So thank you all very much for coming and thanks for the invitation to speak this evening.
I've been asked to talk on this topic, Applied Digital Health,
and what I've done is put a presentation together that talks about how it provides opportunities for better patient care.
I think that will be self-evident from the presentation that I make.
But perhaps more importantly for us at least, why it may assist better or more clinical research.
I'm Richard Hope's I'm a professor of primary care and head of departments here at the University of Oxford.
I'm a clinical scientist, and my main interests are actually cardiovascular disease and cardiovascular research.
But I've always had an interest in better utilisation of health data and interventions that improve patient care as well as beyond drugs.
Hence, I think, why I've been asked to give this talk this evening.
Although methodologically, I think there are all sorts of challenges.
I'm going to touch on a few of them this evening about how you can utilise data in a way that's more reliable.
And I think if you want to ask difficult questions that are methodological in nature,
then I shall immediately transfer that to the methodology in the room who actually do a lot of the work in relation to how the department
uses big data or routine data and therefore are fully aware of the limitations and lengths you have to go to to try and make it worth doing.
Okay. That's really by way of introduction and what I am also happy doing once I've worked to change my slides is taking questions during the session.
So I think I've been asked to talk for an inordinate length of time, 40 minutes or so.
So but I'm quite happy. If you want to interrupt me, please do so because I think it'll it'll break up the presentation as well.
All you got to do is raise a hand or shout, okay, so this is really what I'm going to talk about,
why one might invest in a presence in Applied Digital Health.
And then I'm going to talk about the research impact in those areas of how it
might assist in relation to disease burden or predicting disease outbreak,
how it can improve drug development, how it can assist in better disease prognosis and risk estimation.
And that's a very important topic because for most major disorders, the biggest prognostic indicator is the point at which is diagnosed.
So if you diagnosed important disease earlier for many important diseases, that is the biggest.
That's going to have the biggest impact on prognosis. I'm also a little bit about system change, behaviour change.
We spend a lot of time thinking about how we can get patients to change behaviour.
It's difficult but important, but actually increasingly it's how you can get practitioners and health systems to change.
That's just as relevant and I think there are examples where digital health can provide that as well.
And as I said, it's really all of the above in relation to health care impact.
And as I say, that should be self-evident as I work through. So why is this become more possible?
And it's simply the degree of digitisation.
Digitisation of the clinical record has been particularly pronounced in primary care and in fact particularly advanced in the United Kingdom.
So you can actually see that that pre 2000, in fact most UK general practice was fully computerised.
And what's happened since then is that lots of health systems have caught up and it's
this recognition that an awful lot of the clinical record is now held digitally.
That has meant that people have started thinking that can't be utilised more than it has been historically.
Just in terms of the UK and primary care, I'm really only talking about primary care,
but you could imagine similar examples could occur in specialist settings.
But in primary care, the principal software provider that provides the architecture for primary care records is themis they.
About 55% of the market. And the next big is this TPP.
And we've got relationships with both of these commercial operations and indeed are looking at ways that we
can increase our relationship with them with regard to co-investing in things that they want and we want,
then there may be opportunities for collaboration there and it's already impacts on clinical practice,
at least because the what's what's happened at an increasing trend is that that information is
provided back to practitioners that can help guide them in terms of guideline recommendations,
usually as a prompt. And of course the difficulty of that is that there are now so many prompts within GP systems.
It's a bit like guidelines.
You'll have rooms full of guidelines, you'll now have patient consultations where you might get two or three prompts for every single consultation.
What happens, GP's ignore them. And so thinking about how you can present information that's useful for practice in a way that you could take it up,
I think is, is of itself an interesting research challenge.
It's a sort of thing that Facebook and companies like that have transformed social the way we live our lives socially,
the way we interact with shops, the way we bank.
So in our social lives, it's doing this massive revolution about the way digital applications have changed, the way we behave.
And really what I think is going to happen over the next ten,
15 years is that degree of penetration into practice and the relationships patients have with health systems is going to go the same sort of way.
And one of the good things about Facebook is that they've demonstrated what the
risks and problems associated with these sorts of mass changes in behaviour are,
and we should be thinking about how we can safely develop these systems that maximise, gain and minimise risk.
And even if you think that it's been demonstrated that you can manipulate public voting through mass online campaigns,
in some ways that's a good thing because if if you can do it for the way people vote,
shouldn't we be thinking about things that have a desirable consequence in terms of the way people live their lives and living healthier
lives and accepting more evidence based interventions rather than the sorts of interventions you'd see on the front page of the Daily Mail.
So that's a challenge to us, really,
as to how we can canvass this change in the way people interact with themselves and each other through digital applications.
I think is is going to be quite exciting. So in terms of UK practice,
there is an awful lot of information which is actually available within the clinical record in relation to activity prescribing referral patterns.
This is a typical screen where you'll have demographic and clinical information
provided and increasingly you can actually link this personal level clinical record,
electronic clinical record into other datasets.
And for the UK most notably there are going to be hospital activity data through has or onus particularly
for mortality data but also deprivation scores and you can even triangulate for disease registries,
etc. So it's not just the single clinical record that one would want to be looking into, it's other national datasets as well.
So for the rest of the talk, I'm really just going to run through a few examples and you you can then discuss
whether you think that they're useful things to be doing or not at a later point.
So retrospective clinical epidemiology,
that probably is the thing that's been done the longest that in Pharmacoepidemiology and probably digital clinical records have been
used for about 20 years or so in those two areas and it's just a more efficient way of doing things like epidemiological studies,
which often used to rely on just physical records being filled in on cohorts and then followed up for extended periods of time.
The fact that you can actually start exploring this digitally obviously is hugely less time intensive.
This is a US use. The US are coming to this light but are investing huge dollars, hundreds of millions of dollars in their ability to mine health.
Data in the future. And obviously, you've got the mega data giants,
Google and Apple and and Amazon are spending huge sums of money in what they see is going to be the next big digital transformation,
which relates to health care. But this is just looking at Medicare and Medicaid.
It's easier to use Medicare Medicaid data because it's a single registration, it's universal care.
So it's effectively the equivalent of the National Health Service, but just for a small proportion of the US population.
But what they are able to do looking at these data is look at an outcome that you wouldn't want, which is an early 30 day readmission,
i.e. you've been in hospital for something important, in this case a stroke, and you've been discharged home if you've had a stroke.
What you don't want when you're discharged home is to have to go back into hospital within 30 days.
That's a bad clinical outcome. There are usually issues that you can do that prevent that.
And so what they've been able to do by mapping this is to look at the worst quartile,
which is the bits in dark there of the US that have the worst rates of readmission.
And what you could imagine you might do with that then is think about preventative programmes that are focussed.
What order did you put your initiatives in? Could be dictated by clinical need.
Another important thing that has come from retrospective epidemiology are the
observations that Multimorbidity have become the norm in most developed health systems,
i.e. that patients don't have single disorders anymore but they have multiple disorders.
I don't touch that anymore in case it starts flicking around.
So this is just looking at people with diabetes and showing their own than the 20% of them.
This is Scottish data used in primary care.
It's all the primary care records in Scotland and it's showing that only under 20% of patients only have diabetes.
And if you look at people under 65, then the mean number of additional conditions is about three and over 65.
It's six and a half of the conditions. And so what you could imagine from that is why it's important to think about how interventions
may interact negatively or positively in relation to different sorts of conditions.
And you start to get a feel for why the evidence base needs to be less, you need disease focus.
And I think it's only through having large digitised records that these sorts of observations can occur.
What we're quite interested in, though, at the moment it's all been about talking about multimorbidity, counting it, viewing it as a big issue.
There's very little original research around how that may impact on health care,
but you could immediately begin to think that is it the way they cluster?
Or is it the order that you get diseases in? Or is it a disease exposure issue?
And those are sorts of questions that you might pose of a routine data set and come up with some preliminary questions about that then
might guide you to think about an intervention that you could try to see if it altered trajectory compared to a comparator population.
So that's that's the classic use of routine data sets and still quite important because that last one,
I think you could immediately think about how it could guide you into hypothesis
testing in an efficient way that may come up with some empirical subsequent testing,
but in other important areas, disease, surveillance. And again, this might be more practical from a health system point of view,
but you could imagine how it might impact on the research programme and for that
showing data from the GP War College or GP's Research Surveillance Centre,
Research and Surveillance Centre, which has recently moved to Oxford.
And what that currently do is it provides the best early warning service to the National Health Service about rising rates of of influenza,
and it uses sentinel practices for that.
So it's general practices that get a little bit of additional money to to record things more than in routine practice that is collected twice weekly.
This is just looking at time of year showing that most flu, unsurprisingly,
is in the first part of the year, but occasionally you'll get spikes out of sequence.
And the other thing that they can do because they actually collect serology in these practices,
which again is not done routinely, is look at what influenza subtype it is, which helps the.
Guide how the NHS orders the antibody that goes into the flu, immunisations, it ultimately does.
But what we're quite interested in doing this thinking that could you start mapping disease
burden better at a local and at a regional level using these sorts of surveillance systems
that might give the NHS more granularity about what may become big burden problems or
what may predict a surge in requirements for acute admission in advance of it occurring.
Because at the moment the NHS tends to be very responsive. It can't really predict where demand is going to occur and at what time and where you
might be able to develop intelligent systems around those sorts of service issues.
So another area which is historically has been used for for pharmacovigilance, so a drugs introduced,
you can monitor its safety subsequently once it starts being used in clinical practice.
But I think that's been extended now to think that because the data are more complete and more reliable could
actually start to substitute for what has historically been the way that drug development has occurred,
which is through the randomised controlled trial. And what I've done here is I've plotted the Re-ly trial which is looking at a direct
oral anticoagulants or an alternative to warfarin that was on the bottom there.
The pivotal trial that resulted in Dabigatran, the first of these new warfarin drugs, if you like, these anticoagulants.
That was a pivotal trial against warfarin and showing the benefits in terms of reduced rates of
mortality and less ischaemia stroke and particularly bleeding into the brain with some of the downside,
which is that where there was more GI bleeding than warfarin.
So some of the upside, some of the downside and the top one looks basically at its use in clinical practice, much larger data set.
So that's looking at exposure in hundreds of thousands of people.
This is a huge city, but it was around 20,000 people.
These are tens of thousands of people up the top there in routine data use and it basically is identical results.
And that's very reassuring because it's triangulating what may be a more select population in a clinical trial, in a more routine setting.
And the drug regulators are now wanting real world data to complement complement the CT data.
This is looking at comparison between the other direct oral anticoagulants.
In fact, there's been a fourth one now,
but it gives you an idea of the fact that basically all of them are at least as safe in terms of bleeding as warfarin,
and perhaps some of them are a bit safer. But of course,
there's going to be all sorts of bias in that because the reason why people prescribe a
particular drug to a particular patient may result in some spectrum bias in the observation.
But if you think that the clinical perception out there is that this is the safest when it comes to bleeding,
that's what physicians think, rightly or wrongly.
Then you might hypothesise that actually these patients are more likely to be higher bleed risk because it's perceived as a safer one.
So the fact that it actually comes out despite that that probable spectrum bias as less bleeding, I think is is reassuring.
So it's still observational.
It's still going to caveat it, but it would provide quite a lot of reassurance to practitioners and that those last two were US data.
This is UK data is actually based on key research showing essentially the same thing.
And I think in medicine when you observe what you would anticipate, then that's very reassuring.
So on the bottom here, Rivaroxaban, as you saw on that first side,
is likely to cause more gastric bleeding in the RC to put it into general population.
And yes, it does look as if it causes a bit more GI bleeding than warfarin and apixaban less so.
So again triangulate data from randomised controlled trials.
But that I think is is how it's been used historically and I think that the potential for routine data is that you could actually start doing.
Things that currently aren't done. And two areas of drug development that it may be useful for is looking at subgroup subpopulations,
where it's much more difficult potentially to do a randomised controlled trial or even having a much lower cost way of looking at new indications.
And I'm going to show you a few examples now.
So one of them is Metformin,
which is a drug that's used in diabetes and was contraindicated for use in heart failure because there was a low risk of a serious complication,
lactic acidosis. But in fact, when the FDA,
the regulators in the US looked at all the observational data in relation to people who for whatever reason had been put on metformin,
even though they weren't supposed to be. In fact, the data were very reassuring with all of the studies showing, if anything, safety.
And as a consequence, in the end of what was observational data, the FDA removed that risk label.
You'd never do a randomised controlled trial of that sort of indication where you were concerned about risk in the population.
Here's another one where in fact is the traditional use where you do a clinical trial of various drugs used to treat hypertension,
used in combination at a low level and resulted in a noninferiority of a more intensive regime against the less intensive regime.
And then subsequent to that, a group actually mimicked the trial in routine data sets,
because all of these drugs are routinely available and therefore a lot of
prescriptions occur in clinical practice and showed essentially the same result.
And this trial would have cost about $300 million and that study cost about $4 million.
So you can start to see why people think that efficient study design might be more possible with digital record studies.
And here's another one, which is the other way round, which is that this study had been announced, which is looking at a diabetes drug.
And the observation that these drugs which are used for diabetes might actually
be useful in heart failure as well in terms of reducing hospitalisation.
And that observation came out of observational studies showing a hazard ratio of six,
so a 40% improvement in terms of hospitalisation for heart failure.
And the observational data was sufficient to actually get an RC t done to get the formal indication,
almost identical results, but obviously hugely more expensive to do the randomised controlled trial.
No one has to say though there are risks with the way you do this data because methodologically there are all sorts of challenges you're going to do.
And this is an interesting one because basically what this group did is looked at a restrictive analysis of routine
data to compare of how Pravastatin performed in randomised controlled trials in terms of the amount of mortality,
cardiovascular mortality that was reduced by being on Pravastatin.
And when they just did the simplest type of comparison, which is looking at everybody who's on Pravastatin versus people who weren't,
then you've got this huge difference between observed or actual trial results and what was observed in the routine data set.
But as they did more restrictive analyses doing things like only looking at people who were new onset Pravastatin rather than prevalent,
looking at active comparators, looking at comparators where they started to standardise the patients to be more similar,
then each incremental step they did resulted in a result that was much closer to that observed in the randomised trial.
So it this demonstrates why you need to think methodologically about how you approach these questions.
And here's another example.
There was a group that published the CV, the real data set, which again looked at a type of antidiabetic drug, SGA SGLT2 inhibitors,
which appeared to have this huge effect on vascular mortality with a 50% reduction based on the big data studies.
But actually the this was actually demonstrated in a trial.
It's lost the plot. Sorry, but but you can see in the clinical trial it was only a 13% risk reduction.
Still a worthwhile risk reduction, but nothing like the 50%.
And why did they make the mistake in the CVD rule group?
And they did another basic error in terms of how you would look at pharmaco safety in a trial.
The immortal term bias effect. Because what they were doing is looking at people who were randomised to either initial t one or other
oral glucose lowering drug and then looking for outcomes which was like a death or a vascular event.
But so if you were on an alternative or sl t two, then that's a reasonable comparison.
But there would have been other people who were in this group who were on an old drug
but were randomised or given in clinical practice in this trial to one inhibitor.
And how are you going to count those events where for that bottom group, if that individual died, it counts in the analysis.
But in this group, if they don't die because they live to be able to go into another drug,
then that survival will be attributed to the oestriol t two drug in the analysis is what the mistake they made,
rather than attributing some of that survival to the older drug as well.
So you've got to think about these things if you're going to use the databases sensibly.
So another area, which is going to be a big growth area in medicine because it's about trying to predict risk coming to validated risk scores.
And I'm just used for an example there, all of the ones generated from the Q research database.
There are lots of other risk scores that are used internationally from other databases,
but it gives you a flavour of how you could use a large dataset and actually look at the factors that
are independently associated with outcomes that you can then end up with a weighted composite score.
And indeed, the key Risk three calculator is currently used by the National Health Service as its mandated risk score for predicting the ten year
risk of somebody developing vascular event and then using that to determine whether you should treat them with a statin or not.
But the other thing that you can do with these sorts of data,
so using it in that in a clinical threshold setting is think about the way you present risk to people.
So in this instance,
a ten year Q risk score of 21% means that you've got a 21% likelihood of having an event within a ten year cycle across the population.
But that means that that individual has got a relative risk compared to other people of a similar age and similar agenda of nearly fourfold.
Or you can calculate that into a lifetime risk of 74,
i.e. this 55 year old is on a population basis on average is going to be more like a 74 year old in terms of their outcomes.
And so that immediately then throws up how you present risk may be quite important to helping people
understand that personal risk and then making decisions about what they do about that perceived risk.
You've also got all of these issues is that this is all based upon mean population effects, and you're attributing that to an individual,
which may obviously underestimate the risk in some individuals or overestimate
the risk in some individuals because you're just looking at a mean effect.
So what we're quite interested in looking at,
can you supplement these scores perhaps with biomarkers that may produce a more predictive individualised risk?
So another area that we're pretty plain on, which is part of this behaviour change,
is can you provide contextual information back to practitioners in this instance that helps them change behaviour?
So this is what the GP currently provides back in relation to flu and it's producing all sorts of pieces of information about how this.
Compared with the mean practice and talks about how much money they're losing.
But it's obviously is a pretty complex slide and it does change behaviour, but you'd have to look at that in a practice meeting probably and think,
digest it and think about the implications for your practice and then you might do something about it,
but it's certainly not something that you're going to look at during a consultation to change your behaviour.
Another thing that we've done in terms of routine data is is look at workload.
It may be hard to believe, but since 1948 when the NHS was formed, there was only one analysis,
objective analysis of GP workload and that just looked at the numbers of consultations.
So there was no other objective evidence.
And yet we had this huge concern in British general practice that they were under huge pressure and they were buckling under the workload increases.
And yet the NHS had, you know,
they didn't know whether it was just perceptions of working harder or actual working harder and we managed to using routine data,
look at a seven year period, 2014 sorry,
27 to 2014 and showed that objectively during that period there was this really remarkably consistent increase,
annual increase in consultations, the 16% which were associated also with an increased length of consultations.
So they weren't just seeing patients more,
they were actually spending more on each average consultation and they were doing things to try and reduce the burden,
like increasing them by telephone consultation.
So I think the good thing about these data is it really did demonstrate that they their perceptions were borne out by reality and did
actually change NHS England a view to this in what they then committed to and to try to increase the number of practitioners in the UK.
We've had a number of follow on studies for that and we're still trying to look at issues like complexity.
Can you actually weight workload by complexity of disorders that they're looking after within the population?
Now this is another big data project that's emerged from from the department here in Oxford,
which relates to open prescribing actually is not uncommon.
But what this is doing is using a different data set.
This is looking at the prescription pricing authority which previously was sent manually to CCGs,
and then a pharmacist would pore over it all these tables that were done and look at the differences between practices
and then would target practices to go into and say what they should be doing differently to try and save the NHS money.
But what using big data techniques this group has done, led by Ben Goldacre,
is to provide all of this online real time so that you can immediately start to focus on any particular comparison
that this is looking at basically the generic prescribing of a progesterone drug rather than proprietary prescribing,
and it divides up all the CCG by those doing a lot of the non generic prescribing,
which is you're not supposed to do that sort of thing, but they can also do some time trend stuff which is quite interesting because the NHS
every now and then tries to stop things so they'll have a campaign that will try and
stop GP's using the proprietary drug and you can actually look at whether there are
interventions that they did that are then associated with the change in activity.
So the blue is basically the national trend and then the red is looking at individual CCGs.
So here's one that dropped, had more desirable practice much more quickly.
And you'd postulate did they do something then that resulted in this sudden and very rapid change?
And here's one where they didn't do anything and then obviously they did something that resulted in here, but that is sort of three years difference.
And so you could imagine now that you could think about a CG intervention and
test it in a cheap way of following whether there's been desirable trend trend.
And the group are actually now looking at the use of prompts where practices are.
Actually asking for this information when the prescribing is starting to change to what they'd expect it to.
The final area where I think is going to be a big change in terms of routine data relates to pragmatic trial platforms.
And I think that that should have been self-evident from what I was talking about in real world studies that are looking at retrospective use.
But potentially you could actually randomise people and do most of their phenotypic follow up.
That's the expensive bit of trials is following a patient to capture all of the clinical information.
Then you could imagine that you might be able to start doing these things prospectively.
And one example, for example, might be that you could look at these drugs again, these are a diabetic drug,
but do look as if they may be positive when it comes to vascular risk reduction.
This trial will never happen. Nobody is going to have to do a pivotal primary prevention studies because the cost of it is just too great.
But it's a really important question, and potentially you could get a reliable answer to it through using routine data sets.
Okay. Now, although that's all I want to talk about in terms of data,
I think it is worth me just closing by saying that there are things that we could be thinking about also in terms of kit of things that link to data.
And one example is things that measure devices, that measure things.
And one good example that we've been working on could be in relation to blood pressure management.
And what we postulated was that there was quite good evidence to show that self-monitoring.
Sorry. It's really slow and I'll change it. But self-monitoring actually does have an appreciable effect on blood pressure,
with about a four over 1.5 reduction of blood pressure in patients who self-monitor compared to people who are randomised to non-intervention.
And that's a worthwhile clinical difference.
And we postulated that rather than just self-monitoring, if you provided patients with an algorithm to allow them to actually change their treatment,
so they monitored and self-managed that that might be a desirable thing.
And in fact you had a big treatment effect very similar to the self-monitoring,
but it's on top of self-monitoring and it actually was a bigger treatment effect at 12 months, persisted even after the original trial intervention.
And if you think that doesn't sound very much five millimetres of mercury, then look at the epidemiology.
You can predict that it will have actually quite a significant effect on outcomes.
The trouble is you'd have to do follow up of these cohorts for 15 to 20 years before you could demonstrate that.
But of course, within the dates that you could do that length of follow up,
but you're not going to wait 20 years before you think an intervention is worth doing.
If there are surrogate markers and blood pressure is a very good surrogate marker of clinical outcomes,
we've also done some work in more challenging settings.
So this is in South Africa in townships looking at whether prompts,
using simple messaging on smartphones actually results in desirable change in amongst the
group that often the even more lacking in concordance with medication than in the UK.
And the one thing that is quite good about the world today is that even in really poor settings, everybody's got a smartphone.
I don't know how they pay the I think they're all given free the roaming they get roaming charges.
But it does mean you can think about digital interventions even in poor settings.
And did that make a difference?
Yes, it may have been only two millimetres of mercury difference in the intervention of prompted intervention, but that, as I already demonstrated,
is a worthwhile intervention and that was associated presumably with the fact that 25% more adherence to treatment within that group.
And I think when you think about the future in relation to wearables, different devices linking in to the clinical record,
then clearly there are going to be a lot of opportunities for more active research downstream.
So I think hopefully you'll agree that there are all sorts of exciting new things we can think about
in relation to the use of big data and the linkage of data and information in the clinical record.
And the already there have been some worthwhile research examples that would encourage you in that.
And we currently practice from that building which is the renovated outpatient building on the ARO key site.
And west of efficiently convinced about this is that we are hopefully going to be investing or getting funding
to invest in a new building which will probably be about that sort of size for the rest of the department.
That's the current building, which will be largely around big investment in applied digital health.
So watch this space and look at for future employment opportunities.
Thank you very much.