Edge Talk 1 April 2016 with Tom Lee of Symplur transcript

OLIVER BENSON:

We will start in a minute or so, thank you very much.

Hello, everyone. Welcome to Edge Talk for 1 April, it is 9:30 AM in the UK. Today we're talking about

Twitter, hash tags and data analytics for healthcare.

Just to remind you, as usual, to joining today both using the chat box and Twitter and our hash tag.

You can use the chat box to contribute and talk about anything, we are talking about and you would

like to react to.

We have Janet on standby to pick up your comments and feed them back. If you are using Twitter, use

the hash tag Edge Talks and use the handle school for radicals, or the Edge NHS.

Don't forget you can join our School for Health and Care Radicals and the Edge NHS Facebook

groups, just search for them. As I said, I am chairing today, as I mentioned we have got Dom who was

monitoring Twitter, and Janet who was monitoring chat.

That is all I need to do ahead of just introducing Tom. Tom Lee is based in California in the United

States. He has nearly 4 decades of experience in the business of health care with special emphasis on

leveraging web technologies and new programme develop.

Considerable admin experience in data analytics in a range of healthcare settings. He has been very

involved in start-up organisations, including the integration of data analytics, strategic planning and

reorganising operations to maximise efficiency.

He teaches courses and has been a frequent guest lecturer for doctorate students around the subject

of health care starter businesses. He is passionate about the evolving role social media is playing in

health care and is helping to usher in a new era of enhanced communication of valuable information

on the web.

It is time for me to pass over to you, Tom. Are you there?

THOMAS LEE:

I am here, thank you Ollie. I appreciate that. Let me share my screen here. We don't seem to have the

option, oh, here we go. OK.

I'm hoping everybody can see my screen now, good morning, my name is Thomas Lee, and the co-

founder of a healthcare analytics company by the name of Symplur. It is a real privilege to present

here today. My hope is that by the time we're done here today, I have actually inspired a whole new

legion of data geeks who embrace the stuff as much as we do at Symplur.

NHS IQ Webinar (UKNHSI0104A)

of 24 Downloaded on: 04 Apr 2016 9:04 AM

I wanted to give you a brief preview of some of the things I would like to touch on. The growth of social

media overtime, how that started and how it has been evolving over the last few years.

Specifically, I want to spend time talking about the use of Twitter in the healthcare space. I will

introduce you to the Healthcare Hash Tag project. Some of you may be aware of that, some may not. I

want to talk about the patient voice in some of this.

We will get into some data mining and take a look at some of the healthcare discussions and disease

discussions on Twitter. Time permitting, I would like to take you on a behind-the-scenes tour, we will

pull back the covers and show you some of the types of analytics that we are involved with at Sympler.

I just wanted to give you that quick overview. Starting out with us, a little bit of history, just to put this in

perspective. Taking a quick look back in time. If we look at the evolution of the web, it had a slow start,

not that many users early on.

By the time we got up to 1998, which was when Google launched, there are about 300 million active

users on the internet.

And then there was social media. And that really disrupted things. It really turned everything on its

head. It started several new industries coming out of all of that.

Now when we look at it today, about 16, 17 years after the launch of Google, we have over 3 billion

people currently using the web. A tenfold increase in that time. It is mind-boggling when you start to

think of these numbers, the number of people online.

When we look at the growth of social media, that is a relative newcomer. Back in 2010, there was just

under 1 billion people that were on various social media platforms at the time. We jump forward to

2016, it is in excess of 2 billion in that short span of time.

It is expected to be almost 2.5 billion in a couple more years. What we find is that within healthcare,

Twitter is the 800 pound gorilla when it comes to social media.

If something else is being discussed on Facebook, LinkedIn, or what have you, whether there is an

article being shared or news breaking, if it is not already on Twitter, it will be very quickly.

That is especially true in health care. This seems to be the channel that the healthcare discussion has

really gravitated towards.

Just a few facts for those not as familiar with it. There is an estimated 300 million monthly users of the

platform.

There is about half 1 billion pieces of information exchanged every day on Twitter across the board, on

all different subjects you can imagine. This is a little bit old now, probably from about three years ago.



At the time there was probably about 1 million new accounts being signed up on Twitter every day.

This incredibly rapid rise in the number of users of the platform. Even though that has levelled off,

there are still newcomers coming in every day which are joining in these conversations.

It is about 50-50 male, female. You get figures close to that, on average, it is about 50-50 as far as the

gender mix goes. There are figures thrown around about what percentage of Twitter users have a

college degree, I have 40% on the screen.

It is probably about that, or 40% have some college degree or higher. This is an interesting one, there

are an estimated 500 million non-users of Twitter that read Twitter content each month.

Whether they're going to Twitter.com and reading various fees, or they're being fed from other

platforms or other media outlet. But they are digesting Twitter even though they're not part of Twitter

with their own account.

When we look at the millennials, typically talking about people aged 15-34, a little bit less than one

third are active on Twitter. Of those that are active on Twitter, 81% check their Twitter accounts each

and every day.

15% will check Twitter more than 10 times a day. Very serious Twitter usage. Why Twitter, what is the

thing? What is the attraction? I'll answer that from a healthcare perspective in a little bit. This is a

popular sentiment that you will see posted on the web. This is not a knock on Facebook.

What is interesting is the comment on Twitter, where you are honest with strangers. That does seem

to be the case. Perhaps it is the fact that Twitter is this completely public platform that causes us to

behave a bit more, or what have you.

It all seemed to be the place where people are open and honest with people that they perhaps have

never met. That is not to say Twitter does not have its problems. For those of us who are active on the

platform, let's think back to when you first joined. What those first days were like.

Does this sound familiar? It was noisy, confusing, you could probably come up with more adjectives.

Intimidating even. Really understanding where to start. How do I find those parties that I want to

connect with? That have similar interests to mine?

With healthcare, we're such a specialised industry, things we're interested in, and being able to find

those like-minded people that we would like to connect with and converts, that was a difficult thing.

Try as they may, Twitter itself has seldom been the driving force behind some of the more important

developments on the platform. Most often those types of things actually come from the users. That is

actually the case from the hashtag.

This simple symbol started to allow people to be able to organise on Twitter. Suddenly, they could find



people interested in the same things as them. They could meet virtually and share the same passions

and start to form these online communities.

Healthcare totally bought into that. It just seemed a really good fit. Maybe it is because health care has

so many long words and things like that, or we are used to having abbreviations that lend themselves

to hash tags anyway. Whatever the reason, healthcare jumped on board.

In 2010, at Symplur, one of the things we were really working on was looking for ways to encourage

more healthcare professionals, namely doctors, to get online and start adopting social media

themselves.

The reason we were wanting to see that type of thing happen is so we could have their expertise

online, we know the weather is flooded with information, certainly a lot of health care information. We

know a lot of that information is bad information.

By getting healthcare professionals more involved, by interacting and helping to drive towards these

good sources, we thought that was a worthy cause. To get people to start joining Twitter, we knew we

would have to figure out a way to lower the barrier and help them more easily connected those people

and those conversations relevant to their medical specialties.

So we decided to set out and create a list of healthcare related hash tags. And we found just over 170

that were in wide use at the time. We publish them on our website and did so under the name of the

Healthcare Hashtag Project. You can visit that yourself. This is the web address.

We put this up in an interactive fashion that allowed people to click around and explore the hashtags,

the conversations that are happening.

In hindsight, this whole project just took on a life of its own. If you have not been to the Healthcare

Hashtag Project before, I would encourage you to pay a visit. I think you will be fascinated by some of

the things you will find there. This is what you will find when you go to the homepage.

The first thing I want to draw your attention to is the numbers. We have that arrow on the screen. We

started out of 170 healthcare related hashtags. We now have an excess of 9600 individual hashtags

that we are tracking and capturing information on. When I say capturing information, we capture and

store it all on our servers and categorise them and organise them by subject matter. To date, we are

coming up on 1.5 billion healthcare related tweets in our database. It is the largest number of

healthcare specific traits in the world.

That is growing at a rate of 2 million tweets per day.

You can explore other pages on the site. Across the top of the screen is a menu where you can look at

the homepage or you could explore tweet chat related hashtags.



We have a calendar there was when they were all taking place.

Conferences. Disease conversations. A whole host of things you can do. If you know a specific thing

you are looking for, or you do not and you want to look at the general topic and see what the hashtag

options are, we have a search box.

You can type whatever you want in there and it will whisk you away to what you want to find.

Every hashtag has its own page on the project site. When you go to the hashtag page, in the upper left

you can see the name of the hashtag.

Sorry, did someone ask something?

OK. In the upper left, we see the name of the hashtag and there are some brief information about it.

Some subject matter explaining what it is about.

On the right-hand side, you can find that we will show you some related hashtags. Things that are

parallel conversations. Hashtags related to the same subject matter as the one whose page you are

on.

And a little bit further down the page, if you scroll down, we have these top 10 lists.

We can look at the last seven days of activity, but who is using the hashtag most frequently in the last

seven days? Or the top 10 by the number of times they are mentioned. When we see mentions, that is

the number of times they have been spoken to or about.

On this page, we have the last 15 or 20 tweets using the hashtag. You get some of the flavour of the

discussion going on.

I will go down to the very bottom of the page. This is a very popular feature that we have. It gives you

an opportunity to extract information. You can set your start and end points of date and time right down

to the minute.

Then you can actually pull up a complete transcript of all the tweets using the hashtag during that time

parameter you established. Or you can get some light analytics, quantitative type analytics.

How many people participated in the usage of the hashtag at this time? How many tweets were made,

how many impressions? You can get a sense for how much the conversation was that way.

A lot of people do this right after a tweet chat. Maybe they were not able to attend a tweet chat they

typically attend. They can come here, get a transcript and read through it all.

Some transcripts are incredibly busy and it is hard to capture with your eyes everything that is going

on on the screen.



Afterwards, you can go back and read the things you missed during the chat.

We also allow you to look at topics. In this case, I have got breast cancer as a topic.

If that is what you are looking at, we will give you a list of all the different hashtags we have on the

system regarding the topic you are interested in.

We will identify what type of hashtag it is. A healthcare conference, a disease chat, a healthcare

discussion, what have you.

When you look at the topic areas like this, we have a list of influencers.

That is where we take all the related hashtags, combine them together, and show you the individuals

that are speaking and being spoken about most frequently on this topic.

There is a logic and learn about what ever individual hashtags you are interested in, or find some

additional hashtags and topics you are interested in.

As I mentioned, you can also submit new hashtags to us for consideration or inclusion in the

Healthcare Hashtag Project.

It really does not take that long. The first thing we ask you to do is authenticate with Twitter so we

know who is submitting to us, in case we need to contact you or ask questions.

We also have a few rules. We want to make sure the hashtags or healthcare related. That is all we

except here. We also want to make sure we have a clean tweet stream. That means it is not a hashtag

that is being used by other conversations as well. The scheme should be clearly healthcare related on

the topic you are suggesting that the hashtag is related to.

It also needs to be a hashtag that is in use. A lot of times, people may have good ideas for

conversations they want to start, and have plans for starting it one day, but until there is activity there,

we will not accept that.

We have certain rules we follow, and I would say on an average week, we probably get 120-130

hashtags submissions. We reject 75% of them for one of those reasons I was just mentioning.

But we try to be generous in our consideration of them and we encourage you to please submit them

to us for consideration.

Since we started doing this, looking back over time on this chart or graph we are looking at, this shows

us the number of tweets per day at different points in time since we started this.

You can see, in the very beginning, in year one, we were capturing, on average, about 30,000 tweets



per day. We thought that was a massive amount and could not believe it was such a high level. But

over time, you can see this was growing by leaps and bounds. Especially over the last couple of years.

We are now seeing 2 million tweets on an average day. Sometimes, that will even skyrocket beyond

that, particularly if there is a news event that sparks conversation.

In that same span of time, we have gone from 76 hashtags to over 200. Our database has grown to a

1.5 billion tweets.

With all this that we have been doing in these last several years, we have lots of data. We spend a lot

of time studying it.

This morning, I thought one of the things I could do here was show you some of the things that are

contained in this data and how we are able to slice it in different fashions and some of the things we

observe along the way.

For starters, just to put some general comments to this. Over the last 5+ years, we have seen a lot of

different topics and trends come and go. But there were six things I would like to point out that stand

out in our minds is being notable.

The first one is health information technology.

Health IT was one of the earliest, largest and most well organised healthcare discussions that we saw

taking place.

As a matter of fact, the hashtag health IT was one of the original 170 in the Healthcare Hashtag

Project. It was a perfect marriage between the techie crowd that has an interest in health information

technology and the twitter platform.

The subject matter revolving around electronic health records and mobile health technology, and all

these innovative, disruptive technologies that have so much potential within healthcare. It is still a very

active conversation taking place.

The Ebola crisis. This was probably the single largest healthcare discussion to take place on Twitter

that we have seen.

At its peak, we were capturing over 2 million tweets on that one topic every single day. It is a massive

amount of data that we have on that.

It was a terrible crisis and it was widely discussed. It is an interesting conversation to dive into with all

the factual things that were being discussed. Governments inserting themselves, trying to dispel myths

and rumours and fear.



Listening to public panic, it was a very active time on Twitter in the healthcare space.

I just mentioned government and that is actually another one on our shortlist. Government adoption of

healthcare hashtag usage.

Watching them leveraged this channel has been fascinating from our point of view. It will sound like I

am saying this to be nice to my post to date, but in all honesty, for years at Symplur, we have admired

the NHS and its role in being proactive in this space. I would be willing to bet that more than any other

government entity around the globe, we have properly received more hashtags from various NHS

officers than anywhere else. We think it is great.

Healthcare conferences. We have seen this evolve. It used to be that all these large medical

conferences around the world, people were sharing the information coming out of them.

But what would happen initially with the attendees themselves would submit the hashtag for the

conference to us, and because it was homegrown on the spot like that, sometimes we would get two,

three, or even for hashtags for the same event. So the conversation on Twitter became fragmented.

Two or three years ago, the conference organisers caught on and we get conference organisers

submitting hashtags to us, sometimes a year and a half in advance. They want to make sure they have

corraled the conversation around their event. They recognise the importance of being able to do that

and keeping that conversation cohesive and on one hashtag allows them to study that conversation

later on.

They can really extract insights out of that on what was resonating with the audience, what topics and

speakers, and they can better plan for future things.

We now see an excess of 200,000 health care conference hashtags a year.

Twitter journal clubs. This is an awesome revolution for doctors. We see more and more of these

popping up. It is where doctors of a common medical specialty will meet on twitter each month to

discuss the latest Journal articles related to what their area of expertise is.

There is this ability to interact with peers on the latest research, whether you work in an urban teaching

hospital or a rural outpost. It is amazing, and the ability of doctors to share this information and learn

from one another globally has health implications for all of us.

THOMAS LEE:

This is one we have been waiting for. Leveraging the social media data for other standings as well. At

Symplur, power tools and data have been part of over 60 published journal articles or grant related

projects. This free-flowing, unstructured data is starting to move into the spotlight.



Like I said, those were some of the more notable subjects that we have seen being discussed that I

wanted to point out you. However there is another one I would like to share with you as well. Along the

way, this database as it continued to grow, we began to dive deeper and deeper into it.

Some of the things we found, and one in particular absolutely surprised us. And, yes, that was the

discovery of patients adopting social media. I wish that I could stand here and tell you that we had a

high level of participation of patients on Twitter.

The truth is, we had no clue. We expected to find a lot of industry insider conversations, sort of like the

virtual water cooler where people would gather and talk about issues in their specialty area and what

have you.

Twitter, like I said earlier, a completely open and public platform. We never expected people would

share such personal things as their own health issues with strangers. That caught us off guard, and

began to fascinate us.

I would like to share with you something. In this short video, what you are seeing is a 22 month

timeline of thousands of healthcare topics being discussed.

Each one of those conversations are visualised as a bubble. There are about 100 million tweets here.

The green bubbles symbolise the patient-centred conversations, and the pink everything else. The

larger bubbles are conversations which have more participants, and more tweets going on. They are

more active.

You notice the patient-centred conversations, the green ones, absolutely begin to explode. And so that

is what we were seeing. And we thought, why this growth of patient participation? What is causing

that? My whole life and health care I have had privacy pounded into my thinking.

Why would patients choose to utilise a communication platform that offered no privacy? The next thing

I would like to show you, if you go inside one of those green dots, you can learn a few things.

This is a dynamic (inaudible) the rheumatoid arthritis community. It has taken place over a single

month. The pink nodes represent individual Twitter accounts, the larger nodes in the graphs are more

central and influential in the community. The green lines you see bursting between these nodes, those

lines of communication, actual interaction taking place.

The thicker lines indicate more communication, stronger conversation going on back and forth

between the parties.

This is all the result of an algorithm that we run. Basically, it is all maths, but it is maths visualised. It

helps us to see this as a community, and how vibrant this community is. We start to get a sense that

there is a lot of things happening between the participants in this. Eventually, as we're contemplating

this and diving in more, we were witnessing the flattening of health care.



Let me explain. Here we're looking at a static network centrality graph of the BCSM community, breast

cancer social media. This covers about 3.5 years of data. It looks like a constellation of stars. Instead

of stars, it is people orbiting one another.

The conversation is the gravity that pulls it all together. Boundaries are being broken down, there are

traditional barriers like location, profession, demographics, physical disabilities to a certain extent, that

will not so easily stop people from joining the conversation.

Traditional hierarchies, you are loading because of these new type of communications and

communities forming. That is what I mean when I say the flattening of health care. There is a

disappearance of barriers and hierarchies on Twitter which puts all the stakeholders on a much more

equal level where they can simply talk to one another in a way that is difficult in the off-line world.

If I zoom in, you can see there are different coloured nodes in here. The green node are self disclosed

patients, the purple ones are doctors. We have recognised a lot of patience and social media are very

engaged.

They want to do more than simply impart their knowledge on others. We found a number of them

actually want to change the way healthcare is delivered. Change the system itself.

But change can only come about, if you think about, in a public space. It provides a platform of people

can openly interact and provide ideas with this diverse set of stakeholders all at once.

Twitter has become a melting pot of ideas, people and truly disruptive innovation. The modern-day

public square for these type of things. That is when it clicked with us what the draw was for patients

and it opened up our eyes to why it was happening.

So where is all this going? What is the use of all this data? Is there some useful way it can be applied.

There really is. There is an opportunity for all kinds of things, be it academic research, business

intelligence, crisis management. An interesting one there is a lot of talk about right now is to do with

clinical trials recruitment.

I have heard figures in the neighbourhood of 60% of clinical trials fail because they are not able to

recruit an adequate number of participants in the study.

Within the social space, we can see all these patient discussions around specific diseases taking

place. And the network these people have built around the disease. There seems to be so much

opportunity to better leverage the space. Or even just a rapid dissemination of information.

Understanding the virtual network and how to get, in the event of a crisis, information out to the

general public. And what the hubs are in these conversations to make sure that word gets spread out.

There are countless ways of acquiring this.



What I would like to do right now is jump into some disease conversations, just to give you an idea of

some of what is happening in there. I thought diabetes would be a great place to start with. It is really a

worldwide issue.

This is current as of a few days ago. I took a look at some numbers here. In our system we have

identified 194 diabetes related hashtags, dating back to late 2010. And we have collected about 7.5

million tweets on the discussion to date.

Those tweets were made by roughly 1.4 million individual, unique users. When we look at the

conversation over time, this is looking over nearly the last five years anyway, you can see over time

this has ramped up steadily.

Especially when we got to 2015. There was a surge in the number of tweets on diabetes. You have

these spikes would pop up all over. If I can draw your attention to these five, they're all evenly spaced.

They all seem to be happening at the same time, and that is World Diabetes Day.

People want to draw attention to the chronic condition around the world. You can see year after year,

there is more and more participation with that event. It is a single day, 24 hour event. Then we have

this lone wolf spike right in the centre. And I thought, "What is that, where has that come from?"

After diving into it, it is hard to say it is one thing, it looks like a mix of things happening on that day. It

seems like there were several different campaigns which were not necessarily related to one another.

Diabetes Awareness campaign.

About 60% was in English, 15% Spanish, 15% Indonesian.

There was also a lot of talk about the Jonas Bros. If you are not familiar with them, it is a boy band. I

don't remember if it was a specific brother or the band as a whole, but they won the Teen Choice

Awards for their diabetes advocacy that week.

They started tweeting all of that, that combined with the other discussions, it was like this perfect storm

of tweets about diabetes which caused the surge all in that same week.

When we look at communities online, this is a community that goes by the hashtag DSMA, and it

stands for 'diabetes social media advocacy'. They're going to celebrate six years this coming June or

July, something like that.

It is a very interesting, tightly knit group. They meet constantly once a week, same time each week, a

one-hour discussion on a variety of different topics all centred around diabetes. Just like we were

looking at that dynamic network centrality graph, it is the same thing here. The larger nodes are central

and the conversation.

The arrows are indicating communication, the thickness of the lines indicating the amount of

conversation going on. We can look at a community like this in different ways. When we colour code it



we're running a community algorithm.

This is where we uncovered some subnetworks within the larger network. For example, the group of

yellow participants, the yellow nodes, they tend to talk with one another more frequently than they do

with the rest of the overall community.

The same thing for the pink, purple or red. This is actually quite normal. Sometimes we find there are

smaller, more specific subjects being discussed among some of these groups. When we are looking at

more global conversations, sometimes we find people from shared geographic regions start to cluster

together a little bit more.

THOMAS LEE:

And as I just said there were done this, even though we find more often than not they bring us

together. It is interesting to look at what is happening in the smaller networks.

Another thing we do, and we have been working on this for some time, is the stakeholder

segmentation.

On this one we are colour coding according to 15 different stakeholder groups that we used to code or,

I should say, to categorise these participants.

On this graph, the red nodes are showing who the patients are. Self disclosed patients. This is

overwhelmingly a patient reading community. It was founded by, organised, run by and attended by

patients.

I do not see any doctors in here. I see a few healthcare professionals. People I know are healthcare

dieticians, or certified diabetes dieticians.

But this is one by patients, and there was no doubt that the conversation they have had become the

glue that bonds them and drove them back here every week.

When we look at diabetes globally, and this is a map where the darker coloured areas indicate where

there was a higher concentration of tweets. In this case, it is still about diabetes. This is 2013-2015.

This tells us there is a global concern about diabetes. It covers all quarters of the globe.

Another thing that is interesting to look at is specifically when people are tweeting on different topics

out there.

When you look at this, this is what we call a heat map. The days of the week are running down the left

side over there. If you look at the bottom of the graph, there were different hours of the day. 24-hour

hours.



On the far right is that legend. The blue areas are where the fewest amount of tweets are happening

and the red areas are where the highest concentration of tweets are happening. In the graph, all of

their squares are actually one hour period. We are looking at a random to in the middle of last year,

and this takes the averages for the days of the week here.

Right away, we can see where some hotspots are. Tuesdays, late morning. This was looking at Pacific

time by the way in a few different places.

If we are interested in finding where activity is happening and where we should be planning our time if

we want to participate in this. This is one way we can look at it.

By the same token, it is important to understand that if you are an organisation organising an

awareness campaign or something like that, it is important to know when people are not tweeting. For

what ever reason, we tend to find that Saturdays have lower participation in these discussions then

other days.

I will give you an example of something we observed a couple of years ago. That has to do with Rare

Disease Day. This is a one day a year international campaign to raise awareness about the many rare

diseases out there and the issues related to rare disease.

It is always held on the last day of February each year. But on 2015, the last day of February fell on a

Saturday. When we were looking at that Was Diabetes Day on the graph a few years ago, we saw a

spike on the graph that was going up and up. It was the same thing with Rare Disease Day. Except it

landed on a Saturday and they actually dropped several percent from the last year.

Several years ago, the last day of February was on a Monday, and they saw 550% growth in

participation over the preceding year. How did they know about what Saturdays are like and lesser

participation in advance, perhaps they would have strategised differently.

But statistics out there can help with planning such events.

When we look at all these tweets about diabetes, and in the last case we are looking at last year,

2015, roughly one third of them were re-tweets.

To give you some perspective, about 48% of breast cancer tweets are retrieved. About 50% of

Alzheimer's tweets and 55% of pancreas cancer tweets of the tweets.

I cannot you wait that is, but it points out there are differences in all of these different communities and

disease areas. There is not one blanket rule of thumb. It takes some investigation to see how these

communities are performing and what the norms are.

It would be interesting to do a study to compare different diseases and the styles of interactions like

that.



Another issue is of all the tweets with diabetes shared last year, 22% had images attached. What is

interesting is 52% of tweets with images where we tweets. That was compared to 20% descent of

tweets without images that were re-tweets. It may be that we tartly treated more often if an image is

attached.

Why might that be? Quite honestly, we are visual. The visual sense is so ingrained in our genetic

make-up. And if images connect people, you can take advantage of that. If you are trying to spread a

message, sometimes a link is not necessarily going to catch someone's eye. Images do and that is

something to bear in mind.

It is always interesting to look and see specifically what is being said in these conversations. But when

we are looking at literally millions of tweets, we need to do still that in some fashion. One of the ways

we do this is looking at word frequency.

Here, we are looking at a two month period in the middle of last year. This is what we call a proper

graph. The larger bubbles at the centre of the graph indicates that those are the words that are used

most frequently in these tweets. Then it I was out counterclockwise from there. I will seem zoom in so

you can see better.

When we do this we are looking for things that stand out. We can see things like risk there. We want to

dive into that conversation and see what is being discussed relative to risk factors. It is a way of

starting to identify some areas of exploration.

But it is much more interesting to slice it in a more detailed place. Quite frankly, I like looking at it by

different stakeholders. That makes it interesting for me.

When I look at the 10 most frequently used words by these different stakeholders and tweets over this

two month period, patients, doctors and advocates, the first thing I see is the number one used word

for it is the same. It is diabetes. That is not a surprise.

I also see the words 'type' is in each of those columns. But more interesting thing what is the same is

looking at what is different. We get an idea of the perspective that different people bring to the

conversation.

If we do this, now you can see, when I look at the patients I see some very basic daily issues, daily

concerns. When I look at the doctors, I see this concern for managing risk and perhaps better

understanding of treatment options.

When I look at the advocates, it is very clear. Support, encouragement and urgency.

But the point is, everyone, regardless of who you are in a conversation, everybody is going to bring

their own background and viewpoint to the conversation. They are going to have their own

perspectives.



It is important to expect there will be those differences. It is actually quite fascinating, in some of these

online communities where we have a real mix of stakeholders in the conversation, I think that is where

some of the most fascinating discussions take place.

Links. There are a lots of links shared on Twitter.

When we look at diabetes, the same two months last year, May and June, about 73% of the diabetes

tweets contains a link. That is very high. That is a lot of information that is being shared on the

diabetes stream.

When we look at the most frequently shared link by doctors in the last quarter of last year, this is what

they were linking to. The International Diabetes Federation's Diabetes Atlas. The seventh edition.

And we can look at this and see how it plays out on other social platforms. We can see that overall on

Twitter, not just by doctors, but in this time, we see how many times it was said. But we can see it was

also said on Facebook and LinkedIn and Google +.

But like I said, Google is the 800 pound gorilla. It is where the conversation takes place.

But the information being shared was offering this downloadable PDF file that is 130-140 pages, or

something like that. It is pretty much an overview of everything the lay person will want to know about

diabetes and the care of diabetes.

You can understand why doctors will find that some important to share.

Conversely, this was the article during that same time that was most frequently shared by patients.

This was about a gentleman who is a celebrity chef who has been diagnosed with diabetes. He has

made it his passion to show people with diabetes that the road to good health is attainable. Dietary

issues and things like that.

This is a good representation of what we see over and over when we compare these two stakeholders

in what is being shared, doctors versus patients.

Healthcare professional, it is understandable. They tend to share links to educational material, journal

articles and things that line. Patients tend to share links to stories. People like them, who they can

relate to. We see that time and time again.

One of the things that I thought would be fun to do if we have time, and it looks like we should have

some time, I thought we could actually go over to our analytics platform. Like I was saying earlier on,

we can pull back the covers and together explore one of these online disease discussions that are

taking place.

Perhaps before we do, are there any questions or anything coming up on the tweet stream?



OLIVER BENSON:

I was going to say, it has been fascinating so far and I'm interested in what you are saying. To play

with it would be great, but I know there has been some discussions in the chat room and I think Janet

has got some questions. Janet?

JANET WILDMAN:

Jen wants to know how does a (inaudible) Journal work, that is one of the questions.

THOMAS LEE:

I'm assuming you are referring to the journal clubs? This is something we have seen coming up over

the last two years now. The first one I can recall is perhaps three years ago.

This is where mainly doctors decide there will be a time each month, in their specialty, urology was an

example of the early ones. They convene on Twitter, and before that they will have read their medical

specialties latest journal articles.

They will come ready to discuss their impressions, opinions, ask questions of one another and ask

questions but recent research published in these journals.

It is a way for them to learn from one another, share ideas with their peers. One of the fascinating

things is, you can actually, no matter where you are in the world, all of a sudden you have access to

some incredible minds in your area or specialty.

Some of these journal clubs run for just an hour long period.

Others actually convene without a designated time of day, but they plan for sharing their thoughts over

one or two days.

It is interesting to watch.

SPEAKER:

There is another question from Dom, who do you connect with to get the maximum impact?

THOMAS LEE:

Some of the things I was showing, just understanding the patterns of activity in the subject area that

you are getting ready to plan a campaign for. If we know that was more activity on certain days of the

week than others, you can centre your launch day for a campaign appropriately.

If it is a single day event, consider when most people are participating in discussions already what day

that should be. Another is understanding the network. If you want to get your campaign to have

momentum, then it helps to have relationships with influential parties within those networks.

Because if they share something of yours, again, remembering they carry a bit more influence and are

paid more attention to them some of these networks, your message is likely to get re-shared time and



again.

Understanding where some of those hubs are, and preplanning and developing relationships, if it is a

disease area or whatever it is, but within the topic you're going to be planning a campaign around.

Making sure you have some good connections ahead of time can be worth its weight in gold.

JANET WILDMAN:

Fantastic. This one comes from Tony, "Give us an example where your approach should to data

collection and analysis has enabled and directed change."

THOMAS LEE:

This goes back to the formation of patient communities. One of the byproducts of the Healthcare

Hashtag Project, it allowed people to not only connect to one another, but to see how many other

people like them there are.

I think of the rare disease community as special in that respect. Just by its nature, rare disease can be

very isolating. Often times you don't perhaps even know another person who was going through what

you are going through.

You may not even be able to find a medical professional who really understands. It can be very, very

isolating. What I have seen in some of these online communities that become dedicated to certain rare

diseases, there is a virtual support network, which never used to exist.

Just having that, just having that, I can just see how that has been so invaluable to a number of people

out there. To be able to connect and find that support, knowing you are not alone can mean so much.

Let alone finding things that are working for somebody, something that healthcare professionals in

your part of the world had not considered of whatever.

More than that, it is the comfort factor. That is what has left a big impression on me anyway.

SPEAKER:

Thank you so much, I'm learning a lot from the conversation.

OLIVER BENSON:

Thank you very much, Janet, I was just going to ask Tom something that Sally brought up about the

confidentiality and anonymity of using something like Twitter for Healthcare research.

I guess people are putting it out in a public space. Does that mean as a researcher you feel you can

access that? Are there ethical considerations, I guess, around this kind of research?

THOMAS LEE:

Great question. A couple of things. Certainly, when we get into the example of academic institutions,



they have their rules and ethics policies that they strictly follow.

There is a lot of debate about that in some circles. With Twitter everything is public. Everything is

totally public and it is a matter of essentially a public record out there.

The fact that we identify who patients are on Twitter, the way we do that, and the only way we do that

is by identifying those people who are self disclosing.

When I say self disclosing, they are announcing it right in their Twitter bio. When we sign up for Twitter

we write a short bio of who we are.

Those people who identify themselves as a cancer survivor or something like that in their bio, their

purpose for being on Twitter is, to some extent, related to that fact. They're putting it out there for a

reason.

We feel very comfortable identifying them as a patient because of that. It goes back to what I was

saying earlier, we find that many patients really do want to affect change. They will be upfront about it,

and quite comfortable with it.

OLIVER BENSON:

We have 20 minutes left. This is the interesting, going behind the scenes bit, if you want to continue

with that, that would be great.

THOMAS LEE:

Cool. Let me shift over… So, this is Symplur's Signals. When we first started amassing all of this data,

all of a sudden, researchers started approaching us. They wanted to not only have access to the data,

but also extracting the answers out of it.

OLIVER BENSON:

Tom, we can only see your PowerPoint. I don't know if it is because you have only shared your

keynote. You might have to go and share, if you are using Safari.

THOMAS LEE:

How is that?

OLIVER BENSON:

That is great.

THOMAS LEE:

We started getting approached by researchers, and every time they had a problem, they wanted an

answer to. We had to develop new algorithms and tools to extract that information. We decided about

1.5-2 years ago to put a new user interface on this. And to put these tools out there for third parties to

do their own research with.



The result of it all was what we called Symplur Signals. We launched this at the doctor's 2.0

conference two years ago. It is a very report-driven platform. I'm going to give you an example, I am

going to look at the last 30 day period here.

I would come right in here and this is where we would tell the system what it is we want to explore. I'm

just going to use the example of breast cancer, I start typing breast cancer, and I get this topic of

breast cancer that I can select from. I'm going to select this tweet activity report.

It is a basic report, but a nice place to start, what it does, this 30 day period we're looking at, it gives

me the activity all the different breast cancer related hashtags.

It looks that there might be about 70 more than that different breast cancer related hashtag. We can

see what the activity is on each one of these. What I call the generic hashtag of #breastcancer, which

is the most commonly used one. This BCSM, is the community we were looking at. The graph I was

showing on the slide earlier when we were trying to understand where the patients was so involved in

these discussions. Maybe that is the community we want to dive into. Instead of looking at the broad

subject or topic of breast cancer, we can choose to look at that one community of BCSM.

If it is a community are not familiar with, I will start to look at the network analysis and that is how we

start to understand how these communities are structured, who was talking to who, through the more

influential parties are in some of these.

This is what we would look at. The other graphs that we showed before, those larger nodes by the

more influential parties, those being spoken to and spoken about more frequently than others.

If I was to zoom in a little bit here, you can see all of these lines are actually arrows. They tell us the

direction of the conversation, who is talking to who. The thicker lines, there is more conversation taking

place.

This is where it gets interesting, when we start to seg-node, we have this 15 different categories we

are separating parties into. Actually, 16 now, we're just added a new one. This is where it gives us

much more of a sense of who was involved in this.

What the different positions are. I like to single out certain groups, perhaps I want to zero in and look at

what is happening in the patient population, the discussion that is going on.

Or maybe have the patients are interacting with doctors. That is always interesting to see. And I

mentioned to you before about this community algorithm, remember? This is where we identify those

smaller clicks, smaller networks within a larger network. These people are tighter connected, talking

about specific types of things.

Another thing is really for any of these participants in the conversation. I'm just going to switch back

over to looking at this. What we are able to do is click on any one of these nodes and get a little bit

more information about the individual.



If you want to learn even more about them, typically in the past we would click on this and go to their

Twitter profile. Maybe look at the last 20 tweets or so, who they are following, that type of thing. But it

doesn't teach us much about the individual.

Instead we have some other more unique ways of being able to look at it. For example, I can choose

this "show tweets", and it runs a personalised transcript of everything this individual has contributed to

the discussion during this 30 day period we are looking at.

By the same token, I can see what was being said about this person or to this person. It is a way of

getting a much more in depth look at them.

One thing I will show you today that we are very excited about and this is, in a way, a little bit of an

unveiling. This is in beta. I don't think I have seen this to more than 10 or 15 people outside of

Symplur. Please understand, this is in Beta, so we have some rough edges we are working with. Aut

this is two years in the making, this healthcare social graph.

If we just go to somebody's Twitter page, we cannot learn too much about them. It is such a snapshot

in time. But with all the data we have, dating back use, and the conversations we have captured on

how people participate, it has allowed us to do some unique things.

In this case… Let me take a moment to bring it up.

In this case, we can look at somebody and understand that by going back a year's time, and this

bubble grass at the top here, you can see these different months coming across the year. We are

looking at the last 12 months. You can see some specific conversations that this specific individual is

influential in.

The larger bubbles indicate conversations they are more influential in.

If I scroll down a bit, I can see some overall topics this person is heavily engaged and influential in. We

see breast cancer in here, just knowing we are looking at the BCSM hashtag right now.

But if I click on breast cancer, I can see what other breast cancer related discussions she is involved in

and influencing.

It is getting to know more about where her interests lie and where they end, in all these different

discussions she is involved with.

On the BCSM hashtag here, I can look at the last 52 weeks and see who she interacts with her most

often. Who is talking to her most frequently, and two she is talking to. Right away, I see Alicia in both

columns. They properly took together quite a bit.

Anywhere I see a Twitter handle in the system, I can click on it and I can look at Alicia, in this case, in



the same way and open up her healthcare shows will social graph. This can be done with all

3,000,000+ profiles we have on our system.

We can see what web chat she is most often participating in. Images shared and things like that.

This is beta. This is something that is not open to our set of clientele yet, at this point in time. But it is

probably imminent. It is, for us, anyway, a very fascinating way of understanding the individuals.

Because of that, it is much more about the conversations they participate in.

Another thing I would like to show you. The term 'influencer' gets thrown around so much in these

discussions. It is hard to say, but influential in what way? We have different ways of looking at that.

Oftentimes, we rely on the number of times that someone is being mentioned, spoken to, spoken

about. That is a good indication on many levels. Who is being treated the most and showing the most

engaging content that people want to share with their followers?

The most active people tweeting? It is not really a measure of influence. Some people tweet more than

others.

Impressions get thrown around quite a bit and most people put great emphasis on that. But I can

discount that quite a bit, because I find that large organisations have tens of thousands, or hundreds of

thousands of followers. It does not take but one tweet for them to make more impressions than I can

make in a week.

It skews the result is a little bit. I cannot remember if it was an AIDs conference, perhaps. But Katy

Perry, the singer, made one tweet. She has got 1 million followers or something. She made more

impression on that one tweet then everyone else at the conference did. It skews results a little bit.

Replies is interesting, because it takes effort. It is more than a re-tweet. If someone tweets something

and it spurs someone to write a message back to them, those are people that are creating

conversation and instigating discussion out there.

We can look and see who is generating the most replies. The truth is, any one of these can be gamed

to some sort. There are lots of robots out there that you can pay to re-tweet you and things like that.

So we have developed a healthcare social graph score. We take into account not only how many

times you have been mentioned, but how influential are the people mentioning you? And how

influential are the people mentioning them? And how influential are the people mentioning them? It is a

recursive algorithm that leaps through 1000 times.

We also look at are they influential on the topic you are looking at? Not just influential in healthcare or

cancer. What about breast cancer or BRAC1? We will give that much more weight in the algorithm.

If it is a patient mentioning you. If it is a doctor mentioning you. If it is a known, government public



health account mentioning you. Those carry more weight in our algorithm.

It is highly specialised for healthcare. And if we run an influence report, in this case using that

healthcare social graph, what it does is it will calculate this scoring and we start out with this large pie

graph that lets us know in this influential list of individuals, what is the stakeholder breakdown?

This red slice shows as it is predominately patients. If I scrolled down, you will see this table that has

all these people. They are listed in order of school. The highest score is at the top. The highest score

will be 100. All the other scores will be relevant to that.

We have name and all of that. Here we have Alicia, who we saw in the other list we were looking at.

We have her listed in the stakeholders as a patient. And you can see she lets herself as a three-time

cancer survivor. We will have other things, like where she is from and things like that.

If I wanted to zero in on who all these patients are, I can just drill down and bring up a new list. Now

we are looking at strictly patients. If I scroll down, here they all are.

Sometimes, we are looking at reports that are literally thousands of those long. When it is a table like

this, we can filter for things.

I am looking for a blogger, so I typed that in and just now I identify a list of a dozen or so patient

bloggers. Properly on the subject of breast cancer.

It is a fast way of being able to seek out and find experts, perhaps, that you are looking for.

Another thing that is oftentimes interesting is where people are from. We can look at that. We saw that

one world map of diabetes, and that actually came from this report.

We can hover over countries and see how many individuals lead to these locations. How many are

participating in this 30 day period we are looking at on the hashtag, and we can be seen in and bring

up a list of who these influencers are.

I'm trying to stay conscious of time here. We looked at that word bubble report, and I can filter that. If I

was mostly interested in what patients are saying and wanted to look at the most frequently used

words by just patients on the BCSM hashtag in the last 30 days? Here it is. I see the word guilt right

here. That just caught my eye.

I can drill down and see all the tweets by patients where they are talking about guilt. It is a way of

being able to really uncover some of these things.

OLIVER BENSON:

That is incredibly fascinating, Tom. I'm just aware of the time. I know Janet has some more questions.

I don't if you would be happy to answer them? Janet, ever to you.



JANET WILDMAN:

Thank you. One of the questions that Tony is asking is, "What do we actually mean by impressions?"

THOMAS LEE:

I get asked that a lot. The way we measure impressions is based on the number of followers you have.

It is like a potential audience. If you make a tweet and you have 1000 followers, you have potentially

left an impression on 1000 tweet streams.

It is calculate it by the number of followers you have.

JANET WILDMAN:

Thank you. And there are two other things that have come up. We have asked in the chat room to get

together an action plan following on from the conversation and your fantastic presentation. One of

them is to see whether you would assist the school in setting up a network analysis, an analysis of the

network. I am not sure it if you would be interested in a conversation like that?

THOMAS LEE:

Absolutely.

JANET WILDMAN:

Thank you. I thought you would say that!

Another thing that has come out of the chat room is possibly setting up a journal club for the school.

We can talk to about the kind of support you can offer us on that?

THOMAS LEE:

Sure. Absolutely.

JANET WILDMAN:

Thank you. I really enjoyed the conversation.

THOMAS LEE:

Yes, this has been quite enjoyable. I appreciate the opportunity here today.

OLIVER BENSON:

It has been really interesting. I don't know if you have any final things you wanted to highlight?

One question I have is the Signal stuff, this presumably is a paid service? How does that work?

THOMAS LEE:

The Healthcare Hashtag Project is a service that is open to the public and we have heard online for

five years. The Signal service is a subscription service that is intended for serious research for

potentially large organisations who are trying to study the marketplace and better understand how they

can fit in.



But one is a paid platform.

OLIVER BENSON:

OK, useful to know.

Do you have any final things you wanted to highlight?

THOMAS LEE:

The only thing, to wrap it up, is I hope I have impressed upon people the wealth of information that is

out there and the fact that we have much to learn from it. Also that it is a very dynamic space, and a lot

of people are unsure of what to do on Twitter when it comes to health care.

One piece of advice I like to impart on people is simply be helpful. If you have expertise in a specific

area, if you are a healthcare professionals especially, just be helpful. Be available. Guide people

towards good information. Be supportive. You will reap rewards in return, I promise you.

OLIVER BENSON:

Brilliant. Thank you very much.

Sorry, I keep muting myself for some reason.

Thank you, Tom. That was fascinating. In the last 20 minutes I have learned so much about Twitter,

and I've been a Twitter user since 2008.

I have been an established user, but I think the fascinating things of seeing how, when I send a treat

and put a hashtag in, how build can build into a really detailed piece of research. And actually how

much information you can clean from taking me and then looking at the sort of people I interact with,

and how builds to a fascinating picture.

I want to say thank you very much. I'm sure everyone who has watched this live, or who will watch the

recording, and we have quite a lot of people who will watch the recording afterwards, they will, I'm

sure, be on the Symplur site, checking out their hashtags and also checking out the wealth of

information that is on there.

Thank you very much, Tom. I guess the final thing for me to say is that there will be a record of this,

and we will be doing an Edge talk this time next month, details of which will be shared shortly.

Thank you to everyone who has joined us and taken part in our conversation and chat. We will see

you again shortly.



Edge Talk 1 April 2016 with Tom Lee of Symplur transcript

Healthcare

Transcript of Edge Talk 1 April 2016 with Tom Lee of Symplur transcript