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E-Health: Why Innovation and Connectivity are Vital for our Future Wellbeing

E-Health: Why Innovation and Connectivity are Vital for our Future Wellbeing | meaningful symbols | Scoop.it

Technology has improved our lives in many ways but one area that we are only just starting to scratch the surface of and where there is perhaps the biggest potential in the coming years is healthcare.

Ageing populations in developed countries, rapid population growth in the developing world and issues such as rising obesity rates mean the burden on healthcare systems worldwide will continue to push them to breaking point if it is not addressed. Among the EU member states public health spend has risen from an average of 5.9% of GDP in 1990 to 7.2% in 2010 and that's expected to hit 8.5% in 2060. Especially in these times of economic austerity that kind of growth isn't sustainable.

The potential for technology to ease this burden and both improve healthcare for patients and boost the efficiency of doctors and nurses is huge. Anecdotal evidence shows IT adoption in healthcare lags a decade behind virtually every other sector so there is a lot of catching up to do.

But the market for these technologies is growing. Spend on global telemedicine has grown from $9.8 billion in 2010 to $11.6 billion in 2011 and is forecast to rise to $23 billion by 2015, according to a BCC Research study. 

And, as seen by the gadgets at the CES trade show in Las Vegas earlier this month, there is rapid growth in health and fitness related mobile applications, devices and sensors - everything from wristbands that monitor activity levels and calories burned to heart and diabetes monitors that can report back to your doctor.

Mobile and so-called 'm-health' has a huge role to play in delivering these often life-saving benefits. Here at EE a report we commissioned by Arthur D Little on the benefits of 4G found an example of a hospital in Germany using a 4G-enabled ambulance to send live high resolution CT scans of stroke patients to specialists on route to the hospital, resulting in a 54% reduction in alarm to therapy times during the trial. 

The European Commission has just issued its eHealth Action Plan, outlining goals to support the adoption of better technology-enabled healthcare across the EU by 2020 and Neelie Kroes, Commission Vice President for the Digital Agenda, said: "Europe's healthcare systems aren't yet broken, but the cracks are beginning to show. It's time to give this 20th Century model a health check. The new European eHealth Action Plan sets out how we can bring digital benefits to healthcare, and lift the barriers to smarter, safer, patient-centred health services."

Much of the work outlined in that action plan will focus on reducing the interoperability and regulatory barriers to implementing ehealth services as well as addressing legal issues such as patient privacy around personal health data and records.

Technology will continue to augment our lives in many wonderful ways over the coming decades. It brings with it the potential for greater life expectancy and quality of life through better monitoring and earlier medical intervention, faster and more cost effective treatment and improved communications and management. If the right people make the right decisions, with the right direction and investment, the well-being of citizens in both the developed and developing world could be dramatically improved.


Via Chaturika Jayadewa
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An Augmented Reality Magic Mirror System for Anatomy Education

The system uses the concept of an augmented reality magic mirror to create the illusion that the user has a kind of X-ray view into her own body. Such a syst...

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TechCrunch | 14 Steps To Successful SEO For Startups

TechCrunch | 14 Steps To Successful SEO For Startups | meaningful symbols | Scoop.it
This is a guest post by Ryan Spoon (@ryanspoon), a principal at Polaris Ventures. Read more about Ryan on his blog at ryanspoon.com.

For startups, it is dangerous to entirely separate product and marketing – both strategically and organizationally.

A great product isn’t overly useful without an audience. And a great marketing strategy can’t save a poor product. Product and marketing have to coexist.

So when imaging, building and eventually launching your product, it is important to also hone the marketing strategy. There are five core channels:

- Paid marketing (SEM, display, affiliates, etc)
- Social & viral marketing
- Search engine optimization (SEO)
- Partnerships & business development
- PR

For early-stage companies, advertising at scale is expensive and consequently difficult. Furthermore, PR and business development become easier efforts as the company matures. So where does that leave you as a resource-constrained startup?

Marketing needs to come from the product itself. Last week I explored the role that social and virals play. And while the tech world is fascinated with social media and major platforms like Facebook and Twitter, we shouldn’t overlook the role of SEO (and consequently Google). Like Facebook and Twitter, SEO is another opportunity to expand your funnel and increase your audience — without an advertising budget! Also like social, SEO is far more effective when built directly into the product (“from the ground up”). Here are 14 guidelines for thinking about SEO.

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To make physicians more productive, focus on IT and tools for their supporting staff first

To make physicians more productive, focus on IT and tools for their supporting staff first | meaningful symbols | Scoop.it
Productivity loss and workflow disruptions are commonplace as our industry gets on the Meaningful Use bandwagon and is starting to adopt EHR systems at a slightly more rapid pace than in previous years (things aren’t really as rosy as many...
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Quixey

Quixey | meaningful symbols | Scoop.it
Quixey finds apps on all platforms - including Android, iOS, Mac, Windows, Web and more. Quixey also builds custom search solutions for partners.
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Conceptboard - Realtime Teamwork & Collaboration Software

Conceptboard - Realtime Teamwork & Collaboration Software | meaningful symbols | Scoop.it

Conceptboard goes beyond the idea of online whiteboards and adds realtime collaboration, document sharing, visual feedback, task management, live meetings and more. 

I really liked this platform,impressed by it.

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The value of any data is only as valuable as the information and insights we can extract from it.

The value of any data is only as valuable as the information and insights we can extract from it. | meaningful symbols | Scoop.it

Editor’s note: Dr. Michael Wu is the Principal Scientist of Analytics at Lithium where he is currently applying data-driven methodologies to investigate and understand the complex dynamics of the social Web.

The value of any data is only as valuable as the information and insights we can extract from it. It is the information and insights that will help us make better decisions and give us a competitive edge. The promise of big data is that one could glean lots of information and gain many valuable insights. However, people often don’t realize that data and information are not the same. Even if you are able to extract information from your big data, not all of it will be insightful and valuable.

Data ≠ Information

Many people speak of data and information as if they are synonymous, but the difference between the two is quite subtle. Data is simply a record of events that took place. It is the raw data that describes what happened when, where, and how and who’s involved. Well, isn’t that informative? Yes, it is!

While data does give you information, the fallacy of big data is that more data doesn’t mean you will get “proportionately” more information. In fact, the more data you have, the less information you gain as a proportion of the data. That means the information you can extract from any big data asymptotically diminishes as your data volume increases. This does seem counterintuitive, but it is true. Let’s clarify this with a few examples.

Example 1: Data backups and copies. If you look inside your computer, you will find thousands of files you’ve created over the years. Whether they are pictures you took, emails you sent, or blogs you wrote, they contain a certain amount of information. These files are stored as data in your hard drive, which takes up a certain amount of space.

Now, if you are as paranoid as I am, you will probably back up of your hard drive regularly. Think about what happens when you back up your hard drive for the first time. In terms of data, you’ve just doubled the amount of data you have. If you had 50 GB of data in your hard drive, you would have 100 GB after the back up. But will you have twice the information after the back up? Certainly not! In fact, you gain no additional information from this operation, because the information in the backup is exactly the same as the information in the original drive.

Although our personal data is not big data by any means, this example illustrates the subtle difference between data and information, and they are definitely not the same animal. Now let’s look at another example involving bigger data.

Example 2: Airport surveillance video logs. First, video files are already pretty big. Second, closed-circuit monitoring systems (CCTV) in an airport are on 24/7, and high-definition (HD) devices will only increase the data volume further. Moreover, there are hundreds and probably thousands of security cameras all over the airport. So as you can see, the video logs created by all these surveillance cameras would probably qualify as big data.

Now, what happens when you double the number of camera installations? In terms of data volume, you will again get about 2x the data. But will you get 2x the information? Probably not. Many of the cameras are probably seeing the same thing, perhaps from a slightly different angle, sweeping different areas at slightly different times. In terms of information content, we almost never get 2x. Furthermore, as the number of cameras continues to increase, the chance of information overlap also increases. That is why as data volume increases, information will always have a diminishing return, because more and more of it will be redundant.

A simple inequality characterizes this property: information ≤ data. So information is not data, it’s only the non-redundant portions of the data. That is why when we copy data, we don’t gain any information, even when the data volume increases, because the copied data is redundant.

Example 3: Updates on multiple social channels. What about social big data, such as tweets, updates, and/or shares? If we tweet twice as often, Twitter is definitely getting 2x more data from us. But will Twitter get 2x the information? That depends on what we tweet. If there is absolutely zero redundancy among all our tweets, then Twitter will have 2x the information. But that typically never happens. Let’s think about why.

First of all, we retweet each other. Consequently, many tweets are redundant due to retweeting. Even if we exclude retweets, the chance that we are coincidentally tweeting about the same content is actually quite high, because there are so many tweeters out there. Although the precise wording of each tweet may not be exactly the same, the redundancies among all the tweets containing the same Web content (whether it’s a blog post, a cool video, or breaking news) is very high. Finally, our interest and taste in good content remains fairly consistent over time. Since our tweets tend to reflect our interests and tastes, even apparently unrelated tweets from the same user will have some redundancies, because the tweeter is tweeting similar content.

Clearly, even if we tweet twice as often, Twitter is not going to get 2x the information because there is so much redundancy among our tweets (likewise with updates and shares on other social channels). Furthermore, we often co-syndicate content across multiple social channels. Since this is merely duplicate content across multiple social channels, it doesn’t give us any extra information about the user.

Although data does give rise to information, data ≠ information. Information is only the non-redundant parts of the data. Since most data, regardless of how it is generated, has lots of built-in redundancy, the information we can extract from any data set is typically a tiny fraction of the data’s sheer volume.

I refer to this property as the data-information inequality: information ≤ data. And in nearly all realistic data sets (especially big data), the amount of information one can extract from the data is always much less than the data volume (see figure below): information << data. Since the naïve assumption that big data leads to a lot of information is not true, the value of big data is hugely exaggerated.

 

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The Big Data Fallacy And Why We Need To Collect Even Bigger Data

Dr. Michael Wu

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Editor’s note: Dr. Michael Wu is the Principal Scientist of Analytics at Lithium where he is currently applying data-driven methodologies to investigate and understand the complex dynamics of the social Web.

The value of any data is only as valuable as the information and insights we can extract from it. It is the information and insights that will help us make better decisions and give us a competitive edge. The promise of big data is that one could glean lots of information and gain many valuable insights. However, people often don’t realize that data and information are not the same. Even if you are able to extract information from your big data, not all of it will be insightful and valuable.

Data ≠ Information

Many people speak of data and information as if they are synonymous, but the difference between the two is quite subtle. Data is simply a record of events that took place. It is the raw data that describes what happened when, where, and how and who’s involved. Well, isn’t that informative? Yes, it is!

While data does give you information, the fallacy of big data is that more data doesn’t mean you will get “proportionately” more information. In fact, the more data you have, the less information you gain as a proportion of the data. That means the information you can extract from any big data asymptotically diminishes as your data volume increases. This does seem counterintuitive, but it is true. Let’s clarify this with a few examples.

Example 1: Data backups and copies. If you look inside your computer, you will find thousands of files you’ve created over the years. Whether they are pictures you took, emails you sent, or blogs you wrote, they contain a certain amount of information. These files are stored as data in your hard drive, which takes up a certain amount of space.

Now, if you are as paranoid as I am, you will probably back up of your hard drive regularly. Think about what happens when you back up your hard drive for the first time. In terms of data, you’ve just doubled the amount of data you have. If you had 50 GB of data in your hard drive, you would have 100 GB after the back up. But will you have twice the information after the back up? Certainly not! In fact, you gain no additional information from this operation, because the information in the backup is exactly the same as the information in the original drive.

Although our personal data is not big data by any means, this example illustrates the subtle difference between data and information, and they are definitely not the same animal. Now let’s look at another example involving bigger data.

Example 2: Airport surveillance video logs. First, video files are already pretty big. Second, closed-circuit monitoring systems (CCTV) in an airport are on 24/7, and high-definition (HD) devices will only increase the data volume further. Moreover, there are hundreds and probably thousands of security cameras all over the airport. So as you can see, the video logs created by all these surveillance cameras would probably qualify as big data.

Now, what happens when you double the number of camera installations? In terms of data volume, you will again get about 2x the data. But will you get 2x the information? Probably not. Many of the cameras are probably seeing the same thing, perhaps from a slightly different angle, sweeping different areas at slightly different times. In terms of information content, we almost never get 2x. Furthermore, as the number of cameras continues to increase, the chance of information overlap also increases. That is why as data volume increases, information will always have a diminishing return, because more and more of it will be redundant.

A simple inequality characterizes this property: information ≤ data. So information is not data, it’s only the non-redundant portions of the data. That is why when we copy data, we don’t gain any information, even when the data volume increases, because the copied data is redundant.

Example 3: Updates on multiple social channels. What about social big data, such as tweets, updates, and/or shares? If we tweet twice as often, Twitter is definitely getting 2x more data from us. But will Twitter get 2x the information? That depends on what we tweet. If there is absolutely zero redundancy among all our tweets, then Twitter will have 2x the information. But that typically never happens. Let’s think about why.

First of all, we retweet each other. Consequently, many tweets are redundant due to retweeting. Even if we exclude retweets, the chance that we are coincidentally tweeting about the same content is actually quite high, because there are so many tweeters out there. Although the precise wording of each tweet may not be exactly the same, the redundancies among all the tweets containing the same Web content (whether it’s a blog post, a cool video, or breaking news) is very high. Finally, our interest and taste in good content remains fairly consistent over time. Since our tweets tend to reflect our interests and tastes, even apparently unrelated tweets from the same user will have some redundancies, because the tweeter is tweeting similar content.

Clearly, even if we tweet twice as often, Twitter is not going to get 2x the information because there is so much redundancy among our tweets (likewise with updates and shares on other social channels). Furthermore, we often co-syndicate content across multiple social channels. Since this is merely duplicate content across multiple social channels, it doesn’t give us any extra information about the user.

Although data does give rise to information, data ≠ information. Information is only the non-redundant parts of the data. Since most data, regardless of how it is generated, has lots of built-in redundancy, the information we can extract from any data set is typically a tiny fraction of the data’s sheer volume.

I refer to this property as the data-information inequality: information ≤ data. And in nearly all realistic data sets (especially big data), the amount of information one can extract from the data is always much less than the data volume (see figure below): information << data. Since the naïve assumption that big data leads to a lot of information is not true, the value of big data is hugely exaggerated.

Information ≠ Insights

Although the amount of information we can extract from big data may be overrated, the insights we can derive from big data may still be extremely valuable. So what is the relationship between information and insights? All insights are information, but not all information provides insights. There are three criteria for information to provide valuable insights:

1. Interpretability. Since big data contains so much unstructured data and different media as well as data types, there is actually a substantial amount of data and information that is not interpretable.

For example, consider this sequence of numbers: 123, 243, 187, 89, and 156. What do these numbers mean? It could be the number of likes on the past five articles you read on TechCrunch, or it could be the luminance level of five pixels in a black and white image. Without more information and meta-data, there is no way to interpret what these numbers mean. Since data and information that are not interpretable won’t offer you any insights, insights must lie within the interpretable parts of the extractable information.

2. Relevance. Information must be relevant to be useful and valuable. Relevant information is also known as the signal, so irrelevant information is often referred to as noise. But relevance is subjective. Information that is relevant to me may be completely irrelevant to you, and vice versa. This is what Edward Ng, a renowned mathematician, means when he says “One man’s signal is another man’s noise.”

Furthermore, relevance is not only subjective, it is also contextual. What is relevant to a person may change from one context to another. If I’m visiting NYC next week, then NYC traffic will suddenly become very relevant to me. But after I return to SF, the same information will become irrelevant again. Therefore, insights are an even smaller subset within the relevant information (i.e. signals), which is already a tiny subset of the interpretable information.

3. Novelty. Information must be novel to be insightful. That means it must provide some new knowledge that you don’t already have.

Clearly this criterion is also subjective. Because what I know is very different from what you know, what is insightful to me may be old information to you, and vice versa. Part of this subjectivity is inherited from the subjectivity of relevance. If some information is irrelevant to you, then most likely you won’t know about it, so when you learn it, it will be new. But you probably wouldn’t care because it’s irrelevant. Even if it is novel, it’s of no value to you.

However, once an insight is found, it’s no longer new and insightful the next time you have it. Therefore as we learn and accumulate knowledge from big data, insights become harder to discover. The valuable insight that everyone wants is a tiny and shrinking subset of the relevant information (i.e. the signal).

If the information fails any one of these criteria, then it wouldn’t be a valuable insight. So these three criteria will successively restrict insights to an even tinier subset of the extractable information from big data (see figure). So the big data fallacy can be summarized by a simple inequality: insight << information << data.

The value of big data is hugely exaggerated, because insight (the most valuable aspect of big data) is typically a few orders of magnitude less than the extractable information, which is again several orders of magnitude smaller than the sheer volume of your big data. I’m not saying big data is not valuable, it’s just overrated, because even with big data, the probability for finding valuable insights from it will still be abysmally tiny.

The big data fallacy may sound disappointing, but it is actually a strong argument for why we need even bigger data. Because the amount of valuable insights we can derive from big data is so very tiny, we need to collect even more data and use more powerful analytics to increase our chance of finding them. Although big data cannot guarantee the revelation of many valuable insights, increasing the data volume does increase the odds of finding them.


Via Chaturika Jayadewa
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This is checkthis

This is checkthis | meaningful symbols | Scoop.it

You have to check out checkthis.com.I really liked this platform to express yourself without any hassles.You can also do lot of things through this platform like selling a thing,hiring a person for a job and lot of useful things.It describes itself as between nothing and a blog.Really quick,simple,beautiful and useful...!

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BioDigital Human Intro: Explore the Body in 3D!

We’re all familiar with the 3D glasses by now. But now the 3D technology is also going to be uses to teach medical students about the anatomy of the human body. At the New York University School of Medicine, students now can navigate through a virtual body using a computer and 3D glasses. They can dissect the virtual body, which is projected on a screen.

The virtual human body is made possible by BioDigital Systems, a medical visualization company from Manhattan that we have covered before. BioDigital makes anatomical animations for all kinds of companies and institutions, such as pharmaceutical companies, medical device makers and medical schools.

BioDigital Systems wants to develop the virtual human body further on its medical education siteBioDigitalHuman.com. Their goal is to provide a searchable, customizable map of the human body, which is freely accessible: something like Google Maps for the human body. The website is at the moment only available in beta version, but in the coming months BioDigital plans to offer its code to health websites and health professionals.

It’s still the question whether the virtual body will replace an actual dead body for the sake of anatomy classes. Most likely it will be used in the future to complement current educational methods


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Stop talking about “social” » THINK OUTSIDE IN

Stop talking about “social” » THINK OUTSIDE IN | meaningful symbols | Scoop.it

The leading businesses are recognizing that the web is moving away from being centred around content, to being centred around people. That is the biggest social thunderstorm, and all of us are going to have to understand it to succeed. So stop talking about social as a distinct entity. Assume it in everything you do.

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iDoneThis

iDoneThis | meaningful symbols | Scoop.it

iDoneThis is amazing.Ever day they will mail you and you have to simply reply back to them about what'd you get done today? That's all. It will automatically keep a calendar of your activities day by day.Try it

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