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June 25, 2019
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Consumer-focused healthcare can save lives by focusing on changing behavior

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Everything we do in the $3 trillion healthcare market today only affects 10% of outcomes to premature death.

You read that right. All of that, for just 10% of outcomes:

That 10% exists for a reason. Genetic predisposition is hard to change. So, unfortunately, are social circumstances and environmental behavior. But that 40% of behavioral patterns — why can’t we tackle that? This is what real prevention would look like: nothing comes even close to mattering as much towards whether you will die prematurely as your behavior does.

We can do better than simply focusing on that small 10% slice of the pie; in fact, we’re looking in the wrong place. Doctors, entrepreneurs and founders need to be thinking (and treating with) lifestyle as medicine. Because behavioral change is the best and most powerful way to impact that whopping 40% slice.

Too often we think of this as the “just eat right and exercise” problem. As we know very well, that platitude will not solve our healthcare problem. The true problem is the difficulty of modifying behavior. We know this, because the platitude doesn’t work. We like to eat what we want, to exercise or not exercise if we choose. In short, humans like our patterns. They’re hard to change.

Tech, on the other hand, modifies behavior very well. Just look at the phone you’re probably reading this on, which has foundationally changed the way we communicate — along with huge other swaths of human behavior, in both positive and negative ways — from the ability to call a ride service in practically any city at any time to tracking your health to screen addiction. We know technology modifies behavior; we live this every day. So the question is, how can we target this superpower ability of tech to have 4x the ability to impact that the $3 trillion healthcare budget does?

How does it work?

Let’s think about why technology actually does work for modifying behavior. For one, it’s always there, thanks to the leap in mobile tech, whether that be phones or fitness trackers. Second, technology’s ability to do constant A/B testing essentially enables RCTs, or Randomized Clinical Trials, every moment that technology is present and being used. These RCTs are invaluable laboratories for learning about what is effective therapeutic behavior modification, or improving efficacy — and it’s not toxic. Most medical products are released and then rarely get updated (think about how old the stethoscope is!). Rolling out new versions of products has been difficult and expensive. But that no longer has to be true. The same kind of A/B testing that Amazon does, for example, to optimize ecommerce — everything from the look of the website to the flow of the experience to the nature of the shipping that you get — can be now applied to behavior modification for health. Comparing the immediate efficacy of two algorithms for lifestyle behavior modification on two different populations can happen not just over years or months — as a RCT would have to be — but over weeks and even days, improving our responses and lifestyles that much faster.

Second, applying Machine Learning to vast amounts of new data is identifying all kinds of nuances of human behavior that we aren’t nearly as good, as humans, at noticing. For example, correlating patterns with data like where you shop, when you eat lunch, what activities do you do, what shows you watch, what your exercise routine has been, how much you sleep, even perhaps whether you remember to charge your phone. Identifying the clues in our behavior that eventually add up to significant lifestyle risk is the first step towards changing and improving that behavior. Like it or not, we live our lifestyles now through our phones — ML allows us to learn from it.

And last, technology allows us to scale existing therapies in new orders of magnitude.  Programs which have proven extremely effective at behavior modification through personal interaction — such as Diabetes Prevention Program for Type 2 Diabetes — have been by definition hard to scale; computation can extend their reach into the billions. Or take for another example depression, a complex disease where the molecules involved are poorly understood: drug therapies have been challenging, but therapy, specifically CBT, has a very strong track record, and computational CBT — ie, CBT scaled with technology — the strongest.

Even conditions as mysterious and difficult as cognitive decline can be treated much more effectively with technology. This is another fascinating example where the biology is so complex at the molecular level that breakthroughs have been far and few between. On the other hand, cognitive is painfully clear at the behavior level. And it is also very clear that behavioral treatment in the form of cognitive stimulation helps significantly. In this study, for example, the auditory memory and attention capability of patients who received cognitive stimulation training 1 hour per day, 5 days per week, for 8 weeks improvement was significantly greater than those who did not.

These are big challenges to meet. Behavior is the result of thousands of small decisions at every moment of every day: do I sit or do I stand? Do I drink this beer? Even, do I take regular deep breaths? One of the biggest challenges to face is how we ‘read’ this behavior and turn it into reliable data. There’s also the issue of small sample sizes: in order to narrow down to a meaningful experiment, you need, at the moment, to have very clear definitions of behavior, which often means small sample sizes of people who always do X in Y conditions. The science of behavior and decision making itself is complex, debatable, and often evolving. And there’s the company building practicalities: to build a company in this space, you need to find people who understand clinical science, data science, experimentation approaches, behavioral science *and* product and UI.

But that’s exactly the opportunity. These things are coming; we understanding more about behavior every day, as devices enter our daily lives and health data becomes more and more fine-grained. New conceptions of roles that blend behavioral science and product design are clearly emerging. All of these means are not exclusive and can be combined into powerful ways of modifying behavior for health. Those that can connect all these dots have the ability to build companies that can take a giant bite out of that 40% — and have tremendous impact on mortality for huge swaths of the population.

There’s an old joke that plumbers have saved more lives than doctors, because improving sewers and sanitation (and eradicating the disease that went along with that) was so impactful on longevity for humans. By cleaning up the modern day ‘sewers’ of our lifestyles — not through magical drugs, complex procedures, or platitudes about prevention — but through a real infrastructure of technology that is being built right now — technology will bring an analogous impact.

Spike Diabetes applies social pressure to keep patients safe

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It can be tough for diabetes patients to keep a constant eye on their glucose levels. Spike Diabetes lets family and doctors lend a hand by sending them real-time alerts about the patient’s stats. And the app’s artificial intelligence features can even send helpful reminders or suggest the most diabetes-friendly meals when you walk into a restaurant.

Today onstage at TechCrunch Disrupt Berlin Startup Battlefield, Spike Diabetes is launching its Guardian Portal so loved ones with permission can get a closer look at a patients’ data and coach them about staying healthy.

“Diabetes is an incurable chronic disease that forces diabetics to live a life of carb-counting and insulin injections,” says Spike co-founder Ziad Alame. “Since diabetics are forced to do those mundane tasks for the rest of their lives, they tend to fall off the tracks sometimes simply because of how demanding those tasks can be. As for guardians and parents, they are left in the dark about their loved ones.”

With doctors often only getting data during quarterly or semi-annual checkups, patients are often left on their own. A lifetime of management is very stressful, especially if your life depends on it.”

The startup faces stiff competition from literally hundreds of apps claiming to help patients monitor their vitals. MySugr, Diabetes Connect and Health2Sync are among the most popular. But Alame says many require users to track their levels through complex spreadsheets.

Spike offers customizable mobile charts, and will even read users their stats out loud to make staying safe an easier part of daily life. Spike is invite-only and just on iOS, but it also touts an Apple Watch app plus optimized engineering to minimize battery usage.

“Spike started off as a personal project to help myself adhere better to my medication after reaching critical times in my diabetic life,” Alame tells me. Now he’s bringing to the problem his experience as CTO of the GivingLoop charity platform, TeensWhoCode summer camp and Zoomal crowdfunding site for the Arab world.

Alame has assembled a team of diabetics, engineers and PhDs, plus $200,000 in seed funding from MEVP, Cedar Mundi and Phoenician Funds. They hope to see the premium paid version of Spike’s freemium app overtake longstanding competition through word-of-mouth triggered by bringing loved ones and doctors into the loop.


One of the app’s most interesting features is the proactive info it delivers. “For example, you walk into McDonald’s around 2 PM. Spike would automatically know it’s lunch time for you and suggest the top three options you can have with approximate carb counts,” Alame tells me.

“After some time (~25 minutes) Spike automatically reminds you of your insulin and syncs with your diabetic devices to log all the details. With time, as the app gets to know the diabetic’s taste more, Spike would be able to suggest small behavioral tweaks to enhance lifestyle such as walking routes suggestions or new places similar to the diabetic’s taste but with a lower insulin consumption rate.”

Alame jokes that “The biggest risk [to Spike] is the best thing that can happen — which is finding a cure for diabetes.” But even if that happens, he believes Spike’s app for tracking and actively coaching users could be relevant to other diseases, as well. For now, though, it will have to convince users that an app could make managing diabetes simpler rather than more complex.

How smartphone apps could help keep health records accurate

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Suppose that the next time you go to a new doctor’s office, you wouldn’t have to balance a clipboard on your knee, write down your whole medical history, remember the five-syllable name of every medication you’re taking, and list all your allergies. Suppose that your smartphone could simply tap into the office’s computer system, where you could upload your entire medical history safely, securely, and accurately.

Such an app could ease the frustration patients feel when they fill out the forms for a new doctor. More importantly, it could help solve a serious but lesser-known problem that plagues hospitals and clinics: While the increased use of electronic health records has helped streamline record-keeping, providers aren’t always able to reliably pull together records for the same patient that are held in different hospitals, clinics, and doctor’s offices.

That was the scene in Boston in 2015, when emergency room doctors were struggling to treat a patient named Maureen Kelly—only to discover five different electronic records for Maureen Kelly, each with the same birthday and ZIP code. They had no way of knowing which record matched the patient in front of them. Was she the Maureen Kelly with diabetes? The Maureen Kelly who had only one kidney? And if they were to decide to send her record to a specialist outside the hospital, how could they know which of the five to send?

Fortunately, Maureen Kelly recovered. But to make the best possible medical decisions in cases like hers, doctors need immediate access to accurate patient data—including those from records held in other facilities. Digital systems should be able to seamlessly match records from a pediatrician in Pittsburgh or a surgeon in San Diego each and every time. An inability to do so—which could mean physicians not having important details, such as a patient’s drug allergies, chronic illnesses, or past surgeries—can mean the difference between life and death.

Doctors using digital tablet together in hospital (Photo: Ariel Skelley/Getty Images)

It’s hard enough keeping records straight within a single large hospital system; transferring them among different doctors’ offices and other hospitals is even more challenging. As digital health care systems have proliferated, they’ve used a variety of formats to record essential pieces of information, such as addresses and birthdates, that don’t easily transfer from one system to another. And, of course, patients’ identifying information isn’t static—birthdates don’t change but people move, change names through marriage or adoption, and more. Matches among different systems have also been stymied by data entry errors.

And while patient harm is the primary risk posed by inaccurate records, cost is no small consideration. The Office of the National Coordinator for Health Information Technology reported that each instance of a misidentified record cost the Mayo Clinic roughly $1,200—and that’s just within the Mayo system. These administrative costs are magnified when data are exchanged on a nationwide scale.

No one solution can solve every patient-matching problem. But The Pew Charitable Trusts is investigating several ideas. Pew recently asked the nonprofit RAND Corporation to evaluate solutions that would let patients exercise more control over how their records are matched. RAND looked at a variety of options and concluded that the growing use of smartphones offers a particularly promising opportunity to improve record matching in two ways.

Photo: Hero Images/Getty Images

First, smartphones could allow patients to verify their phone numbers at the point of care, perhaps by responding to a text message—a strategy already used in banking, travel, retail, and other industries. Once a number was confirmed by the patient, the hospital’s computer could use it automatically to match other records against that number with a higher degree of certainty.

Second, patients could use an app to enter their information—such as an address or even a driver’s license number—and have that information sent directly to the hospital when they check in for their visit. This would let patients update their information and voluntarily provide more accurate data to facilitate a match. Smartphone apps could eventually aggregate and transfer even more information—such as medication lists or health histories—and replace the paper on clipboards used today.

The smartphone approach will not solve this problem by itself. There are potential limitations—patients would need to own phones and know how to use them, and the system might not work in emergency situations when a patient didn’t have or couldn’t operate a smartphone—but the Pew Research Center found earlier this year thatmore than three-quarters of Americans now use smartphones, including nearly half of people older than 65.

To address the larger problem of patient matching, stakeholders must pursue a variety of solutions, including smartphone apps. Technology developers would be wise to advance and start pilot projects now of smartphones and a variety of other solutions, and demonstrate how they could be used to save lives, improve care, and reduce health care costs.

Implantable 3D-printed organs could be coming sooner than you think

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At MBC Biolabs, an incubator for biotech startups in San Francisco’s Dogpatch neighborhood, a team of scientists and interns working for the small startup Prellis Biologics have just taken a big step on the path toward developing viable 3D-printed organs for humans.

The company, which was founded in 2016 by research scientists Melanie Matheu and Noelle Mullin, staked its future (and a small $3 million investment) on a new technology to manufacture capillaries, the one-cell-thick blood vessels that are the pathways which oxygen and nutrients move through to nourish tissues in the body.

Without functioning capillary structures, it is impossible to make organs, according to Matheu. They’re the most vital piece of the puzzle in the quest to print viable hearts, livers, kidneys and lungs, she said.

“Microvasculature is the fundamental architectural unit that supports advanced multicellular life and it therefore represents a crucial target for bottom-up human tissue engineering and regenerative medicine,” said Jordan Miller, an assistant professor of bioengineering at Rice University and an expert in 3D-printed implantable biomaterial structures, in a statement.

This real-time video shows tiny fluorescent particles – 5 microns in diameter (the same size as a red blood cell) – moving through an array of 105 capillaries printed in parallel, inside a 700 micron diameter tube. Each capillary is 250 microns long.

Now, Prellis has published findings indicating that it can manufacture those capillaries at a size and speed that would deliver 3D-printed organs to the market within the next five years. 

Prellis uses holographic printing technology that creates three-dimensional layers deposited by a light-induced chemical reaction that happens in five milliseconds.

This feature, according to the company, is critical for building tissues like kidneys or lungs. Prellis achieves this by combining a light-sensitive photo-initiator with traditional bioinks that allows the cellular material to undergo a reaction when blasted with infrared light, which catalyzes the polymerization of the bioink.

Prellis didn’t invent holographic printing technology. Several researchers are looking to apply this new approach to 3D printing across a number of industries, but the company is applying the technology to biofabrication in a way that seems promising.

The speed is important because it means that cell death doesn’t occur and the tissue being printed remains viable, while the ability to print within structures means that Prellis’ technology can generate the internal scaffolding to support and sustain the organic material that surrounds it, according to the company.

The video above, courtesy of Prellis Biologics, shows real-time printing of a cell encapsulation device that is useful for producing small human cells containing organoids. The structure is designed to be permeable and the size is 200 microns in diameter and can contain up to 2000 cells.

Prellis isn’t the first company to develop three-dimensional organ printing. There have been decades of research into the technology, and companies like BioBots (which made its debut on the TechCrunch stage) are already driving down the cost of printing living tissue.

Now called Allevi, the company formerly known as BioBots has seen its founders part ways and its business  strategy shift (it’s now focusing on developing software to make its bioprinters easier to use), according to a report in Inc. Allevi has slashed the cost of bioprinting with devices that sell for less than $10,000, but Prellis contends that the limitations of extrusion printing mean that technology is too low resolution and too slow to create capillaries and keep cells alive.

Prellis’ organs will also need to be placed in a bioreactor to sustain them before they’re transplanted into an animal, but the difference is that the company aims to produce complete organs rather than sample tissue or a small cell sample, according to a statement. The bioreactors can simulate the biomechanical pressures that ensure an organ functions properly, Matheu said.

“Vasculature is a key feature of complex tissues and is essential for engineering tissue with therapeutic value,” said Todd Huffman, the chief executive officer of 3Scan, an advanced digital tissue imaging and data analysis company (and a Prellis advisor). “Prellis’ advancement represents a key milestone in the quest to engineer organs.”

Matheu estimates that it will take two-and-a-half years and $15 million to bring implantable organs through their first animal trials. “That will get a test kidney into an animal,” she said.

The goal is to print a quarter-sized kidney that could be transplanted into rats. “We want something that would be able to handle a kidney that we would transplant into a human,” Matheu said.

One frame of a 3D map of animal tissue from 3Scan .

Earlier this year, researchers at the University of Manchester href=”https://newatlas.com/working-kidney-cells-grown-mice/53354/”> grew functional human kidney tissue from stem cells for the first time. The scientists implanted small clusters of capillaries that filter waste products from the blood that had been grown in a Petri dish into genetically engineered mice. After 12 weeks, the capillaries had grown nephrons — the elements that make up a functional human kidney.

Ultimately, the vision is to export cells from patients by taking a skin graft or blood, stem cell or bone marrow harvest — and then use those samples to create the cellular material that will grow organs. “Tissue rejection was the first thing I was thinking about in how I was designing the process and how we could do it,” says Matheu.

While Prellis is spending its time working to perfect a technique for printing kidneys, the company is looking for partners to take its manufacturing technology and work on processes to develop other organs.

“We’ll be doing collaborative work with other groups,” Matheu said. “Our technology will come to market in many other ways prior to the full kidney.”

Last year, the company outlined a go-to-market strategy that included developing lab-grown tissues to produce antibodies for therapeutics and drug development. The company’s first targeted human tissue printed for clinical development were cells called “islets of Langerhans,” which are the units within a pancreas that produce insulin.

“Type 1 diabetics lose insulin-producing islets of Langerhans at a young age. If we can replace these, we can offer diabetes patients a life free of daily insulin shots and glucose monitoring,” said Matheu in a statement at the time.

Matheu sees the technology she and her co-founder developed as much about a fundamental shift in manufacturing biomaterials as a novel process to print kidneys, specifically.

“Imagine if you want to build a tumor for testing… In the lab it would take you five hours to print one… With our system it would take you three and a half seconds,” said Matheu. “That is our baseline optical system… The speed is such a shift in how you can build cells and fundamental structures we are going to be working to license this out.”

Meanwhile, the need for some solution to the shortage in organ donations keeps growing. Matheu said that one in seven adults in the U.S. have some sort of kidney ailment, and she estimates that 90 million people will need a kidney at some point in their lives.

Roughly 330 people die every day from organ failure, and if there were a fast way to manufacture those organs, there’s no reason for those fatalities, says Matheu. Prellis estimates that because of the need for human tissue and organ replacement alternatives, as well as human tissue for drug discovery and toxicology testing, the global tissue engineering market will reach $94 billion by 2024, up from $23 billion in 2015.

“We need to help people faster,” says Matheu. 

Siren raises $3.4 million for smart socks that track diabetic health

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Siren, a startup that has develops fabric with embedded microsensors, has unveiled its product, a sock for people with diabetes. Siren also announced a $3.4 million investment from DCM, Khosla Ventures and Founders Fund.

Powered by its Neurofabric technology, the diabetic sock can monitor foot temperatures with the idea that those with diabetes will be able to detect potential foot injuries.

“What we do is we take proven technology and we put it inside of socks so people can use it easily,” Siren co-founder and CEO Ran Ma told me. “If you get injured and have diabetic nerve damage, it’s hard to feel pain. The pain can go unnoticed, become infected, turn into an ulcer and lead to an amputation.”

Each sock is fitted with six sensors, so 12 sensors for every pair of socks. They’re also machine-washable and don’t need to be charged.

Siren sells for $19.95 a month, which gets people an initial pack of five pairs of socks, access to fresh socks every six months and access to the Siren Hub for monitoring.

Since its conception, Siren has had a few versions of its socks. Over the years, Siren has fine-tuned its development process to more seamlessly integrate the technology into the socks. Last January, Siren Care won TechCrunch’s Hardware Battlefield at the Consumer Electronics Show.

Moving forward, the plan is to create additional products in the health space with this technology.

“It’s very easily scalable,” Ma said. “It’s the same process. You just switch out the component. We already know sensors and electronics are getting cheaper and smaller every single day. So we just take advantage of this trend. Most of this stuff is made for wearables but we’re hijacking it and putting it in your clothes. And we think that’s the best use case.”

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