We now turn to the Healthcare business in our Creative Destruction 7 Act Play series.

Healthcare accounts for about 15% of GDP in America. This is very, very big business. We have also seen that Healthcare is one market that is spending serious money on Ontology development; so it matters in our Semantic Web world. Healthcare also matters to all of the 6 billion people on this planet. So, this is a big subject. It is hard to do it justice, but I will try!

Lots of money does not equate to outcomes as this chart from National Geographic shows:

That line way up to the left that is literally "off the charts" is how much America spends. And we live about as long as everybody else. So that chart really introduces this series by emphasizing;

• 1. There is a lot of money at stake.

• 2. More importantly, healthcare determines how long you will live a healthy life.

• 3. 1 & 2 are not as well correlated as they should be - "could do better".

• 4. Better knowledge dissemination could impact the healthcare outcome (ie how long we will live a healthy life) and Semantic Web is certainly going to play a big part in knowledge dissemination.

Prospects for defeating aging altogether

We like to indulge in a bit of science in this series, to "skate to where the puck is moving". Another way to look at this what in management speak is called setting "Big Hairy Audacious Goals".

So here is a serious scientist (Aubrey deGrey) indulging in some major Big Hairy Audacious Goal setting by describing the "prospects for defeating aging altogether".

It is not embeddable and is 45 minutes long, but it is surprisingly approachable for something with a lot of deep science. It is also a subject we can all get interested in.

I will key off some elements of deGrey's talk to look at three key concepts as they apply to the Semantic Web's impact on Healthcare:

• 1. The rate of technological change is something we can plan around. This is not just Moore's Law for semiconductors. Aubrey de Grey has a great slide illustrating change in the airplane business:

His point is that we can plan on similar rates of change in other sciences, such as Biology (which is what he is concerned with). This kind of thinking is second nature in the computer business. We plan around Moore's Law without having the faintest idea how that doubling of capacity will be achieved technically. But as he points out, we cannot imagine what these breakthroughs will be, they will involve concepts that we are not even thinking about today.

Which brings us onto the second point.

• 2. The big breakthroughs are often cross-discipline. The really big "aha moments" tend to come when you are looking for something else or looking at a pattern in a totally different domain. For example, Aubrey de Grey's breakthrough came from applying proven techniques in Ecology to Biology:

To a traditional scientist this is heresy. But to people working on Complexity Science, this is increasingly accepted. And to a non-scientist this is intuitively obvious. Of course we live in one universe so patterns from one science should be applicable in other sciences.

• 3. Many of these breakthroughs may come from highly motivated amateurs. They have "beginners mind", the ability to see something without the obstruction of layers of accumulated knowledge. In the case of healthcare, the motivation is obvious. People with life-threatening diseases are motivated. We now have the extraordinary situation where thousands of highly intelligent people gather in forums such as PatientsLikeMe to research their disease. Making their research task easier is a Big Hairy Audacious Goal for semantic web entrepreneurs. Don't worry about the revenue model; if you can really crack that problem, the money will come pouring in.

Highly Motivated Users: Patients Like Me

Today's web is being used by patients to search Google before visiting a doctor. Maybe they want to avoid an unnecessary doctor visit. Or they want to make the best use of the short time that they will have with the doctor by being prepared. Or they want an unofficial second opinion.

Google does a pretty good job using Rich Snippets, so that consumer health sites that have RDFa embedded can present some data direct to the Google search page. That is good for relatively simple searches.

There is still a lot of room for improvement in basic consumer healthcare sites. But the area where game-changing innovation can occur is in long term disease management. These are "big nasties" that you see on PatientsLikeMe:

PatientsLikeMe is for people who have already been diagnosed with one of these diseases. This is not the simple "my knee aches, should I see a doctor" use case that Google search to consumer health sites focus on today. Once you are in that unfortunate position of having a long term disease, your motivation to research changes. You realise:

• 1. That when you ask multiple specialists, you get different answers and the big decisions come back to you.

• 2. The state of the medical art is changing all the time. There may be something new that is right for you but there are risks (as well as costs) and you need to weigh a lot of different factors.

In short, you need to be your own Chief Medical Officer.

The Web's Golden Triangle And Long Term Disease Management

The web's golden triangle is social, mobile and real time. We describe this here and how semantic web technology is the key to this golden triangle.

You can see how this applies to long-term disease management:

• Social. In this case "social" means other people with this disease so you can share your research notes directly with them.

• Mobile. Long-term disease management requires patients to change behavior, simple things like taking medications on time and changing diet. Recording those behavior changes and getting alerts to ensure patients take the required action clearly involves mobile phones - the only device we "never leave home without".

• Real Time. This is less critical than the other two points of the triangle. Some alerts are needed as described above.

Semantic Web Technology To Make Sense Of All This Complexity

Attempting to know enough about how to combat a nasty long term disease is hard enough. It is much harder when you are facing the emotional and physical trauma of the disease itself.

The subject itself is complex. But even greater complexity comes from the overlapping and contradictory knowledge frameworks of the different participants:

• Patient and the close relatives/friends who are advocates and caregivers

• The trusted General Practitioner who really knows the patient but is not a specialist in the disease.

• Many specialists. What is exciting about medical advances today is the cross-disciplinary cooperation. The breakthrough may come from outside the mainstream. But each specialist has their own framework for looking at the problem.

• The Pharma companies who have drugs that are already FDA approved and others in the pipeline where they want patients for clinical trials.

• Academic and scientific researchers.

Of course the patient has to be the center of this "medical graph". Their framework is the one that matters in the end.

The "Googley" Approach To Science

The latest issue of Wired has a great article on Sergey Brin's search for a Parkinsons Disease cure after he discovered that he had a genetic disposition to that horrible illness.

He of course approaches it as a scientific problem, specifically looking to see how "big data" can help. This chart shows how access to data can dramatically speed up results:

Networks such as PatientsLikeMe can clearly help to create more data. The system exports anonymized data for researchers and their users can be easily recruited for research surveys and at the right time, clinical trials.

There is one other area where huge amounts of data are starting to come online and radically change medicine:

10 Year Anniversary Of Genome Map

The Economist has a report on Biology 2.0, where they start with:

"TEN years ago, on June 26th 2000, a race ended. The result was declared a dead heat and both runners won the prize of shaking the hand of America’s then president, Bill Clinton, at the White House. The runners were J. Craig Venter for the private sector and Francis Collins for the public. The race was to sequence the human genome, all 3 billion genetic letters of it, and thus—as headline writers put it—read the book of life."

The series of articles points out that progress from that to breakthrough medicine was not as fast as the headlines of the time. But the go on to show how it is likely to have huge impact in the coming years. This proves the old adage about new technology:

"People always over estimate the impact in the short term and under estimate the impact in the long term".

One chart shows this dramatically. This is the equivalent of Moore's Lazw for Genomics (as yet unnamed), which makes the computer industry look slow and incremental:

The result? yes, lots more data. Lots and Lots!

Work On Stuff That Matters And Where You Can Solve A Problem

Tim O'Reilly urges entrepreneurs to "work on stuff that matters".

Healthcare qualifies on that score.

I added "and where you can solve a problem". There are too many entrepreneurs using hard core technology to solve trivial problems that just make us smarter consumers.

Healthcare is a massively complex domain. The data is massive and complex. The consumer is now a patient who really does need help. There are entrenched interests. This is one domain where all the smarts of the IT world are needed - great information architectures, magical user interfaces, infrastructure technologies that speed up time to market and reduce cost to deploy.

Healthcare qualifies on that score as well. it is the ideal domain for breakthrough Semantic Web systems.
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