Without further ado, please allow me to introduce our two main speakers today.
So first up will be Dr Nick Scott RAM, MBA.
He is the managing director of Life Sciences for Sense on Health.
I was also director of the Oxford Martin School here at the University of Oxford.
He's got over 25 years of experience in commercial and business developments in the life sciences sector,
particularly of pharmaceuticals, as an MBA in natural sciences and a Ph.D. in the philosophy of Science from Cambridge University.
He's worked in blue chip life science companies such as Oxford Biomedica, How the Jet Pharmaceuticals,
where he was vice president for corporate affairs responsible for strategy.
Previously, he was darch of the commercial developments of the Oxford Academic Health Sciences network,
where he was responsible for the development of strategic partnerships with major pharmaceutical companies.
He's led the Life Sciences Industry Position on Biotechnological Technology Patents Directive to the
European Parliament in the 90s and in 2001 it was awarded the NBA for services to biotechnology.
In 2001, after neck health halted his heels would come Dr Brian Heinz.
Brian is the technical director for Global Non-woven for Campbell Kimberly-Clark and is responsible for driving key technology initiatives,
intellectual property and developing strategic vision for the application of non-woven technologies within Kimberly-Clark.
Prior to his current assignment, Dr Heinz has held various roles,
including pilot facilities manager Mel Technical Team Leader and director of Frontend Innovation, so he knows everything about this during his career.
He's obtained 43 US patents,
16 trade secrets as the recipients of the I.A.E.A Lifetime Technical Achievement Award at the Society of Women Engineers Rob Rodney Dietrich Award.
So the speakers are going to come one after another and after that, we're going to progress into Q&A.
You can type your questions in the chat and they will be fed through the backstage process up to me and I will fire them off to speakers.
So enough for me. I see that Nick has appeared on screen. Thank you very much.
Welcome, Nick. Thanks, Phil. and good afternoon, ladies and gentlemen,
it's a pleasure to be here and very much appreciate the opportunity to talk a
bit about what happened to his stance on health during the COVID pandemic.
Now there should be a slide deck that should be sort of appearing imminently in this so I can talk through some of the things that we did.
But by way of background, I work for a company called Sunshine Health.
We are about 150 people and our roots go back to a deep embedding within the University of Oxford
and also the local hospital trust called Oxford University Hospitals NHS Foundation Trust.
Next slide, please. Our model, which we set up about three years ago,
was trying to balance a different type of relationship between industry and the health care system and particularly hospitals.
And at the heart of that was really the notion of building a trusted,
collaborative community that would enable us to get access to fully anonymized data and clinical
AI to improve patient care and accelerate the discovery and development of new medicines.
And in essence, what it was as a model that allowed NHS healthcare trusts to participate,
become equity holders in the company to get investment in IT infrastructure,
to support data access and analysis,
and then to get a commercial return out of the partnerships that we would then generate through working with life science industry companies.
And when we started this out, we really had to trust Oxford and Chelsea and Westminster as our partners.
Next slide, please. And went out and did an initial public offering on AIM back in 2018, raising 60 million pounds.
And since that time, when we had about access to two million unique patient records,
that's now grown to over 8.5 million in the UK and a further nine million in the US.
And you can see from this slide the partnerships we've built across the different hospital systems were working with the other side
of the equation with being able to work with the pharmaceutical industry and effectively act as a docking station across NHS data,
applying our in-house analytics capability and machine learning and computer vision expertise,
and then partner with pharmaceutical companies, in particular therapy areas.
So, for example, we've been working with bear and cardiovascular disease, the Brit with Bristol-Myers Squibb looking at blood cancers.
The foundations of this is really around having close partnerships,
and that's the key to the success in terms of being able to build up novel approach.
So the words that Charles spoke earlier on in this, I think, are very pertinent in trying to find new models of collaboration and ways of doing this.
Next slide, please. As a business, we operate across two domains.
One is health care facing where we started out with a number of remote patient monitoring apps.
A number of those which really were birthed within the University of Oxford collaborating with the
hospital system while the first one for gestational diabetes through Lucy MacKillop and Lionel Tarasenko.
And then following that through Send,
which was a vital signs technology platform through Peter Watkinson at the hospital and again with Lionel Tarasenko.
We then added onto that clinical algorithm engines, which is really about developing real time support mechanisms for healthcare practitioners,
and we see this offer back into the health care systems where we don't make a profit out of this work
as being key within the partnership that we have with different hospitals on the life sciences side.
We focussed really around clinical AI enabled clinical trial optimisation and R&D of new medicines,
particularly focussing around how you can actually optimise and improve clinical development in different areas.
Next slide, please. At the point when the pandemic really started to hit in the UK,
I remember very clearly about a week before we went into lockdown thinking that things were really going to change very, very dramatically.
And as a company, we took very rapid steps in terms of ensuring that we could still continue to operate.
One of those was being able to actually work remotely with the NHS data, which have been fully anonymized onto an information governance procedures.
And we were grateful being able to do that. We also transitioned as a company completely to remote working.
And what we found interesting was that actually this was very successful and as a company now we have a permanent remote working policy.
So we have colleagues in different parts of Europe, in the US and further afield who all work remotely.
And we have found that an effective way of working the two other main domains that we're
really focussed on was what can we do in terms of helping the public and communities?
And then also, equally importantly, was what can we do to help the NHS?
And in this talk, I wanted to concentrate on a couple of these areas.
The first is in Sign Cove, which is new near real time prediction of COVID 19 patients in the NHS.
On the second is in Magnifier, which has a lateral flow reader technology platform that we developed as well.
It's also fair to say that we worked closely with the NHS in terms of the clinical needs and the requirements,
and we were very grateful for the continued engagement with our trust partners during the pandemic.
Next slide. So this just touches on an app that we developed for the community, which was ready for remote monitoring of patients symptoms,
quote CVM Health and here really, it was a question of being very nimble to build this out very, very rapidly.
And that was done in a matter of weeks going into months to actually build the architecture and then build this up, which we then rolled out.
Next slide, please. The other component was with the North Gestational Diabetes app,
which is for remote monitoring of mothers in their third trimester with gestational diabetes.
We took the decision very early on to offer this free to all NHS trust partners.
And as that was rolled out over the last year or so, that's now been used in over 50 percent of trusts within the UK.
So it's a fantastic testament to the team that we're actually responsible for doing this,
but also the fact that we thought we could make a difference in supporting.
A component on a pathway and make a difference in terms of the outcomes for diabetic mothers in the UK.
Next slide.
One of the key areas that we then started to focus on, and this started out very early on in relationship to one of our other trust partners,
Chelsea and Westminster, where we were approached by one of the leading ICU consultants,
which was how can we predict the only progression of disease and patients that are admitted into
a hospital and try and understand what their clinical pathway through the hospital would be?
And how can we actually manage that within the hospital setting?
So when patients are presented, obviously, although in a hospital system about 14 percent, you may go into hospitals suffer severe symptoms.
Of those, six percent would require critical care.
And one of the challenges really early on in the pandemic was the availability of resources, access to mechanical ventilation.
And there was evidence that really showed that the prolonged waiting time between the emergency department patients would
be admitted and that admission into intensive care may lead to delayed intensive care treatment and increased mortality.
Next slide.
So we set out really to identify a solution that would enable better prediction to reduce waiting times for those patients which were most at risk,
and then finding a way in which you could actually track that journey through the
hospital and be able to then for the clinician to side on the basis of that prediction,
which patients should be moved into intensive care and subsequently onto a ventilator if required.
And on the back of that, we developed two app solutions, or clinical decision tool solutions.
The first was signed Cove,
which really was an algorithm that predicted three potential clinical outcomes for COVID 19 positive patients that was risk of intensive care,
admission, risk of invasive mechanical ventilation and risk of in-hospital mortality.
And then the second was an operational algorithm that provided near real time decision making
support to hospital managers coping with the pressures and resources within the hospital.
Next slide, please. This just gives you an overview in terms of the clinical question we were asking,
which is kind of COVID 19 patients early clinical features predict the risk of severe disease and then
in terms of the data that we access through the clinical data from any admission using that data,
cleaning, curating it, applying that clinical algorithm approach to that and then being able to predict a risk of ICU admission,
risk of ventilation and risk of mortality.
And this was something that we started off at a weekend hackathon and very, very quickly moved into a prototype product within the following weeks.
Next slide. So if you could just share this, this is a video with the leading clinician from Chelsea in Westminster.
What's around the 27th of January? And I remember texting one of my colleagues from the clinical informatics team and I said,
Look, it seems like, you know, this pandemic may affect us all.
Part of it was the uncertainty. We really had no concept of the numbers of patients would appear with these symptoms, the severity of those symptoms.
The impact on our own staff while we're trying to achieve our point was to prevent progression of disease because you get patients earlier,
you intervene and you improve outcomes simply like that.
The key here was, I know your risk of dying, your race of needed intensive care or your risk goal need mechanical ventilation.
What's these machine learning algorithms can do is to take vast quantities of data and give it back to the clinician as quickly as possible,
because that could be the difference between life and death.
I don't think that artificial intelligence or algorithms will change the way that we practise medicine.
What it does enhances it. Thanks.
Next slide, please.
So that just gives us sort of a snapshot and what was very interesting in this whole experience where we were developing the algorithm,
testing it and validating it was really the close partnership we had with colleagues at Chelsea and Westminster on the ITU group.
This enabled us to develop the algorithm, test it live within the hospital, look at outcomes and then do further work around validation.
And what we found actually with the algorithm was that there was a increase in predicting
performance by 24 points against clinical standards with a five fold increase in precision.
And this then enabled the clinicians there to use that support tool for their clinical practise that they were delivering next slide.
So some of the benefits around this work really were improving patient based clinical care.
So earlier risk prediction would improve potentially clinical outcomes.
All of this was run on a cloud based architecture,
which was something that we were developing the Chelsea and Westminster and enables actually a much more rapid turnaround in terms of data analytics.
This went through a regulatory process and also it informed that decision making as well
all done within strict information governance requirements from the NHS side of this.
And since that time, we've gone to work with the hospital in terms of developing improving this as the clinical pathways modified itself as well.
Next slide. At the same time, in terms of the risk protection stratify patients,
this is also being used as a support for operational decision makers to support understanding the resourcing,
so you can then look at the expected numbers of admissions to patient to intensive care of patients.
The number of patients that would require ventilation or the expected outcomes in terms of mortality,
and the algorithm enables one to do that virtually in a real time setting.
Next slide. And what we found with this was really that the hospital clinicians,
administrators found this really an invaluable tool in terms of resource planning, and this is one of the benefits really.
I think clinical decision support tools not only those related to COVID,
but in other disease areas to really support and optimise planning in terms of patient journeys and what's actually required in doing that.
Next slide. Turning now to a second development that we did, and again, this was done very much at a pace within the business and as a company,
we have about 150 people working across different disciplines.
And this was another example of bringing together different disciplines within the company to identify a problem
which had been highlighted by the government and then to build a solution that actually could address that problem.
And this was specifically with lateral flow testing for COVID 19.
The number of you probably will have experienced using these lateral flow tests on one of the questions was actually,
was there a way that we could actually automate and improve the reading performance of those tests against different backgrounds?
And also then ultimately to be able to actually upload those results into the NHS?
And this project started back in December and was really based on the computer vision
team that we had within the company who had been doing more traditional imaging work,
looking particularly around pathologies and lung disease.
But very quickly they adapted themselves actually to work in a totally different domain,
which is to develop novel algorithms to support better reading.
And this really is a highly magnified, which is the product that was developed as a highly accurate set of deep learning algorithms,
which really automates the reading lateral flow tests and enables you actually to do this beyond what the human eye could actually see.
And also at the same time, this was a very, very fast,
secure and scalable solution and enabled one actually to outperform the human by detecting faint lines and improve actually those readings.
And we started working with a range of different datasets to actually prove this once we develop the algorithms.
Next slide, please. One of the things at the start was to actually work out what the architecture was.
And we developed a system which was able to read 500 plus tests per second and also sub to second machine to machine round trip response,
which is pretty important. And also with the scalability to handle up to 40 million tests per week.
So this was the fundamental building blocks were put in place, which we did in partnership with Microsoft on their zero platform.
Next slide. What we then put together was a range of eight different algorithms built specifically for the lateral flow reading,
which really would enable us to identify test which were positive, negative or void.
There was also an anomaly or fraud detector and then quality control algorithms,
which actually would enable one to actually review the brightness and actually understand what the test was of
the context of the background lighting to be able to then understand whether you had a positive or a negative
on that and that really sat at the heart in terms of the innovation that was brought into this for something
that superficially similar looks quite easy in terms of getting better precision and lateral flow testing.
But recognising that if you have to do that from lots of different backgrounds, then that is something that is much, much more complex.
Next slide, please. At the same time, we found with this that actually we were able to support identity fraud as well,
so that if it actually trained to seek out those things where there been some
lines that were abnormal or fraudulent in the control and or the test line,
and that's something then that could be uploaded using a QR code, which is an important part of this test.
Next slide. So what we found really was in being able to easier read the test.
Then there's the potential to use Magnifier to increase the use of lateral flow testings in a home setting or other
settings that then gives greater confidence as those tests are done in terms of the readings that are generated.
And this is something that we've seen is very, very significant. And the building and the development of this, which started in December,
took us two to three months and really again comes back to having multidisciplinary teams coming together very,
very quickly, having very strong project management and a very clear idea of what the end goal is.
Charles earlier on was talking about what the objective is another example of actually
having a shared objective that really everyone could work to and identify with.
Next slide, please. We also found that we could build in this COVID 19 app for machine learning, for lateral flow testing.
And this also working this of mobile phones was an important component of what we were doing with this next slide.
Finally, then with this, there's the opportunity to create a digital certificate,
which, if required, would support organisations to keep workplaces and events safe.
And also in terms of understanding epidemiological trends as well.
That comes with training videos and questions to ensure that the testing is done properly.
As with all of this, the focus is on having proper regulatory framework around GDPR compliance on a quality management system.
We see it's been absolutely essential and integral to the work that we've been doing on this next slide.
All of this then gives the confidence to enable secure reporting,
and then that is something that ultimately public bodies would be able to receive these reports and
also can have importance in terms of supply chains into manufacturers and inventory management as well.
Next Slide. So the final piece around this, which I've mentioned a little bit earlier, was around the regulatory piece,
and there was a great deal of effort done by our regulatory team to ensure that the certification
and regulatory compliance in this in terms of security controls over data handling and processing,
tracing and auditing were actually done as well.
And the magnifier has been approved for use for lateral flow tests, for professional use testing across Europe.
And also it's being worked with. The test to go up is anticipated to receive regulatory approval in the future.
So all of this was built into the whole process going forward. Next slide, please.
So that's just sort of giving a snapshot on two particular projects that we undertook within the company in very short order.
And this was really done against the backdrop of remote working.
So everyone be familiar with back to back teams meetings, but really required a deeper collaboration within different disciplines with the company,
within the company and also critically with our NHS partners.
That really enabled us to do these things. Some of the lessons that came out of this for us was very much a greater emphasis on project planning,
the importance of defining roles and responsibilities,
which then flow into a shared vision of what the target output of everything that is being done.
I think also what I witnessed was really a willingness and adapt to modify different development pathways and timelines repeatedly.
So what we found in all of this is that the goalposts did change slightly because the disease progression, the impact of it,
the decisions in terms of how things were managed also had an impact both within the NHS and in some of the other work that we did.
I think one of the bigger things from a health care system perspective is that the
COVID pandemic has shone a light on on the importance and impact of digital solutions.
And certainly we saw a major sea change within our trust partnerships in terms of recognising now digital
solutions could really have a future understanding of what could be done better within a healthcare system.
So a shift from looking at things that hospital or moving to the hospital at home are all part of this where actually remote monitoring,
vital signs tracking stay at home. A better understanding of patient tracking enables then clinicians to have more confidence
in the decisions they make in terms of patient treatment is really the way forward,
and this will continue to develop and grow. I think one of the challenges that remains within healthcare systems, though,
is the disconnect between who benefits from the products, who pays for them,
and there is a lag in the system in terms of changing those perspectives,
not only at the system level, but also where you're trying to integrate new product solutions.
And I think within the NHS, although great strides have been made in this direction,
there's an awful long way still to go in terms of how to connect up the funding benefits with the clinical benefits,
with the patient benefits across multiple stakeholders. And I think over the coming years, that's going to be an area of great concentration.
The other thing in the final thing to say, and this is when there's a collective need and the collective requirement,
it is incredible what people can do in terms of moving the dial and moving very,
very quickly on the speed that we had in terms of building algorithms and apps.
Validating and testing them within the business with our partners was something that was really incredible,
and I really give great thanks to our partners in terms of the work and the commitment that they had.
So for me, I think for the company changing to remote working, but being able to really partner and have a deep,
engaged partnership is really been the key in the fundamentals to making some changes in the pandemic and the impact of digital solutions.
Thank you very much.