Category Archives: quantified self

The Story of Digital Health

http://www.nuviun.com/nuviun-digital-health

good infographics…

 

Digital Health Venn Nuviun

 

http://storyofdigitalhealth.com/infographic/

 

Digital_Health_Infographic

Infographic

I created this conceptual infographic illustrating the increasing health benefits achievable with digital health with the great team at Misfit Wearables. You can download a high-resolution version by clicking on the image.

Digital_Health_Infographic

References:
Number of people sequenced
“250,000 human genomes will be fully sequenced by the end of 2012, 1 million by 2013, and 5 million by 2014″ -Topol, Eric (2011-12-02). The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care (p. 102). Perseus Books Group. Kindle Edition.

Also, compliments of Story of Digital Health strategic partner nuviun, there’s this interactive diagram of the digital health landscape…

nuviun-digital-health-landscape

 

Quantified Diet Findings

  • sdfsdfsdfsdjf’;klj

People have more goals than they have willpower for. That’s just the way our ambition works. They give up, get distracted, or prioritize some other goal.

https://medium.com/inside-lift/be4809e34563

TLDR; This is the story of how we used the Lift Goal Coaching app to build an ongoing 15,000+ person experiment to compare popular diets. The good news is that dieting works, especially if it means giving up sugar and fast food. See our charts below or take our weight loss calculator. Or better, join one of our diets and contribute to science.

About a year ago, we ran a one-off research project into the Slow-Carb Diet™ that turned up surprisingly strong results. Over a four week period, people who stuck to the diet showed an 84% success rate and an average weight loss 0f 8.6lbs.

But are those results legit? If I picked a person at random out of a crowd, could they expect to see the same results? Almost immediately after publishing the results we started getting feedback about experimental bias.

This first study was biased, which means it doesn’t carry any scientific confidence. That’s a fixable problem, so we set off to redo the study in a bigger and more rigorous way.

That led to the Quantified Diet, our quest to verify and compare every popular diet. We now have initial results for ten diets. This is the story of our experiment and how we’re interpreting the diet data we’ve collected.

Understanding Bias

To understand bias, here’s quick alternative explanation for our initial Slow-Carb data: a group of highly motivated, very overweight people joined the diet and lost what, for them, is a very small amount of weight. In this alternative explanation, the results really are not very interesting and they definitely aren’t generalizable.

However, we had some advice from academics at Berkeley aimed specifically at overcoming the biases of the people who were self-selecting into our study. The keys: a control group following non-diet advice and randomized assignment into a comparative group of diets.

Our Experimental Design

The gist of our experimental design hinged on the following elements:

  • We were going to start by comparing ten approaches to diet: Slow-Carb, Paleo, Whole Foods, Vegetarian, Gluten-free, No sweets, DASH, Calorie Counting, Sleep More, Mindful Eating.
  • Lift wrote instructions for each diet, with the help of diet experts, and provided 28-day goals (with community support) for each diet inside our app.
  • We included two control groups, one with the task of reading more and the other with the task of flossing more.
  • Participants were going to choose which of the approaches they were willing to try and then we would randomly assign from within that group. Leaving some room for choice allowed people to maintain control over their health, while still giving us room to apply a statistically relevant analysis.
  • Participants who said they were willing to try a control group and at least two others were in the experiment. This is who we were studying.
  • A lot of people didn’t meet this criteria, or opted out at some point along the way. We have observational data on this group, but they can’t be considered scientifically valid results for the reasons around bias covered above.
  • Full writeup of the methodology coming.

Top Level Results

At the beginning of the study, everyone thought we were going to choose a winning diet. Which of the ten diets was the best?

Nine of the diets performed well as measured by weight loss. Here’s the ranking, with weight loss measured as a percentage of body weight. Slow-Carb, Paleo and DASH look like they led the pack (but keep reading because this chart absolutely does not tell the whole story).

If you don’t like doing math, the above chart translates to between 3-5lbs per month for most people. If you really don’t like doing math, we built acalculator for you that will estimate a weight loss specific to you.

Sleep, which never really had a strong weight loss hypotheses, lost. We ended up calling this a placebo control in order to bolster our statistical relevance.

Before moving on, lets just call out that people in the diets were losing 4-ish pounds over a one month period on average. That’s great given that our data set contains people who didn’t even follow their diet completely.

The Value of the Control

The control groups help us understand whether the experimental advice (to diet) is better than doing nothing. Maybe everyone loses weight no matter what they do?

This sounds unlikely, but we were all surprised to see that the control groups lost 1.1% of their body weight (just by sleeping, reading and flossing!)

Is that because they were monitoring their weight? Is it because the bulk of the study occurred in January, right after people finished holiday gorging? We don’t actually know why the control groups lost weight, but we do know that dieting was better than being in the control.

Here’s the weight-loss chart revised to show the difference between each diet and the control (this chart shows the experimental effect).

The Value of Randomized Assignment

Randomized assignment helps us feel confident that the weight loss is not specific to the fans of a particular diet.

Because of the randomization, we can ask the following question. For each diet, what happens if we assigned the person to a different diet?

This is an indicator of whether a diet is actually better or if the people who are attracted to a diet have some other characteristic that is effecting our observational results.

The obvious example of bias would be a skew toward male or female. Bigger people have more weight to lose (male), plus we observed that males tended to lose a higher percentage of their body weight (2.8% vs. 1.8%).

Comparing the diets this way adds another promising diet approach: no sweets. But let’s, be real, the differences between these diets are very small, less than half a pound over four weeks, as compared to doing any diet at all, five pounds over four weeks. Our advice is pick the diet that’s most appealing (rather than trying to optimize).

Soda is bad! And other Correlations.

What else leads to weight loss?

  • It helps if your existing diet is terrible (your new diet is even better in comparison). People who reported heavy pre-diet soda consumption lost an extra 0.6% body weight.
  • Giving up fast food was also good for an extra 0.6% (but probably not worth adding fast food just to give it up).
  • Men lost more weight (2.6% vs 1.8%).
  • Adherence mattered (duh). Here’s a chart with weight loss by adherence.

How much of the time did people follow the diet advice?

Choosing a Diet

Ok. Now I think I’ve explained enough that you could choose one of these diets. All of them are available via the Lift app available on the webiPhoneand Android.

Given that all the diets work, the real question you should be asking yourself is which one do you most want to follow.

I can’t stress that enough. It’s not just about which had the most weight loss. Choose a diet you can stick to.

Let’s Talk Success Rate

Adherence matters. Even half-way adherence to a diet led to more than 1% weight loss (better than the control groups).

This brings up an interesting point. So far, our data is based on the people who made it all the way to the end of our study. This is the survivor bias. We don’t know what happened to the other people (hopefully the diets weren’t fatal).

In order to judge the success rate of dieting you’ll have to use some judgement. But we can give you the most optimistic and most pessimistic estimates. The truth is somewhere in between.

Of people who gave us all of their data over four weeks, 75% lost weight. Let’s call this the success rate ceiling. It includes many reasons for not losing weight, including low adherence. But at least they paid attention to the goal for the entire time. The weight loss averages are based on this group.

Of people who joined the study, only 16% completed the entire study (and 75% of those lost weight). So, merely joining a diet, with no other data about your commitment, has a success rate of 12%. Let’s call this thesuccess rate floor.

Read that floor as 12% of people who merely said that they were interested in doing a diet had definitely lost weight four weeks later. There’s no measure of commitment in that result. If we filter by even a simple commitment measure, such as the person fills out the first survey on day one, then the success rate jumps from 12% to 28%.

If you are making public policy, then maybe that 12% number looks important. People have more goals than they have willpower for. That’s just the way our ambition works. They give up, get distracted, or prioritize some other goal.

If you are an individual, I’d put more weight in the ceiling. You want to know that whatever path you choose has a chance of succeeding. 75% is a number that should give you confidence.

Losing Weight?

We’ve focused on losing weight for two reasons. One, it’s a very common goal. But, two, it’s also the strongest signal we got out of our data.

We also measured happiness and energy but the signal was weak. We didn’t measure any other markers of health. That’s important to note.

We are behavior designers, so we’re looking at the effectiveness of behavior change advice. You should still consult a nutritionist when it comes to the full scope of health impacts from a diet change. For example, you could work with our partner WellnessFX for a blood workup (and talk to their doctors).

Open Sourcing the Research

We’ve open sourced the research. You can grab the raw data and some example code to evaluate it from our GitHub repository.

All of the participants were expecting to have their data anonymized for the purposes of research. Take a look and please share your work back (it’s required by the CC and MIT licenses).

There was some lossiness in the anonymization process. We’ve stripped out personal information (of course), but also made sure that rows in the data set can’t be tied back to individual Lift accounts. For that reason some of the data is summarized. For example, weight is expressed as percentage weight loss and adherence is expressed on a 1-5 scale.

If you want to go digging around in the data, I would suggest starting by looking at our surveys where we got extra data about the participants: day 1,week 1week 2week 3week 4.

Citizen Science or No Science

I’m expecting that our research will spark some debate about the validity of scientific research from non-traditional sources. I expect this because I’ve already been on the receiving end of this debate.

Here’s how we’re seeing it right now. I acknowledge that we already have a robust scientific process living in academia. And I acknowledge that the way we ran this research broke the norms of that process.

The closest parallel I can think of is the rise of citizen journalism (mostly through blogs) as a complement to traditional journalism. At the beginning there was a lot of criticism of the approach as dangerous and irresponsible. Now we know that the approach brought a lot of benefits, namely: breadth, analysis and speed.

That’s the same with citizen science. We studied these diets because we didn’t see anyone else doing it. And we’re continuing to do other research (for example: meditation) because we’re imagining a world where everything in the self-improvement space, from fitness to diet to self-help, is verifiably trustworthy.

Continuing Research

One of our core tenants with this research is that we can revise it. We didn’t have to write a grant proposal and it didn’t cost us anything to run the study. In fact, we’re already revising it.

To start with, we’re adding in one more diet: “Don’t Drink Sugar.”

We wrote this diet based on the study results and a belief in minimal effective interventions. So, if you’re at all interested in losing weight while contributing to science, please sign up for the Quantified Diet.

Thanks

Special thanks to many academics who commented on our process along the way, along with our sponsors who helped drive people into the study:The Four Hour BodyNo Meat AthleteFoodistZenHabitsNerdFitness,PaleoHacksDeborah EnosDr. Kevin CampbellTania MercerSarah StanleyWithingsGreatistHintZicoWellnessFXO’Reilly Media,Dreena’s Plant Powered KitchenEat TribalPolarRunHundredFeast,BasisZestyKinduBiome.

an idea of earth shattering significance

ok.

been looking for alignment between a significant industry sector and human health. it’s a surprisingly difficult alignment to find… go figure?

but I had lunch with joran laird from nab health today, and something amazing dawned on me, on the back of the AIA Vitality launch.

Life (not health) insurance is the vehicle. The longer you pay premiums, the more money they make.

AMAZING… AN ALIGNMENT!!!

This puts the pressure on prevention advocates to put their money where their mouth is.

If they can extend healthy life by a second, how many billions of dollars does that make for life insurers?

imagine, a health intervention that doesn’t actually involve the blundering health system!!?? PERFECT!!!

And Australia’s the perfect test bed given the opt out status of life insurance and superannuation.

Joran wants to introduce me to the MLC guys.

What could possibly go wrong??????

Dodgy wearables…

Dodgy wearables indiegogo pitch.

Airo gets a mention.

OK, I get it.

http://pando.com/2014/03/20/on-indiegogo-a-miracle-health-device-raises-730k-and-a-whole-load-of-red-flags/

On Indiegogo, a miracle health device crowdfunds $730k. One problem: it might be total bullshit

319462_10150807895075313_1360808361_nBY 
ON MARCH 20, 2014

healbe
It’s the stuff that crowdfunding dreams are made of.

An Indiegogo campaign for a gorgeous piece of wearable tech, shown off in a slick video with some great visuals. Speaking with a thick Russian accent, Healbe CEO Artem Shipitsyn describes what his company calls the ‘The Original 100% Automatic Body Manager.’ It’s called the ‘GoBe’ and it does everything a Fitbit can but so, so much more. Using Healbe’s “Flow” technology – pressure and impedance sensors mixed with an accelerometer – the device is capable of reading glucose levels through your skin to give an accurate calorie count of everything you’ve eaten, against all the energy we’ve burnt. Despite Shipitsyn’s accent, the device’s Indiegogo page says that the company is based in San Francisco.

“Tell it nothing. Know everything. Go be you,” the video signs off. The GoBe will be delivered by June of this year, to anyone who stumps up just $199.

This, ladies and gentlemen, is a market changer. Step right up!

And people have certainly stepped up. As of midday today, Shipitsyn’s campaign has raised $730,294 in two weeks, from 3253 backers — more than seven times its initial funding goal. Thirty-three backers have paid $1175 for a “Club Pack” including ten GoBe-s.

No shame in admitting it: I was impressed. If GoBe did what it claimed, this was the end of the Fitbit, the Up and just about every other weight loss technology.

And so, keen to be the first reporter to cover this marvelous piece of technology, I started asking questions. What I discovered was something far from the slick, bay area startup Healbe purported to be. Rather, I found a publicity shy company, operated remotely from Russia, promoting a device unsupported by any medical or scientific evidence whatsoever. One that thousands of backers have supported to the tune of almost three quarters of a million dollars, and one that Indiegogo says raises no red flags. In the exact words from an Indiegogo spokesperson: ”We have no reason to believe that this company’s Indiegogo campaign is at all fraudulent.”

[UPDATE: The day after publication, a different spokesperson for Indiegogo took issue with the idea that no red flags were ever raised by the campaign, finally confirming to us that the campaign was indeed investigated– and cleared– by Indiegogo’s usual anti-fraud methods. She declined to explain why the basic mistruths and inconsistencies we found in our reporting– which haven’t been denied by Healbe– didn’t concern Indiegogo. She also declined to explain what types of discoveries would lead Indiegogo to conclude an offering was fraudulent.]

My initial doubts were raised last week when I contacted the company’s information line and received no response. This is odd. Normally when I contact the folks behind crowdfunding campaigns, the response is prompt, and enthusiastic. The more publicity, the more money, after all.

I try again this past Monday. Finally, Meghan Donovan, from MicroArts Creative Agency in Greenland, New Hampshire replies, asking if we can talk the next day. There’s just one problem, she explains when we speak: everyone at Healbe was travelling through the end of the week. She promises to get back to me within a couple of days.

A Google search shows that the GoBe has been the subject of about two dozen press articles, but all of them either quote from the press release or the Indiegogo campaign itself. No major tech website has covered the device, and no scientists seem to be as excited as I am about its apparent medical breakthrough. Healbe might be the most press-shy successful startup on earth.

Artem Shipitsyn (also spelt as Shipitsin) and five of his six colleagues listed on the Indiegogo page – George Mikaberydze, Stanislav Povolotskiy, Michael Rubin, Eugene Sokolov, Pavel Mussel – are traceable online only in relation to this one Indiegogo campaign. Shipitsyn lists himself on the page as “a major developer of market solutions and new products for global brands such as Rostelcorn, Sberbank, L’Oreal, Valio, Reebok, Hearst Shkulev, Discovery Channel and more.” And yet on hisLinkedIn profile he lists none of that, describing himself instead as the owner, since 2004, of Iridium, a marketing company in Russia with little discernible online footprint. And now the CEO of Healbe.

Healbe’s website lists no contact details except for the email address that connected me to Donovan’s PR agency in New Hampshire. An address listed for Healbe Incorporated on an old version of its website leads to a law firm, White Summers, in Redwood City. A receptionist for White Summers confirms that Healbe is a client. The company itself is registered in Delaware. This is apparently the extent of its American infrastructure.

Meanwhile, some of GoBe’s backers are getting cold feet: requests for refunds are starting to trickle on to Healbe’s Indiegogo page, dissent is growing on the company’s Facebook and a few Redditers are getting twitchy.

Michelle MacDonald, a clinical dietician at the National Jewish Health hospital in Denver, tells me that her eyebrows were raised almost immediately when she read Healbe’s claims that, through an “algorithm,” it can work out from glucose levels in our cells what our caloric intake was. “Of course they’re claiming an algorithm, because it’s a fun word,” McDonald laughs.

The problem is, MacDonald explains, the three main nutrients that determine caloric intake are carbohydrates, protein and fats. Glucose provides only a small part of the picture. A company that invented a non-invasive way to measure glucose would be a huge hit with the treatment of diabetes. Currently, diabetes sufferers have to prick their skin and make themselves bleed. The technology is probably coming soon, MacDonald says, but when it does it will be the size of a shoebox. It will also likely involve some form of infrared light shone through the skin that will measure the fluid in interstitial cells to approximate the blood glucose level in a simple milligrams per deciliter figure. It will come from a big lab, will be huge news and make a lot of money.

“If you actually had this technology, Indiegogo would be the last channel you’d go through,” MacDonald says.

Let’s imagine that Healbe really has perfected this technology, though. Even so, MacDonald says, nothing described in the video could do what it claims to. The impedance monitor could look at hydration, the pressure monitor could examine pulse and the accelerometer could tell us about action. But none of those three things could tell you anything about glucose levels. A graphic of Healbe’s accompanying smartphone app even shows it measuring fat and carbohydrates, which is doubly ridiculous.

MacDonald takes particular umbrage at Healbe listing its chief scientist Eugene Sokolov as having a background as a rocket scientist. “I wish I was a standup comedian. I could really run with that,” she laughs. Maybe they’ve left out the key part from the video, she hedges. “But when you fail to explain it, that’s always a red flag.”

In fact there are multiple red flags.

I call Meghan Donovan back. She assures me that the GoBe is a real, working device. Her company was employed by Healbe in Fall 2013 and Shipitsyn came into the MicroArts office to film the Indiegogo video in January. He bought two models in for the video, but Donovan admits that she never saw the device in action. Shipitsyn told her that they were doing their own internal tests. But, she tells me, Healbe displayed its product at CES. That’s something.

On closer inspection, Healbe’s Indiegogo page talks about having “unveiled” the GoBe at CES. Except when I look, there’s no reference to Healbe in the CES directory of exhibiting companies in 2014.

I talk to Healbe’s industrial designer Jozeph Forakis, who has done work for Motorola and Swatch in the past. Over Skype from Italy, Forakis confirms that he has worked with Shipitsyn and Healbe for a year on several different prototypes, “the most recent of which were shown in January in CES.”

Why, then, can’t I find any reference to Healbe in the CES directory? Well, Healbe wasn’t technically at CES Forakis admits. But Shipitsyn was in Las Vegas at the time, taking meetings in his hotel room.

Forakis and Donovan are the only two people I can find who claim to have seen a GoBe in real life. Neither are willing to vouch for the science behind it.

At least Indiegogo believes in Shipitsyn. The Healbe campaign raises no red flags, a company spokesperson tells me. Indiegogo, she says, has a vested interest in security. It prides itself on its “equal opportunity, open platform… literally anyone from anywhere in the world can raise money here.”

In October last year, a Canadian company called Airo Health promised a wearable that could do the same thing as Healbe, with a slightly different technique – looking at nutrient levels in our blood by shining a light through it. The company took pre-orders through its website, but a month later refunded all of its customers. “Through conversations with others in the industry, we have come to realize that it requires further testing,” the company said in a release.

Indiegogo protects itself against fraud with an algorithm — that word again — that detects troublesome accounts, alongside human vetting and the group mentality of crowdfunding picking  out bad eggs.

Fraud is a slippery term, though. It doesn’t account for more subtle manipulations. Healbe’s Gobe activity tracker looks enough like a Fitbit that the average shopper can grasp what it is, and what it might be able to do. The automatic calorie reader claim is an advancement that we can all appreciate the significance of, but few of us can pick apart the science behind. Indiegogo can protect against an outright fraudster, but a snake oil salesman with an unproven product is a different matter. Since the beginning of recorded history, opportunists have been using impressive pitches to sell miracle health potions and devices — really the only thing that’s changed is the technology (although it used to be that if you were conned by a snake oil salesman, at least you’d end up with a pretty glass bottle. Indiegogo can’t even promise that.)

Indiegogo wants to keep these concerns inside the domain of the campaigner-funder relationship. Despite $730,000 in pledges, Indiegogo’s position is that as long as there’s a real company claiming to make something that doesn’t violate its terms of service, it has no moral or legal obligation to ensure that the GoBe is legit. All backers can do is wait until June to see if their miracle band shows up and does what it says in the video.

I was finally able to reach Shipitsyn and Healbe’s managing director George Mikaberydze this morning in Moscow, Russia, via Skype, two hours before my deadline for this piece. Their schedules had apparently become more flexible since I started asking questions.

Shipitsyn holds the device close to the camera — it seems to be the same as in the video — while Mikaberydze points to a fuzzy line on an app that, he says, breaks down his energy consumption over the last 20 minutes since he ate a Snickers bar. They were at CES, they say, as the guest of Levin Consulting, with their own meeting room. They hold up an attendee badge showing, at least, that they visited the conference.

So what about the science? Shipitsyn says that the impedance monitor in the Gobe can measure glucose by monitoring the water moving in and out of cells. Insulin opens up the cells when you eat sugar, he says. The company will publish their own clinical tests soon and are discussing with a third party clinic in America, Shipitsyn insists. They’ve slipped off the medical radar because the accuracy rates range between 80 and 90 percent. The head of Samsung Russia is apparently a huge fan.

It’s a breezy, confident pitch. Shipitsyn and Mikaberydze have a ready response to all of my concerns — the subtext being that I really ought to trust them.

But here’s the rub: I don’t. Or at least not enough to part with $199 for a device for which they haven’t yet released any clinical test results (despite their insistence that these apparently do exist) and which, right now, only exists as a demo on a screen.

I’ve jostled myself right up to the soapbox, pushed my face as close to the screen as it’s possible to get, and I still have absolutely no hard evidence that this device is any more than a smart mock up. Shipitsyn says its a miracle machine, at least one expert says it can’t possibly exist.

What I know for a fact is this: in about three weeks, Healbe will be close to three quarters of a million dollars richer, at least. Indiegogo says they have no reason to withhold the money raised, or to doubt that it will be used to deliver GoBes to three-and-a-bit thousand backers who have, presumably, weighed up the risks for themselves and decided to put their faith in Shipitsyn and his partners. Shipitsyn himself says there’s nothing to worry about, but it won’t be until at least June — plenty of time for the money to have moved from a Delaware corporation to a bank account in Moscow — before we know the truth.

Pando will keep pushing HealBe to publish their trial results and I’ll embed them in this post if and when they do. In the meantime, anyone who is inclined to bet $199 or more on a miracle weight loss device might recall the old maxim: if something seems too good to be true, it probably is.

Shipitsyn has a hell of a pitch, but my $199 is staying in my pocket.

See here for the latest updates on this story.

319462_10150807895075313_1360808361_n

James Robinson is a staff writer for PandoDaily covering hardware, advertising technology and the Internet of Things… among many other general goings on. Follow him on Twitter: @jalrobinson

Illumina’s $1000 genome

This article nice frames the immaturity of the technology in the context of population health and prevention (vs. specific disease management), and even references the behaviour of evil corporations in its final paragraphs.

 

Cost breakdown for Illumina’s $1,000 genome:

Reagent* cost per genome — $797

Hardware price — $137**

DNA extraction, sample prep and labor — $55-$65

Total Price = $989-$999

* Starting materials for chemical reactions

** Assumes a four-year depreciation with 116 runs per year, per system. Each run can sequence 16 genomes.

http://recode.net/2014/03/25/illuminas-ceo-on-the-promise-of-the-1000-genome-and-the-work-that-remains/

Illumina’s CEO on the Promise of the $1,000 Genome — And the Work That Remains

March 25, 2014, 2:18 PM PDT

By James Temple

Illumina seized the science world’s attention at the outset of the year by announcing it had achieved the $1,000 genome, crossing a long-sought threshold expected to accelerate advances in research and personalized medicine.

The San Diego company unveiled the HiSeqX Ten Sequencing System at the J.P. Morgan Healthcare Conference in January. It said “state-of-the art optics and faster chemistry” enabled a 10-fold increase in daily throughput over its earlier machines and made possible the analysis of entire human genomes for just under $1,000.

Plummeting prices should broaden the applications and appeal of such tests, in turn enabling large-scale studies that may someday lead to scientific breakthroughs.

The new sequencers are making their way into the marketplace, with samples now running on a handful of systems that have reached early customers, Chief Executive Jay Flatley said in an interview with Re/code last week. Illumina plans to begin “shipping in volume” during the second quarter, he said.

The Human Genome Project, the international effort to map out the entire sequence of human DNA completed in 2003, cost $2.7 billion. Depending on whose metaphor you pick, the $1,000 price point for lab sequencing is akin to breaking the sound barrier or the four-minute mile — a psychological threshold where expectations and, in this case, economics change.

Specifically, a full genomic workup of a person’s three billion DNA base pairs starts to look relatively affordable even for healthy patients. It offers orders of magnitude more information than the so-called SNPs test provided by companies like 23andMe for $99 or so, which just looks at the approximately 10 million “single-nucleotide polymorphisms” that are different in an individual.

With more data, scientists expect to gain greater insights into the relationship between genetic makeup and observable characteristics — including what genes are implicated in which diseases. Among other things, it should improve our understanding of the influences of DNA that doesn’t directly code proteins (once but no longer thought of as junk DNA) and create new research pathways for treatments and cures.

“The $1,000 genome has been the Holy Grail for scientific research for now over a decade,” Flatley said. “It’s enabled a whole new round of very large-scale discovery to get kicked off.”

Cost breakdown for Illumina’s $1,000 genome:

Reagent* cost per genome — $797

Hardware price — $137**

DNA extraction, sample prep and labor — $55-$65

Total Price = $989-$999

* Starting materials for chemical reactions

** Assumes a four-year depreciation with 116 runs per year, per system. Each run can sequence 16 genomes.

Source: Illumina

Some have questioned the $1,000 claim, with Nature noting research centers have to buy 10 systems for a minimum of $10 million — and that the math requires including machine depreciation and excluding the cost of lab overhead.

But Flatley defended the figure, saying it’s impossible to add in overhead since it will vary at every research facility.

“Our math was totally transparent and it is exactly the math used by the (National Human Genome Research Institute),” he said. “It’s a fully apples-to-apples comparison to how people have talked historically about the $1,000 genome.”

He also questioned the conclusions of a recent study published in the Journal of the American Medical Association, where researchers at Stanford University Medical Center compared results of adults who underwent next-generation whole genome sequencing by Illumina and Complete Genomics, the Mountain View, Calif., company acquired last year by BGI.

They found insertions or deletions of DNA base pairs only concurred between 53 percent and 59 percent of the time. In addition, depending on the test, 10 percent to 19 percent of inherited disease genes were not sequenced to accepted standards.

“The use of [whole genome sequencing] was associated with incomplete coverage of inherited disease genes, low reproducibility of detection of genetic variation with the highest potential clinical effects, and uncertainty about clinically reportable findings,” the researchers wrote.

Or as co-author Euan Ashley put it to me: “The test needs some tough love to get it to the point where it’s clinical grade.”

Flatley responded that the sample size was small and that the sequencing platforms were several years old. But he did acknowledge they are still grappling with technology limitations.

“What’s hard is to determine whether there’s a base inserted or deleted,” he said. “That’s abioinformatics problem, not a sequencing problem. That’s a software issue that we and others and the whole world is trying to work on.”

But, he stressed, that shortcoming doesn’t undermine the value of what the tests doread accurately.

“There are many, many, many things where it’s clinically useful today,” he said.

Flatley pointed to several areas where we’re already seeing real-world applications of improving sequencing technology, including cancer treatments targeted to the specific DNA of the tumor rather than the place where it shows up in the body. There are also blood tests under development that can sequence cancer cells, potentially avoiding the need for biopsies, including one from Guardant Health.

Another promising area is noninvasive prenatal testing, which allows expecting parents to screen for genetic defects such as Down syndrome through a blood draw rather than an amniocentesis procedure.

The technology can delineate the DNA from the fetus circulating within the mother’s bloodstream. It’s less invasive and dangerous than amniocentesis, which involves inserting a needle into the amniotic sac and carries a slight risk of miscarriage. Because of that risk it’s generally reserved for high-risk pregnancies, including for women 35 and older.

Illumina, which offers the blood screening for out-of-pocket costs of around $1,500, recently funded a study published in the New England Journal of Medicine that found the so-called cell-free fetal DNA tests produced more accurate results than traditional tests for Down syndrome and Trisomy 18, a more life-threatening condition known as Edwards syndrome.

“It gives some earlier indicators to women in the average risk population if their babies have those problems,” Flatley said. “I think that it will broaden the overall market, and there are other tests that can be added over time.”

But there are ethical issues that arise as prenatal genetic tests become more popular and revealing, including whether parents will one day terminate pregnancies based on intelligence, height, eye color, hair color or minor diseases.

For that reason, Illumnia refuses to disclose those traits that are decipherable in the genome today.

But Flatley said they couldn’t stop purchasers of its machines from doing so, nor competitors like BGI of China (for more on that issue see Michael Specter’s fascinating profile of the company in the New Yorker ). Flatley said there needs to be a public debate on these issues, and he expects that new laws will be put into place establishing commonsense boundaries in the months or years ahead.

“This isn’t something we think we can arbitrate,” he said. “But we won’t be involved directly in delivering [results] that would cross those ethical boundaries.”

Ornish on Digital Health

The limitations of high-tech medicine are becoming clearer—e.g., angioplasty, stents, and bypass surgery don’t prolong life or prevent heart attacks in stable patient; only one out of 49 men treated for prostate cancer benefit from the treatment, and the other 48 often become impotent, incontinent or both; and drug treatments of type 2 diabetes don’t work nearly as well as lifestyle changes in preventing the horrible complications.

http://www.forbes.com/sites/johnnosta/2014/03/17/the-stat-ten-dean-ornish-on-digital-health-wisdom-and-the-value-of-meaningful-connections/

3/17/2014 @ 11:09AM |1,095 views

The STAT Ten: Dean Ornish On Digital Health, Wisdom And The Value Of Meaningful Connections

STAT Ten is intended to give a voice to those in digital health. From those resonant voices in the headlines to quiet innovators and thinkers behind the scenes, it’s my intent to feature those individuals who are driving innovation–in both thought and deed. And while it’s not an exhaustive interview, STAT Ten asks 10 quick questions to give this individual a chance to be heard.  

Dean Ornish, MD is a fascinating and important leader in healthcare.  His vision has dared to question convention and look at health and wellness from a comprehensive and unique perspective.  He is a Clinical Professor of Medicine, UCSF Founder & President, nonprofit Preventive Medicine Research Institute.

Dr. Ornish’s pioneering research was the first to prove that lifestyle changes may stop or even reverse the progression of heart disease and early-stage prostate cancer and even change gene expression, “turning on” disease-preventing genes and “turning off” genes that promote cancer, heart disease and premature aging. Recently, Medicare agreed to provide coverage for his program, the first time that Medicare has covered an integrative medicine program. He is the author of six bestselling books and was recently appointed by President Obama to the White House Advisory Group on Prevention, Health Promotion, and Integrative and Public Health. He is a member of the boards of directors of the San Francisco Food Bank and the J. Craig Venter Institute. The Ornish diet was rated #1 for heart health by U.S. News & World Report in 2011 and 2012. He was selected as one of the “TIME 100” in integrative medicine, honored as “one of the 125 most extraordinary University of Texas alumni in the past 125 years,” recognized by LIFE magazine as “one of the 50 most influential members of his generation” and by Forbes magazine as “one of the 7 most powerful teachers in the world.”

The lexicon of his career is filled with words that include innovator, teacher and game-changer.  And with this impressive career and his well-established ability to look at health and medicine in a new light, I thought i would be fun–and informative–to ask Dr. Ornish some questions about digital health.

Dean Ornish, MD

Dean Ornish, MD

 1. Digital health—many definitions and misconceptions.  How would describe this health movement in a sentence or two?

“Digital health” usually refers to the idea that having more quantitative information about your health from various devices will improve your health by changing your behaviors.  Information is important but it’s not usually sufficient to motivate most people to make meaningful and lasting changes in healthful behaviors.  If it were, no one would smoke cigarettes.

2. You’ve spoken of building deep and authentic connection among  patients as key element of your wellness programs.  Can digital health foster that connection or drive more “techno-disconnection”?

Both.  What matters most is the quality and meaning of the interaction, not whether it’s digital or analog (in person).  Study after study have shown that people who are lonely, depressed, and isolated are three to ten times more likely to get sick and die prematurely compared to those who have a strong sense of love and community.  Intimacy is healing.  In our support groups, we create a safe environment in which people can let down their emotional defenses and communicate openly and authentically about what’s really going on in their lives without fear they’ll be rejected, abandoned, or betrayed.  The quality and meaning of this sense of community is often life-transforming.  It can be done digitally, but it’s more effective in person.  A digital hug is not quite as fulfilling, but it’s much better than being alone and feeling lonely.

3. How can we connect clinical validation to the current pop culture trends of “fitness gadgets”?

Awareness is the first step in healing.  In that context, information can raise awareness, but it’s only the first step.

 4. Can digital health help link mind and body wellness?

Yes.  Nicholas Christakis’ research found that if your friends are obese, your risk of obesity if 45% higher.  If your friends’ friends are obese, your risk of obesity if 25% higher.  If your friends’ friends’ friends are obese, your risk is 10% higher—even if you’ve never met them.  That’s how interconnected we are.  Their study also showed that social distance is more important than geographic distance.  Long distance is the next best thing to being there (and in some families, even better…).

5. Are there any particular area of medicine and wellness that might best fit in the context of digital health (diet, exercise, compliance, etc.)?

They all do.

6. There is much talk on the empowerment of the individual and the “democratization of data”.  From your perspective are patients becoming more engaged and involved in their care?

Patients are becoming more empowered in all areas of life, not just with their health care.  Having access to one’s clinical data can be useful, but even more empowering is access to tools and programs that enable people to use the experience of suffering as a catalyst and doorway for transforming their lives for the better.  That’s what our lifestyle program provides.

 7. Is digital health “sticking” in the medical community?  Or are advances being driven more by patients?

Electronic medical records are finally being embraced, in part due to financial incentives.  Also, telemedicine is about to take off, as it allows both health care professionals and patients to leverage their time and resources more efficiently and effectively.  But most doctors are not prescribing digital health devices for their patients.  Not yet.

 8. Do you personally use any devices?  Any success (or failure) stories?

I weigh myself every day, and I work out regularly using weight machines and a treadmill desk.  I feel overloaded by information much of the day, so I haven’t found devices such as FitBit, Nike Plus, and others to be useful.  These days, I find wisdom to be a more precious commodity than information.

 9. What are some of the exciting areas of digital health that you see on the horizon?

The capacity for intimacy using digital platforms is virtually unlimited, but, so far, we’ve only scratched the surface of what’s possible.  It’s a testimony to how primal our need is for love and intimacy that even the rather superficial intimacy of Facebook (or, before that, the chat rooms in AOL, or the lounges in Starbucks) created multi-billion-dollar businesses.

My wife, Anne, is a multidimensional genius who is developing ways of creating intimate and meaningful relationships using the interface of digital technologies and real-world healing environments.  She also designed our web site (www.ornish.com) and created and appears in the guided meditations there; Anne has a unique gift of making everyone and everything around her beautiful.

 10. Medicare is now covering Dr. Dean Ornish’s Program for Reversing Heart Disease as a branded program–a landmark event–and you recently formed a partnership with Healthways to train health care professionals, hospitals, and clinics nationwide.  Why now?

We’re creating a new paradigm of health care—Lifestyle Medicine—instead of sick care, based on lifestyle changes astreatment, not just as prevention.  Lifestyle changes often work better than drugs and surgery at a fraction of the cost—and the only side-effects are good ones.  Like an electric car or an iPhone, this is a disruptive innovation.  After 37 years of doing work in this area, this is the right idea at the right time.

The limitations of high-tech medicine are becoming clearer—e.g., angioplasty, stents, and bypass surgery don’t prolong life or prevent heart attacks in stable patient; only one out of 49 men treated for prostate cancer benefit from the treatment, and the other 48 often become impotent, incontinent or both; and drug treatments of type 2 diabetes don’t work nearly as well as lifestyle changes in preventing the horrible complications.

At the same time, the power of comprehensive lifestyle changes is becoming more well-documented.  In our studies, we proved, for the first time, that intensive lifestyle changes can reverse the progression of coronary heart disease and slow, stop, or reverse the progression of early-stage prostate cancer.  Also, we found that changing your lifestyle changes your genes—turning on hundreds of good genes that protect you while downregulating hundreds of genes that promote heart disease, cancer, and other chronic diseases.  Our most recent research found that these lifestyle changes may begin to reverse aging at a cellular level by lengthening our telomeres, the ends of our chromosomes that control how long we live.

Finally, Obamacare turns economic incentives on their ear, so it becomes economically sustainable for physicians to offer training in comprehensive lifestyle changes to their patients, especially now that CMS is providing Medicare reimbursement and insurance companies such as WellPoint are also doing so.  Ben Leedle, CEO of Healthways, is a visionary leader who has the experience, resources, and infrastructure for us to quickly scale our program to those who most need it.  Recently, we trained UCLA, The Cleveland Clinic, and the Beth Israel Medical Center in New York in our program, and many more are on the way.

 

Reflections on trackers…

It’s about healthy living, not quantifying oneself…

http://www.medgadget.com/2014/03/an-interview-with-the-monitored-man-albert-sun.html

An Interview with “The Monitored Man”: Albert Sun

Posted: 13 Mar 2014 12:04 PM PDT

Albert Sun, a young journalist at the New York Times, recently authored an article entitled “The Monitored Man” chronicling his experience using a multitude of health fitness trackers over the last few months. I wanted to ask him about his fitness tracking adventure and gain further insight into this booming sector from a “super user” who at times was simultaneously wearing up to four fitness tracking devices.

Tom Fowler, Medgadget: Albert, tell me about why you decided to put fitness tracking devices to the test.

Albert Sun An Interview with The Monitored Man: Albert SunAlbert Sun: I think it started with a really simple graphic that my colleague Alastair put together last year listing a few interesting wearable health monitors and what things they measured. For that he put together this google spreadsheet and we sort of tried to keep it up to date with all the different gadgets as we heard about them. I was constantly adding things to it and at a point felt that if I was having this much trouble keeping track of all of them that probably other people were as well. My original idea was actually to put them all to the test in accuracy and be able to chart which ones were the most accurate. I had plans to reverse engineer their drivers and access the raw data they were recording. But once I actually started wearing them I realized that, yes there was a lot of data, but it was actually this idea of motivation and behavior change and how you understand the data that was much more interesting.

 

Medgadget: You mentioned that many trackers were lacking in detecting exertion and activities like biking and fidgeting. Are the device makers missing the point, or are these merely due to current technical limitations?

Albert Sun: It’s definitely due to current technical limitations. If companies could make devices that could track everything perfectly, I think they absolutely would. And I think a lot of people see that kind of tracking as a kind of holy grail and are trying very hard to make it to that goal. I’m not so sure that’s a good idea. No tracker is going to be able to fully track everything about you and we’ve all already got a perfectly good “tracker” that’s wired in to every part of our body: our brain. My colleague Gretchen Reynolds writes about that in her article on why she decides to remain a “tech nudie.”

Yes an objective measure of your activity level is useful, but it’s just one view, and it has to be integrated into the broader subjective view of how you feel.

 

Medgadget: If every fitness tracking device producing company CEO was reading this interview, what tips would you like to give them?

Albert Sun: I think many of these CEO’s are already thinking about the things and experiences I wrote about. From talking to their users they know what experiences people are having and they’re definitely improving rapidly. Just in the time I’ve been using them they’ve improved a lot.

There are two things that I think they could do that would improve people’s experiences though. First is they could be a little more upfront in their marketing of these devices about what they can and can’t do instead of presenting them as magic.

The other thing that I think would be really helpful would be for them to put some error bars on the data they show and indicate that they are estimates and the true values lie somewhere in a range. I think that would go a long way towards helping people interpret their data in the proper context.

I might be sounding overly pessimistic about activity tracking, but I actually really like these devices and think they’re very cool and useful. But to be very cool and useful I think people have to approach them the right way and that means having realistic expectations of how they work. Otherwise people will be disappointed.

 

Medgadget: Would you say your conclusion “I don’t need a monitor anymore. I’m tracking me.” is a reflection of a large part of the market, in that many will initially use but then no longer have a need for trackers?

Albert Sun: Yes, absolutely. It’s maybe not a permanent thing, but it could be a now and again thing. I mean, are we really expecting people to start now and wear something that tracks their movement continually until they’re in the grave?

The goal here is to be healthier and happier — to live well — not to be perfectly quantified. Once an activity tracker has helped you do that it should ideally fade to the background to the point where you can almost forget about it. I obviously haven’t been able to do that while I’ve been working on this story, I’ve been juggling a lot of different gadgets and apps and chargers trying to keep everything straight. It’s quite taxing and it takes a toll on all the other things that life is about.

Anne Wojcicki lays out 23andMe’s vision…

 

http://www.engadget.com/2014/03/09/future-of-preventative-medicine/

Anne Wojcicki and her genetic sequencing company 23andMe are locked in abattle with the FDA. Even though it can’t report results to customers right now, Wojcicki isn’t letting herself get bogged down in the present. At SXSW 2014 she laid out her vision of the future of preventative medicine — one where affordable genome sequencing comes together with “big data.” In addition to simply harvesting your genetic code, the company is doing research into how particular genes effect your susceptibility to disease or your reaction to treatments. And 23andMe isn’t keeping this information locked down. It has been building APIs that allow it to share the results of its research as well as the results your genetic tests, should you wish to.

It’s when that data is combined with other information, say that harvested from a fitness tracker, and put in the hands of engineers and doctors. In the future she hopes that you’ll see companies putting the same effort into identifying and addressing health risks as they do for tracking your shopping habits. Targetfamously was able to decode that a woman was pregnant before she told her father, based purely on her purchase history. One day that same sort of predictive power could be harnessed to prevent diabetes or lessen a risk for a heart attack. Whether or not that future is five, 10 or 15 years off is unclear. But if Wojcicki has her way, you’ll be able to pull up health and lifestyle decisions recommended for you with the same ease that you pull up suggested titles on Netflix.

Big data in healthcare

A decent sweep through the available technologies and techniques with practical examples of their applications.

Big data in healthcare

Big data in healthcare

big data in healthcare industrySome healthcare practitioners smirk when you tell them that you used some alternative medication such as homeopathy or naturopathy to cure some illness. However, in the longer run it sometimes really is a much better solution, even if it takes longer, because it encourages and enables the body to fight the disease naturally, and in the process build up the necessary long term defence mechanisms. Likewise, some IT practitioners question it when you don’t use the “mainstream” technologies…  So, in this post, I cover the “alternative” big data technologies. I explore the different types of big data datatypes and the NoSQL databases that cater for them. I illustrate the types of applications and analyses that they are suitable for using healthcare examples.

 

Big data in healthcare

Healthcare organisations have become very interested in big data, no doubt fired on by the hype around Hadoop and the ongoing promises that big data really adds big value.

However, big data really means different things to different people. For example, for a clinical researcher it is unstructured text on a prescription, for a radiologist it is the image of an x-ray, for an insurer it may be the network of geographical coordinates of the hospitals they have agreements with, and for a doctor it may refer to the fine print on the schedule of some newly released drug. For the CMO of a large hospital group, it may even constitute the commentary that patients are tweeting or posting on Facebook about their experiences in the group’s various hospitals. So, big data is a very generic term for a wide variety of data, including unstructured text, audio, images, geospatial data and other complex data formats, which previously were not analysed or even processed.

There is no doubt about that big data can add value in the healthcare field. In fact, it can add a lot of value. Partially because of the different types of big data that is available in healthcare. However, for big data to contribute significant value, we need to be able to apply analytics to it in order to derive new and meaningful insights. And in order to apply those analytics, the big data must be in a processable and analysable format.

Hadoop

Enter yellow elephant, stage left. Hadoop, in particular, is touted as the ultimate big data storage platform, with very efficient parallelised processing through the MapReduce distributed “divide and conquer” programming model. However, in many cases, it is very cumbersome to try and store a particular healthcare dataset in Hadoop and try and get to analytical insights using MapReduce. So even though Hadoop is an efficient storage medium for very large data sets, it is not necessarily the most useful storage structure to use when applying complex analytical algorithms to healthcare data. Quick cameo appearance. Exit yellow elephant, stage right.

There are other “alternative” storage technologies available for big data as well – namely the so-called NoSQL (not only SQL) databases. These specialised databases each support a specialised data structure, and are used to store and analyse data that fits that particular data structure. For specific applications, these data structures are therefore more appropriate to store, process and extract insights from data that suit that storage structure.

Unstructured text

A very large portion of big data is unstructured text, and this definitely applies to healthcare too. Even audio eventually becomes transformed to unstructured text. The NoSQL document databases are very good for storing, processing and analysing documents consisting of unstructured text of varying complexity, typically contained in XML, JSON or even Microsoft Word or Adobe format files. Examples of the document databases are Apache CouchDB and MongoDb. The document databases are good for storing and analysing prescriptions, drug schedules, patient records, and the contracts written up between healthcare insurers and providers.

On textual data you perform lexical analytics such as word frequency distributions, co-occurrence (to find the number of occurrences of particular words in a sentence, paragraph or even a document), find sentences or paragraphs with particular words within a given distance apart, and other text analytics operations such as link and association analysis. The overarching goal is, essentially, to turn unstructured text into structured data, by applying natural language processing (NLP) and analytical methods.

For example, if a co-occurrence analysis found that BRCA1 and breast cancer regularly occurred in the same sentence, it might assume a relationship between breast cancer and the BRCA1 gene. Nowadays co-occurrence in text is often used as a simple baseline when evaluating more sophisticated systems.

Rule-based analyses make use of some a priori information, such as language structure, language rules, specific knowledge about how biologically relevant facts are stated in the biomedical literature, the kinds of relationships or variant forms that they can have with one another, or subsets or combinations of these. Of course the accuracy of a rule-based system depends on the quality of the rules that it operates on.

Statistical or machine-learning–based systems operate by building classifications, from labelling part of speech to choosing syntactic parse trees to classifying full sentences or documents. These are very useful to turn unstructured text into an analysable dataset. However, these systems normally require a substantial amount of already labelled training data. This is often time-consuming to create or expensive to acquire.

However, it’s important to keep in mind that much of the textual data requires disambiguation before you can process, make sense of, and apply analytics to it. The existence of ambiguity, such as multiple relationships between language and meanings or categories makes it very difficult to accurately interpret and analyse textual data. Acronym / slang / shorthand resolution, interpretation, standardisation, homographic resolution, taxonomy ontologies, textual proximity, cluster analysis and various other inferences and translations all form part of textual disambiguation. Establishing and capturing context is also crucial for unstructured text analytics – the same text can have radically different meanings and interpretations, depending on the context where it is used.

As an example of the ambiguities found in healthcare, “fat” is the official symbol of Entrez Gene entry 2195 and an alternate symbol for Entrez Gene entry 948. The distinction is not trivial – the first is associated with tumour suppression and with bipolar disorder, while the second is associated with insulin resistance and quite a few other unrelated phenotypes. If you get the interpretation wrong, you can miss or erroneously extract the wrong information.

Graph structures

An interesting class of big data is graph structures, where entities are related to each other in complex relationships like trees, networks or graphs. This type of data is typically neither large, nor unstructured, but graph structures of undetermined depth are very complex to store in relational or key-value pair structures, and even more complex to process using standard SQL. For this reason this type of data can be stored in a graph-oriented NoSQL database such as Neo4J, InfoGrid, InfiniteGraph, uRiKa, OrientDB or FlockDB.

Examples of graph structures include the networks of people that know each other, as you find on LinkedIn or Facebook. In healthcare a similar example is the network of providers linked to a group of practices or a hospital group. Referral patterns can be analysed to determine how specific doctors and hospitals team together to deliver improved healthcare outcomes. Graph-based analyses of referral patterns can also point out fraudulent behaviour, such as whether a particular doctor is a conservative or a liberal prescriber, and whether he refers patients to a hospital that charges more than double than the one just across the street.

Another useful graph-based analysis is the spread of a highly contagious disease through groups of people who were in contact with each other. An infectious disease clinic, for instance, should strive to have higher infection caseloads across such a network, but with lower actual infection rates.

A more deep-dive application of graph-based analytics is to study network models of genetic inheritance.

Geospatial data

Like other graph-structured data, geospatial data itself is pretty structured – coordinates can simply be represented as pairs of coordinates. However, when analysing and optimising ambulance routes of different lengths, for example, the data is best stored and processed using a graph structures.

Geospatial analyses are also useful for hospital and practice location planning. For example, Epworth HealthCare group teamed up with geospatial group MapData Services to conduct an extensive analysis of demographic and medical services across Victoria. The analysis involved sourcing a range of data including Australian Bureau of Statistics figures around population growth and demographics, details of currently available health services, and the geographical distribution of particular types of conditions. The outcome was that the ideal location and services mix for a new $447m private teaching hospital should be in the much smaller city of Geelong, instead of in the much larger but services-rich city of Melbourne.

Sensor data

Sensor data often are also normally quite structured, with an aspect being measured, a measurement value and a unit of measure. The complexity comes in that for each patient or each blood sample test you often have a variable record structure with widely different aspects being measured and recorded. Some sources of sensor data also produce large volumes of data at high rates. Sensor data are often best stored in key-value databases, such as Riak, DynamoDB, Redis Voldemort, and sure, Hadoop.

Biosensors are now used to enable better and more efficient patient care across a wide range of healthcare operations, including telemedicine, telehealth, and mobile health. Typical analyses compare related sets of measurements for cause and effect, reaction predictions, antagonistic interactions, dependencies and correlations.

For example, biometric data, which includes data such as diet, sleep, weight, exercise, and blood sugar levels, can be collected from mobile apps and sensors. Outcome-oriented analytics applied to this biometric data, when combined with other healthcare data, can help patients with controllable conditions improve their health by providing them with insights on their behaviours that can lead to increases or decreases in the occurrences of diseases. Data-wise healthcare organisations can similarly use analytics to understand and measure wellness, apply patient and disease segmentation, and track health setbacks and improvements. Predictive analytics can be used to inform and drive multichannel patient interaction that can help shape lifestyle choices, and so avoid poor health and costly medical care.

Concluding remarks

Although there are merits in storing and processing complex big data, we need to ensure that the type of analytical processing possible on the big data sets lead to valuable enough new insights. The way in which the big data is structured often has an implication on the type of analytics that can be applied to it. Often, too, if the analytics are not properly applied to big data integrated with existing structured data, the results are not as meaningful and valuable as expected.

We need to be cognisant of the fact that there are many storage and analytics technologies available. We need to apply the correct storage structure that matches the data structure and thereby ensure that the correct analytics can be efficiently and correctly applied, which in turn will deliver new and valuable insights.

Rock Health visits Australia – preview

 

 

 

FUELLING CHANGE IN AUSTRALIA’S HEALTHCARE THROUGH TECHNOLOGY; LESSONS FROM ROCK HEALTH

By Melia Rayner | February 27th, 2014 in Intelligent Thinking First, Technology Second

Cellscope oto

Above: Rock Health funded startup CellScope are reinventing the otoscope (image courtesy of Yahoo)

Social change through technology is all around us, in the way we shop, communicate, pay bills and arrange services. So why has the incredibly important area of health been so slow to move in line with the digital economy? Australia has led medical breakthroughs in the past; from the implementation of the first bionic ear in 1982 to the cervical cancer (HPV) vaccine in 2007, but the past few years have seen our healthcare landscape struggling to get further than the ‘middle of the pack’.

Elsewhere, the digital health movement is growing rapidly. In Washington, a startup called KitCheck helps hospital pharmacies process medication kits faster and without error, whilst in San Francisco CellScopehas built a smartphone-enabled diagnostic toolkit, including a digital otoscope. Even global magnates have put resources and teams into developing health innovation, such as General Electric’s Logiq; which is an ultrasound for the whole body, and Walgreens’ Pill Reminder app and Find Your Pharmacist web tool.

All the companies above have capitalised on the need for social change in healthcare through the vehicle of technology. Utilsing innovations in technology to solve human problems is behind everything we do at Portable. The point at which culture and technology meet is where social change can really happen. It’s in this mission that our maxim ‘Intelligent thinking first, technology second’ hits home; in the utilisation of technology to support social change rather than commandeer it.

This is why we’re bringing out a digital health innovator like Dr. Nate Gross as part of our Portable Talksseries. Nate’s company Rock Health provides startups (such as KitCheck and CellScope) with funding and full service support to advance the healthcare industry through technology. Their partnerships across the industry – from medical institutions to venture capital firms and corporates – give them unparalleled knowledge of how to innovate change in a highly regulated industry. In addition, Nate’s successful development of healthcare game changers such as Rock Health and Doximity makes him uniquely qualified to present to Australian audiences on lessons in innovation from Silicon valley and how to break down barriers to entrepreneurship and communication in this sector.

We spoke to Nate about the importance of change in healthcare and some of his other key maxims in advance of his recently announced tour for Portable Talks in May.

https://www.youtube.com/watch?v=V7Q9y1zRdXQ

Above: Nate speaks briefly about the story of Rock Health.

 

Why did you decide to come all the way to Australia for Portable Talks?

Well, that’s easy: Australia and Portable Talks. It seems to be a very exciting time in Australia, where the next engine of growth could be technology, healthcare, or both — and the [Australian] people are consistently applauded for living healthy lives. And of course, Portable’s reputation preceded itself as I had watched several previous [Portable Talks] series online, thus knowing there were amazing and in-depth conversations to be had.

 

How can the USA learn from Australia’s approach to health innovation, and vice versa?

I think we can learn from the approaches and we can learn from the businesses themselves. The startup community in Australia is smaller but accelerating, and most importantly the quality is high, a recipe for wins that will attract more entrepreneurial ideas and capital.

We can also learn from the businesses themselves — many successful concepts may be translated or shared due to some similarities between the mixed private and public components of our healthcare systems. That’s not to call our systems too similar, of course, as there is much to learn from Australia about accessibility and affordability.

 

In your opinion, what are the three biggest hurdles facing digital health innovation internationally?

Differences in incentives is often at the top of the list, which can make cost a barrier to different parties in different healthcare systems.

The funding environment is another. I think many cities and countries are ready to scale up their innovation efforts, but it can be a chicken-and-egg problem where some local wins are first required to attract capital to the area.

Language itself is a barrier, which Australia is perhaps more cognizant of than Silicon Valley, as it’s a leader in the Asia Pacific region. And there are many other hurdles that may become more relevant depending on the venture: market size, privacy, interoperability, the US regulatory process, infrastructure, consumer readiness.

 

Health is often perceived to be a topic that individuals outside the industry don’t actively engage with. Why should individuals from outside health and medical fields engage with in this industry?

Two reasons: First, because you don’t want to wait until you get sick to start solving these problems. And second, healthcare is an entrenched industry, which means there’s a lot of entrenched thinking. Outside perspective can lead to fantastic innovation, and many of the startups that have come through Rock Health have been founded by “outsiders”.

What is the key message you’d like to bring to your talks in Australia?

It’s the right time to get involved in digital health. There has never been a better time to be a health entrepreneur, and there are many ways you can get involved to transform the healthcare sector.

Nate will be discussing topics such as trends in digital health, innovation in heavily regulated industries and breaking down barriers to entrepreneurship and communication in healthcare throughout his Portable Talkstour in early May. This event is a must-see for those working in digital innovation, healthcare, technology, startups, or high-tech funding. The tour will cover Melbourne and Sydney with tickets available here – be quick to secure an earlybird discount. Nate will also spend a day as Portable’s ‘Entrepreneur in Residence’, delivering a new agenda with the team to help encourage innovation and creative thinking in cross disciplinary fields.

To find out more about the Rock Health Portable Talks tour or to enquire after a private company consultation with Dr. Nate Gross, please contact Kate at kate(at)portablestudios.com.au

For all other tour enquiries please contact Mikala Tai at mikala(at)portablestudios.com.au