HealthGraph.png

This is part of our Creative Destruction 7 Act Play series. The market we are currently focused on is Healthcare. In Part 1 we looked at the big picture. In Part 2 we drilled into consumer health sites that are leveraging semantic web technology.

In Part 3 we looked at innovation in the enterprise space, how semantic web technology is being used by researchers in pharma and biotech firms

In this final Part 4, we look at how all the participants in the “health graph” can start to work around a common set of data standards in what may be the first glimpse of 21st century healthcare.

In our first post on Healthcare we wrote:

“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 “health graph”. Their framework is the one that matters in the end.”

The answer of course is the fabled Electronic Health Record (EHR). I say “fabled” as this has been forecast by people for a looooong time. Cynics might write it off. They would be wrong. Technologies that take a long time to come to the mainstream sometimes do so just after their demise has been declared by “sensible” folks.

What caught our eye was a case study related to Diabetes – a current “scourge” in America and other countries. So we decided to focus this post on that case study and the product behind it. This may point the way to what we are calling the “health graph”.

HealthGraph.png

This is part of our Creative Destruction 7 Act Play series. The market we are currently focused on is Healthcare. In Part 1 we looked at the big picture. In Part 2 we drilled into consumer health sites that are leveraging semantic web technology.

In Part 3 we looked at innovation in the enterprise space, how semantic web technology is being used by researchers in pharma and biotech firms

In this final Part 4, we look at how all the participants in the “health graph” can start to work around a common set of data standards in what may be the first glimpse of 21st century healthcare.

In our first post on Healthcare we wrote:

“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 “health graph”. Their framework is the one that matters in the end.”

The answer of course is the fabled Electronic Health Record (EHR). I say “fabled” as this has been forecast by people for a looooong time. Cynics might write it off. They would be wrong. Technologies that take a long time to come to the mainstream sometimes do so just after their demise has been declared by “sensible” folks.

What caught our eye was a case study related to Diabetes – a current “scourge” in America and other countries. So we decided to focus this post on that case study and the product behind it. This may point the way to what we are calling the “health graph”.

Personal Knowledge Management: Who Needs Ya Baby?

The product being promoted in this case study is called Kyield from a seasoned entrepreneur called Mark Montgomery. It is one of those hard-to-categorize products. This categorization problem is common with emerging technology. Analysts like to have categories, it just keep things tidy. But existing categories don’t usually look like good entrepreneurial opportunities – there will be many entrenched competitors. Trying to invent a category that does not yet exist is fruitless, that comes much, much later after the product is very successful, it does not make the product successful in the first place.

This is a classic entrepreneur dilemma. The answer is of course to just focus on solving problems and ignore categories. Savvy investors also ignore categories and just look for hard problems being solved in different ways. Customers also obviously just want their problem solved.

In the case of Kyield, one analyst positioned it as “personal knowledge management”. This does not resonate at all. Firstly, “knowledge management” (KM) is a category that never really happened. It is sort of related to Intranets, collaboration and semantic web, but as a label it is not useful. It is too broad and conceptual.

KM may not be dead, but it is in a coma. Or sleeping. Or walking wounded. Pick your metaphor.

As for “personal” KM, who needs ya? Actually the people who need it are the analysts who write about it. Analysts need something like KM to do their job, but we all evolve our own tools to do this and these tools have to be dirt cheap and the market of analysts is too small to monetize via advertising.

Actually, there is a use case for Personal KM. If you have a life-threatening disease, you need to manage an awful lot of complex knowledge. You have the MOTIVATION.

And one disease that sadly a lot of people have is Diabetes.

Diabetes

These are frightening facts:

DiabetesFacts.png

What jumps out of that data is a simple conclusion:

“the patient has the power to cure themselves”

This is not like cancer, where scientists have to crack the problem. The most effective cure and prevention strategies for Diabetes all center around lifestyle changes – diet and exercise basically.

So the “health graph” has to be patient-centric.

The medical establishment has a role to play in that and they have to treat the symptoms of those who fail to change their lifestyle.

So all the other participants in the health graph also have to be involved. This is the complex problem domain in this case study.

The Case Study

The case study is available as a PDF download here (hint to Mark and any entrepreneur, make marketing materials an embeddable Slideshare or YouTube if you want distribution).

It is a 13 page document and worth a read if you work in the healthcare informatics business. It tells the story of a specific patient with Diabetes, George Strasburg. The hero in this story is Dr. Wendy Davis, his physician, who was:

“impressed with many of the specialists in the treatment of diabetes and other diseases, she was frustrated by the affects of the overall healthcare system on her patients. Diabetic patients spend much of their life running between specialists, dealing with a toxic bureaucracy, and worrying about the financial impact, rather than focusing on health priorities; reducing stress, maintaining a strict diet, exercising, and monitoring blood glucose.”

The case study goes on to describe online interactions that would seem normal in other markets but radical in healthcare:

“The next morning George logged on to his Kyield module, finding a note and link from Dr. Davis recommending a glucose monitoring system that stored results and could be uploaded to his module at any time.

Another note from the physicians network introduced Sandy; a registered nurse who published a blog on diabetes and would be assisting the CIO; posting links and structured data for automated feeds and search. She posted several links to diabetes management
applications and articles on diet and exercise.”

Later, the data from all this gets used by researchers who:

“found that 22% of the patients who registered for the system initially failed to provide meaningful data. However, the majority of the data proved invaluable; detailed information on patients, nutrition, exercise, blood glucose, and therapies were extracted on a nearly continuous basis.”

Imagine that at scale! This is what Sergey Brin is doing in relation to Parkinsons Disease as reported in Wired and covered in our part 1.

Read the whole case study to get the details. To me, this was a glimpse of a 21st century healthcare system.

The Technology

I can see why Mark Montgomery struggles to explain this concisely:

Kyield.png

“The AWSE engine combines, compresses, routes, and analyzes data from patients, approved devices, care givers, hospitals, payers, suppliers and researchers.

Each file is further represented by a semantic wrapper that includes additional structured client data prior to compression and securitization for delivery to the pre-approved device, person, or organization.

Client healthcare organizations manage relationships, security, and data with an administrator module which provides access to analytic programs that generate rich metrics for diagnoses, therapies, prevention, behavior, innovation, research, economics,
forecasting and prediction.

The physician module empowers relationship management, visual analytics, monitors devices, and assists in managing patient care all with one simple to use browser interface. The module can be integrated with any certified application, practice management
program, prescription programs, and partners.

The patient module empowers the individual to proactively manage his/her healthcare, set permissions for sharing their EHR, access related research, integrate third party programs, and participate in employer, community, or payer incentive programs.”

Semantic Web Technology For Electronic Health Records

The Kyield system in this case study looks like what we are calling the “health graph”, connecting all the participants around common data structures.

That sounds reasonably simple in technical terms and has been at the core of the Electronic Health Record (EHR) concept for a long time. There are three reasons why I believe that the EHR is about to go mainstream:

1. Funding. Specifically, $10 billion for the National Institutes of Health (NIH), $650 million to support prevention and treatment of obesity, smoking, and other risk factors for chronic diseases; and $500 million for training programs via the HITECH (Health Information Technology for Economic and Clinical Health) Act as part of the American Recovery and Reinvestment Act of 2009 (ARRA).

2. Legislation. The recent legislation against Insurance Companies denying coverage based on pre-existing conditions. The fear that the data would be used in this way would have been a showstopper.

3. Technology
. The technology is now ready, specifically the mix of social, mobile and real time with semantics at the core that we write about here.

If you are a semantic web entrepreneur and want to choose one market to focus on, healthcare would be a great choice.
———————————————————————–
CONVERT BREAKS: __default__

Personal Knowledge Management: Who Needs Ya Baby?

The product being promoted in this case study is called Kyield from a seasoned entrepreneur called Mark Montgomery. It is one of those hard-to-categorize products. This categorization problem is common with emerging technology. Analysts like to have categories, it just keep things tidy. But existing categories don’t usually look like good entrepreneurial opportunities – there will be many entrenched competitors. Trying to invent a category that does not yet exist is fruitless, that comes much, much later after the product is very successful, it does not make the product successful in the first place.

This is a classic entrepreneur dilemma. The answer is of course to just focus on solving problems and ignore categories. Savvy investors also ignore categories and just look for hard problems being solved in different ways. Customers also obviously just want their problem solved.

In the case of Kyield, one analyst positioned it as “personal knowledge management”. This does not resonate at all. Firstly, “knowledge management” (KM) is a category that never really happened. It is sort of related to Intranets, collaboration and semantic web, but as a label it is not useful. It is too broad and conceptual.

KM may not be dead, but it is in a coma. Or sleeping. Or walking wounded. Pick your metaphor.

As for “personal” KM, who needs ya? Actually the people who need it are the analysts who write about it. Analysts need something like KM to do their job, but we all evolve our own tools to do this and these tools have to be dirt cheap and the market of analysts is too small to monetize via advertising.

Actually, there is a use case for Personal KM. If you have a life-threatening disease, you need to manage an awful lot of complex knowledge. You have the MOTIVATION.

And one disease that sadly a lot of people have is Diabetes.

Diabetes

These are frightening facts:

DiabetesFacts.png

What jumps out of that data is a simple conclusion:

“the patient has the power to cure themselves”

This is not like cancer, where scientists have to crack the problem. The most effective cure and prevention strategies for Diabetes all center around lifestyle changes – diet and exercise basically.

So the “health graph” has to be patient-centric.

The medical establishment has a role to play in that and they have to treat the symptoms of those who fail to change their lifestyle.

So all the other participants in the health graph also have to be involved. This is the complex problem domain in this case study.

The Case Study

The case study is available as a PDF download here (hint to Mark and any entrepreneur, make marketing materials an embeddable Slideshare or YouTube if you want distribution).

It is a 13 page document and worth a read if you work in the healthcare informatics business. It tells the story of a specific patient with Diabetes, George Strasburg. The hero in this story is Dr. Wendy Davis, his physician, who was:

“impressed with many of the specialists in the treatment of diabetes and other diseases, she was frustrated by the affects of the overall healthcare system on her patients. Diabetic patients spend much of their life running between specialists, dealing with a toxic bureaucracy, and worrying about the financial impact, rather than focusing on health priorities; reducing stress, maintaining a strict diet, exercising, and monitoring blood glucose.”

The case study goes on to describe online interactions that would seem normal in other markets but radical in healthcare:

“The next morning George logged on to his Kyield module, finding a note and link from Dr. Davis recommending a glucose monitoring system that stored results and could be uploaded to his module at any time.

Another note from the physicians network introduced Sandy; a registered nurse who published a blog on diabetes and would be assisting the CIO; posting links and structured data for automated feeds and search. She posted several links to diabetes management
applications and articles on diet and exercise.”

Later, the data from all this gets used by researchers who:

“found that 22% of the patients who registered for the system initially failed to provide meaningful data. However, the majority of the data proved invaluable; detailed information on patients, nutrition, exercise, blood glucose, and therapies were extracted on a nearly continuous basis.”

Imagine that at scale! This is what Sergey Brin is doing in relation to Parkinsons Disease as reported in Wired and covered in our part 1.

Read the whole case study to get the details. To me, this was a glimpse of a 21st century healthcare system.

The Technology

I can see why Mark Montgomery struggles to explain this concisely:

Kyield.png

“The AWSE engine combines, compresses, routes, and analyzes data from patients, approved devices, care givers, hospitals, payers, suppliers and researchers.

Each file is further represented by a semantic wrapper that includes additional structured client data prior to compression and securitization for delivery to the pre-approved device, person, or organization.

Client healthcare organizations manage relationships, security, and data with an administrator module which provides access to analytic programs that generate rich metrics for diagnoses, therapies, prevention, behavior, innovation, research, economics,
forecasting and prediction.

The physician module empowers relationship management, visual analytics, monitors devices, and assists in managing patient care all with one simple to use browser interface. The module can be integrated with any certified application, practice management
program, prescription programs, and partners.

The patient module empowers the individual to proactively manage his/her healthcare, set permissions for sharing their EHR, access related research, integrate third party programs, and participate in employer, community, or payer incentive programs.”

Semantic Web Technology For Electronic Health Records

The Kyield system in this case study looks like what we are calling the “health graph”, connecting all the participants around common data structures.

That sounds reasonably simple in technical terms and has been at the core of the Electronic Health Record (EHR) concept for a long time. There are three reasons why I believe that the EHR is about to go mainstream:

1. Funding. Specifically, $10 billion for the National Institutes of Health (NIH), $650 million to support prevention and treatment of obesity, smoking, and other risk factors for chronic diseases; and $500 million for training programs via the HITECH (Health Information Technology for Economic and Clinical Health) Act as part of the American Recovery and Reinvestment Act of 2009 (ARRA).

2. Legislation. The recent legislation against Insurance Companies denying coverage based on pre-existing conditions. The fear that the data would be used in this way would have been a showstopper.

3. Technology
. The technology is now ready, specifically the mix of social, mobile and real time with semantics at the core that we write about here.

If you are a semantic web entrepreneur and want to choose one market to focus on, healthcare would be a great choice.
———————————————————————–
• Don’t forget to propose your startup for our Semantic Web Impact Awards. The deadline is Sept. 15.

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