Thoughts
Start Here: Core Ideas
This site explores how information, technology, and crisis are reshaping healthcare and other institutions.
If you're new here, these essays lay out the central themes:
Disruption for Doctors, Part 1: What Is Disruption?
A clear explanation of Clayton Christensen’s disruption theory, applied to medicine.Disruption for Doctors, Part 2: Examples in Healthcare
How Walmart Health, AI diagnostics, and automation are already changing the system.Disruption for Doctors, Part 3: The Rise of Self-Care
The biggest disruptions bypass doctors entirely — and move care into consumers’ hands.
You can browse all posts below.
Hidden Connections: What John Muir Can Teach Us About Apple’s New Hypertension Notifications
The naturalist John Muir saw how everything in nature is connected — and today AI is showing us the same truth inside the body. From Apple Watch studies on atrial fibrillation to new hypertension alerts, hidden links in long-collected data are transforming how we understand health.
“When we try to pick out anything by itself, we find it hitched to everything else in the universe.”
John Muir, circa 1902
When the naturalist John Muir penned that quote, he was writing about the natural world after working as a shepherd in California’s Sierra Nevada, immersed in the mutual dependence of mountains, forests, rivers, and animals.
More than a century later, his insight is proving just as true for the wilderness inside of us. We’re discovering that nothing in our physiology exists in isolation. The eye, the heart, the skin, the voice — each carries hidden connections to other systems. And increasingly, it is AI that is uncovering those links.
An Early Example: Apple Heart Study
In 2017, Apple and Stanford launched the Apple Heart Study, enrolling more than 400,000 participants. Researchers asked a simple question: could the Apple Watch’s pulse sensor — a photoplethysmograph (PPG) LED originally designed just to measure heart rate — be used to detect something far more serious?
The answer was yes. By applying machine learning, Apple showed that irregular pulse patterns captured by PPG could flag atrial fibrillation (AFib), a condition often silent until it causes a stroke. The connection between a consumer watch’s pulse sensor and a dangerous cardiac arrhythmia was a hidden connection — one not previously recognized or used at scale. And gaining the valuable additional information didn’t involve any new hardware at all.
More Hidden Connections Revealed by AI
Since the Apple study, machine learning has surfaced hidden links across medicine, and not just with watches and phones and other mobile devices:
Retinal images — long used to check eye health — can reveal sex, race, smoking status, blood pressure, and even cardiovascular risk.
Gait and voice patterns — captured casually by smartphones or microphones — can detect the earliest signs of neurological disease without a visit to a neurologist
Electrocardiograms (ECGs) — the same tracings used for decades to diagnose heart rhythm — now allow AI to screen non-invasively for anemia, electrolyte imbalance, and left ventricular dysfunction
CT scans — often ordered for one purpose — can be re-analyzed at minimal cost to estimate bone density or coronary calcium, each tied to long-term risk — without needing additional scans (or radiation)
The information was always there. What’s changed is that AI gives us the ability to see it.
The Apple Watch and Hypertension
Perhaps the end of the beginning
This is particularly true for Apple’s new hypertension notifications, because they don’t use any kind of new sensor in the Apple Watch. The same green LEDs still measure blood oxygen levels. The electrodes still capture heart rhythm. The accelerometer still tracks movement. What changed was Apple’s recognition that these existing signals already carried the imprint of something new: hypertension.
For years, millions of Apple Watch users have been streaming this data. But it’s only now — after training AI on the right relationships — that the watch can flag blood pressure risk with confidence.
Another hidden connection uncovered.
Beyond the Watch
We can expect that this pattern will repeat itself everywhere: applying AI to old data streams to produce new insights. Combined, of course, with new data streams — very likely including non-invasive blood glucose monitoring in the near future.
Credit card purchases — will allow us to understand not just spending, but nutrition and mental health
Browsing histories — can understand cognition and promote education
Medical images — will be repurposed by AI to surface cardiovascular, skeletal, or metabolic risk factors unrelated to the original reason for the scan
Muir’s observation was ecological, but it applies just as well here. Health is not a set of isolated numbers. It is a network of hidden links — and we are only beginning to trace them.
Closing Thought
Apple’s hypertension notifications are not the end of something — they are the beginning. They can help us imagine how every stream of data we collect may reveal more hidden connections between body systems than we ever imagined. And the future of health will be built on our ability to recognize those links connecting what we already measure to what we most need to know.
Forget the EHR — Your Health Data’s On Your Phone
The overwhelming majority of health-relevant data —movement, behavior, speech, sleep — is now generated outside the clinical setting. As a result, health innovation is increasingly shifting toward consumer devices and tech platforms that actually hold the data — not the EHR or the healthcare system.
In 2020, I asked a provocative question:
“What does it mean for the future of health when Apple makes more money from AirPods than the top ten EHR vendors combined?”
At the time, it might have seemed like an unfair comparison—enterprise software versus global consumer electronics. But five years later, it’s clear that this contrast was not only valid, but also predictive of a broader shift: health data—and thus health innovation—has moved out of the clinic and into the consumer’s pocket.
The Real Center of Health Data Is Not the EHR
“We have 50,000 times more consumer health data than EHR data. So where is AI health innovation going to happen?”
The Electronic Health Record (EHR) remains the data centerpiece of traditional medicine. But in terms of data volume, it’s no longer the main source of information about our health. And it’s not even close.
U.S. EHR Data Volume
Each U.S. patient generates an estimated 80 megabytes of EHR data annually, including clinical notes, lab results, and imaging. Multiply that by roughly 250 million Americans interacting with the healthcare system annually, and you get:
→ 20 petabytes per year of U.S. EHR data
U.S. Consumer Health Data Volume - Calculated by Total World Data
There is no question that the real volume, and the real growth, in health-related data is happening outside the exam room, and outside the EHR.
Every day, we generate health-related data through wearables, smartphones, browsers, keyboards, credit cards, sleep monitors, voice assistants, and fitness apps. Not to mention smartwatches, rings, and earbuds.
One way to estimate this is by looking at the total data produced worldwide:
Global data generation reached approximately 147 zettabytes in 2023. If we very conservatively estimate that only 5% of this is health-related consumer data (i.e. somehow related to, or useful for, our health), we get approximately 7.4 ZB/year globally.
Adjusting for the U.S. — which has about 4-5% of world population but likely 10-15% share of digital activity — we can conservatively estimate that the U.S. generates roughly 1 zettabyte (1,000,000 petabytes) of consumer health data annually..
→ That’s 50,000 times the volume of U.S. EHR data
Another way is to look at individual data generated.
For example, everyone with a smartphone — which Pew estimates to be 91% of the roughly 230 million US adults, or 209,000,000 people. Even if we just look at the accelerometer data for those people, leaving out all the other health-related information on their phones (like speech patterns, word choices, grocery bills, etc) continuous low-frequency sampling (1–2 Hz) on an iPhone can generate 1 GB of raw movement data annually. Which is 209 pettabytes for the adult population.
→ That’s still ten times the volume of U.S. EHR data
So even our low-end estimate shows that we’ve got 90%+ of our health data sitting on our phones, not in the EHR. And it’s not hard to believe that if we wanted to predict, for example, cardiovascular risk, using either the average person’s EHR data (which is likely minimal) versus that person’s continuous data on how fast, how far, and how often they move . . . it’s the phone data that will produce a better prediction.
Although as far as I know, no one is yet doing that. Sigh.
Lark health coaching
Innovation Follows Data
If the EHR holds such a tiny part of U.S. health-relevant data, where should we expect most of the next breakthroughs in diagnostics, prediction, and prevention to occur?
Not in the hospital. Not in the clinic. Not in the EHR.
Innovation is flowing to where the data is—in consumer-facing platforms and tools. We’re already seeing this play out in:
AI Symptom Checkers: Platforms like K Health and ChatGPT-based tools provide users with AI-driven assessments of their symptoms, offering preliminary insights before consulting healthcare professionals.
Wearable-Based Sleep Optimization and Stress Tracking: Devices such as the Oura Ring and WHOOP monitor sleep patterns and stress levels, providing users with actionable insights to improve rest and manage stress effectively.
Early Illness Detection: Wearables like the Oura Ring have introduced features such as Symptom Radar, which can detect early signs of respiratory illnesses by analyzing physiological data, enabling users to take precautionary measures promptly.
Fitness and Glucose Feedback Loops: Dexcom G7 offers continuous glucose monitoring that now connects directly to Apple Watch, allowing users to track their glucose levels in real-time. Similarly, Levels and Ultrahuman provide platforms that integrate glucose data with lifestyle factors to optimize metabolic health.
Personalized Coaching and Risk Scoring: Apps like Lark Health utilize AI to offer personalized health coaching, helping users manage conditions such as diabetes and hypertension through tailored advice and monitoring.
Mood Inference and Mental Health Support: Ellipsis Health has developed voice-based technology that analyzes speech patterns to assess mental health, providing insights into conditions like depression and anxiety.
These advances are possible not because of structured ICD codes—but because of rich, continuous consumer data at massive scale.
Conclusion: The Real Medical Record Is Distributed
Ultrahuman metabolic tracking
In 2025, the EHR remains essential—but it’s nothing like central. The richest, most frequent, most predictive health data lives outside the clinic.
And that means the future of health won’t be driven by Epic, or Cerner, or Allscripts. It won’t even be driven by United Healthcare, or Kaiser, or Mass General.
It will be driven by whoever best understands—and leverages—the flood of health-relevant data being generated by consumers, not patients. And that is likely to be the consumer tech companies, both giants like Google and tiny startups or that haven’t even left the garage yet.
Is Autonomous Driving Healthcare’s Most Important Competitor?
Hospitals worry about retail clinics and other healthcare competitors. But real disruption may come from outside healthcare entirely: cars that don’t crash. As autonomous driving becomes safer and more widespread, the revenue ripple effects on emergency departments, orthopedics, and imaging will be profound—and sooner than most systems expect.
While I’ve spoken extensively about how healthcare is not paying enough—if any—attention to the technological changes that threaten its business model, I haven’t talked much about improvements in auto safety, including Tesla Full Self-Driving (FSD) and similar efforts in “autonomy” (i.e. autonomous driving).
Let me correct that with this post.
In 2024, U.S. traffic deaths dropped nearly 4%, the sharpest single-year decline in half a decade. The National Highway Traffic Safety Administration (NHTSA) credits this trend in part to technologies like automatic emergency braking (AEB), which will be mandatory on all new light vehicles by 2029. NHTSA estimates that AEB could prevent 360 deaths and 24,000 injuries annually once fully deployed.
That’s before you even factor in what’s coming next: widespread autonomous driving. Tesla’s Autopilot — definitely not yet a fully autonomous driving system — already reports one crash for every 7.4 million miles: seven times safer than the U.S. fleet average. Waymo’s driverless fleet in Phoenix has now driven tens of millions of miles with zero at-fault fatalities. These systems are getting better, quickly. They don’t speed. They don’t text. And they don’t fall asleep at the wheel.
And even now, before full autonomy is widespread, the downstream effects of safer driving technologies are already being felt—just not yet on hospital dashboards.
For 2019–2020, an average of 3.8 million emergency department (ED) visits for motor vehicle crash injuries occurred annually — about 3% of ED visits. And car crashes are not low-cost visits. They trigger trauma activations, CT scans, orthopedic surgeries, ICU stays, and weeks or months of rehabilitation.
In 2022, motor vehicle accidents in the US resulted in over $470 billion in total costs, including medical costs and the cost of lost lives, according to the CDC.
Which is exactly why a slow decline in motor vehicle trauma should be setting off alarms in every hospital boardroom.
Trauma, orthopedics, imaging, rehab—many of healthcare’s most dependable, well-reimbursed services depend not just on illness, but on accidents. And no single type of accident has been more reliable, more predictable, and more lucrative than the car crash.
Autonomy changes that.
Not overnight. But incrementally, and often invisibly. There’s no press release saying “trauma cases will drop 15% this year.” There’s just a software update. And then another. And another. Every version pushes trauma volumes a little lower.
If you run a trauma center, your real competitor might not be the hospital across town—it might be the software quietly reducing your patient volume from the outside.
And it is a major threat. Trauma centers are high fixed-cost operations. They can’t scale down neatly when volume dips. You still need the surgeon on call, the CT scanner, the blood bank, the full staff. When volume becomes unpredictable, the economics start to break.
Healthcare tends to expect disruption to look like a healthcare competitor—CVS Health, Walmart, Amazon Clinic. But true disruption rarely comes from within the market. Blackberry didn’t fall to another mobile phone company, it fell to Apple — a company that had never made a phone. Taxi companies didn’t fall to another transport company. They fell to software layered onto private cars.
Healthcare’s trauma volumes may fall to something even simpler: safer roads. And any strategic plan built around growing orthopedics, expanding trauma capacity, or maximizing downstream imaging needs to be re-examined through the lens of this shift.
What if car crashes drop 20% in five years — from software updates?
If you’re a healthcare executive, or board member, you might not need need to change your mission. But you do need to change your assumptions. You can’t control the crash rate — but you can control how prepared your system is for the rapidly-approaching world in which crashes are rare.
References:
The Empowerment of Consumers for Health: A Long Trend, Accelerated by AI
The public conversation about AI in healthcare swings between extremes—some predict it will replace doctors, others that it will usher in a golden age for medicine. So which is it? In my recent American Family Physician editorial, I explore how AI is less a disruptor of doctors than a powerful accelerator of consumer-driven health.
I recently published an editorial in American Family Physician titled “The Empowerment of Consumers for Health: AI Accelerates a Long-Standing Trend.” While headlines often frame artificial intelligence (AI) as either an existential threat to physicians or a helpful clinical assistant, I argue that both views miss the broader context: AI is the latest in a series of technologies that empower consumers to manage their own health—often without a doctor at all.
And this didn’t begin with ChatGPT. For decades, consumers have used online search engines, wearables, and over-the-counter medications to bypass the traditional gatekeepers of care. AI simply supercharges a longstanding trend: the democratization of health knowledge and decision-making.
“As with OTC drugs, the rise of AI represents another step toward empowering consumers to take control of their health decisions.”
What happens when free, always-on tools help patients adjust their insulin, distinguish between a cold and pneumonia, or monitor key metrics continuously rather than once a year? The answer isn’t a dystopian future without doctors—but it is a future where clinicians must reorient their value toward what cannot be automated.
In the piece, I encourage physicians to embrace AI, not fear it—to find new roles, contribute to the development of patient-centered tools, and show the unique value only human clinicians can provide.
If you’re a health leader, technologist, or clinician, I’d love to hear how you’re navigating this shift. The question is no longer whether change is coming—but whether doctors play the role of leader or observer.
🔗 Read the full article in American Family Physician:
In the Future, You'll Need Your Doctor Less
Snack food CEOs are planning for a world without obesity. Why aren’t healthcare execs?
What If Healthcare Just… Fades?
What happens to healthcare when obesity goes away?
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.
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.
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 2: Healthcare Examples
Smartphone apps that can diagnose pneumonia? FDA-approved machines that can diagnose conditions without a doctor? Robot psychotherapy? It’s not coming, it’s here now.
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:
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.
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.
This post draws on themes from my keynote talks.