Interview by Alan Tonelson
Talk on Amazon and the Book Business – explaining how Amazon’s grip on the book business became unbreakable, and lessons for the rest of the economy. Click here for youtube.
New technology, patient-driven medicine, and the power of iteration:
How I solved my sleep apnea
Obstructive sleep apnea is a potentially dangerous disease. As the airway closes during sleep, it causes victims to wake up repeatedly, often with a loud snort. It also causes heavy snoring, which of course isn’t great for sleeping partners. It affects 18 million people in the US, and I am one of them.
I was diagnosed about 15 years ago, after doing a sleep study in hospital. I was wired up with all kind of monitors, slept overnight, and found that I was waking around 28 times an hour – I had moderate obstructive sleep apnea. The doctor prescribed a C-PAP machine, which pumps air into the lungs during sleep through a sleep mask covering the face. Some people tolerate this and get used to it, but I hated it.
So I then tried a dental device, a mouthpiece that holds the lower jaw slightly forward to keep the airway open. Miracle! It worked and I could dump the C-PAP! As confirmed by a second in-hospital study, my sleep apnea was officially under control. It was expensive – $1,800 for a dental device – but it was worth it even though I had to pay out of pocket.
The device worked great for several years, even after my dog chewed part of it off. Eventually, I replaced it after I got better insurance; I only paid $300 for the new device. Better still, the necessary before and after sleep studies were now conducted at home using a portable sleep-tracking device.
Then disaster struck. I left the device on a plane! I couldn’t really afford the full cost of a new device, so I started exploring online. Pretty much immediately, I found SnoreRX, which is functionally identical to my lost dental device, except it is self-installed using moldable plastic (like in sports mouthguards). And it cost $99 instead of $1,300. It’s explicitly marked “not for sleep apnea,” because it is not FDA-approved.
Well, SnoreRX seemed to be working, but I wasn’t entirely convinced, or at least wanted more data. So I looked for a way to track my sleep at home. A number of wearable products do that, from companies like Fitbit, but the product reviews on Amazon seemed spotty. Then I found SnoreLab. It’s a free phone app that tracks snoring via audio recording and analysis (I paid $7 for the premium version). Put the phone down by your bed, turn SnoreLab on, and when you wake up it offers a comprehensive picture of your night’s sleep – every sigh and snort is captured, and for convenience, SleepLab aggregates the data into a single “Sleep Score.”
That’s all good, but SnoreLab is much more than an audio recorder. It allows you to track both specific interventions – like using SnoreRX, or a chinstrap I tried that failed miserably. And it also lets you track your environment – a late heavy meal perhaps, too much wine, or a cold. These are essentially fields in the database you are building with SnoreLab. And SnoreLab also lets you export the data to Excel where you can do extremely detailed analysis correlating outcomes (snoring) with any combination of multiple inputs (interventions). This is far more granular than peer-reviewed studies.
What did I find? Well, SnoreRX worked. Not perfectly, but pretty well, and probably about as well as the expensive dental device I’d lost. On average, SnoreLab told me that my sleep had score dropped from high twenties to around 10 or even below. But SnoreLab was also indirectly nudging me to try lots of other options – it listed about 20 different interventions on which you could collect data, and those were presumably the most popular solutions for snoring. So, based on the wisdom of crowds, I tried using a bed wedge – lifting the head of the bed about 4-6 inches.
Holy Cow! Transformational. Tilting the bed eliminated snoring: my new score was around 2 or 3. Mostly not snoring at all. Even better, when traveling, I found I could use a blow up bed wedge, and that worked just as well.
One final step. I tried eliminating the SnoreRX mouthpiece. And that worked too – I found that my overt sleep apnea symptoms vanished even without the mouthpiece, provide I was using the wedge. I confirmed this with a couple weeks of SnoreLab monitoring
There is plenty learn from this:
- You have to iterate. Only with iteration can the patient adjust interventions and gradually work toward an effective solution. And iteration requires monitoring at home. I would never have been able to find a way without SnoreLab.
- Patients will drive their own treatment. It would never even have occurred to a doctor to suggest that I experiment with sleep apnea cures. But I quite quickly found what works for me. Doctors will have to adjust to a role that is more partner than God.
- Personalized medicine means patient-driven medicine. I am the expert on me, and I increasingly have tools to deepen that knowledge. I can then use it – guided by humans or machines – to find what works for me. Doctors essentially deal in aggregate data – they know what works on average. But I can become the expert on “me medicine,” because I have all the data and all the incentives.
- Tools must be easy to use. If I had to wire myself up to do sleep studies I would never do it. If I had to fiddle with a separate machine I probably wouldn’t do that either. Turning on an app in my phone is entirely doable.
Solving my sleep apnea problem was deeply empowering. I don’t expect to ever walk into a doctor’s office and simply accept a diagnosis or a prescription. And as other apps like SnoreLab become more available for other conditions, I will be using them too for tracking the rest of my health.
- Initial snoring plot, showing two days
- Possible snoring remedies to track
- Final 0utcomes plot
Despite all the productivity we see around us, most mainstream economists believe we are in era of low productivity growth, reflecting the reported productivity numbers. That’s a problem for the Great Disruption argument, because if productivity growth is low, then surely talk of disruption is at best overblown. Maybe the robots aren’t coming after all.
Those of who believe the data – “true believers” – offer several explanations for this disconnect: low productivity growth in the service sector which dominates the economy; innovations that aren’t sufficiently important to move the economy; lags between innovations and the subsequent diffusion of innovation; and growing gaps between leading companies and lagging ones in many sectors of the economy, fueled perhaps by a disinclination to invest.
But maybe these true believer theories explain outcomes that don’t really exist. Maybe the productivity data are bad.
There are a lot of problems with the data:
- They are poor at capturing both quality changes and new products.
- They are especially bad at measuring services (about 80% of the economy), where it’s hard to conceptualize outputs, let alone measure them.
- They struggle mightily with free (Facebook/Google) and near-free (Netflix) services.
- They completely fail to address nonprofit and government sector productivity, and don’t include non-market activity (like environmental gains).
- They don’t account properly for rapidly growing investments in intangible assets.
- And they miss out entirely on the black economy and on much of the gig economy.
Together, these arguments easily explain the “missing” 1.5 percent of annual growth that separate low and high growth economies.
The heretics can also adopt some of the true believer arguments: it may be that productivity is indeed depressed by lags and the growing innovation gap. But the overall conclusion is inescapable: the productivity paradox is mostly driven by bad measurement. There is nothing here to suggest that the Great Disruption is unreal, or even that it will be much delayed.
Amazon only accounts for only about 5 percent of all US retail sales. And online retail as a whole has only 11 percent. So there are plenty of folks who believe that the transition to online retail will happen slowly, and that while Amazon is growing fast it will be long time before it reaches the top of the heap. They see what’s happening as just another twist in the long and winding tale of US retail, the latest installment in a story where department stores rose to replace clusters of small stores, malls and chains exploited the growth of the suburbs, and then Walmart and the big box category killers sliced off large chunks of the US retail market, leaving the big mall anchor stores alive but bleeding. On this view, Amazon is just another shark in a sea of predators, and not a very big or dangerous one at that.
They could not be more wrong. The current “Retail Apocalypse” is just the start. Online retail is entering a period of explosive growth; Amazon is quickly becoming entirely dominant in ecommerce; and these changes will have a massive impact on retail employment as 3-4 million of retail salespeople lose their jobs over the next ten years.
So this post is about three things:
- Why Amazon is winning online now and will continue to win right across the retail sector by exploiting the network effects that will make the biggest online player effectively the only online player. Amazon is systematically building strategic competitive advantage to become impregnable. 85 million US households already have Amazon Prime subscriptions.
- How online shopping is just entering the acceleration phase of technology adoption. Steady growth of about 10 percent annually in recent years will become 20 percent as Amazon’s tools and strategy provides reinforce normal adoption patterns.
- The impact on work. Workers in retail face a catastrophic future. 2.7-4 million existing jobs at bricks and mortar retailers (B&Ms) will vanish over the next ten years. They will be replaced by far fewer jobs at online retailers, and those new jobs will require different skills and will be located in different places. Amazon also has every intention of automating many of these jobs, as soon as possible, and B&Ms will respond by cutting wages and benefits for those that remain.
Striking that total TV viewing time is collapsing, and there is no growth in DVR viewing
Remarkable that the share of Wharton women not expecting to have children is up from 3% to 27% in 2 decades
Source: Stewart Friedman, Baby Bust 2013