Uber joins Apple, Amazon as the next tech company to jump into healthcare

The Uber Health dashboard brings Uber rides to healthcare facilities for its patients and caregivers.
Image: uber

Healthcare is big business, and tech companies don’t want to miss out just because they’re busy building smartphones, apps, and self-driving cars.

Uber announced its new Uber Health platform Thursday. It’s a way for healthcare organizations to order rides for patients who are receiving care at their facilities. Uber also launched an API so the ride-hailing service can be built into existing healthcare tools.

This isn’t an exact replica of the existing Uber app. Instead, health facilities can schedule rides for patients and caregivers (up to a month ahead of time, so patients can get to appointments and make sure they get their follow-up care). The facilities also pay for the rides. Uber Health hopes to replace the transportation options facilities usually book to pick up patients.

Unlike the Uber app, rides work for patients without smartphones. Instead, the rides are coordinated through text message, and there’s a forthcoming plan to set up calls to landlines or cellphones instead of texting. 

Hospitals, rehab centers, and senior care facilities —and more than 100 organizations across the U.S. — are already using a beta version of the program. 

Uber isn’t the first major tech company to break into the healthcare space, which the World Economic Forum values at $6.5 trillion worldwide. Companies like Apple and Google are collecting tons of data on their users, which means it’s not much of a stretch for them to start using information about our eating, sleeping, and heart rate patterns to create new tools and products. 

Last month Google announced an algorithm that can detect, via an eye scan, whether someone has high blood pressure or is at risk for a heart attack or stroke. The researchers were part of Google’s health research division, Verily, which became part of the company in 2015. The AI has a 70 percent accuracy rate, and it’s improving.

Apple is using its Apple Watch product to track health signals like heart rate. With the KardiaBand, wearers can accurately detect irregular heartbeats and other indicators about poor heart health.

Apple introduced the health platform to its operating system back in 2014 and has big goals for diabetes care and other tools for health. 

This week Apple announced its own health clinic system, AC Wellness, for its employees.

Even Amazon is rumored to be thinking about creating its own healthcare company. Depending on how you look at it, that’s either very far — or super close — to selling everything from books to yoga pants to succulents through its online store. Alexa, Amazon’s digital assistant, already knows a lot about users’ habits and health problems, like if you’re asking which pharmacies are open or inquiring as to home remedies for a sore throat.

Health and tech are blurring together. But instead of prescription slips, it’s apps, wearables, and AI. Get ready for the future. 

Read more: https://mashable.com/2018/03/01/uber-health-healthcare-tech/

Apple now stores some iCloud files on Google Cloud Platform

Image: jaap arriens/Nur Photo via Getty Images

After some speculation, Apple has finally confirmed that it uses Google Cloud Services for iCloud storage. The admission was made in the company’s most recent update to its iOS Security Guide, spotted by CNBC. 

The Security Guide has previously indicated that iCloud services relied on Google competitors Amazon Web Services and Microsoft Azure. But the most recent version, released in January, states that iCloud files are stored using “third-party storage services, such as S3 [a product of Amazon Web Services] and Google Cloud Platform.” 

According to the guide, all items stored on iCloud, including contacts, calendars, photos, and documents are stored with such third-party services. Each file is broken into chunks and encrypted. The encrypted chunks are then stored on Google Cloud Platform without any user-identifying information. 

The guide does not specify when or why Apple switched from Microsoft to Google, or whether the company is taking advantage of Google Cloud services for anything other than object storage. 

Coca Cola, Best Buy, Niantic, Spotify, Motorola, and Airbus rank among the platform’s almost 300 corporations that use the platform. Apple is not listed among Google Cloud Platform’s prominent customers

Read more: https://mashable.com/2018/02/26/apple-uses-google-cloud-services/

iMessage and FaceTime goes down for users in Australia

Some Australian users weren't able to use iMessage or Facetime.
Image: lili sams/mashable

Well, this is odd.

Some customers of Australian carrier Telstra were unable to use iMessage and FaceTime on Wednesday morning, as technicians worked to fix the issue with the two Apple services.

The carrier’s outages page confirmed the problem. In a tweet, Telstra said customers could continue to send and receive SMS messages, but later stated those services were coming back to normal.

“Earlier today some customers experienced a disruption to Apple iMessage and FaceTime services. We worked with Apple to resolve this issue. Services are now being progressively being restored. We apologise for any inconvenience caused,” a Telstra spokesperson said via email.

One Twitter user, Nikolai Hampton, reported that Apple’s servers were not reachable by Telstra. 

Hampton suggested updating the phone’s DNS to get access to different servers, while another user Nathan Bujega, said a VPN would get around the problem.

Wednesday’s outage is not helped by the fact the iMessage app bundles SMS and iMessage services together, leading Telstra to instruct users how to force send an SMS message.

Apple’s iMessage has a history of not playing well with carriers. 

As the service gained popularity, those who switched from iOS and forgot to turn off iMessage reported missing messages, something that’s arguably been a headache for carriers.

Read more: https://mashable.com/2018/02/20/telstra-imessage-facetime-outage/

With AI, Your Apple Watch Could Flag Signs of Diabetes

Before modern chemistry brought doctors blood and urine tests for diagnosing diabetes, they had to rely on their taste buds. Sweet-tasting pee has long been the disease’s telltale biomarker; mellitus literally means honey. Too much sugar in your bodily fluids means your metabolism has gone haywire—either your cells aren’t making insulin or they’re not responding to it.

But a little over a decade ago, a group of researchers discovered a less obvious link. One of the complications of diabetes is nerve damage, and in the cardiovascular system that damage can cause irregular heart rates. Which you can measure, either with electricity or light. So one day soon, doctors might diagnose diabetes with their patients’ wrist bling instead of blood pricks or pee strips. Oh, what difference a few centuries make.

In 2005, heart rate sensors were something only elite athletes and very sick people used. Today, one in five Americans own one. Which is why there’s now a deep learning company trying to make something out of the connection between heart rate and diabetes. On Wednesday, at the annual AAAI Conference on Artificial Intelligence in New Orleans, digital health-tracking startup Cardiogram presented research suggesting the Apple Watch’s heart rate sensor and step counter can make a good guess at whether or not a person has diabetes—when paired with the right machine-learning algorithms, of course.

Apple has been eyeing a career change—from personal trainer to personal physician—for its signature wearable for a while now. In November the company teamed up with health insurer Aetna to give away more than 500,000 Apple Watches as part of a pilot to try to reduce health costs. And it embarked on a study with Stanford to test the watch’s skills at detecting irregular heartbeats, which can lead to stroke or heart attack. This most recent collaboration between Cardiogram—a San Francisco-based startup staffed by former Google engineers—and a landmark UC San Francisco heart health study is just the latest in these moves.

Cardiogram offers a free app for organizing heart-rate data from the Apple Watch and devices with similar sensors—from companies like Fitbit, Garmin, and Android Wear. It uses the same kind of artificial neural networks that Google uses to turn speech into text, and repurposes them to interpret heart-rate and step-count data. On its own, that data is mostly meaningless for detecting disease, and not just because the sensors themselves have significant errors. Training a model that can pick out condition-specific patterns requires labeled data. To learn what a diabetic heart rate signature looks, it needs some diabetics.

That’s where UCSF comes in. In 2013 it kicked off a major heart disease project called the Health eHeart study, aiming to collect massive amounts of digital health data on one million people. As of mid-January, the study had registered 196,000 participants, who each fill out a survey about known medical conditions, family histories, medications, and blood test results. About 40,000 of them have also opted to link that information with their Cardiogram app.

“That’s where we get our labels,” says Cardiogram co-founder Brandon Ballinger, who previously worked as a tech lead on Google’s speech recognition software. “In medicine, your labeled answers each represent a life at risk. Compared to what an internet company is working with, it’s actually a very small number of examples.”

So Cardiogram has had to adopt some tricks from the tech world to train its neural network, DeepHeart, to spot human disease. One of these is a technique called semi-supervised sequence learning, which was originally invented to work on text data like Amazon product reviews. But instead of a sequence of words, they sub in a sequence of heart rate measurements—about 4,000 per week. Some fancy math compresses that information into a single number summarizing the amount of heart rate variability. Then those summaries are what get tied to labeled patient data, and the real training can begin.

Using this method, DeepHeart was able to spot diabetics who weren’t part of the training group 85 percent of the time. The results are on par with the company’s previous work: Last year, the Cardiogram and UCSF released results showing that DeepHeart could wrestle one week’s worth of a person’s Apple Watch data into predictions for hypertension, sleep apnea, and atrial fibrillation with accuracy rates between 80 and 90 percent.

So how do Cardiogram’s algorithms make good guesses without directly measuring the amount of sugar in someone’s blood? Nobody really knows.

“Diabetes is very clearly a cardiovascular condition, but it’s not one with an obvious physiological connection to heart rate variability,” says Mark Pletcher, one of the principal investigators of the Health eHeart study and a co-author on the paper presented Wednesday. When you train machine learning algorithms on data without knowing the mechanisms behind the underlying patterns, you often get a signal without understanding why. “It makes me nervous, frankly. We’ve had a lot of internal discussions about whether this could be picking up medications diabetics use or some other extraneous factor. But we haven’t come up with anything.”

That’s the kind of thing that sends up red flags for Eric Topol, a cardiologist and Director of the Scripps Translational Science Institute, where he’s leading the digital health arm of the NIH’s billion dollar Precision Medicine Initiative. “This combines features of the black box of algorithms and the black box of biology,” he says, of the Cardiogram study. “It’s unconvincing and shaky. At best it would be considered hypothesis-generating.” The hypothesis here being that DeepHeart might be picking up a diabetes signal. But it might be picking up something else.

Ballinger is quick to counter these kinds of criticisms. If your wearable tells you you’re at increased risk for diabetes, and you go to the doctor and get diagnosed by traditional means, then you’re still getting the standard quality of care, he says. So what if it’s a black box that gets you in the door? Still, he recognizes the need for prospective validation to really demonstrate the AI’s accuracy—screening people who have not yet been diagnosed with diabetes, and following them to see if they did in fact develop the disease. He says the company is actively investing in those kinds of future studies.

With the right testing, Ballinger sees business potential in his black box intelligence. Cardiogram’s app for Apple Watch and other devices is free today. But the startup plans to add features that advise a user be tested for atrial fibrillation, high blood pressure, sleep apnea, or diabetes as soon as later this year. To stay on the right side of the US Food and Drug Administration, the app can’t function as a standalone diagnostic, more like some friendly advice. But the kind of advice an insurer might cover if they thought it would get people into treatment earlier and save healthcare costs.

Which leaves them a long way to go, given the evidence that’s currently out there. Or rather, lack thereof. “Setting aside the accuracy piece, which is something the FDA would want to know about, there’s almost no data out there on whether or not these wearables can actually change patient outcomes,” says Brennan Spiegel, a gastroenterologist and the director of Health Services Research at Cedars-Sinai in Los Angeles. “Creating the tech isn’t the hard part. The hard part is using the tech to change patient behavior. And that’s really hard to do. It’s not a computer science, it’s behavioral and social science.”

Still, if the Health eHeart and Cardiogram studies can say one thing pretty definitively at this point, it’s that people are eager to engage with apps capable of medical-grade measurements, if and when they become available. The question is if a healthier you is truly just a push notification away.

Intelligent Wearables

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Don't know the difference between supervised, semi-supervised, and unsupervised deep learning? The WIRED Guide to Artificial Intelligence can help you with that.

Read more: https://www.wired.com/story/with-ai-your-apple-watch-could-flag-signs-of-diabetes/