IT (especially smartphones). And we’ve all gained so many new capabilities via smartphones and apps that it’s hard to keep track.

In healthcare, as reflected in its laggardly IT spending? Not so much.

The biggest IT spending in healthcare in the last ten years has been to purchase “EMRs” (electronic medical record systems), partly with $30+ billion in federal subsidies. Many hospitals systems have spent upwards of $100 million on their EMR, and the big EMR companies (like Epic and Cerner) are tremendously influential in the healthcare system.

But guess what: consumers spend more money each year on AirPods than healthcare spends each year on EMR! And because of IT, consumers have been gaining new capabilities much more rapidly than the healthcare system.

Yes, Apple makes more money just on AirPods than the combined revenue of the biggest EMR vendors

This wouldn’t matter for healthcare if consumer capabilities were unrelated to healthcare capabilities. But they are very related: many of the things that IT now lets consumers are do for themselves are things that used to be “healthcare.”

You don’t need AI to replace doctors

Over the last few decades, hundreds of medicines have been moved from prescription to OTC. The FDA said in 2016 that there were more than 700 drugs that “would have required a prescription only 20 years ago” and this number has presumably grown since then. Those would include commonly used drugs like Claritin (loratadine) or Advil (ibuprofen) — hard to remember now that you used to have to visit a doctor to take Advil!

More than 700 current over-the-counter drugs would have previously required a prescription from a doctor

Part of the reason why FDA can move drugs to OTC, of course, is that consumers today have many more tools to find out medical information than thirty years ago: computers, the internet, Google, drugs.com, Medscape.com, smartphones, to name just a few. Consumers equipped with Google are much better able to educate themselves about their health.

I’ve been asked many times whether I think that AI will replace doctors. Never once have I been asked if I thought that OTC drugs could replace doctors. But that’s exactly what they have done: every time a drug switches from prescription to OTC, the total number of doctor visits drops.

In fact, a 2012 study by Booz concluded that

“if OTC medicines did not exist, an additional 56,000 medical practitioners would need to work full-time to accommodate the increase in office visits by consumers seeking prescriptions for self-treatable conditions.”

Fifty-six thousand doctors that we don’t need because of OTC drugs; that’s almost 6% of the practicing doctors in America. Think of the effect on healthcare costs.

But AI is an accelerant

If we thought that Google-equipped consumers were better able to manage their health, AI is going to blow that away. A product called ResAppHealth is a powerful example.

We’ve probably all heard something about the uproar in radiology concerning AI. Most of this was sparked by a 2016 comment by eminent AI researcher Geoffrey Hinton saying that new types of AI were going to be better able to read images than expert human radiologists.

So Hinton was really describing the possible disruption within healthcare of substituting an algorithm for a radiologists but leaving the rest of healthcare intact, as in the image below:

Will AI disrupt radiologists, but leave the rest of the healthcare system intact? I don’t think so.

ResAppHealth

ResAppHealth (now a subsidiary of Pfizer) gives us an idea of what Hinton missed, and how AI is more likely to disrupt the whole of healthcare, rather than just a piece here and there.

ResApp was created by Australian research physicians who thought “when I want to know if a patient has lung problems, I listen to their breathing with a stethoscope to detect any pattern characteristic of pneumonia. I wonder if we could automate that process with AI.”

So . . . now they have a smartphone app that listens to your cough and tells you if you have pneumonia. And they are marketing it to healthcare providers and directly to consumers.

Now, imagine the market for a tool that gives every person the ability to diagnose, or rule out, pneumonia (every parent on earth, for starters). And imagine how that affects healthcare:

Just as with OTC drugs, information technology gives consumers a capability they didn’t have before. And suddenly, a skill requiring 10+ years of training becomes a smartphone app. And that aspect of healthcare — reading x-rays to check for pneumonia — suddenly . . . isn’t healthcare anymore. Costs go down dramatically, more people in need of care are able to get it.

Even the in-healthcare examples I’ve provided in Part II, like IDx-DR (the AI system that diagnoses diabetic retinopathy without an ophthalmologist) are not likely to stay in-healthcare very long. Although for business (and regulatory) reasons that company is now marketing the product to primary care practices, there is no technological reason why that machine couldn’t be sitting right next to the automated blood pressure cuff in the drugstore.

What happens now

As Yogi Berra famously said, “it’s hard to make predictions, especially about the future.” Nonetheless, it’s very, very clear at this point that even without AI, information technology is able to shift the balance between the capabilities of the healthcare system and the capabilities of individual consumers.

Twenty years of over-the-counter drug switching by FDA, at least partly enabled by consumers access to information technology, has already dramatically reduced our reliance on healthcare for a variety of conditions, and reduced the number of clinicians needed by US healthcare by something like 5-10%.

As those same consumers gain access to the automated expertise that is AI, there’s no question that this process will accelerate.

Consumers can sit back and enjoy the process.

Healthcare needs to be thinking of how other organizations in other sectors — like news or travel — survived digital disruption, and pulling lessons out of those examples.

And policymakers need to be revising their predictions of “physician shortages” in light of the last thirty years of selfcare, and the promise of the next thirty.

The new Blockbuster?

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This post draws on themes from my keynote talks.

Revisited: FDA's AI Medical Device Approvals

About a year ago, I dug into FDA’s newly-released list of its AI medical device approvals. In that post, I noted some surprising findings: AI approvals were (1) becoming more concentrated, with a small group of companies winning a large percentage of the approvals, and (2) most approvals were going to established medical device titans, like Siemens and GE, rather than to startups. Both findings showing that AI in medicine was providing sustaining innovation for established companies more than disrupting the established ecosystem.

A few weeks ago, FDA provided new data:

October 19, 2023 update: 171 Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices were added to the list below. Of those newly added to the list, 155 are devices with final decision dates between August 1, 2022, and July 30, 2023, and 16 are devices from prior periods identified through a refinement of methods used to generate this list. 

With this release, I wanted to see if my earlier findings still held, how fast innovation was accelerating, how quickly were more companies getting involved — and what other surprises lay within the data.

Anti-exponential: medical AI innovation is not accelerating

Scoring points: approvals by year

Probably most interest of all findings is that AI innovation in medicine — at least by the measure of AI device approvals — isn’t accelerating at all! The graph below shows good, steady growth in approvals per year (an average 26% increase per year over the last 4 complete years) but not acceleration:

Of course the number of approvals depends on at least two variables: how many applications are submitted, and how many are approved. It’s possible that the rate of invention (i.e. the creation of new algorithms and tools) is accelerating but that the FDA approval process is the rate limiter.

Joining the team: new companies per year

In terms of broadening the group of innovators, the list this year includes 19 entities that previously had not appeared, including stalwarts of diagnostics like Beckman Coulter and Biomerieux:

• AgaMatrix Inc.

• AlgoMedica

• Appian Medical Inc.

• Beckman Coulter, Inc.

• Biomerieux, Inc.

• Bruker Daltonics, Inc.

• ClearView Diagnostics Inc.

• Cydar Ltd.

• Gauss Surgical Inc.

• IRIS Intelligent Retinal Imaging Systems, LLC

• LabStyle Innovations Ltd.

• Matakina Technology Ltd.

• Monarch Medical Technologies

• Pathwork Diagnostics Inc.

• QView Medical, Inc.

• Renalytix AI, Inc.

• Stratoscientific, Inc.

• Tyto Care Ltd.

• Zepmed, LLC

Liked approvals per year, this is an increase but not an acceleration. As shown in this graph:

So neither the number of approvals nor the number of companies winning those approvals is accelerating.

Less lonely at the top

Last year I noticed that approvals were becoming more concentrated — with only 18% of companies on the list gaining more than half of total approvals (this became 20% with revision of the list by the FDA; see notes at bottom). So far in 2023, you’ve got to include 30% of total companies to get to 50% of total approvals — so approvals are going to a broader group. Hard to say from the data whether this is a trend or just variation year to year, and I’ll plan to revisit when next year’s data comes out.

Your grandfather’s medical AI?

Age of the players

I was surprised last year to find that the average age of the top 6 companies in the approvals list (including all years) was 89 years!

The most surprising point hidden in the dataset was that a lot of the action in medical AI isn’t coming from Big Tech companies known for AI like Apple or Amazon or Google/Verily but from much much older Big Med Tech industrial companies, with GE (founded 1892) and Siemens (founded 1847) taking the number 1 and 2 spots, respectively.

That has changed a bit. The top 12 companies for 2022-23 have an average age of 58 years - so we can count that as a major shift towards new players like Annalise-AI (4 years old), Viz.ai (7 years old), Hyperfine (9 years old), and Aidoc (7 years old).

This is also reflected in the continued drop in the median age of companies receiving at least one approval, as shown in the graph below.

The bump in average age for 2023? That’s in part from the addition to the list of Cedars-Sinai Medical Center (founded 1902) and MD Anderson Cancer Center (founded 1941). Will such venerable institutions continue to move onto the list? Given the accumulated medical knowledge there, let’s hope so.

The graph above just looks at the age of companies “in the game,” with at least one approval. But that means that for 2022, for example, it counts GE, with 17 approvals, the same as United Imaging — with just one.

The graph below gives us more insight into that game, showing what percentage of players are “older” (>= 20 years) versus younger.

Points scored by young vs old

It’s also important to know whether older vs younger players are racking up points (i.e. approvals). And here we see that despite the influx of younger companies the older companies are more than holding their own. In fact, as noted last year, the percentage of approvals going to older companies is steady or possibly increasing.

Big tech MIA

Note that although older companies are holding their own, those companies mostly aren’t Big Tech. Since 2017, Apple’s only gotten 2 approvals, Google’s Verily 2, Microsoft 1, and Amazon 0. That’s a total of 5 out of 531 approvals: less than 1%.

What’s it all mean

At least on the medical side, as measured by FDA AI device approvals, AI innovation is chugging along — but not accelerating. This comes as a bit of a useful corrective to the relentless AI hype. It also raises the question as to whether the rate-limiting factor here is the FDA approval process itself (not hard to believe considering the slow pace of drug approvals), or something intrinsic to the market.

More companies are entering the space and getting approvals. Most of these are younger, but the entrance of MD Anderson and Cedars-Sinai stands as an example for the well-established hospital systems; an example which I hope will spur more participation from the healthcare establishment not just in adopting AI innovation but in helping to create it.

The percentage of approvals going to the older companies is, if anything, increasing. This is classic sustaining innovation in the medical device space, as companies like GE add AI components to improve their existing product line. It remains to be seen who in the space will start to significantly outpace the innovation at those older companies and seriously disrupt the space.

Finally, despite great stated and demonstrated interest in health, Big Tech is still not a major presence on FDA’s list. Given the health moves made by Apple, Google, Amazon, Microsoft and others, this is a little surprising — especially on the Apple side, given their focus on device innovation. Will this change, whether through in-house innovation or acquisition? Time will tell.

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Notes

1. I’m definitely not an FDA expert, just a doctor and tech guy trying to figure out what’s going on in the world. If you’ve got special insight into any of this, or corrections to the above, please let me know.

2. The FDA’s AI list appears to be incomplete, leaving out at least 4 de novo approvals utilizing AI that I was able to find by cross-checking other FDA references: one from 23andMe, one from Apple, one from Renalytix AI, and one from Viz.ai. Those 4 are in FDA’s searchable approvals database, but not in their AI list. I’ve included those four in the analyses above.

3. The FDA’S list includes this note: “Of those newly added to the list, 155 are devices with final decision dates between August 1, 2022, and July 30, 2023, and 16 are devices from prior periods identified through a refinement of methods used to generate this list.”

4. I was unable to find out when these two companies were founded, and so they’ve been left out of age calculations. If you know, please share!

   • Yukun (Beijing) Technology Co., Ltd, filer of item K213986 for CerebralGo Plus

   • AssembleCircle Corp., filer of item K220903 for WebCeph

Disruption for Doctors 2: Healthcare Examples

Recap of part I: disruptors are worse, not better

In my last post, I introduced the concepts of innovation developed by Clayton Christensen of Harvard. In Christensen’s paradigm, “sustaining” innovation enables incumbent organizations to better please their existing customers, while “disruptive” innovation weakens incumbents by creating new markets with cheaper but arguably worse technology.

As an example of “worse” disruptive technology, I talked about Netflix, which originally sent customers DVDs by postal mail. That seemed ridiculous to many who liked being able to just walk into a Blockbuster video store and then walk out with a video in a short time.

And the incumbents at Blockbuster apparently laughed and laughed . . . but Netflix was able to siphon off Blockbuster customers who were more price-sensitive. Then, when Netflix adopted streaming technology, they took all the remaining customers because at that point they were both cheaper AND faster.

But healthcare is not video streaming, so what about some examples of disruption in healthcare?

Non-IT-based healthcare disruption

There has been a lot of disruption within healthcare in the last decades; with some of it mostly related to information technology (IT) and some not. Let’s look first at some of the non-IT stuff.

Some examples:

• doctors getting disrupted by ancillary providers like nurse practitioners, who have many fewer years of expensive training, and are paid less

• primary care clinics and emergency departments getting disrupted by urgent care centers with a limited menu of services, and without the legacy costs of giant hospital-centric systems

• specialists getting disrupted by other specialists (see next section, below)

Note that, as with our Netflix example, the disrupters are arguably worse in certain ways than the doctors they are trying to replace (sorry, “augment” 🤣). It’s hard to argue, for example, that nurse practitioners are better than primary care doctors. The important thing, however, is that they are good enough for some section of the patient population — and that they are cheaper.

Also, although these disruptive innovations aren’t mostly related to IT, they are dependent on the IT innovations of the last 40 years: it wouldn’t be so easy to run an efficient urgent care center without inexpensive personal computers, etc.

A simple menu means costs can come down.

Example of non-IT healthcare disruption: heart disease treatment

Back in 2000, in an article entitled “Will Disruptive Innovations Cure Health Care?” Christensen himself provided an example of healthcare disruption. He and his coauthors talk about coronary angioplasty (or PCI, for “percutaneous intervention”), which enabled cardiologists to treat heart disease patients by inserting tiny tubes called stents into blood vessels — instead of requiring cardiac surgeons to perform cardiac bypass grafts (CABG). This is disruptive technology, for sure, and it’s a good illustration that disruptive technology doesn’t have to be information technology.

PCI didn’t eliminate heart surgeons, of course, but it did drastically reduce their activities: between 2001 and 2008, the annual rate of CABG in the US went down by 30%! So the incumbents (cardiac surgeons) lost a lot of revenue to the disruptive practitioners (interventional cardiologists) doing the PCI.

Keep in mind that CABG operations were, and are, better in certain circumstances but the disruptive PCI was generally cheaper because cardiologists are cheaper than cardiac surgeons, and PCI can often be outpatient. So the general idea of “disruption by worse, but cheaper” holds for this example.

The latest disruptor?

Interestingly, more recent data to 2016 demonstrated a roughly 40% decrease in both CABG and PCI — possibly due to more effective medicines (like statins and beta-blockers) reducing the need for surgery. So perhaps medical treatment for heart disease is now disrupting PCI, which previously disrupted CABG? Which might shift revenue back to primary care providers, whether doctors or nurse practitioners or other.

IT-based healthcare disruption

Disruptor of many things

Disruptor of many things

The reason that nowadays we focus on IT when we think about disruption is that for about 40 years, most business disruption in the world has been driven by the IT revolution (further reading: Why Software is Eating the World).

Think about the efficiencies and cost savings made possible by the PC, the internet, cloud computing, and the mobile phone.

Now think about the fact that healthcare has really only adopted one of those innovations: personal computers. Ouch.

Which means that companies that are very, very good at IT are turning their attention to healthcare to see if they can begin picking off the low-hanging fruit from set-in-their-ways old-school health systems. And this includes those using the very latest technology: artificial intelligence (AI).

Some examples:

• efficient lower-overhead systems (like Walmart Health) disrupting hospital-based systems (AND the urgent care companies!) with lower-cost systems that provide only the most commonly-needed services, and that can leverage their enormous portfolio of existing stores for office space. Also remember that Walmart got to be Walmart by being really good at IT (for supply chain management): we can expect efficiencies that most hospital systems can’t approach.

• deep-pocketed tech companies, like Amazon Care, disrupting primary care by using tech to run clinics at lower cost. How can Amazon be successful? By using the same online tools they use as the biggest online retailer in the world and, less well known to the public, as one of the leading providers of cloud computing services to other tech companies, via their AWS division. And they are on a healthcare spending spree lately, having just purchased One Medical [Note: the day after publishing this, Amazon announced they’re shutting down Amazon Care and focusing on One Medical].

• AI-based tools pulling some services away from expensive specialists, with one example being IDx-DR (see next section, below).

These IT-based disruptors are trying to use IT to create simpler, easier to use, cheaper versions of existing healthcare services.

Example of AI disruption: IDx-DR

Digital Diagnostics is a company based in Coralville, Iowa, far from tech centers like Silicon Valley or Boston. Using artificial intelligence computer vision techniques, they’ve built IDx-DR: the “first and only FDA authorized AI system for the autonomous detection of diabetic retinopathy.”

Translated, that means they have a machine that can take pictures of your retina and determine if you have diabetic retinopathy (DR, a debilitating problem that often occurs in poorly controlled diabetes).

Before IDx-DR, the only way to diagnose DR was for a very highly-trained and highly-paid ophthalmologist to look at your eyes. With Idx-DR, the diagnosis is automated and requires no input from any doctor, much less an expensive specialist.

This is the first machine capable of making a medical diagnosis by itself, and according to the website is “now part of the American Diabetes Association’s Standard of Diabetes care”.

Interestingly, it is marketed as a tool for primary care clinics, who can now avoid sending diabetic patients to expensive ophthalmologists for this diagnosis. That is a boon to primary care docs (and their patients) — but with 37 million diabetics in the US alone there is no real technological reason why this shouldn’t eventually find its way to a drugstore near you, right next to that automated blood pressure cuff.

Used to require a doctor

Also used to require a doctor

Example of AI disruption: ChestLink

Another great example of AI-based disruptive innovation has been developed by Oxipit. Their ChestLink app, which is approved by European regulators but not yet the FDA, uses AI to read chest x-rays. It is apparently sensitive enough to identify the presence of problem, but not (yet) as good as a human radiologist to identify what the specific problem is.

ChestLink’s main use, as a result, is to automate the reading of the up-to-80% of chest x-rays that are normal, freeing up radiologists to focus on those images flagged by the software as suspicious. Note again that the software isn’t necessarily as good as a radiologist in identifying a specific problem, but it is good enough for a very common task that takes up a lot of human radiologist time.

How is this disruptive? Well, because using the software to verify x-rays as normal is so much cheaper than using a radiologist for the same purpose:

1. rich countries with radiologists won’t need as many, so the cost of this one small facet of healthcare (i.e. reading chest x-rays) will go down.

2. in poor countries, the software isn’t competing against radiologists, it is — in Christensen’s terms — competing against “non-consumption.” It can thus create a new market for the use of chest x-rays, bringing that beneficial medical technology to many more people.

From https://oxipit.ai

From specialists to generalists, from physicians to nurse practitioners, from hospitals to supermarkets

In Christensen’s 2000 article (echoed later in his 2016 book The Innovator’s Prescription) Christensen presciently wrote about many of the now-established trends in healthcare:

We need diagnostic and therapeutic advances that allow nurse practitioners to treat diseases that used to require a physician’s care, for example, or primary care physicians to treat conditions that used to require specialists. Similarly, we need innovations that enable procedures to be done in less expensive, more convenient settings—for doctors to provide services in their offices that used to be done during a hospital stay, for example.

All of this, of course, is commonplace in the healthcare of 2022: urgent care centers staffed by nurse practitioners, outpatient surgicenters, more techs working throughout healthcare.

But Christensen wrote that in the world of 2000: Google was 2 years old, AI was still frozen in a decades-old “winter”, and the iPhone existed only in the mind of Steve Jobs. The advent of two decades of consumer technology has not only accelerated the healthcare changes that Christensen envisioned, but also accelerated something he doesn’t seem to have thought much about: selfcare. That is, the ability of the consumer to treat their own illness, and to maintain their own health.

And in my next post, I’ll talk about healthcare moving not from doctors to nurse practitioners, but from nurse practitioners to . . . us.

Stay tuned.

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This post draws on themes from my keynote talks.

Disruption for Doctors 1: What’s Disruption?

We’re number 46!

Healthcare in the United States is a $1.27 trillion dollar industry known for

1. very high prices

2. terrible customer service (long waits, short visits, poor coordination)

3. impossible-to-understand billing

4. antiquated information technology (e.g. using fax machines in 2022)

While the saving grace for a long time was that the healthcare system got outstanding results, that doesn’t seem to be the case anymore. While the US spends much more on healthcare than other rich countries, for example, the country is number 46 for life expectancy at birth — right behind Estonia.

And a recent study published in the Journal of the American Medical Association demonstrated that for many common health issues (e.g. colon cancer, heart attack, infant mortality), even the richest Americans often had worse healthcare outcomes than the average citizen of many European countries.

Ripe for disruption — whatever that is

Whenever you have a very expensive product with major problems, there is always an opportunity to find ways to improve or replace it. One important type of replacement using technology is called “disruption”, and it is one of the main strategies that Big Tech companies and others use to take over major segments of whole industries (see: advertising, retail, music, travel, photography, etc).

Unfortunately, most doctors, nurses, PAs, techs, and others aren’t familiar with the term — and are unaware of how technological trends have already begun disrupting their current business models.

The purpose of this and some following posts is to give folks working in healthcare a framework to better see what’s happening in healthcare and better prepare for the future — a future which will be very different from the traditional doctor-and-hospital-centric healthcare model.

What does “disruption” mean?

If the fire alarm goes off at the grocery store while I’m looking for tomatoes, that would definitely “disrupt” my shopping — but this is not the disruption I’m talking about. Likewise, I don’t just mean the introduction of something “new” or “innovative.” And I definitely don’t mean a new style of denim shorts:

Ah . . . no.

I’m using the concept of disruption as defined by the late Clayton Christensen in his incredibly influential book The Innovator’s Dilemma (TID), originally published in 1997. TID talks about how technological change can sometimes improve existing companies’ products, while other times putting such companies out of business.

The Bible of Disruptive Innovation

As a way to explain these two very different outcomes Christensen divides tech innovation into two categories: sustaining and disruptive.

Sustaining innovations

A sustaining innovation is a new technology that helps existing companies (“incumbents”) make their products better for their existing customers.

A great innovation — but not a disruptive one

LED light bulbs are a great example of a sustaining innovation. They’re definitely way better than incandescent bulbs — much longer-lasting, way more energy efficient, cooler — and they’ve helped big incumbent light bulb manufacturers like Philips and GE to make better products, and serve their customers better, and to increase their profits. This sustaining innovation makes incumbents more profitable.

Other examples of sustaining innovation are not hard to find: many of the products we use everyday get better over time. Detergent pods? New versions of your phone’s operating system? Bigger (flatter!) TV screens? Better cushioning material in your running shoes? Self-checkout at the grocery store? Waterproof/breathable rain gear?

Sustaining innovation example #3,452,778

None of these innovations are putting anyone out of business. They’re “just” incremental improvements that make useful products even better.

Disruptive innovations

A disruptive innovation is a new technology that lets new companies make a product or service more affordable and accessible to more people in a way that puts incumbents out of business.

“Telephone did not come into existence from the persistent improvement of the postcard.”

— Amit Kalantri

Characteristics of disruptive innovations usually include:

1. they are typically worse in many respects than existing tech (this one is a bit counterintuitive, but read on)

2. they are typically better in at least one respect, usually some combination of price and simplicity

3. by being cheaper, or simpler to use, they create a new market of users that couldn’t afford to participate using older technologies

4. they are ignored — or laughed at — by incumbents because they don’t (initially) make much money. Plus, they’re clearly worse (see #1, above).

A great example of disruptive innovation that most of us have lived through is Netflix’ original business model (before they became a streaming giant). You might remember that when Netflix came online in 1997 there was no streaming (the internet wasn’t fast enough yet): Netflix was a DVD rental company. They pioneered a new way to rent DVDs, by ordering them online and receiving them through the mail, which was in direct competition with Blockbuster and other physical video rental shops.

How was this classic disruption? Let’s go through the points:

worse — the first thing I thought when Netflix came out was “who wants to wait 3 days to get a DVD in the mail??” Most people liked the instant gratification of going to the video store and walking out with a movie a short time later. Netflix was definitely much worse in this respect, and was the butt of many jokes when it started.

better — the thing people hated most about Blockbuster and other video retail shops was . . . the late fees. You’d get the DVD or VHS tape for cheap but if you forgot to return it on time you were nailed with a big late fee. In fact, according to Netflix lore, Netflix founder Reed Hastings got the idea when he forgot to return a copy of Apollo 13 on time and got hit with a $40 late fee. Netflix had NO late fees: you could keep a DVD as long as you wanted and just paid the same subscription fee each month.

new market – the market for Blockbuster was obviously limited to places with a Blockbuster store. Netflix, on the other hand, could bring in more customers because they were (1) cheaper and (2) available to anyone in the US that could get mail (i.e. everyone).

ignored/laughed at — a lot has been written about why Blockbuster didn’t/couldn’t adapt to the challenge from Netflix. Christensen would say that it’s because Netflix was offering a mail-order subscription service that — at least initially — Blockbuster’s customers weren’t asking for, and Netflix wasn’t making much money doing it. In fact, in 2000, the Netflix founders tried to sell Netflix to Blockbuster, and they remember that at that meeting the Blockbuster CEO “was struggling not to laugh.”

Netflix initially got subscribers who were angry about late fees, or who didn’t have a good video shop nearby, but as they got more popular their business grew. The last straw was in 2007, ten years after Netflix started, when they announced that they would start streaming movies.

This removed the last objection anyone had to Netflix (having to wait for the mail), and actually made them a faster option than Blockbuster. Three years later, as everyone got used to picking and immediately watching movies without getting up from the couch, Blockbuster went out of business.

Other examples of disruptive innovation include social media — which has put many traditional news media like newspapers out of business — and electronic messaging like email, WhatsApp, and Skype, which has nearly eliminated traditional paper-based messaging. Not to mention the smartphone — which by making computing simpler and cheaper (especially with low-cost Android phones) has brought BILLIONS of new computing users into the market.

Sometimes people argue about whether a new technology is sustaining or disruptive, and the answer can depend on who, exactly, is getting disrupted. The iPhone, Tesla, Uber . . . all of these clear innovations have had people arguing both sides. To read more about the example of Tesla, try “Is Tesla Disruptive” by Ben Evans. To read why even Clayton Christensen, the father of disruption theory, got the iPhone’s disruptive potential wrong, try “What Clayton Christensen Got Wrong” by Ben Thompson.

So what does this have to do with health?

When you work in a very expensive system with arguably poor results, it’s a natural question to think “how can we improve this?”

If you work outside the system, and you understand disruption theory, the natural question is “how can we disrupt this system?”

These questions are being asked throughout healthcare, and by companies outside healthcare. We’re seeing sustaining innovations like better drugs and electronic medical records, as well as potentially disruptive innovations like urgent care centers run by Walmart, and smartphone apps that treat depression without a human therapist

In my next post I talk about the many, many ways that information technology is acting as a catalyst for both sustaining AND disruptive innovations, and the implications for traditional healthcare.

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This post draws on themes from my keynote talks.

A Closer Look at FDA's AI Medical Device Approvals (2022)

On October 5th, 2022, the FDA added 178 new devices to its list of (approved) “Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices.” The full list is conveniently available at that page for download as an Excel file, and I decided to take advantage of this to dig a little deeper into the data. Mostly I was interested in the pace of FDA approvals — which of course reflects both the number of applications and the speed of the FDA process. What I found was interesting, and not exactly what I expected.

Picking up the pace of approvals

Not too surprisingly considering the hype around AI — and its potential —  we can see that the pace of FDA approvals has picked up substantially in the last 10 years. While 2022 is not yet complete as of this writing, if it continues at the current pace it should just exceed 2021:

FDA AI-Powered Medical Device Approvals by Year

Lonely at the top

With a bit more digging, we can also see that the number of companies receiving at least one approval is on the upswing, as well:

Number of companies receiving at least one FDA approval by year

Despite the increase in the number of companies playing in the space, success in FDA medical AI approvals also seems to be becoming more concentrated.

For example, in 2022 so far, just 10 of the 55 companies winning at least one approval (18%) were responsible for more than half of total approvals. Five years ago, that percentage was 55%:

Percent of Companies Winning Approval Each Year Getting 50% of Approvals

Your grandfather’s medical AI?

The most surprising point hidden in the dataset was that a lot of the action in medical AI isn’t coming from Big Tech companies known for AI like Apple or Amazon or Google/Verily but from much much older Big Med Tech industrial companies, with GE (founded 1892) and Siemens (founded 1847) taking the number 1 and 2 spots, respectively.

[Note that I’ve counted related company divisions as a single unit. For example: GE Medical Systems, GE Healthcare Japan, and GE Healthcare are just counted as “GE”.]

Here are the companies taking the top 5 spots for “number of approvals in 2022 so far,” with number of approvals in parentheses (and note that Philips and Canon tied for 4th place with 4 approvals):

Top 6 Companies for FDA Al Approvals in 2022 (so far)

The average age of these top six companies is 89 years old! Leaving out Hyperfine and Aidoc, that figure rises to 130. Not exactly startups.

Nonetheless, the oldsters are being crowded out gradually as the median age of companies getting approvals goes down:

Mean and mean age of companies receiving FDA approval

Sustaining innovation rules medical AI

So we’ve established that some pretty ancient companies are among the dominant players in medical AI. But is the tide shifting towards the startups (i.e. towards disruption)?

I’ll define an “older” company as >= 20 years old.

Certainly we can see that the percentage of companies winning at least one approval each year that are older is decreasing:

Percentage of companies winning at least one approval that are >=20 years old vs younger

This may reflect the increasing simplicity and economy of AI tools available, and the increasing number of coders who understand how to use them: the cost in time and money of playing in the AI space is going down, and that means younger and smaller companies can join the game.

At the same time, older companies gain the benefit of those decreasing costs, too, and they have more resources with which to deploy them. Here’s a chart showing the percentage of approvals going to older vs younger companies:

Percentage of FDA approvals going to companies >= 20 years old vs younger

What the last two graphs shows us is that while younger companies are increasingly in the game, the older companies (with presumably greater resources) are more than holding their own in terms of productivity. They make up a smaller percentage of companies winning at least one approval each year, but are taking an increasing share of those approvals.

So using FDA approvals as the metric, it doesn’t look like there’s much disruptive innovation going on in AI-powered med tech: we don’t see established medical device makers like GE being pushed aside (at least not yet). Instead we see:

1. increasing numbers of new companies entering the space and winning FDA approvals

2. continued dominance by established med tech players as they add AI on to their existing products to improve performance and appeal to their best existing customers.

In other words, classic sustaining innovation.

Does this mean that AI isn’t causing disruption related to healthcare? No, not at all. But, as I’ve explained in previous articles on disruption within healthcare and disruption to healthcare, it shows that the same technology (AI) can be used to

1. sustain current healthcare practices (e.g. GE and Siemens and Canon, along with some youngsters, making hospital-based imaging cheaper, more useful, and more accurate)

2. enable players outside traditional healthcare to shift low-hanging fruit to another part of the healthcare system (e.g. Walmart Health)

3. allow companies completely outside healthcare to shift other low-hanging fruit completely outside healthcare (e.g. ResApp)

Thoughts about the above? Inside knowledge you’d like to share? Feel free to contact me privately.

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Further Reading

• If you’re interested in learning more about the FDA approval process for AI medical devices, I strongly recommend “How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals” by Eric Wu et al in Nature Medicine (April 2021).

• A good, but now somewhat out-of-date discussion of the types of AI medical devices, can be found in “The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database” by Stan Benjamens et al in NPJ Digital Medicine (September 2020).

• Nice discussion of current FDA rules for software-as-a-medical-device (SaaMD) at Emerge (June 2022).