The Year "Digital" Crested as a Magical Wave
First published December 29, 2022
Updated March 25, 2024 to integrate insights from a study by Peterson Health Technology Institute concluding diabetes monitoring apps "do not deliver meaningful clinical benefits, and result in increased healthcare spending”
Updated March 26, 2024 to integrate Christina Farr’s *call to action* for digital health companies.
Updated March 28, 2024 to integrate perspective from The Economist’s data journalism on health technology (“The AI doctor will see you…eventually”)
Updated April 8, 2024 to integrate the strategic collapse of Teledoc (“Teladoc CEO departs the company after stock’s 95% fall from 2021 highs”)
Mark 2022 as the year the magical wave of "digital" crested.
Technology is never decisive, and is often strategically distracting. It can be expensive confusing new technology for a modern strategy. And it can be lethal buying the sell that new technology is the path to progress. Brooke Masters, the US Financial editor at the Financial Times, yesterday in her year-end piece (Three ways Big Tech got it wrong):
It’s time to unlearn the lessons of Big Tech.
For 20 years, the Silicon Valley giants and their peers have set the standard for corporate success with a simple set of strategies: innovate rapidly and splash out to woo customers. Speed rather than perfection, and reach rather than profits proved key to establishing dominant positions that allowed them to fend off, squash or buy potential rivals.
Entrepreneurs everywhere took note, and an assumption that scaling up and achieving profitability would be the easy part took hold far beyond the internet platforms where these ideas originated.
Investors, desperate for growth and yield amid historically low interest rates, were all too happy to prioritise the promise of growth over short-term earnings. During the pandemic, the trend became extreme, as the shares of companies with big dreams and equally large losses soared to dizzying heights.
Those days are over. Inflation and rising central bank rates have changed the financial calculus. When investors can earn measurable returns from bank deposits and top-rated bonds, speculative investments that promise growth lose their edge. The share prices of Google, Amazon and Facebook are down between 40 and 60 per cent year on year, and their younger emulators have done even worse. A Goldman Sachs index of unprofitable technology companies has fallen by 77 per cent since its February 2021 peak.
There is also a growing sense that most important challenges of our time — improving health, cutting carbon emissions, basically anything that involves a real world rather than purely digital product — will require a different approach.
Indeed.
And where tech entrepreneurs, and the press outlets that adore them, go to sell and cover the next new possibility is a big question. As Silicon Valley works to design the world in its image, reconfiguring our ideals in order to fit their business models, it’s easy to bask in the eternal sunshine of it all, to simply sit back and let the warm glow consume every corner of our consciousness.
“Silicon Valley is good at "reframing” questions, problems and solutions,” explains Adrian Daub in What Tech Calls Thinking, his lively dismantling of the ideas that form the intellectual bedrock of Silicon Valley. “Equally important to Silicon Valley’s world-altering innovation are the language and ideas it uses to explain and justify itself. And it’s easy to come away with the sense that the original way of stating the problem is made irrelevant by the reframing.”
“Consider how much mileage the tech industry has gotten out of its technological determinism. The industry likes to imbue the changes it yields with the character of natural law: If I or my team don’t do this, someone else’s will. Or consider how important words like “disruption” and “innovation” are to the sway the tech industry holds over our collective imagination. How they implicitly cast you as a stick-in -the-mud if you ask how much revolution someone is capable of when that person represents billions in venture capital investment.”
Edison's great advantage was in systems thinking.
Writing on a century of American innovation and technological enthusiasm, Thomas P. Hughes (American Genesis: A Century of Invention and Technological Enthusiasm, 1870-1970) described invention as the process of solving new problems. Radical success, he said, comes at a system level, from striking breakthroughs or improvements in nascent systems rather than from the incremental improvements in well-established technological ones.
“In general, today’s accounts of the information revolution focus upon artifacts, such as computers and the Internet. This approach is myopic. We should broaden our concept of the information revolution. The other industrial revolutions involved far more than hardware. Besides technical artifacts, these earlier revolutions involved political, economic, social, organizational and cultural changes….”
If not completely irrelevant, the word "digital" is incidental as a bullet in the PowerPoint. "Disruption" as a war cry hasn't delivered economic growth at scale, and the mind-numbing flow of headlines from the media on what technology "can" do have all failed to spark the kind of systemic change in direction needed to sweep aside the status quo.
Mediocre Results
Better diagnoses. Personalised support for patients. Faster drug discovery. Greater efficiency.
“Artificial Intelligence is generating excitement and hyperbole everywhere, but in the field of health care it has the potential to be transformational,” writes The Economist in a special report (published March 28, 2024) dedicated to medical artificial intelligence. “In Europe analysts predict that deploying Ai could save hundreds of thousands of lives each year; in America, they say, it could also save money, shaving $200bn-360bn from overall annual medical spending, now $4.5 trillion a year. From smart stethoscopes and robot surgeons to the analysis of large data sets or the ability to chat to a medical Ai with a human face, opportunities abound.”
There is already evidence that Ai systems can enhance diagnostic accuracy and disease tracking, improve the prediction of patients’ outcomes and suggest better treatments. It can also boost efficiency in hospitals and surgeries by taking on tasks such as medical transcription and monitoring patients, and by streamlining administration. It may already be speeding the time it takes for new drugs to reach clinical trials. New tools, including generative ai, could supercharge these abilities.
Yet as our Technology Quarterly this week shows, although Ai has been used in health care for many years, integration has been slow and the results have often been mediocre.
Which is a consistent theme.
Saving money using “innovation” is tricky. But defining “innovation” in technical terms is part of the problem. And framing healthcare within the context of “cost” under-conceptualizes and under-powers, keeping things stuck in low-earth orbit. Without simultaneous and interactive economic innovation, all the things that technology “can” do is the sound of one hand clapping.
New technology may account for as much as half of the annual growth in health spending. Layering on new systems — trying to “fix” legacy operating models or make them more efficient —, will increase costs and complexity. But redesigning processes to make efficient use of new digital tools is likely to be resisted by patients and medics. Though Ai may be able to triage them over the phone or provide routine results, patients may demand to be seen in person.
Many health systems are set up to reward the volume of work. They have little reason to adopt technologies that cut the number of visits, tests or procedures. And even publicly run health-care systems may lack incentives to adopt technologies that reduce costs rather than improve outcomes, perhaps because saving money may lead to a smaller budget next year. Unless governments can change these incentives, so that ai combines better treatment with new efficiencies, innovation will increase costs.
A new analysis pours cold water on the effectiveness of widely used digital diabetes management solutions, stirring up discussion about how best to evaluate the growing market of digital health tools.
The blistering report, released by the Peterson Health Technology Institute (PHTI), concluded that diabetes monitoring apps "do not deliver meaningful clinical benefits, and result in increased healthcare spending."
“When these digital diabetes management tools launched more than a decade ago, they promised to improve health outcomes for people with diabetes and deliver savings to payers. Based on the scientific evidence, these solutions have fallen short, and it is time to move toward the next generation of innovation,” said Caroline Pearson, executive director of the PHTI, in a statement accompanying the report,
Strategic problems don’t have technical solutions.
New Concepts Required
Technical problems are relatively easy to solve. The harder problem is constructing new economic systems.
Which is another way of saying that the shape and texture of transformational visions will come from discovering new language to frame action, and new management techniques that dissolve and work horizontally across organizational, market, industry, state and even national boundaries. This is what an ‘ecosystem-centered market strategy’ is really all about — a management innovation to steer and sustain the ‘progressive integration’ of an infinite flow of technical potential into a new growth model, one that positions the ‘production of health’ as the objective on the roadmap (‘ecosystem-centered market strategies’).
Success will take strategic imagination, a new kind of cognitive pattern that goes against the very fabric of experience that has up to know given most people, industries and countries their identity. It works on the interplay between markets and governments. Much of the burden for boosting Ai in health care falls on governments and regulators. However, companies have a part to play, too.
For the next phase, smart strategy starts with disassembling the technology industry narrative that pretends to be novel but is actually an old motif playing dress-up in a hoodie. We are in desperate need of a different kind of creative leadership, a new economic theory. Says Christina Farr:
“We are all facing the same tough stuff in the shadows, so things are not changing.
Digital health companies are fighting a war on every front.
It is so hard out there to be a health-technology founder in 2024. I’m exhausted by osmosis, and I’m not even the one doing the grind that operating teams are doing every day. And to be doing it in this venture market… well it’s tough.
Adding to all that, most people who’ve been in the space for any length of time will tell you that it’s a lot harder to be in health-tech than almost anywhere else. The sales cycles are long and slow; the incumbents dominate and lack the incentives to innovate; and even just getting to the individual or individuals who can actually make a buying decision can take months. Months that companies don’t have to waste.
So where has this left us, about two decades into this experiment to modernize U.S. healthcare? Well, it looks like we're still funding companies that aim to finally rid us of fax machines, so I'd argue it's been a whole lot of ‘one step forward and two steps back.’
If you’re a digital health startup you have one big choice to make starting out, she says:
Do you work within the system?
Do you work outside of the system?
“Those working within the system are likely to have an easier time fundraising, because there’s an opportunity for enterprise-wide contracts that are highly sticky. In order to land these contracts, what’s also required here is that companies pay the piper. They must integrate with and partner with incumbents, or they have no chance of tapping into the money flow.
For those brave souls who choose the red pill and sit outside of the system, you have a lot more freedom. This is not a bad call, and I’m personally excited by the trend, even though it's harder to fundraise. It does allow companies to do things like prioritize the user experience. You can also do some health care, versus purely doing sick care. But it is harder to build a truly scaled business, because there's still some resistance amongst consumers to paying for health care out of pocket.”
Another Broken Piece of Technical Potential
The 'strategic collapse' of Teledoc Health ‘s market has been fierce.
It has posted top-line growth that has now decelerated for 11 consecutive quarters. It's gone from a 151 percent year-over-year surge in revenue during the first quarter of 2021, when its stock was peaking and its business was rotating around an orbit of gravitational interaction with Covid, to less than 4 percent now. Teladoc was founded more than 20 years ago, but it still isn't producing profits -- the company lost $220 million last year and expects significant losses this year too.
Its Livongo deal, which closed in late 2020 to create a $37 billion "health tech behemoth uniquely positioned to unlock the full potential of virtual care" -- helped juice early results, but it's been organic deterioration most of the way down.
Call it the Big Misread.
And as far as the kinetic potential of all that data and all those customers, this yesterday from Cory Renauer in The Motley Fool is, I think, the right frame to assess value:
"Tinkering around the edges with help from its data trove probably helps providers make better recommendations. But before getting too excited about Teladoc's data advantage, it's important to remember that there's a lot of low-hanging fruit when it comes to lowering healthcare expenses. For example, a majority of American adults don't even bother with annual flu shots.
The company's client list is huge, but it doesn't seem like these members appreciate their access very much.
There were 80 million Americans with access to one or more of the company's products and services at the beginning of 2023, but less than one in four used Teladoc to visit providers. In 2023, the company completed just 18.4 million telehealth visits."
Which is another lesson in this:
Poor strategy is expensive, commercially as well as professionally. Bad strategy is lethal, professionally as well as commercially. And confusing operations (i.e., M&A, technology and technical potential) for "strategy" falls somewhere in between.
/ jgs
John G. Singer is Executive Director of Blue Spoon, the global leader in positioning strategy at a system level.
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