In 1980 Ron Dennis bought what was an ailing company, an ailing motor sport company, and
merged it with his own.
The adage goes that in order to make millions in Formula One you need to start with more
millions and so it’s not a business.
So you need to be able to use it as a sandpit for other activities and Ron recognised that
very early on and developed two things in the same year; the first was our car company
but equally we developed a technology company and the technology company that was first
established to commercialise sensors in Formula One, that first sensor was an accelerometer
something that measures whether you’re accelerating or decelerating and how fast you happen to
be doing that.
Critical if you’re a racing driver because racing is won in the corners and braking late
into the corner is the way to win races.
It wasn’t just one sensor it was up to 400 sensors on board a car and they were collecting
data at anywhere between once every ten seconds and 960,000 times a second at the centre of
an internal combustion engine.
They were processing data on the edge of the network, they were pushing data to the pit
wall for engineers and strategists to make sense of that data with the use of analytic
tools, a visualisation platform and some decision support tools and ultimately act upon that
data in order to optimise your chance of winning the race.
Indeed today what you see if you stand in a garage on the Formula One grid is one team
participating in what is probably the largest science experiment on Earth and it’s being
performed every other weekend with up to 80 gigabytes of data generated per car and 40
data scientists sat behind every single car on the grid.
So what does that mean?
Well it gives us pedigree in capturing data, moving data from one place to another, analysing
data in real time and near to real time and deriving decisions off that data set which
is of course relevant in other industries.
We see there being huge opportunities through the use of this sort of approach to continuous
improvement and the technology components that support that in targeting the right intervention
for the right patient at the right time, leveraging the potential distributed wearable sensors
to make clinical decisions in real time, to better integrate the potential of companion
technologies in disease management and increase early diagnosis and improve treatment prevention
and reduce downstream systemic costs.
A most recent piece of work that we have done is in identifying novel biomarkers of disease
progression post stroke for the assessment of anti‐myelin associated glycoprotein.
Neurological disease is great actually for the application of this sort of companion
technology or digital therapeutic because often its onset and progression is relatively
insidious and difficult to measure and many of the tools used to determine whether an
intervention has had an impact are subjective and clunky.
So digital interventions are very relevant but there is no substitute for capturing really,
really good data at the point of initial capture and so we’ve worked in this space.
A great example is a smart Inhaler and not the sort of smart inhaler that monitors whether
you open or close a cap but indeed whether you open and close the cap, you’ve depressed
the MDI canister, whether you’ve inhaled rather than exhaled and also estimating what
percentage of that powder has been deposited within your lung field.
The final example I’ll leave you with we worked with a relatively forward thinking
primary care group that were interested in prescribing exercise therapy to patients with
a BMI over 35 and they had been doing so for about two years but unfortunately hadn’t
seen a reduction in weight as they had hoped despite the fact that they built and opened
a gym on their own facility.
What they wanted to do in the first instance was just understand whether these patients
that were being prescribed exercise therapy were actually being compliant or indeed just
signing in to the gym and so we developed a device that not only demonstrated that the
patients were being compliant but also gave the patients an idea of how much energy they
were burning every time they did turn up and do an exercise session and two remarkable
things happened.
The first is that of course the group started to lose weight but secondly when the device
was removed after about twelve weeks the group continued to lose weight and so a small amount
of insight provides education which will persist or the effects of which will persist beyond
the use of the initial device was the preliminary findings that we made during this pilot.
The world is changing, it’s changing rapidly.
We do need to engage with the technology industry because it is desperately trying to have an
impact and have an impact upon health outcomes but without guidance and a steer from the
clinical community it just won’t get there on its own.
Thank you very much