Smart Scales, Blood Pressure and other connected devices


This post is by rich from Tong Family

Well I’ve had really good luck during this pandemic in connecting all these health devices together, but today, my GreaterGoods Wifi Scale finally broke. It’s always been pretty flaky. The Wifi couldn’t handle special characters and now it won’t connect to Wifi and the iOS application that programs it is definitely busted. Doesn’t even render correctly, so time to find the next solutions. But here is what is working:

  1. Scosche Heart Rate Monitor. Some say this reads high and low, but the main thing is that the Bluetooth LE works flawlessly when connected to both a Garmin bike computer and also to an old Mac when using TrainerRoad. Pretty impressive and not too expensive. Plus as an arm band it is way easier to put on. I have not tried their latest version, but it even has a single LED to give you a small read. The lower cost $80 version doesn’t have that. I’m not sure why you need it as you are probably using a watch or something.
  2. Withings Body Plus or Wyze Smart Scale. Ok, this is definitely a first world problem, but it sure is convenient if you don’t have to record your weight all the time. There are plenty of reviews on this at Tom’s Guide, PC Magazine (although these reviews are four years old). They all say the same thing, the $99 Withings has lots of features (and integrates with Strava in addition to Healthfit and the Wyze at $28 is an incredible value. The main issue is the Wyze is Bluetooth only, so you need a phone application to make it work (like the Scosche) but supports both Apple Health and Google Fit.. And for the Withings Body seems like another good choice at $59 it throws away the heart rate monitor and weather (why do you need that on a scale?). The main issue is the proprietary app. The bane of all these devices. The main issue with Wyze is how to get the data to all the places I need it mainly TrainingPeaks and Apple Health.

Blood Pressure Monitor

When there is time you want to measure your blood pressure then having a wireless system for it. And there are reviews from Very Well Health and Wirecutter. The main issues are the actually fake spot reviews of the Omron are not that high:

  1. Omron Healthcare Platinum. This was top rated by Very Well Health, but has a terrible Fakespot rating.
  2. Omron Evolv. This is a small cuff type that has a C rating and seems pretty convenient

The nice thing about these is that they are bluetooth connected and then work with Apple Health. They have something called Fitness Syncer at Training Peaks that knows how to talk with Omron

The post Smart Scales, Blood Pressure and other connected devices appeared first on Tong Family.

Are You Offering the Mental Health Benefits Your BIPOC Employees Need?


This post is by Andrea Holman from HBR.org

A one-size-fits-all plan isn’t good enough.

A Better Approach to Fighting Chronic Diseases


This post is by Alan M. Trager from HBR.org

Public-private partnerships are more common in infrastructure, but they offer potential for global health.

Funding for mental health-focused startups rises in 2020


This post is by Alex Wilhelm from Fundings & Exits – TechCrunch

Turning away from the public markets, IPOs, SPACs and Palantir for a moment, would you like to talk about startups again? I would.

This morning, I pored over venture capital funding patterns for wellness-focused startups. Broadly, according to a new report, these startups raised less money in the first half of 2020 than they did in the first two quarters of 2019. Deal volume fell from nearly 600 in H1 2019 to just under 500 in H1 2020, and dollars invested slipped from $6.1 billion to $4.6 billion in the same timeframe.

But, if we peer a bit deeper and look at the subcategories of wellness startups, interesting hotspots become clear.


The Exchange explores startups, markets and money. You can read it every morning on Extra Crunch, or get The Exchange newsletter every Saturday.


Inside the subcategories of wellness startups that CB Insights dug through while compiling the dataset, some, like fitness tech and sleep tech, saw fewer deals and dollars than they did in the first half of 2019. But one particular varietal is doing very well this year: mental health-focused companies.

The strong venture results that these startups have recorded in 2020 are not entirely due to a pandemic, a recession and political unrest that’s causing more anguish than usual, though I’d be surprised if those factors didn’t provide a tailwind of sorts.

Stepping back a few quarters, there’s a bit more to the business side of mental health startups that I want to unpack.

This morning, let’s remind ourselves about how startups like Calm and Headspace proved that their market was large and lucrative, review the venture capital data and see if the pattern of strong investment in the space is continuing in the current quarter.

We should see another unicorn or two out of the group, we reckon, before the eventual tech downturn. So let’s work to understand where the category is today.

Covid-19 Created an Elective Surgery Backlog. How Can Hospitals Get Back on Track?


This post is by Amit Jain from HBR.org

Five steps healthcare providers can take right now.

MIS2019: Fireside Chat with Vinod Khosla & Dr. Toby Cosgrove


This post is by Micah Kasman from Khosla Ventures

Video

MIS2019: Fireside Chat with Vinod Khosla & Dr. Toby Cosgrove

Toby Cosgrove:

Vinod, I am so pleased that you’re here. I have to tell you that about three or four years ago a mutual friend John Doerr gave me your tome, if you will, of about a 100 pages saying that we probably didn’t need doctors much anymore. They were going to be taken over by the various computer capabilities. That was a tough swallow for me. Happily, I hope they don’t learn to do cardiac surgery right away, so there may be some people employed. But tell us about the theory and tell us about why you wrote it. Let’s start with why you wrote it.

Vinod Khosla:

About eight years ago I was on a ski vacation, tore my ACL, took an MRI, took it to three surgeons that recommended very different procedures for recovery. So I said, the treatment I get is a function of the doctor I get, the surgeon I get, not the condition I have, that’s just patently wrong on its surface. At the same time I was looking at the other end of the spectrum, which is how do you scale healthcare in India? And with an unlimited budget, with hundreds of billions of dollars, I couldn’t come up with a way within 25-30 years of getting the same patient doctor ratio, just a simple metric, in India as it exists in the U S. So it forced me to think about nonlinear solutions. How do we broadly provide highest quality care?

As I did more work on it, and it was eight years ago, I first wrote this blog called Do We Need Doctors? I realized that AI, which was still well buried within the surface is talked about a lot now was going to be the solution that would scale skills, deep skills, whether it’s physicians or technicians or cardiac technicians. The way to scale that skill to make it broadly accessible to all 7 billion people on the planet was through technology and AI. So, four years ago I wrote this piece called 20% Doctor Included, which is a 100 pages or so. I drew heavily from the Institute of Medicine studies, in that it’s heavily referenced, many other sources, and I came up with this idea that we could dramatically increase the quality of care while dramatically reducing the cost of care, only through technology. It was the only way to do it and we had to get past intermediate steps in the way.

The role for the humans, and I actually define it in this paper, is the human element of care, not complex disease diagnostics. If Craig Mundie gets 5,000 proteins, you can’t ask a physician to look at 5,000 proteins, and you can’t say that a disease is caused by one biomarker when there’s 3,000 connected metabolic pathways in the human body. It’s not complicated, which is how physicians explain it, the human body’s complicated. It’s mathematically defined as a complex system. Complex systems. Complicated is hand waving, complex systems is a science. So, I like to reduce it and I said this in my paper, we’ve had very good practice of medicine and every year, every decade it’s been improving for the last 100 years. It’s better than it has ever been, but it is far below what it can be. If you take a complex systems view of both healthcare and sick-care, sick-care is what we do today.

Toby Cosgrove:

So what are the factors that are going to limit our ability to do this?

Vinod Khosla:

Probably more than anything else, humans.

Toby Cosgrove:

Humans in terms of the complexity of their system?

Vinod Khosla:

Well, their resistance to change.

Toby Cosgrove:

Ah, Really? I never noticed that.

Vinod Khosla:

It’s often stated as why this is not going to be important. People cite to me, you develop something new, like a 23andMe test, to say, “Are you at risk of breast cancer for a woman or a carrier for a male?” People will say, “Take 25 years for it to become standard practice.” That is the biggest barrier. The other thing to keep in mind is, we worry much more about acts of commission than acts of formation. I like to say, for all physicians here, if you took that Hippocratic Oath, please renounce it because it is mathematically doing more damage to society than it’s saving lives. So if you first do no harm, then you’re saying, I’d rather by through an act of omission, not save a 1,000 lives as long as I avoid a 100 deaths. That’s the Hippocratic Oath. It’s fundamentally wrong and we as humans still love it. We need to experiment a lot with human safety or patient safety in mind, and that’s the balancing act while allowing for innovation to progress.

Toby Cosgrove:

Okay. Now let’s talk a little bit about innovation, because I think that’s the key about really why we’re all here. Why do you think it takes so long and why is healthcare so resistant to innovation?

Vinod Khosla:

Well, there’s a complex set of reasons. First, we as human beings are comfortable with what we’ve done in the past.

Toby Cosgrove:

Absolutely.

Vinod Khosla:

Extrapolate the past in a new patient case. If it’s a radically different approach, then we tend not to like it, but as organizations, imagine if you could make healthcare twice as cost effective tomorrow. That’s half the revenue at the Cleveland Clinic tomorrow. I suspect you will have strong resistance. The American Medical Association today in most States, except for nine States, doesn’t allow RN, a registered nurse to prescribe a Z-Pak.

Toby Cosgrove:

Right.

Vinod Khosla:

Right? Because it takes away a primary care visit.

Toby Cosgrove:

Right.

Vinod Khosla:

The American Dental Association doesn’t allow a dental hygienist to provide preventive care to patients except under the supervision of a dentist in most States. So, at every level making healthcare more cost effective is a revenue reduction, it’s that revenue for a PCP if they can’t do, if you eliminate a face to face wizard, it’s institutions like the AMA, they all have resistance to change and they’re not looking at aggregate benefits to society and you can always story tell why their benefit … you can find an example why it would hurt one patient, and in that story you lose the fact that might save 10,000 lives this year.

Toby Cosgrove:

Do you think the selection and the training of physicians has anything to do with their reluctance to change, allergy to change?

Vinod Khosla:

Yes. There’s many, many characteristics and we’ve talked about this. You get through organic chemistry as you like to say by memorizing things, and then you get through med school by following rules and then get through early training by doing what the chief of staff tells you, to use your words. You’re taught to behave a certain way not explore and experiment, which I can see the reactions of people saying, “Well, that’s bad for patients.” Except every conceivable experiment you can think of is being tried by some physician somewhere.

Toby Cosgrove:

Okay. So, let’s realize that we are reluctant to change and innovation is not a gene that is dominant in most of us. What do we do about that? How do we begin to take what now is the 13 years from proven to standard of care and shorten it?

Vinod Khosla:

Yeah. Now this problem is not endemic to just healthcare. I looked across the last 40 years that I’ve dealt with technology and innovation and that’s all I’ve done in my life. I couldn’t think of, and this is surprising to people that challenge me. Any major example of large innovation coming from within the system in that area in 40 years and doesn’t matter if you look at biotechnology, no pharma company innovated, in fact, Bob Swanson was an associate at my previous firm, Kleiner Perkins, left to start Genentech and biotechnology sort of originated there. Space innovation came from SpaceX and Rocket Lab not from Airbus or Boeing or Lockheed. Media didn’t come from CBS or NBC or Fox, it came from Twitter and Facebook and YouTube. Retailing didn’t come from Walmart or Target, it came from Amazon.

I couldn’t think of one major innovation in any area, electric cars didn’t come from Volkswagen or General Motors, they came from Tesla and self driving cars came from Google and Waymo. So if no example exists in the last 40 years of a major innovation coming either from people who were within that industry or even had backgrounds, all these examples I cited of innovation, major innovation happen from people. Nobody from the taxi industry could have created Uber.

Toby Cosgrove:

What about Skunk Works?

Vinod Khosla:

Have they worked? 

Toby Cosgrove:

I don’t know. Have they? 

Vinod Khosla:

I don’t know of a single example of major innovation. Now, minor innovation, like going from one semiconductor process to the next generation, Intel can do pretty well. And the financial industry is interesting because they’re so profit motivated and transaction oriented, they’ve done things like collateral debt obligations, which are financial innovations. But I think you need somebody outside the system to rethink how something should be done.

Toby Cosgrove:

Okay. So let me ask the hard question. Should we forget our discussions with Microsoft, Google and Amazon and turn to the startups? Do they have a place in the healthcare universe?

Vinod Khosla:

So I should say innovation-

Toby Cosgrove:

You have to understand he’s a venture capitalist, so you know where the answer’s coming from.

Vinod Khosla:

Well, I’m just looking at the historical fact.

Toby Cosgrove:

Okay.

Vinod Khosla:

Right? Because I have spent a lot of time on this question of expert opinion, and for those of you who are interested, there’s a great book called Expert Political Judgment, makes the argument that no area, whether it’s political science or economics or radiology have experts generally done very well with their opinions. They can talk very well on stage and the better they are on stage, the worst they are in practice. He makes the argument, this is a guy called Philip Tetlock, who’s done 20 years of research. He’s a social scientist, first at UC Berkeley and I think now he’s at Yale or something, on expert opinion. That generally is very poor at predicting the future when there’s any change involved So yes, you have to look at startups, you have to look, all founder-driven companies. I mean, Waymo is from a large company, but it’s the vision of a single person. I can tell you Larry Page drove that one thing.

Toby Cosgrove:

And Sebastian Thrun.

Vinod Khosla:

And Sebastian, but Larry got Sebastian to drive it. So, founder-driven companies do much more innovation, whether they’re a startup or a larger company. If I were to say, how do you make Google Health much more effective? And I’ll get in trouble for saying this, you don’t hire Dave Feinberg from Partner’s Health to run it, right? It is likely to make it much more compliant with healthcare norms than to radically reinvent healthcare.

Toby Cosgrove:

Okay. I’m trying to avoid the hits on Google at the moment but-

Vinod Khosla:

I love Google. One of my favorite companies, very good friends with everybody there.

Toby Cosgrove:

So, let’s change the topic here a second, because there’s now in healthcare a tremendous discussion that is important around the social determinants of health. We know that your health is essentially 20% the medical care you get, 20% your genes and the rest of it is the social determinants. What is the potential for machine learning to begin to help with dealing with those social determinants?

Vinod Khosla:

Yeah. So what you’re really asking is how can you live five miles apart and have 10 year difference in life expectancy?

Toby Cosgrove:

Actually, here in Cleveland, in the city of Cleveland, there is a 23 year difference in life expectancy.

Vinod Khosla:

Okay. So, the first thing you have to say is, this is a very high dimensionality problem.

Toby Cosgrove:

Absolutely.

Vinod Khosla:

Is it the street you live on, and everybody’s attitude? Are there trees on that street or is it bars on windows because people are afraid, and if there’s bars on windows, is it the bars or the psychology of the person who has to feel they have to protect themselves in this high levels of chronic stress. It’s a very high dimensionality problem, and only machine learning with the right data collection can really say what are the social determinants of health. How do you force this complex problem and say, “What is it? Is it income levels? Is it your unwillingness to spend that extra $20 for a vaccination, flu vaccination, or is it something else? Are you avoiding visits because you’re afraid of copay and it’s an income problem? Is it the loving care of a family that Dean Ornish would argue for? Is it changing gene expression because your environment is more or less meditative as Deepak Chopra?”

Vinod Khosla:

This is all signs we’ve hand waved our way into saying, “Hey, food is the best medicine, but we don’t know what food does, parsing it into details. So, the FDA database on food has 9,000 foods characterized, if you look at FDA labels, about 150, 147 components to be precise. There’s a guy called Barabási doing network science at Northeastern. He’s found about 28,000 components of just the first 1,000 foods he’s analyzed. So we have a project with him called the Foodome to do a full network map of food, and interestingly people will love this. We can hand wave and say, processed food is bad, he can actually prove it. The ratio of zinc and potassium and other components of any food follows a curve. When you process it, certain components become outliers in this map.

So you can tell the degree of processing or ultra processing or not, of food, by doing a mass spec analysis of all 28,000 compounds. There’s 100s of versions of flavonoids or polyphenols, and they make a difference. Then you can turn this hand waving about nutrition of food into a science of food, and we have a project to do that and hopefully in two or three years we’ll have better results to show. And Greg talked about doing the same thing with your proteome.

Toby Cosgrove:

Yes.

Vinod Khosla:

And we’re also doing it with your metabolome and your lipidome and your methylome and your … I can go on.

Toby Cosgrove:

A lot of omes.

Vinod Khosla:

Lot of omes.

Toby Cosgrove:

So let me … I want to change the topic again and I’ve got two things more that I really want to ask you. First of all, how do you decide as a venture capitalist who you’re going to invest with? Is it the idea? Is it the people, is it the timing, what is it? Because I’m sure a lot of people here have gotten an idea that they’d like to get some capital from.

Vinod Khosla:

Well, first it’s highly uncertain and speculative. So, if I’m right 20% of the time I do really well. I’m very comfortable with the ambiguity of being wrong 80% of the time. Large institutions can’t do that, which is also a reason large institutions don’t innovate as much. There’s a 100 reasons something can fail, something can fail because the science failed, something can fail because its clinical implementation failed, something can fail because its organizational impact failed. 

Many, many years ago we did a procedure called heartport that we’ve talked about. Instead of spending weeks in the hospital for open heart surgery patient like a valve replacement, it was a less invasive approach that let the patient walk out in two days. It failed because it dramatically reduced the revenue for cardiac surgeons, that really was the reason, if you looked at why a Heartport adoption rate was so slow. That then becomes … I always look at, is it a large enough innovation to impact patient care? Personally I am a little bit different, I don’t start with CPT codes, I don’t look at CPT codes. I start with the level of change and impact on patient health.

Toby Cosgrove:

Okay, now wait a minute, level of change. So are you more after investing something that has a very high level of change or something that is going to be easier to change?

Vinod Khosla:

I much prefer very high levels of change. So, this is probably a good point to say, there are enough innovative healthcare organizations. So if it is incremental change, the organizational infrastructure of large organizations actually does better at incremental change than a startup does.

Toby Cosgrove:

Yes.

Vinod Khosla:

A startup has too many disadvantages, and so if it can be adapted as an assistant for radiologists to better read and reduce the error rate on radiology graphs, a perfectly reasonable thing for a larger organization to leverage some startup that’s doing the algorithms, incremental change like that is pretty easy. If you sort of say, and I’ll give you an example, we’re reducing a one hour cardiac MRI, which is not done in most MRI centers because it’s too complex, a 100 or 200 button presses on a windows like interface on an MRI machine. We’ve reduced it to one click and 15 minutes for much more data content than a standard cardiac MRI, and we don’t need cardiac MRI technicians. That kind of change is best done outside the system.

Vinod Khosla:

So, it depends if it’s large change, you’re better off starting and controlling the process and not requiring others to adapt it. And especially the AI driven changes where the AI is superior to human judgment, you can’t have human doctors say, “Oh, I trust this more than I trust myself, or that I’m willing to change my workflow to accommodate it.” So what do you do? You build it outside the system. And no question we can provide far better mental health care at $2 per patient or per member per month than Magma can at a much higher price point. When we do that, our peak load doesn’t start till after 8:00 PM and continues till 3:00 AM, mental health care when you need it, not when you can schedule an appointment with a psychiatrist in three weeks.

Vinod Khosla:

Same thing with cardiac care on a live call, you can do it and our average patient is taking more than 10 average ECGs a month. You can’t do that within a healthcare system. Not only that, we have their voice imprint so we know what their voice is saying, if they have a comorbidity for depression at that time or a manic depressive episode at the time, they are feeling something in their heart. Did they just finish exercising? Did they just eat a heavy meal? We have all that data, not when you can schedule an ECG in three weeks when your condition may or may not exist. So that kind of broad scale data collection is much more important to this new model, and I think it’s best done outside the system.

Toby Cosgrove:

Okay. So I want to give a little fodder to the group here. Tell us your favorite of your investments in healthcare, your favorite five.

Vinod Khosla:

My favorite five, like asking me to pick my children?

Toby Cosgrove:

Yes, I am.

Vinod Khosla:

I’ll think of the top of my head. The annual physical has been repeatedly, both in studies and meta studies proven to be totally worthless, expense with no benefit, maybe negative benefit. You can reinvent it, I’d recommend trying an annual physical called Q.Bio, full body MRI, which will soon be less than 10 minutes in the next year or two for a full body MRI with a lot more information. And they’re using old machines and new physics, which seems odd, but that’s what they’re doing. They can use a GE magnet, but do new physics with it and not be just restrained to T1 T2 pulses.

Toby Cosgrove:

Good. 

Vinod Khosla:

And computations-

Toby Cosgrove:

Do you want to tell us the name of the company?

Vinod Khosla:

It’s called Q.Bio.

Toby Cosgrove:

Q.Bio?

Vinod Khosla:

Yeah. DiscernDx can track your health care along the lines Craig was talking about 10,000 biomarkers for under a 100 bucks per dried blood spot, including shipping of kits, blood samples, everything. Right?

Toby Cosgrove:

Okay.

Vinod Khosla:

You can do that.

Toby Cosgrove:

What’s the name?

Vinod Khosla:

DiscernDX

Toby Cosgrove:

Okay, that’s two.

Vinod Khosla:

Okay. Microbots, about the width of your hair, 300 or 400 microns untethered that can travel anywhere in your body, magnetically guided from the outside. You can watch them move, so they could go up your spinal cord, deliver a drug at the nerve that’s causing you pain instead of washing your whole body with opioids or travel into your brain, pick a brain cancer sample, bring it back out and have no tether and very little damage because of that.

Toby Cosgrove:

And the name is?

Vinod Khosla:

The company … I don’t know if they have announced their name yet.

Toby Cosgrove:

Okay.

Vinod Khosla:

They’re still only in animals, not in humans yet.

Toby Cosgrove:

That’s very exciting.

Vinod Khosla:

All right?

Toby Cosgrove:

Okay, four.

Vinod Khosla:

Fourth. Both in ultrasound and I will cluster this as a category because I think this is important, we have replaced the technician for either cardiac ultrasound or other ultrasound or MRI machines. So, what do you do? You change the cost of an ultrasound by 80-90%. If you do that in a primary care visit, you should do an ultrasound, you shouldn’t be doing a physical exam, because many papers to establish much more data can be done in an ultrasound and, you can Google this, then a physical exam that a primary care physician can do with their hands.

Toby Cosgrove:

I’ve been telling cardiologists for years that the heart is a pump, not a music box, and get rid of that stethoscope.

Vinod Khosla:

Yeah. The last example I’ll give you…

Toby Cosgrove:

You didn’t give him the name.

Vinod Khosla:

Oh, HeartVista does cardiac MRI, self-guided, so autonomous cars instead of driving a car, you drive a machine, you don’t stop in between. You can cut the time from more than an hour for-

Toby Cosgrove:

So, what’s the name of the company?

Vinod Khosla:

It’s called HeartVista.

Toby Cosgrove:

Okay, great.

Vinod Khosla:

And the ultrasound machine company is called Caption Health. Last one is my personal favorite. I wrote about it eight years ago when I said Do We Need Doctors?, four years ago when I detailed the 100 page thesis on Transforming Medicine, and I got my son two years ago to start doing this, which is build AI-driven, free primary care for the world. The only way to do free primary care for all 7 billion people on this planet is through AI. So my son’s working on that, they’ve just announced in California. Unfortunately, when somebody needs a prescription, they have to have a California physician do the final prescription, but the cost per session will approach $3 or $4, and even in $3 or $4, the biggest component of cost by the way, is medical malpractice insurance per session. If you do it at $4, $2 goes to medical malpractice and $2 to everything else. So, I’ve given you five, I could probably give you a dozen more.

Toby Cosgrove:

All right, all right. So, you gave me the five, you didn’t tell us the name of your son’s company.

Vinod Khosla:

It’s called Curai, C-U-R-A-I.

Toby Cosgrove:

Good.

Vinod Khosla:

But look, primary care should be accessible 24/7, anywhere in the world at any time without needing to go to urgent care or an emergency department.

Toby Cosgrove:

At home.

Vinod Khosla:

At home, over text messaging because when it’s text messaging, telemedicine is really bad for AI learning. When it’s text messaging, you can structure the messaging to maximize the rate of learning for the AI. And what we found, and this is really interesting, literally there’s an exponential curve of the percentage of the text messaging dialogue that you can automate. So the way they do this, and this is the way to do it, the patient only talks to the AI, the AI only talks to the physician, and they have 50 physicians in India, and at the right time they can bounce to a position in California to do the prescription, but the physician only talks to the patient.

So every text message that AI generates, sort of your suggested reply in Google mail, the physician either forwards it to the patient, changes it and forwards it or discards it, whichever of those three it is, it’s a learning opportunity and the percentage that the physician forwards without change is measured every month to make sure they’re making progress. This is the way to build AI systems with humans in the loop training the AI and eventually it’ll get to 95% AI does and 5% humans do.

Toby Cosgrove:

Vinod, I know that you and I can carry on this conversation for the next two hours and I’m sure that the audience would probably be champing for more. I can’t thank you enough. You have stimulated all of us enormously and I enjoyed very much sharing your thoughts with us.

Vinod Khosla:

Well, it’s great to be here and talking to an audience.

Toby Cosgrove:

Thank you very much. Thank you sir.

Vinod Khosla:

Thanks.

 

The post MIS2019: Fireside Chat with Vinod Khosla & Dr. Toby Cosgrove appeared first on Khosla Ventures.

Vinod Khosla + Lisa Weiner Intrator on Deep Tech Health | ApplySci Silicon Valley


This post is by Micah Kasman from Khosla Ventures

Video

Vinod Khosla + Lisa Weiner Intrator on Deep Tech Health

Vinod Khosla:

Hi everybody.

Lisa Weiner:

Hi everybody. Vinod is … Actually I’ve been talking about you. I talked about you in my opening remarks. You weren’t here this morning, but I noted that eight years ago you said that AI was going to dominate medicine. And it was controversial then and now it was … It was visionary actually. So, first of all-

Vinod Khosla:

Or a lucky guess.

Lisa Weiner:

So, Vinod is very modest, obviously. And very visionary. And very open. He likes to structure this talk by opening it to questions from the beginning. I would like to start actually talking about AI and maybe talking about some of the things that Nathan and Nikail brought up. Then we’re really open. So, just feel free to jump in and ask questions. Is that okay with you?

Vinod Khosla:

Sure.

Lisa Weiner:

First of all, thank you for being here. It’s the fourth time that you’ve done this. 

Vinod Khosla:

It’s a good audience.

Lisa Weiner:

It seems from some of the talks today that there appear to be two approaches to AI and healthcare. One, as Nathan said and as Nikail said, is taking kind of off the shelf AI and applying it to massive data sets. For example, what Amit Etkin published yesterday about depression and biomarkers.

Another is what Mary Lou Jepsen, who has been at this conference several times is doing.

Vinod Khosla:

We are investors in Mary Lou’s

Lisa Weiner:

I know. Yes, that’s great. She should be here today, I don’t know where she is. But taking novel miniaturized sensors and doing things that are maybe higher risk, but maybe much higher payoff in changing the world. What do you see as having the greatest impact on society over the next five years?

Vinod Khosla:

Oddly, you mentioned eight years ago it was highly controversial.

Lisa Weiner:

Say something controversial now.

Vinod Khosla:

I’m sure during the course of this conversation I’ll say something controversial. So, I’m never lacking for that. But if you look at it logically, wherever there’s lots of data, AI plays a large role. Sensors of course playing to generating lots of data. It doesn’t matter what the data source is. If you’re collecting a lot more brain data, then AI has a lot more to work with and you’re going to see results that are far beyond what any of us expect.

One of the odd things about AI we’ve discovered is more than almost anybody expected even two years ago, the scale of the compute … Which one would expect scaled sub linearly … Is superlinear. It means the larger a network gets, your compute network gets, the more data, more insight you get out of the AI data. Which is surprising to most people.

So, data and sensors are key to this. It doesn’t matter what area of AI we are talking about. Eight years ago, everything was controversial. Today almost nobody expects humans to do better than AI in any image based sensory

Lisa Weiner:

Not almost nobody, nobody.

Vinod Khosla:

Yeah. In fact the editor of the Medical Association Journal, I think it was the JAMA Journal said last year that they’re no longer accepting papers proving AI is better than humans at any kind of image analysis. Just, period. Which is pretty stunning compared to where people were eight years ago.

Lisa Weiner:

And actually stunning compared to where you and I had this conversation four years ago. People in the audience were still like, “Oh, well…”

Vinod Khosla:

Yes. People were very skeptical. I actually recently looked at a 100 document PDF I’d done about five years ago. It didn’t need that much reworking. So, I didn’t rework it. I left it as is, which is pretty telling. I think it’s pretty clear on some axes I’ve been more conservative.

But take an area like drug discovery. Not an area you’d expect. If you sort of take 400 million compounds  participle and say I want to in high-throughput screening screen for four million of them, some expert selects there. It’s very, very clear now for anybody working in that field, if you have the right data, then you can do easily three orders of magnitude. I just saw a project that screened 11 billion molecules. Many of them not even synthesizable for effectiveness on a particular receptor.

Whether it’s imaging or that, it’s very clear that’s happening. Let’s look at the other areas. If we have better EEGA data, or FMRI data, highly complex data, or most activity data, of your variable sensors, the Apple Watch, you’re going to get a lot more insight out of it.

Take the coronavirus. How would you monitor that? I suspect … This is just speculation, not any proof … That when you get an infection you don’t know, feel it for 14 years, but your body starts responding. If we have enough sensor data, whether it’s your Apple Watch data or other biomarkers that we’ve heard about at this conference, almost certainly it’s going to be the only way to detect two weeks in advance … Or in the case of Lou, two days in advance … That your body is starting to respond and starting to fight that infection or not.

Long before you feel it. That’s a data problem, a sensor problem and an AI problem. One of the largest drugs in the world is NTTNF inhibitors like Humira. Fifteen billion dollars. For those of you who don’t know the drug, only about half of the patients respond to the drug. The drug costs about $50,000 a year in the U.S. per patient.

It would be extremely valuable if you could tell who is going to respond to this or not. A guy who would best be described as a mathematician, Barabasi for those of you who know him at Northeastern, did a network theory based analysis. He has no background in biology. And can predict with something like 90% sensitivity and specificity based on network theory, which is lots of biomarker data, that we can predict who will respond to this very expensive drug.

That, for Humira, it will cut out half their sales. Great for insurers. It will save them a lot of money. And great for patients because they won’t wait a year to find out if it works or not. Those are examples.

That leads me to one other thing which is important to all of this audience. One of the fundamental things we have to discard in medicine is the notion that humans have to be able to look at the data we collect.

In 128 channels of EED you’re going to see stuff that humans can’t make sense of but is not hard for an AI to. If you look at a company like SomaLogic, they measure 5,000 proteins nearby. No human physician could look at that body. But it’s not surprising if there’s a few thousand metabolic pathways in the body. This complex of data represents how the system is behaving. And that’s what we want to know.

Almost certainly if you’re going to get Alzheimer’s, your system is starting to change 20 years in advance with that. Same is true of cardiac disease. So, we need to go to data driven medicine and collect data, not twice as much data. But a thousand times more data. I have a funny joke. Around the time I was talking about data in medicine, Stanford was planning still to build their new hospital.

The CEO brought the design team from IDEO over to my office and said, “What should we be thinking about in designing the new hospital?” It was a multi-billion dollar project, they were just looking for opinions. I looked at what they were doing and I said, “You’re off by a factor of a thousand in how much data you think you’ll have per patient.” I’ll tell you, recently I was talking to the same person, who’s no longer at Stanford. And I said, “I was wrong by a factor of a thousand in the wrong direction.” That’s the scale of these changes that are going on. And the opportunity in front of us.

Lisa Weiner:

So, what is going to be the role of doctors and hospitals? You know, we’re saying AI can read scans and sensors can detect Alzheimer’s disease so early. What are doctors going to do? We talk about the human aspect of medicine a lot, but-

Vinod Khosla:

First you say, “What’s the right treatment for the patient?” That’s where you have to start. Our job is to quite provide great care for patients, not to provide great employment for doctors. As our first task. There’s a secondary task we can talk about. So, if we have a company that, they can do thousands of biomarkers, let me say. And tens of thousands of unidentified but constant biomarkers. That means biomarkers you can’t name, they’d be just features in a chart.

For under a hundred dollars, for less than the cost of taking your normal blood test at Quest for example. That’s incredibly valuable in categorizing disease. And Freenome just published a study saying that pretty good sensitivity and specificity in detecting colon cancer using very large scale biomarkers.

I think we have to realize that’s how disease should be diagnosed. We have a company called Informatics that looks at a pattern of gene expression to detect sepsis well ahead of any other way to detect sepsis. If you’re late by a day detecting it, your mortality rate goes through the roof. So, what should we be doing? Caring about patients.

Having said that, at least for the foreseeable future, there’s a human element of care. I think doctors will do that. Now, it leads to the question, do you select the highest IQ doctors? I guarantee you the people who get into Stanford Medical School or Harvard Medical School are the highest IQ, but not the highest EQ.

If I’m looking for the human element of care, which is very important … And I actually think nurses do better than doctors. So, who’s best equipped to provide the human element of medical care? It is a role for humans, for sure. For the foreseeable future.

Lisa Weiner:

I can ask questions all day. Are there any questions from the audience before I continue? I see a hand, a few.

Audience Member:

Hi. I’m curious about your thoughts of the future of mental healthcare. So, in psychiatric illnesses there’s enormous heterogeneity in the manifestation of illness, enormous societal costs. Unlikely to have sensors that consistently cross cutting populations will detect illness, and so, what’s the vision-

Vinod Khosla:

Did you say unlikely?

Audience Member:

For sensors, and so if you … Well, this is debatable, but for illnesses that are at baseline ill defined and probably don’t fall neatly into categories, how can these approaches be applied well to reconfiguration of mental health care?

Vinod Khosla:

The first thing to understand is we know very little about the brain. If you read the DSM manual … And I remember when DSM 5 came out, Scientific American actually did an assessment of the DSM manual. And the exact quote I remember is looking at two diseases, I think it was bipolar and manic depressions. The DSM 5 said something like kappas of 0.2 are acceptable. Scientific American called them two pathetic kappas. Scientific American is not the National Enquirer on these topics.

My point is we don’t have data to know what diagnosis … The Diagnosis you get is the psychiatrist you get, not what the disease you have. Mostly, in mental health.

Lisa Weiner:

And it’s a psychiatrist who sees you once a week or a month.

Vinod Khosla:

Yeah.

Lisa Weiner:

Or one time.

Vinod Khosla:

Yeah. We don’t even know the very basics of … SSRI is a solution for almost everything. But we don’t know whether … And if you look at the new theories around inflammation in the brain, that’s a very different set of causal conditions than serotonin deficiency or something. Or anxiety as a GABA glutamate imbalance. If you can’t measure GABA glutamate, how do you know whether there’s an imbalance or not. And do you give the same drug?

So, my point is the following. We have nowhere near the right amount of data. Whether it is about brain activity, EEGs, FMRIs, and I think we need not 10 times more, not 100 times more, we need 1,000 or a million times more. There’s a large opportunity in working on those sensors. So, we can understand the brain, which is hopefully the most important part of the body. Without that, we’re not going to have good solutions. And today, with psychiatry, psychology, other areas, we do the best we can with relatively imperfect tools.

It’s not the psychiatrist’s fault. Their set of tools is fairly limited. A paper and pencil questionnaire for a PHQ-9 square is sort of pathetic. We’re starting to do much better. You heard about EEG. Voice as a biomarker in mental health is becoming much more common. One can now prove that one can get a relatively good PHQ score from a 30 second speech sample. Things like that will increase the amount of data, don’t just think very traditional data.

I think we’ll do much better at diagnosis. But I don’t think we improve diagnosis without first improving measurement. We have a company called Neurotrack that actually looks at your eye movement when playing a game. It turns out Alzheimer’s brains respond to novelty differently and reflected in the eye motion than normal brains. So, by watching somebody’s eye movement during a test you can actually predict the level of severity of Alzheimer’s, or even if they have it.

Should need measurement, I would say, in general. In many, many dimensions, whether it’s speech, FMRI, EEG, MEG, I can go on. But way more. I’m encouraged, I think we’ll sit here five years from now saying we have way more data than we imagined five years ago.

Lisa Weiner:

Are you committing to sitting with me five years from now?

Vinod Khosla:

We’ll see.

Lisa Weiner:

We’ll see. And I’m also encouraged by companies like Mindstrong Health who are using the phone to capture mental health data. And tomorrow we have a talk by Dror from the University-

Speaker 4:

That’s me.

Lisa Weiner:

Oh hi, Dror.

Vinod Khosla:

What’s their talk about?

Lisa Weiner:

So, technology based mental health. When Dror said to me, “Why do you want me to speak at your conference?” And one of the things that really stood out to me was using the phone to discreetly treat mental health. For example, as he does it in the West Bank where there’s a population of PTSD and in Ghana. Sorry I didn’t recognize you.

Vinod Khosla:

We don’t even know whether PTSD is a mental health condition or not. There’s a research project that I’ve funded to see if it’s a cell danger response condition. And that the mental health piece is a consequence of cell danger response to extreme stress. In some situations the cell danger response lasts for years. I don’t know. So we can’t answer these, we can have opinions, but opinions don’t matter. We need to get the data to prove it out one way or another.

I am pretty encouraged that we are paying attention to it. In mental health I just read a paper speculating that there’s not one … As you know, the efforts at identifying genes for particular conditions has been relatively unsuccessful. There is no gene you’d say contributes more than one or 2% to a particular condition like ADHD or depression.

But the research seems to be indicating that a pattern of genes, hundreds of genes, each contributing in some way to … Propensity to have mental health issues is likely what the genetic basis is. That means your brain’s preconditioned. If one sibling has a depression, the other sibling is more likely to have other conditions we think are unrelated to depression.

That’s pretty interesting data. That, together with some nonlinear dynamical models, for those of you are engineers of brain activity, and patterns are firing. I suspect we know the direction to look in, not what the answer is yet. Sorry for a long answer.

Lisa Weiner:

There was another question…

Audience Member 2:

Hi, coming back to the original question about the future of AI in medicine. I know we talked a little bit about this today, previously. It seems like most of the efforts are on data collection, sensor data collection for prediction of disorders. You also mentioned early detection as important … My question is, how do you see AI for treatment optimization?

Audience Member 2:

Obviously I’m asking this because you know I’m involved in that area, or in the direction. Our startup made a switch. And focusing rather on data collection for diagnostics rather than … We have a wearable therapeutic that actually delivers neuromodulation. But we’re looking at AI for treatment optimization. Especially with the era of home care therapy where we will be collecting a lot of data. We can get feedback on outcomes.

Vinod Khosla:

Well, there’s two ways. You can do a blind search or we can do a directed search. And we are sort of doing blind search because we have no ways to do directed search. I’ll explain what I mean by that, of what the solution is. Whether it’s VR therapy, or simple things like exercise helps with depression or exercise helps slow progression of Alzheimer’s, we don’t know the answer to that question.

Why? Because we can’t quantify the degree of Alzheimer’s, the degree of depression. Without that measurement system, it’s a blind search. We try 16 things and intuitively some things work. If we can measure we can close the loop. Is there a certain pattern of VR activity that reinforces certain brain circuits that then leads to certain consequences?

Unless we have those measurement systems, I don’t think we’ll make progress rapidly. Directed search with feedback loops or closed loops is what we need for rapid progress and we’re not there yet.

Audience Member 3:

I’ll do a followup to that question. How do you see the path of all these AI devices like Neurotrack or VR devices to get into clinical practice? So, I mean, I’m a practicing clinical neurologist at Stanford. Most commonly, like, Neurotrack. They came to me. Or other companies like VR company came to me and they’re like, “Can you implement this?”

So, where do you see the clinicians going to their administrator and asking, “I need this.” And what basis do you think it will happen?

Vinod Khosla:

That’s a very important question. And the answer is highly variable. But let me define two really different paths. One is what you’d expect. Prove clinical efficacy. Do the trials, randomized controlled trials. Then you have something you can, I can come to you and say, “Here’s the results.” Then there’s 10,000 of you in the U.S. or 100,000. It’s spread slowly.

If I have a great breakthrough today, it’s 10 to 15 years before 80% of the physicians accept it as practice, probably five to seven years before 5%. The penetration rate, even after you prove efficacy, is very slow. What I’m encouraged by is there are alternative paths that don’t deal with the healthcare system to do this.

Take Ginger.io. Mental health was a question. They go to employers and say, “Offer this as a benefit,” and you know within six months, whether your employees love it and value it as a benefit. You can measure the level of participation in this benefit and it doesn’t matter what the degree of clinical validation is. You have a different validation.

Do your employees love it? Of course you don’t want to do harm. And you have to do internal tracking and studies. If you increase the number of suicides after you offer the service, that’s a problem. I think some of these companies, whether Livongo is doing diabetes, or Hello Heart is doing hypertension, or Ginger.io is doing mental health, we’ve seen companies in almost all these specialty areas.

We’re in a great company in physical therapy post surgery. Complete AI driven physical therapy. Monitored by a physical therapist, but you use one fifth the number of physical therapists. Guess what happens? People can do their own appointment at 8:00 at night. Of their 1,000 patients who were active last Christmas, shockingly 71% of their patients did physical therapy on Christmas Day. Because even if every physical therapist was open, you’d never see that kind of compliance.

Of course they’re not open. And they’re not open after 5:00. I can go on. They get average session times are between 5% to 7% or seven times a week of physical therapy, depending upon the population. These are normal employees of companies that offered this as an alternative to physical therapy. I think making it better, trying things and then iteratively, with appropriate safety guides, trying these things and improving them.

If you’re going to improve physical therapy through the regular means, it’s going to take you forever. Not only is the AI doing physical therapy, it’s collecting an incredible amount of data and outcomes. Because they measure outcomes. For physical therapy there’s very clear outcomes. What angle does your knee bend to? And when you have quantitative measures it becomes really easy.

In mental health there is a PHQ9 score, poor as it may be. But it’s accepted as the standard of care. You can take these, in hypertension it’s BP reduction. Behavioral health we are finding you can do 20 millimeters of BP reduction through digital therapy only.

Across all these, if you can measure, which is why I was focusing so much on measurement, you can actually develop these much faster ways to evolve these therapies then going through trying to convince Humana in that five year pilot that this is going to work.

Both are valuable. And sometimes, if you’re doing heart surgery, you can’t iteratively try it. There are situations. But in many cases you can. These alternative health plans, especially direct to employer, are creating channels with clear measurement and efficacy measurements. And customer satisfaction. Those are really promising channels

The other is the uninsured population, or people who have no access to care. Any care with appropriate, again, safety and ethical guidelines helps you develop those. I grew up in India for the first 20 years of my life. I’d never even heard of a psychiatrist. I never met anybody who’d ever seen a psychiatrist.

Lisa Weiner:

Or at least told you they did.

Vinod Khosla:

But you never heard of anybody being a psychiatrist. I used to know the number of psychiatrists in the country for a billion people. It was ridiculously low. There are environments in which the next best alternative, poor or rich, is something that’s better than nothing. Michael Bloomberg is very interesting. He’s running for president now.

One of the projects he was doing is teaching high school graduates in Tanzania to do C sections on women. Because if you needed a C section, you had a death sentence in almost all cases. If you look at the number of physicians who could do C sections versus the population, the ratio was off more than 10 to 1. Probably 100 to 1 off of what it is in the U.S. So, what is better, death or somebody who’s watched 100 such operations do an operation?

It’s not perfect. But there are ways. So, there’s ways to be opportunistic. And entrepreneurs are very good at hacking the system to find ways to test things rapidly. Fundamentally I believe for rapid progress we need rapid learning loops. Which means you have to do things, learn things and do them again a little bit better. Once you get on this exponential learning curve, things move much faster. But through the traditional healthcare system, it’d be very, very hard.

Audience Member 4:

Quick question and fairly general. Speaking more of neuro technology or ancient neuro technology, where do psychedelics play a role in this space?

Vinod Khosla:

Ooh. How many people have read the book How to Change Your Mind? Very few. Everybody should read it. As someone who figured I’d never try psychedelics, I actually thought the best thing to do was, next best thing is read this book.

Clearly, because the area has been taboo for reputable research, we’ve not understood it as well as we should. I think we’re starting to see a change in whether it’s psychedelics, or micro dosing or psychedelics, to study the phenomenon. So, could I tell you anything? I don’t have any opinions. I do think fringe areas are always worth studying if there’s a scientific approach that’s possible. I think we’re getting to the point where a scientific approach to that is possible.

Lisa Weiner:

We had this conference at Harvard Medical School a few months ago. Brad Ringeisan, who is the new head of the DARPA Biological Technologies office gave his first public speech after being appointed. He acknowledged that while the U.S. Army cannot ever support psychedelics in its treatment of PTSD, he seemed to acknowledge that there is some good science there.

Vinod Khosla:

Even five years ago, I do not think anybody reputable would say it’s worth studying. And that’s a shame. It’s a little bit like cold fusion, too. It’s just taboo to study it. Which is a shame. And most good progress happens at the edges of the system in unconventional ways. And this is the key. If there’s a reasonable scientific methodology applicable to the area, we should study.

Audience Member 5:

I have a question which is probably not as exciting because it’s not about a technology but more about an infrastructure, regulatory questions. So, it’s highly sensitive data. But how would the infrastructure need to be so that it leads to the biggest benefit to the patient. That is also because if you have only one company with that, for example, measure biomarker A. Another company measuring biomarker B and so on.

How do you combine these? Because, like, at these intersections where you have access to multiple biomarkers you probably get the best results. How do you put an infrastructure in place…

Vinod Khosla:

This is a much simpler problem than people make it out to be. How many people have heard of PicnicHealth? One person. A couple of people. Not one of our companies. But what they do … And it’s very cost effective … I have most of my medical data at Stanford. I’ve seen some UCSF people. When I had my skiing accident it said, “In the mountain in Utah,” they’ll collect all this data and put it into a form that’s actually usable. Not just PDFs.

So they can know what my BP and my blood glucose was 10 years ago and see it as a graph. There’s simple solutions. They don’t solve the problem of … I don’t know how many people heard, the Chinese Army was behind the big Equifax hack. When the credit card data for so many people got exposed. I think fundamentally most systems are hackable. So, we have to worry. When you put data together like Picnic Health does … And there’s a couple of companies doing that kind of thing.

And good efforts there are needed. But there’s a much, much better solution. How many people have heard of a company called Nebula Genomics? Two hands. Three hands. They mostly deal with genomic data. They’ll put your genome in the block chain. They can’t access it, but the consumer can. Consumer can then, through this system, permission them to sell it to Pfizer or whoever wants their data with the consumer’s permission.

I think that is ultimately the only real solution to data security. Put it in some form of blockchain. It doesn’t have to be the Bitcoin blockchain. Make it only permissible by the end user. It’s also a great point of integration. Because data has to come together from every medical or nonmedical … wearable, Apple Watch, health kit, source you have data in. And put it some place where the user has control over it.

Hacking, let me just say, is extremely difficult. Let’s say they hack 300 million peoples’ worth of data in a record like this. If I’m a hacker, I could hack it and get a lot of data. What’s it worth? A billion dollars? Billions of dollars? If I could hack the blockchain I could steal 100 billion dollars like that. Which one would I rather steal? The bigger prize, all of Bitcoin? Or your health data? I’d directly take cash.

So, you have this insurance, like, if somebody hacks the system, there are much better things to steal than your personal data. But it is so far proven unhackable. With relative certainty it’s very, very hard to hack today. We know that quantum key distribution, other technologies are coming along to make it even more secure. There is a solution to that, just no health system wants to give their data to the patient and for the patient to be the integration point for it and put it in a place outside the reach of the health system in a secure blockchain.

It’s one of the few places the blockchain is extremely valuable because it’s distributed to us as opposed to trusting some party, whether it’s your bank, or your healthcare provider or somebody else.

Lisa Weiner:

Vinod, we’re out of time.

Vinod Khosla:

Great. I talk a lot.

Lisa Weiner:

I really want to thank you. Because this is the fourth time you’ve been here and this is really a lot of fun for me.

Vinod Khosla:

It’s always fun to talk to an audience like this. I’m a techy nerd, so I love talking to technical people mostly. Hopefully no financial people here. But there’s a lot of room for more data, more sensors and what we can do and the insight we can derive from data. Thank you all very much.

The post Vinod Khosla + Lisa Weiner Intrator on Deep Tech Health | ApplySci Silicon Valley appeared first on Khosla Ventures.

Anxious About What’s Next? Here’s How to Cope.


This post is by Rasmus Hougaard from HBR.org

Three tips for rewiring your brain.

Reframe How You Think About Self-Care


This post is by Liane Davey from HBR.org

It’s about resilience, not indulgence.

Reframe How You Think About Self-Care


This post is by Liane Davey from HBR.org

It’s about resilience, not indulgence.

Withings raises $60 million to bridge the gap between consumer tech and healthcare providers


This post is by Darrell Etherington from Fundings & Exits – TechCrunch

Since being re-acquired from Nokia in 2018 by a group including its original founders and some of its original investors, health tech company Withings has been focused on evolving their offering of consumer health hardware to provide medical-grade data that can be shared with, and leveraged by healthcare professionals to deliver better, more personalized care. The company has now raised another $60 million to continue pursuing that goal, a Series B funding round co-led by Glide Healthcare, along with existing Withings investors IDinvest Partners, Bpfrance and BNP Paribas Développement, ODDO BHF and Adelle Capital.

Withings will use the funds to ramp up its MED PRO division, a part of the business formed last year that focuses on the company’s B2B efforts, placing its medical-grade consumer health devices in programs and deployments managed by medical professionals, health institutions, insurance payers, researchers and more.

In an interview, Withings CEO Mathieu Letombe explained that following the re-acquisition of the company, the team set out to “pivot slightly” in regards: First, the company would only focus on medical grade products and services from here on out, something that Letombe said was done at least in part because of how crowded the general ‘wellness’ tech category has become, and in part because players like Apple had really, in their view, made the most of that category with their Apple Watch and other health features.

The second was to shift on their business side to better address the B2B market – primarily due to inbound requests to do so.

“We were getting a lot of requests from the healthcare industry,” Letombe told me. “And by the healthcare industry I mean major healthcare programs, like the diabetes prevention program, the hypertension program. Also hospitals, insurers and Pharma, so we decided to dig into it and we saw the there was a huge demand for medical connected devices from this world.”

According to Letombe, Withings was well-positioned to address this need, and had an advantage over other traditional medical device suppliers for enterprise and industry. The company’s DNA was in building accurate, user-friendly devices to help them keep an eye on their wellbeing at home, and so they put their focus on evolving those products so that the results they provide pass the standards of governing medical device regulatory bodies around the world.

Withings’ special advantage in this pursuit was that it knew very well how to build products that customers want to use, and have opted to pay out of pocket for in the past. Most medical equipment for at-home monitoring that comes from a payer or a healthcare institution hasn’t had to face the challenges and focusing rigor of the consumer technology market, and it’s foisted upon users, not selected by them from a field of choices. Letombe says that this consumer edge is what has helped Withings with its B2B business, and notes that both sides of the market will continue to be of equal importance to the company going forward.

The company had been turning its attention to building out a suite of products, from smart blood pressure monitors, to scales that measure body fat percentage, to contactless thermometers and much more, long before there was any hint of the current COVID-19 pandemic, obviously. But that demand from the healthcare industry has stepped up considerably in the wake of the coronavirus, which has accelerated plans from insurers, care providers and healthcare pros to develop and deploy remote care capabilities and services.

“We also got a ton of requests from a company that wanted to create back-to-work packages, were there was a thermometer or a scale or blood pressure monitor for them to help the employee understand if they are at risk for COVID,” Letombe said, noting that the B2B opportunities the company has seen extend beyond the healthcare industry itself.

Image Credits: Withings

To assist with its new medical B2B focus, Withings has also formed a Medical Advisory Board, which Letombe says they’ve actually been working with for a year but that they’re only announcing publicly alongside this funding. The board includes Mayo Clinic Platform President Dr. John Halamka, former head of Clinical Pharmacology in Hôpital Européen Georges Pompidou Dr. Stéphane Laurent, and former head of Clinical Innovation at Pfizer Craig Lipset – top medical professionals across respected institutions and one of the largest therapeutics companies in the world.

Letombe notes that Withings also has a number of medical physicians and professionals on staff, as well as a psychologist and a physicist, and so they’re involved in building the products themselves throughout their design and creation, rather than just validating their results after the fact.

Withings would seem to be in a great position to address not only the growing need for connected medical monitoring tools, but also to understand exactly what makes those products work for consumers, and become something they actively want to use as part of their lifestyle. This new $60 million round is a vote of confidence in that strategy, and in its ability to become something bigger and still more ambitious.

Withings raises $60 million to bridge the gap between consumer tech and healthcare providers


This post is by Darrell Etherington from Fundings & Exits – TechCrunch

Since being re-acquired from Nokia in 2018 by a group including its original founders and some of its original investors, health tech company Withings has been focused on evolving their offering of consumer health hardware to provide medical-grade data that can be shared with, and leveraged by, healthcare professionals to deliver better, more personalized care. The company has now raised another $60 million to continue pursuing that goal, a Series B funding round co-led by Glide Healthcare, along with existing Withings investors IDinvest Partners, Bpfrance and BNP Paribas Développement, ODDO BHF and Adelie Capital.

Withings will use the funds to ramp up its MED PRO division, a part of the business formed last year that focuses on the company’s B2B efforts, placing its medical-grade consumer health devices in programs and deployments managed by medical professionals, health institutions, insurance payers, researchers and more.

In an interview, Withings CEO Mathieu Letombe explained that following the re-acquisition of the company, the team set out to “pivot slightly” in regards: First, the company would only focus on medical-grade products and services from here on out, something that Letombe said was done at least in part because of how crowded the general “wellness” tech category has become, and in part because players like Apple had really, in their view, made the most of that category with their Apple Watch and other health features.

The second was to shift on their business side to better address the B2B market — primarily due to inbound requests to do so.

“We were getting a lot of requests from the healthcare industry,” Letombe told me. “And by the healthcare industry I mean major healthcare programs, like the diabetes prevention program, the hypertension program. Also hospitals, insurers and pharma, so we decided to dig into it and we saw the there was a huge demand for medical connected devices from this world.”

According to Letombe, Withings was well-positioned to address this need, and had an advantage over other traditional medical device suppliers for enterprise and industry. The company’s DNA was in building accurate, user-friendly devices to help them keep an eye on their well-being at home, and so they put their focus on evolving those products so that the results they provide pass the standards of governing medical device regulatory bodies around the world.

Withings’ special advantage in this pursuit was that it knew very well how to build products that customers want to use, and have opted to pay out of pocket for in the past. Most medical equipment for at-home monitoring that comes from a payer or a healthcare institution hasn’t had to face the challenges and focusing rigor of the consumer technology market, and it’s foisted upon users, not selected by them from a field of choices. Letombe says that this consumer edge is what has helped Withings with its B2B business, and notes that both sides of the market will continue to be of equal importance to the company going forward.

The company had been turning its attention to building out a suite of products, from smart blood pressure monitors, to scales that measure body fat percentage, to contactless thermometers and much more, long before there was any hint of the current COVID-19 pandemic, obviously. But that demand from the healthcare industry has stepped up considerably in the wake of the coronavirus, which has accelerated plans from insurers, care providers and healthcare pros to develop and deploy remote care capabilities and services.

“We also got a ton of requests from a company that wanted to create back-to-work packages, where there was a thermometer or a scale or blood pressure monitor for them to help the employee understand if they are at risk for COVID,” Letombe said, noting that the B2B opportunities the company has seen extend beyond the healthcare industry itself.

Image Credits: Withings

To assist with its new medical B2B focus, Withings has also formed a Medical Advisory Board, which Letombe says they’ve actually been working with for a year but that they’re only announcing publicly alongside this funding. The board includes Mayo Clinic Platform President, Dr. John Halamka; former head of Clinical Pharmacology in Hôpital Européen, Georges Pompidou; Dr. Stéphane Laurent; and former head of Clinical Innovation at Pfizer Craig Lipset — top medical professionals across respected institutions and one of the largest therapeutics companies in the world.

Letombe notes that Withings also has a number of medical physicians and professionals on staff, as well as a psychologist and a physicist, and so they’re involved in building the products themselves throughout their design and creation, rather than just validating their results after the fact.

Withings would seem to be in a great position to address not only the growing need for connected medical monitoring tools, but also to understand exactly what makes those products work for consumers, and become something they actively want to use as part of their lifestyle. This new $60 million round is a vote of confidence in that strategy, and in its ability to become something bigger and still more ambitious.

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