A little over two years ago, the Abbott Freestyle Libre launched, to much fanfare and significant consumer trialling and direct patient marketing (anyone see a theme there with how another new entrant into Diabetes care is marketing themselves?). Since that point it has changed how people view their diabetes and their relationship with healthcare professionals in a dramatic and far reaching way.
The Libre was blogged about, widely reviewed, with given away product and Abbott managed to generate much excitement, indeed, this topic in the diabetes.co.uk forum has generated 4172 comments, up to now and started on the release of the product.
The UK based Facebook group focused on the Libre has more than 7,500 members and there are multiple Libre groups across social media. Abbott’s results also include commentary about how the Libre has lifted their Medical Device group’s sales and annual results. From their Q3 2016 results:
Worldwide Diabetes Care sales increased 11.5 percent on a reported basis in the third quarter, including an unfavorable 1.0 percent effect of foreign exchange, and increased 12.5 percent on an operational basis. International sales growth was driven by continued consumer uptake of FreeStyle Libre, Abbott’s revolutionary continuous glucose monitoring system that eliminates the need for finger-sticks.
From pretty much every perspective you look at it, the Libre has been a success, not only for Abbott, but for people with Diabetes of all types. Speaking to the Abbott Customer Care line, the helpful representative told me they had “Hundreds of thousands of registered Libre customers”, which isn’t to say they are all active but gives some idea of the uptake. But why?
Very simply, Price and Data.
First up, low price = disruption in the product market place…
For the early adopters amongst us, it was a way in to Continuous Data that many of us felt we could afford. I’ve written before how the price of CGM had put me off, but all of a sudden, at a price of £100, I could get up and running with something giving me continuous data. Two years ago, the equivalent set up costs on Dexcom were around £1000.
The resultant uptake in Europe outstripped Abbott’s estimate of demand to such an extent that they were forced to stop supply to new customers around 6 weeks after launch while they ramped up a new manufacturing facility. This supply restriction lasted six to nine months.
18 months after the launch by Abbott, and with their supply in full swing, Dexcom moved their sales in-house and massively reduced prices of their products, making it possible to get set up on Dexcom for around £250. The #wearenotwaiting crowd rejoiced, and Abbott went on to launch their Android App making it possible to start using continuous data for the price of a single sensor. £50. That’s now all you need to spend to get 14 days of data, really useful data, accessible in Diasend and we’ll move on to that shortly.
Demographic information out of Scotland shows that, as you’d expect, uptake has been amongst members of the population with higher and middle incomes, as they are the ones that can, or have made the sacrifices to, afford it.
There is no denying that it has changed the way that people purchase continuous data systems, and has brought a fundamental adjustment to the non-prescription diabetes device market, especially in the UK.
Similarly, the data presentation has caused other providers to change theirs. Diasend, for example started to include an AGP graph after Libre users started uploading data and wondering where it was!
Second up, Data = Democracy
Why did I put price first? Well without the product at the price, data would be irrelevant. By disrupting the pricing model of incumbent CGM providers, Abbott has been able to get access to many thousands of users that weren’t using anything like this before.
The data that it provides has proved to be a revelation to most. You only have to take a look at the many facebook groups where users discuss overnight hypos, the time it really takes for insulin to act, carb spikes, exercise reactions, etc, to realise how enabling it has proven to be. For the first time, many not hugely engaged T1s and a fair few T2s have started to look at what they do on a day to day basis in a way they haven’t had the opportunity to before. In a survey that I am currently running, just under 50% of the responses have come from Libre users. If that’s anything to go by, it has doubled the market for continuous data consumers.
And it shows.
I’ve mentioned previously about attending clinics where the registrar I’d been speaking to hadn’t a clue about AGP graphs and how to interpret them, and how the data I’d received enabled me to demonstrate very clear dawn phenomenon, or how it showed me how long insulin really took to act and just what my post prandial spikes really looked like rather than guesses based on what I had been told be HCPs. And I’m not the only one.
Now, these graphs are providing significantly more people access to data that they never had before.
But the question then becomes, how is this democratising diabetes care?
For the first time, by combining data with social media, people are able to find out much more about themselves. Partha Kar often talks about patients being experts in their diabetes and healthcare professionals being specialists.
Previously, the amount of expertise varied, and many weren’t that engaged, but now, more people than ever are engaged and know far more about themselves. And they are helped to be experts by their peers, reviewing data that is easily accessed, and more importantly, easily shared.
This means that HCPs are being presented with patients that know far more about how all the inputs they have everyday affect them, and are looking for help with dealing with them. They are providing immediate and often compelling evidence that clearly shows why a pump would make sense, or perhaps a change of insulin, or even a low carb diet.
The key here is the transfer of power. Now that we know about ourselves in such detail, we have pulled a lot of power away from HCPs. We are able to ask questions that we weren’t able to before, and more importantly, provide easy, full, hard-to-fake evidence to back up what we’re asking and why.
As these systems become adopted by insurance companies and national healthcare schemes, this will only happen more frequently, hopefully enabling some of those that really aren’t engaged to get involved.
Why is this a good thing? It’s a good thing because, whilst it transfers power into the hands of those who need to act, it also should free up time for HCPs to deal with those who really need help. It also enables HCPs to learn from the data that they are seeing more and more frequently about what T1D really looks like and from non-Diabetic users, that 3.8mmol/l is a normal glucose reading.
Essentially, as the use of products like this filters into everyday life for more and more people, what they learn about themselves enables them to better understand and manage what they do. But that’s not all. They also provide a much more complete picture of what is and isn’t normal and provide insights into how it should all be handled.
And it doesn’t stop there…
The Freestyle Libre and WeAreNotWaiting
As I’ve written about previously, multiple attempts have been made to allow the Libre sensor to be used as CGM, with a number of homebrew effots created. The LimiTTer, Glimp on an Android Phone which now supports a CGM mode, a version for the Sony Smartwatch 3, plus others. The Libre sensor has been adopted as part of this ecosystem, and remains something that people are interested in hacking.
Finally, there’s also the benefits that Abbott have gleaned from the device…
Abbott’s Data Uploads…
Early in its life, a number of people identified that the Libre software updated an anonymised version of all the data you downloaded onto your PC or Mac up to Abbott servers in the states. It’s written in the small print, as part of the license that you allow this. With “hundreds of thousands of customers” using the product throughout Europe, that’s a lot of glucose data. But it’s also a lot of Insulin, Carb ratio and ISF data as well.
In fact, it’s a lot of data that you might want if you were going to build yourself something like a better bolus calculator or insulin prediction algorithm. It’s certainly a vast amount of data that can be mined to generate very interesting statistics. So how will Abbott use it? Well that’s something we don’t yet know. But it creates some interesting options.
I suspect the answer is, “Watch this space”….