I'd like to welcome Karina Jones.
Professor Karina Jones is here to talk to us about some of her work relevant to the big state epidemiologist motto, which just talks part of.
She's an associate professor at the University of Swansea and she's worked on a number of big data initiatives which she will tell us about.
She's also really involved in information governance and public engagement, which I believe this talk is really focusing.
Her role is to ensure data protection and maximise the social accessibility of the data used across these various sources.
Okay, thank you very much. Thanks very much for the invitation.
It's great to be doing something on this really prestigious course and such a lovely summer as well,
which is I know it's wonderful, but you know, we've got this air con, which is great, but isn't it good?
We've got a good summer at last. And so okay, I'm going to talk about I like intriguing titles, the jugglers and the Black Cat,
just to think, well, you've had a long day, you've been being taught now and people have been teaching you.
So I'm just thinking I'll give you a title so that you think, Well, what's that going to be about then?
But before I talk about that, I'll just tell you a little tiny bit about some of the big data initiatives I work with at the very beginning there.
That's our building, that's the data science building where we house all the work that we do.
So we have the same data bank which you may have heard of,
which is an anonymous data safe haven of records about the population of Wales, so that about 3 million people in Wales.
But this because it's been running now for ten years, but it's gotten retrospective data as I've got data on about 4 million people.
So it can be GP records, hospital records, all sorts of things, screen in cancer registries.
We've got awareness, births and deaths, but we've also got some of the data.
That's not health. We've got education, housing, fire and rescue, that sort of thing.
And the way it works is the data are anonymized by an NHS body and then they come to us,
but they didn't link form because it goes via a trusted third party that in link up form.
So we can have a lot of records, a lot of details about people, but not in a way that has any personal information attached to it.
We only have an anonymous linking field that has no currency outside the system,
and there's a lot tons and tons of research going on with the same databank, if you want to look.
I mean, I think they will distribute the slides for you if you want to.
And you can you can look up the same data bank if you're interested then as well
as what we've just become one of the health data research UK substantial centres.
That's the Amnesty's new investment now in place of the FA Institute though it is a little bit different.
That is set up a number of consortia across the UK to apply cutting edge techniques and to do research.
It's really important for our public benefit and we're also an administrative data research centre funded by the ISC that are four in the UK,
that also rejig in that investment as well. But that's to take forward the use of administrative data.
It doesn't exclude health data, but it's more focussed on government data, local authorities,
economic and social data, benefits, work and pensions and also things like education and on suchlike as well.
And as as Emily mentioned, I'm the academic lead for data governance and public engagement.
So that's about looking at how to keep the data as useful as possible, but also to be safe and to be able to be used in a socially acceptable way.
Because if you think you can lock, date it up and make it aggregated and it's incredibly, incredibly safe,
but it may not actually be useful for answering research questions or you can make it very open,
but then perhaps you haven't got sufficient safeguards.
So it's about finding that proportionate balance so that the data are useful but also can be done within a robust governance framework.
So let's come to the jugglers. So we have a little bit of context.
It's been suggested it can be easier to donate blood and even organs than to genetic data.
This is something that came my way in another meeting I was at in Oxford, actually, in April.
And that's an interesting sort. And if you think about donate, it comes from the Latin for donor, which means a gift.
And the French donor is a very, you know, familiar derivative.
But do you think then donating data is about gifting data?
Donating organs is about gifting them? It's an interesting concept if you think about it in terms of a gift.
So I'm just going to pause a few questions. Are we moving in the right direction with legislation and regulations around this?
Is it possible to find a bioethical balance between individual autonomy, personal exploitation and concepts of social responsibility?
And can we, as individuals and as decision makers, make properly informed choices?
Now, the thing with data is there's a tangibility issue.
Debt is not like a thing. And when you think about ownership and rights, they usually about the right or the act or state of possessing a thing.
And data is not tangible. It's not a thing.
So the laws around data tend to deal with data protection, safeguarding privacy and confidentiality and take and just as a rule of thumb.
Privacy would be about a person confidentiality about the data.
But it's not about ownership of data as much as it is about safeguarding privacy and confidentiality.
Because if you think about it, how can you own what you can't control?
Once someone else is aware of it? If I tell you two things about myself that you don't know.
One is I really, really don't like butternut squash. I think it's awful.
But I want to say, Lambert, you know, now you now know two things about me that you didn't know.
And I really don't mind, you know, in those things.
But there are lots of other things about ourselves that perhaps we don't want people to know or we only want some people to know.
An analogy I heard, which I quite like, is you can think about data.
It's a bit like music. You can hear it, you can't it's not it's not so easy to own it.
You can copy that music. Okay.
But it's quite similar, isn't it, in that, you know, when somebody is aware of data, aware of information, how can you own it?
So the laws and the regulations are more about safeguarding your privacy and the confidentiality of the data rather than an ownership concept,
though we do tend to say, my data, your data, but it is a bit tricky.
And of course we haven't even discovered every type of data that could be considered personal.
So that's a challenge in itself. And there have been recent changes.
If we go back however many years we need to,
when there was no social media that couldn't be considered in data protection legislation because it didn't exist.
And so legislation gets changed to add the concepts of these different kinds of data as we progress and they become available.
But let's think about what it is we could be donating.
So if we think about our little data subject, you know, I've called him Schrodinger's pot and I guess you're going to work out why.
So Pat could donate all sorts of things that pertain to himself.
It could be paired organ, could be a vital organ, could be some tissue, some blood, some stem cells,
perhaps when he was very small, or perhaps his needs to decide about that for a baby or a change of his his DNA,
his health records, social media data, data collected as he uses his phone, something collected via an app like Mapmyrun or store loyalty card data.
Or he could donate his whole body after he dies, which is why he should take part, because, of course, if you don't, it's all about your liver.
Then he must be dead before all of the others he can do while he's alive.
But whatever he donates, even if it's tissue or organs, data are being generated.
So when we say Is it harder or is it easier to donate data or tissue,
we've got to think that even when it's tissue, it's donating data as well, which is another interesting twist.
So if we think about the different sort of data to whom and how we donate it,
we have a store loyalty cards which we go into Sainsbury's or Tesco's and we swipe the card and we think, Great, we're going to get some points.
But the truth is that data, this is of much more value to the shops and the little points we get back and then we get £10 to spend in the shop.
But really it's for the market advantage.
They do it something nice for the customer, but the primary reason is to gain an advantage.
Similarly with social social media, we can share things with friends and family,
but they are gaining information on us which they can use for their own purposes.
And of course we know that they do that.
We'll all be aware of the recent Cambridge Analytica debacle, as it tends to be called debacle after Cambridge Analytica.
That wasn't that. It's a bit like the conciliatory service, ACOSS.
You know, it's one of those things that goes together where they basically Facebook allowed them
to put on a personality test where people filled in details about themselves and then
the company profile people according to the things they clicked like on and then use
that information to target adverts to try and influence the US presidential elections.
And so there's this, things like that. And then just by reason of using our mobile phones or smartphones, every time we make a call or send a text,
the company collects a call detail record which gives the location where.
We are where the recipient of the call is, whether it's a text message or a call.
The duration of the call. So there's some sorts of information just being collected there.
They can also profile the URLs you've visited as well.
And we use all sorts of apps, you know, just like Mapmyrun for GPS locations or all sorts of other reasons.
And then we have medical data, which does tend to be better regulated, of course,
where we have our health status shared, perhaps with a practitioner or a researcher, a genetic data similarly.
But we may also choose to buy some sort of genomic testing via a direct to consumer company.
So that's a different sort of thing because that isn't really the same as someone who's sitting down with us and explaining things to us.
That's a matter of pay. In $200 engaging a company, they send you a little thing to do, a cheek swab, and they send you back a result.
So we provide our data to these companies in different ways, and we may also have apps to monitor our health a health condition.
And depending who owns that app, the data will go to a different place.
Perhaps if it's owned by the health provider, that's one thing.
But if it's owned by a third party, then they will benefit from the data being collected from that app.
And we have everything from properly informed consent to scrolling like crazy and
agreeing to terms and conditions which we I know I'm guilty of just tending to do that.
And there was this story, it was in the BBC News just on I think it was Friday.
What you might have seen. And there are lots of stories like this about what the tech giants really do with your data.
What they did there was they talked to some children up to about age 13, I think they were.
And they got them to read the terms and conditions and they found it very difficult to understand them to be fair, which I think is fair enough.
And the researchers concluded that you probably needed a university degree in order to be able to understand the terms and conditions,
to give a meaningful decision on the sort of things that happened is your location can be tracked even if you don't specifically allow it.
So when you're carrying your phone around, you're just being tracked.
Basically, data passed to affiliates. They're not just capped with the company that collects them and were then bound by the third party terms.
Who's now got the data? And as I mentioned, Cambridge Analytica.
Facebook tracks you if the app is on your device, even if you haven't got an account, that's what they found there.
Yes, I know it's not just if you're not logged in, but even if you haven't got an account,
if the app is on the phone, Facebook is follow in that phone.
And LinkedIn tracks your scans with private messages.
There's often good reasons for these things.
You can see the inverted commas on the podcast where, you know, it may be done for privacy,
it may be done for security, making sure that nothing inappropriate is done.
But the fact is it's still going on. And what we need to think about is anything you put in those situations don't just assume it's private.
You know, it may not be. It may be, but there are cases where it isn't because these are platforms of largely unaccountable power very often.
But are we, at least with Socrates, do we know what we don't know? No, we don't know what we don't know.
I recently did a study.
I don't know if anybody remembers Moth and if it did remember Moth yet I managed to mangle the title of a project into a word acronym MOFT,
which I won't spell out for you now, but it was on looking at how should data governance be done for using mobile phone data for research?
And one of the things we asked people in a series of workshops which I could repeat here is,
okay, has anyone read the terms and conditions for their mobile phone contract?
Anyone know? Okay, you're just the same as everybody else I've ever asked in the study.
Apart from I was doing a presentation in a law conference and it wasn't part of the study,
but I just asked the question and in the whole room there were about 30 people and
three in a law conference full of legal experts had read the terms and conditions.
So it's something we don't do. Okay.
And then there have been some interesting social experiments and over 500 students signed up to a fictitious social media channel.
None of them had read the terms and conditions well enough to notice they'd agreed to hand over their first born child.
There was another example where the terms of a different study, the terms and conditions, said,
if you have read these terms and conditions, you can claim and it was a decent prize and it ran for three months.
And then one lady emailed and then she said,
I've read the terms and conditions and they gave it the place because nobody else had read them, you know.
And even with quite sensitive information like these, direct to consumer genomic companies with 550 people who used the company,
most of them thought they were aware of privacy issues and thought the risk of trouble and repercussions were negligible.
But also 50% of men and about 30% of the women said they hadn't read the terms and conditions anyway,
so they didn't really know whether there were privacy issues to be concerned about.
They were just taking it as I want this thing, therefore I am going to do it.
But what can we think about in terms of perceiving public opinion?
And we can ask ourselves the question as well. It's not just the public out there.
We are the public in this context as well. Do we really engage with information or are there very good reasons why we don't or can't?
So if we look just at the legal position for a minute, Wales has adopted an organ opt out system, organ donation, opt out after death.
We've done this since 2015. England is doing the same. I'm not quite sure when it's coming in.
And the donation of human tissue while you're alive is under the Human Tissue Act, as you know, and the Human Tissue Authority.
There was a study done by the Wales Cancer Bank and they found that over 95% of patients
were willing for surplus tissue to be used by the Wales Cancer Bank for further studies.
That's the sort of thing you often see with people who are close to an issue like that.
On the GDPR, as we now know, has now come in and the new UK Data Protection Act,
which has renewed provisions and some new ones for process in general and special category data as part of this opt out consent.
Is no longer acceptable. So using it in research, let's say you can't just say this is going to happen unless you say no.
And the same with using identifiable data.
There's more clarity on data processing and the need to be more upfront about what the data controller is going to do.
It's not just about having the information. If somebody wants it, you need to be more upfront, more more prospective about it.
And there's an improvement on legacy tiny word escrow. Nobody reads it and sees as well.
And a bit of a a no no on this automated profiling that was going on.
Now, whether that'll have an effect on these massive, powerful platforms.
But that's the idea. And clear provisions for using research in the public interest, which is a good thing,
because the guidance now is it may not be necessary to rely on consent for some research,
provided you can show and defend your position that it's in the public interest.
I mean, it wouldn't be something like a clinical trial, let's say,
but if it's research that's using data, there may be cases where that can be relied upon.
But coming back to the organs, the organs of deceased people can generate DNA and other health related data about living individuals.
So we've got an opt out consent for organ donation after death.
But that organ donation can generate DNA profiles about people that are alive that are related to this person.
So there are some incongruities there, at least it appears so far on some ethicists actually argued against that opt out organ donation after death.
And another query that's come up is, does a donation bid passively lose the spirit of a gift?
If we don't know we're giving something? I don't mean the organ donation after death, because that's got to be specified.
But in terms of passive data collection, if we're doing it passively and we haven't really engaged or been able to engage,
maybe we shouldn't really be thinking about that as a donation, but need to be thinking about do we really want to do that or should it be considered
in another way because we don't have opt out donation for posthumous data?
You know, which is interesting. If we can do it for organs, we can't do it for data.
But of course, Pat isn't an island entire of himself.
And when people make decisions for all of us, we need to think about the other people that we implicate.
And we generally take these as four bioethical principles in making decisions based on bioethics.
So autonomy, the right to make informed decisions free of coercion.
Not maleficent's. Not intentionally harming someone through acts of commission or omission.
Beneficence. The duty to benefit patients, individuals and society and justice.
Equity in provision of care and resources.
But coming back to that question from the beginning, where's that bioethical balance between individual autonomy,
personal exploitation and concepts of social responsibility in these different data contexts where you've got data provided to practitioner,
that's okay. Data provided to a researcher with all the correct procedures in place,
but data provided to some where else, where we don't actually know where it's going.
You know, we could be thinking, well, there are different sorts of ethical philosophies going on here.
We could think, well, autonomy is that individual autonomy, or if that's in relation to a company,
is it more related to we want this regardless of what effect it has on other people?
Because we know that some of the companies I pick on Facebook, again,
some people who work for them have come upfront and said, we've actually got psychologists working.
We know what you know how to manipulate people.
And an engineer, they're thinking in order to get them to do what we want.
And they've they've come up and said that that's what is being done.
Altruism,
we think of as something the benefits are the people without necessarily benefiting ourselves and sometimes with a bit of a sacrifice attached to it,
such as perhaps someone would be prepared to provide data about themselves.
It may take a lengthy interview. They may need to go through some procedures, but it's not going to help them.
But it may help research in the future, or utilitarianism where it may have benefits for a large number of people, including ourselves.
But even so, to what extent can it be said decisions and practices are actually guided by bioethics when
we've got this huge interplay of actors and where the might or right comes to the front.
So if we think of ourselves and everybody, really, we are bombarded by information data from all sorts of places.
We've got this little kind of data splat over here where all sorts of information about health comes to us books,
news media, magazines, social media, health professionals of friends, all sorts of places.
Information comes our way now for health care practitioners. And I worked in the NHS and I work in a university.
We've got an advantage position because we're aware that there are good sources of information where to go to find them.
But for the general public they may not be aware of that and they're more subject to this with these people being quite small,
what is for us perhaps that a bigger influence and research may be a bigger influence?
So when individuals make decisions to donate or not donate their data, we've got this multiplicity of data contexts.
We've got active and passive data donation, we've got the getting something versus the giving something.
If I want a mobile phone or if I need to Skype in the next 10 minutes and I don't have Skype got installed on, I must go through this long thing.
I'm just going to press accept because I need the thing or I want the thing compared to
giving where we see ourselves as data subjects in a study or a clinical trial where okay,
I may be able to benefit someone in the future. There's a getting giving concept going on across these data domains.
We also have the effect of distance from the issues where the whales, cancer bank patients, they were close to the issue.
They could see, well, I've been helped by this. It's going to benefit others if I allow my data to be used.
But if we're in a situation, let's take blood donation. Maybe we donate blood too.
Maybe we don't. But if we think we could, yes, we will do it.
But on the other hand, if there was a local need where there's a big problem near us or to people related to us,
it may be much more foremost in our minds and we think we really need to do this.
But some issues are important, whether or not they're important in our perception.
And they the other thing, of course, is data and things like this.
And research is not the only show in town. And I spent five I was on the train today.
And sometimes I just think to myself, if I was to ask any of these people on this train, have you donated data today and who did you donate it to?
I would imagine people say, no, no, I do that.
No, I haven't donated any data. But the truth is, it's very unlikely that we haven't you know,
if we're carrying around a mobile phone or even travelling in the car with cameras and such like,
there's different sorts of information being collected.
If we've got a Fitbit and all sorts of things like that, there will be information being collected.
But then sometimes it gets personal and it hits that threshold and we have to make some sort of a decision,
whether for ourselves or for somebody else. And then it's about finding a way through moral measures.
And we come back to this situation now.
How does Paterno, which is the bit of information he can trust, how does he know how to make his decision?
It's it's quite a difficult thing when there's so much information.
It's not about getting information anymore, is it, when, you know, maybe 50 years ago you needed to look hard for things and find things in books.
Now it's easy to get information, but it's hard to be judicious and to know what we can trust.
So we find ourselves juggling the ones and zeroes.
We are, wittingly or unwittingly, we are the jugglers because we've got all sorts of things coming our way.
Decisions we have to make, decisions we don't know. We've made all sorts of things going on.
We can donate the same or similar data via different processes.
We can sit down face to face with a researcher or a practitioner and talk about sensitive health data.
Or we can put it on an app and we're not quite sure where it goes,
or we can contact a direct to consumer genomic sequencing company and provide it to them.
Or we can provide different data using the same or different processes, general data or very sensitive data, again, via an app, let's say.
So different things in the same way, similar things in different ways.
And there are limitations on individual choice because we're not always aware of who we are donating data to.
What data do we mean? What will they do with it? Will they pass it on and so on.
So sometimes I think it's too easy to donate data.
It may be quite easy to donate tissue. Yes. Okay.
But sometimes it's too easy to donate dated as well when we're subject to this information overload and distortion.
So we just look at this as a little illustration here. If we think of ourselves as this thinking capital,
we have to make some sort of decision either for ourselves or for the people we're caring for as practitioners or working with us researchers,
whether it's about research and big data, all about this is trying to be packed again with an unusual skin condition this time.
If we think about information coming from primary sources and we just allow us, us to call this objective, depending how it gets to us,
it'll go through different channels, different conduits, and depending how it's interpreted, it may be magnified, diminished or fragmented.
By the time it gets to us and we may not know that.
So all we can really do is to think to ourselves, well,
we need to get as close as possible to the primary source and think about, you know, how does data come to us?
Where can we go to be as close to the primary source as possible?
Because I'm sure we've all been in a situation where someone has said something and it could be something quite ordinary,
but you think that really doesn't work like that.
However, they thought that, well, obviously somewhere down the line they've got some information from somewhere and it's got a bit mangled on the way.
So I think this sort of thing is just with thinking about when you read information as part of your course,
as part of your work, as part of just being a person, you know, think about where does that come from?
And can I get back to as primary a source as possible?
So having got this far, I'm sure there's got to be time for a bit of alliteration, though.
Everybody brings a bit of a little rationing. So minding our P's and Q's, we've got a series of unknowns.
When we deal with data, we've got the unknown package, the data content.
We may not know what data someone holds about us, or we may not know, for example, with, let's say, genomic data,
what the data actually mean, because only some of it is known, you know, it's not as if it's all fully known as yet.
We've got unknowns in who might be using the data. We've got unknowns in how they might use it, on where that data might sit.
Is it somewhere in a data safe haven where it's under secure governance or
is it sitting on somebody's desktop which could be shared with someone else?
We may not know, but I think we need to stop asking questions that can't be answered properly.
Now, what I mean by that is it is something that is changing naturally,
but there's been a lot of work asking the public, do you think your data should be used for research?
Well, let's face it, data are being used for research, and asking that question isn't really going to help anybody anymore,
because if people say no, well, what are we going to do about it anyway?
Because data are being used on a lot of data,
they're being used by large companies who may not being may not be being as caring towards the data subject,
perhaps as others are, as as as generally we should be for research integrity.
So I think it is moving more towards how do you think data should be used?
Who do you are you happy for to use your data or under what conditions are you happy
for your data to be accessed rather than should it be used in the first place?
And I think we are moving on from that.
But we need to be transparent about information and custodians need to be able to show trustworthiness to us all,
us as people, to show that is risk mitigation controls in place.
Be clear, but also be clear about the limits and admit, yes,
there are some unknowns and perhaps we can't totally protect your data against every possible attack that could come from anywhere.
For example, the Personal Genome Project, where people are able to share genomic and health data about themselves actually
tells people We cannot totally guarantee the confidentiality of your information,
so at least people can go in with your eyes open if they want to.
They can. If they don't want to, then that's fine. So can we make informed choices?
Well, I think only if we are asked and we ask the right questions and we're able to engage
and we do engage because otherwise it's quite difficult if we keep that distance.
There's a lot of work with public engagement, but I do think there needs to be a recognition.
We've all got that responsibility to think about it more for ourselves.
And there are now, as I say, some alternatives to consent and we need ways, interpretations of legislation and regulations,
because the GDPR provisions for use in research in the public interest is very helpful for researchers just about data,
because data can be anonymized by a trusted third party as long as that authorised to hold that data, and then the data can be used in anonymous form.
The Digital Economy Act has provisions for data processing, for anonymization,
for non-healthcare organisations, so for government and administrative data organisations.
The Digital Economy Act allows the data to be anonymous by a trusted third party, whereas there wasn't a provision for that before.
Now, as I mentioned, we have the opt out organ donation which is moving in England and we now have in England the National Data Guardian,
opt out where people can object to their data being used for anything beyond the direct care.
Some of the thoughts that are flying around are about licensing data uses compared to actually donating data.
Now if we think back to our full piece, the what, the who, the how and the where,
I think it would be challenging in terms of the practicalities and it would need some really clever,
clever technical solutions and governance frameworks around that.
So I'm not quite sure, to be honest, how that would work.
If we think of donating and forgetting, is it licensing and remembering,
but how would you then engage with that every time you need to be able to engage every possible time your data were going to be used.
But we want to promote data sharing, whether it's donated or licensed.
We know the Data Saves Lives campaign.
This is really, really important and we need to be able to follow the rules and the regulations, but without being rigid.
And we need to be able to make decisions that have consequences for people without being partial so that we have socially acceptable ways.
To use data. So we need wise interpretations of sorry.
And the it's really important to think about what can we lead, what can we shape and what do we need to follow in.
So we come to the black cat. Now, as context for this, I was asked to carry out a study commissioned by the Nuffield Trust,
along with a couple of other colleagues to review the harms for health and biomedical data.
And we did that. And what we found was the largest problem was actually the maladministration of information governance regimes,
not about not having the policies in place, but having them, but there being problems in actually how they were interpreted and implemented.
I won't talk about that now, but what I want to talk about is the alternative, the harms that occur when data are not used.
Now this can be difficult to pin down and there are lots and lots of domain specific efforts going on, like the All Trials campaign,
where a lot of effort is going on to make sure trial data is registered, information is provided properly so people know what's going on.
But the non-use of data, that's quite a tricky concept, and there's a lack of publications in this problem area.
So what we did was an international case study using the published literature looked at why is health related data non-use difficult to ascertain?
What are the sources of data non-use? What are some of the reasons?
What are the implications for citizens in society? And I looked across clinical records, research domains and governance frameworks.
It was focussed on health, but it is applicable more widely.
And it's really important to point out that the harm due to the non-use of data is more than not gaining the benefits of data use.
It's not just one or the other. It's not if you don't use the data, you will get those benefits.
Yes, you will get those benefits, but other things will happen as well.
So if we have a look, first of all, at the clinical regimes, first of all, with sources,
there were a variety of different sources of why data are not use or sources of data non-use.
One of them was the way in which case notes are managed.
Now, this is something that you'll be all very familiar with.
Often a patient has a number of conditions, and so the case notes maybe in one clinic or the other clinic or another clinic.
But that is something that if if everything was beautifully joined up with electronic records, then that obviously wouldn't be a problem.
But while we depend on Steele a lot on on paper records, that can be a problem,
because when the records are looked for, they're not where they meant to be.
Because possibly the interesting case is where someone has got a number of conditions that could be in a number of places,
that could be data entry errors that can happen to anybody.
I've got two really unusual examples for you.
I was working in the Clinical Audit Office at one time and we had a clinical audit assistant
and she was given a spreadsheet with all sorts of information from different departments,
numbers of procedures, bed length, time stay, stays in hospital and suchlike.
And she was adding and she was also needed to sort by date and by department.
But when she added and then sorted, she sorted by date and sorted that column and didn't start the whole spreadsheet,
the dates went in the column, the row stayed the same. Then she sorted on department and the column changed and the roles stayed the same.
So basically as she was adding and sorting and adding and sorting, it was well, it was just completely useless.
It was a shambles.
It was in reverse, it was irreversible because it was randomised basically, you know, and it was found because of some guy, any man.
No, no, that's not right. You know, on this, this obviously became quite a big problem.
Now, I can make this one step worse. After she was found not to be able to work in clinical audit, she was given a job in medical records.
And this is true. I've got another example for you where this was a researcher and he had 4000 records,
quite wide database records, and he had them in SPSS and he wanted to put them in Excel.
Okay. And he took a whole weekend, he printed the whole lot out and he literally hand copied them all in.
And the only reason we found out was he produced a bar chart and he produced a batch and showed it to me.
And I looked at the one it produced the previous week and it bore very little resemblance that it just wasn't even the same shape.
I saw what happened there, and he had actually copied it in row by row.
So some strange things do happen. Unfortunately, sometimes it's about limited data recording.
We don't we don't record all data. We know some information is just not for recording.
That could perhaps be useful. Sometimes there are no clinical systems that are suitable or they don't fit and data are in silos.
We all know that people go to hospital and they're asked.
Medication are you on? And people find it odd because they don't know why the GP has given them certain
medication and the hospital doesn't know that they're taking that medication.
But of course, as we know, it's not joined up, but the public often think it is.
And find it odd when they go to outpatients and they're asked about things and think, well, well, I told that to, you know, and they just don't know.
You really should be more joined up than that. So some of the problems that have happened.
The National Audit Office did a huge review of case notes across 120 hospitals.
And as an example, they did they looked at around two need to audit.
And what happened was when they investigated the results of the audit were better than they should have been because the problem cases,
as I mentioned with the case notes, the case those but in other places and they hadn't been included in the audit.
So where a pregnant lady had a culpability or diabetes or something on the case notes weren't available.
So what about 3% of the case notes that weren't available? But it tended to be a bit systematic in that they were the ones who had the conditions.
We did a survey when we set up the UK EMS register and we asked all the neurology clinics in the UK how they collect their data.
There were about 85 at the time and about 40 something replied to the survey and about six of them still only used paper or a word processor,
no clinical system at all.
And for many specialities I guess that's still the case where it may be people are using an Excel sheet or or an access database.
I don't know if there's any nods going on. Or is everything where you work all beautifully put into a clinical system or is it
still in some cases anybody know large amounts of paper with large amounts of free text?
Yes. Free text which isn't coded or. Yeah, searchable.
Yes, yes, indeed. Yes. Thank you. Yes. Yes.
Although this is improving, it's still something that needs a lot of work and of course different coding standards
so that one system doesn't talk to another and systems themselves are incompatible.
The reasons I mean the NHS is wonderful. I totally believe that I've worked in the NHS.
I wasn't clinical myself but totally aware of the work that goes on.
But it's so massively overstretched and it's about the priority, it's about patient care.
The work of the NHS is to try and make sure all the data is done right that needs to be brought in and made possible
rather than expecting it to be done by magic in some countries as performance a payment performance model,
which also influences the way things are done. And it's not by design.
These things are done not from the beginning where the systems are created in order to be interoperable and suitable.
It's a bit bolt on, isn't it?
Well, I think from the top and not really looking across the big picture, the implications are hugely this happens misdiagnosis,
unnecessary intrusion, where if information is missing, people may be asked the same questions again.
They may need to go through the same tests again because information is just not
available in order to proceed or procedures that they need to have may be delayed.
There may be poor outcomes, emotional damage, deaths, litigation then against the NHS and of course additional costs.
In research. If we're dealing with information based on clinical records, we may find our study is subject to selection bias.
Because, of course, people that are sicker have more data entry.
You know, they have more data records.
So that in itself can cause a selection bias or they may be improper study design that influences how data are used.
Sometimes results are held back for personal or political reasons.
Sometimes this cherry picking where someone might do a study is X, influenced by, let's say,
and choose a lot of variables on a huge data set so that something will be significant and then report,
Hey, this is significant, but really it's been done in a way that's cherry picking, which calls into question research, integrity.
Some examples. I don't know if you remember, this trial was in 2006.
It was a an immunomodulatory drug.
It was a first in man trial. And they gave the drug to four or five people.
They had an awful immune response. I think a couple of them died.
Sorry, that's the one. Yes, indeed.
It later transpired during the investigation that a close enough drug to be relevant had been tried in one person and it caused a big problem.
But this information hadn't been available at the time.
Clinical trial success rates. Commercial clinical trials are reported to have 85% of them have positive results,
whereas in non-commercial trials, only about 50% of them have positive results.
That's a little quote from Ben Goldacre, and obviously he's thinking about that and that's interesting.
How does that quite work then? This one about the arrhythmia drug that was quite a big disaster.
About 100,000 people died before the problem was realised.
And what happened was it was in the 1980s and a drug was developed for arrhythmia and it was given to
people who'd had a heart attack and it was fine as long as it was given to people who'd had a heart attack,
who had arrhythmia. But when it was given to people who didn't have arrhythmia but had had a heart attack, the died basically on 100,000 people.
Over a hundred thousand people died before that mistake was realised.
Sadly, there had been a small study but it wasn't published.
But in the investigation it came forward and said, look, we we weren't able to publish this,
but if we'd been able to publish it, then we may have been able to to stop this happening.
The new anticoagulants. This is an interesting one and one of the big drug companies has marketed dabigatran.
And the marvellous thing about the new anticoagulants is that they're meant to be this meant to be.
No need for titrating one dose suits everybody and no need for the frequent monitoring.
Well, it then transpired that hundreds of people died or had a severe bleed many, many more, many times more than on the older style warfarin.
And when it was investigated, it was found that the drug company had wilfully withheld the data,
and they knew that it did need to be titrated and it did need to be monitored.
And they were fined something like 5 million by the FDA in America.
But that drug is a blockbuster drug and it has an annual revenue of $1,000,000,000 and they were fined $5 million.
Then after that, they also produced and marketed the antidote and then no able to sell that as well.
So that's some of the problems that can happen with non-use of data.
And of course, we have this problem with journal publications where we know it's so much easier to publish something that's got a positive result,
a really clear finding than a negative result. Or basically this just didn't work.
So why does this sort of thing happen? Well, there are few incentives for data sharing and few penalties for not.
Sometimes it's about market share, intellectual property investment, either in money or effort.
Kudos by data. You know, sometimes we're just under pressure and some people want to hold it up to themselves.
Pressure to publish on publication bias, as I mentioned.
But as a result,
sometimes we're dealing with working with untrustworthy information because unreliable findings are taken forward because they look shiny.
Whereas the second best or third best, which was actually true, isn't seen as so good and it doesn't get taken forward.
True findings may be concealed with multiple, multiple deaths in terms of governance frameworks.
These, of course, there to safeguard individuals, everything to be done properly.
Often we'd be all familiar with lengthy and duplicative processes,
inconsistent advice and subjective interpretation like that life through a lens image and sometimes excessive disclosure control even.
And when data are anonymized, sometimes there's a drive for or we must do more.
We must do more to it in order to make it safer.
Sometimes there's non-acceptance by an organisation after the regulatory and governance approvals have been gained.
Sometimes a data provider will still say no, they won't provide data, even though ethical approval and everything that's necessary is in place.
Sometimes you get consent by us.
There's an article in the UN Malformation Study where there was a difference, systematic difference between those who consented and those that didn't.
And so that can affect the the study that also happens with what people have been up to,
wicked problems such as substance misuse and psychosocial problems in young people.
What is really hard to get hold of the people in order to get consent.
But as a result, if you are if this insistence on consent, then you can't study the issue properly.
There was a particular problem with substance misuse stated in the US where
the data provider just decided with no changes in regulations or legislation,
that they would now withhold any record that contained an instance of substance misuse from research.
But that means when the subject can't be studied properly.
But that was just something that needed probably needed a lot of fighting back.
There was an article published on it. So I would hope that that has now been resolved.
The reasons can be, of course, there may be framework changes.
Often it's because responsibilities are unclear and there is fear of reputation, risk, fear of getting it wrong.
It's easier to say no than it is to think what happens if I say yes and I get this wrong?
There's this little term here, privacy protectionism, which was coined by some Australian researchers.
And what they mean by that is adding extra controls on the data and doing more and more processing to the data to make it safer,
but actually not making it safer at all.
So it doesn't actually add safeguards, it just adds further and further controls and limits the utility of the data.
And, of course, the to the extent public support for the use of all kinds of data isn't known.
As a result, we get an information deficit which can be delayed or abandoned and benefits left and attained.
I don't know about you, but sometimes we maybe I'm find and I'm seeing more of a caution since the GDPR and not necessarily because of the GDPR,
but because of interpretations.
All of a sudden, everybody's a legal expert because it's a new piece of legislation and lots of people have had to engage with it.
I don't know about you, but you know, just the range of emails I have received from companies I do and don't know.
You need to do basically nothing for us to continue to send you this thing or you need to do an awful lot of
things and do lots and lots of clicking and saying yes and reading in order for us just to send you a newsletter.
And I think, you know, it's just, again, an evidence of that, differences in how things are interpreted.
And of course, the result, again, multiple deaths of financial burdens to societies.
But it's not just the one thing at a time.
There are combined effects.
So if you think of this doctor's handwriting here, which we're probably very familiar to doctors, I could probably read that.
And it doctors among us, if we think of how the data collected, there are issues there in data non-use,
how it's managed and stored, what is actually managed and stored,
how it's used for research, how it's taken on to inform policies and guidelines,
and then how it is received back at the bedside or at school and what is collected at that point.
That comes back around to the state treasury. So it's a kind of big synergistic effect.
It's an effect that's that has a knock on effect. It's a trail and it can compound itself.
So how serious is this problem? Well, as we know,
the non-use of health data is implicated in the deaths of hundreds of thousands of people and billions of dollars in financial burdens for societies.
And this is quite a nice quote from the managed health care executive.
And what they said was the greatest threat, the biggest risk to people with diabetes or heart disease, cancer, HIV, etc.,
seems not to be from unauthorised sharing or use of their personal health information,
but rather from a failure to share or inadequate use of that information.
And sometimes even valuing privacy or overprotected an individual's life,
their health and the health of their families, their friends and their neighbours.
I think that's a really, really interesting point because sometimes even it's not even you know what we mean by this, isn't it?
What do we mean by protecting privacy, which I could waffle on about for quite a long time,
because if we think about is something disclosed, if well, what happens if it is disclosed?
Because sometimes the disclosure of information doesn't cause any problems and sometimes it does.
So not everything needs to be private, but obviously some things do.
And it's quite a difficult balance. So the recommendations from the.
They were. There needs to be support for a culture change and data capture.
How do we do this? It needs to be thought about and built in, not a bolt on solution.
Decision makers need to think beyond the specific settings.
Data capture systems need to be fit for settings and need to be interoperable.
Need to promote onward data sharing. But recognising the investments that have been made by people.
People may have worked hard for years to build a big cohort of data for research and
it's understandable that they they need to be recognised for having done that work.
But the need to be greater repercussions for wilful non-use of data, okay, accidental or unintentional or data is just not available.
But where data are withheld because I don't want to publish yet because or I'm going to market this drug and then I'll
deal with the repercussions because I know I'm going to make far more money than the fine them ever going to get.
You know, we need something more than a tiny fine on a massive revenue.
Governance models need to be proportionate.
We don't need the same level of ethical scrutiny on such like for something that's dealing with something non-intrusive as we do with an intervention.
We need to rebalance that tension between data privacy and utility with greater support for trustworthy re-use.
I came across a term which was data obsolescence, and I just think, no, you know, we really need to be thinking about reusing data,
not creating data for one project and then saying, okay, we can't use that again.
That massive investment that's gone into that needs to be taken into account.
We need to present the public with a balanced view of data use.
Yes, there are some risks on the S that are huge benefits,
but also there are problems when data are not used and if people choose not to allow their data to be used,
even to say, okay, I don't know my data to be anonymized and used, well then there needs to be an understanding of what happens when data aren't used.
And that's so that social responsibility can work both ways.
Just some brief thoughts on the recent developments.
It's a great thing that there's no the public interest provision for data use for research rather than needing to rely on consent all the time.
Because obviously with the GDPR, the bar for consent has been raised and it is much more stringent.
It's good that the Digital Economy Act allows data sharing for anonymization by the
government departments that have been some comments on the National Data Guardian opt out,
the opting out of personal data being used beyond direct care understanding patient data.
I've written a couple of blogs on this because there is some concern that if people opt out,
it could bias research and audit and service development because of,
let's say, rare conditions, rare events, diversity, ethnicity and possibly differences in geography.
So that, I think, needs to be carefully looked after.
But it's obviously understandable that it needs to be done properly and not done in any way that the public wouldn't be finding acceptable.
So there's a need for innovation in data governance.
However, whatever data is generated from where Schrodinger's pot produces his his donations so that in order to get from data to information,
there needs to be innovation.
It is about but it's not just about following the legislation and regulations and the ethical, legal and social implications and due diligence.
It is about them, but there's more to it than that.
We need to take into account how these things are interpreted, how they're perceived, what sort of reservations are there,
what are the aspirations of people present in the different perspectives that are stakeholders to consider the literature,
the debate, the media, that information splat we looked at with that sort of shifting body of knowledge, it floats around shaping social realities.
So if we're going to do this successfully, we need to be innovative.
You need to do it properly because otherwise it risks becoming unethical through not using data.
It would swing too far. So in terms of final words, we need to hone our juggling skills, all of us.
And and as researchers thought responsible, trustworthy and socially acceptable use of data,
because the non-use of data is like a black cat and it's like a large, agile, polymorphic, lethal black cat.
And he certainly is out there and a better understanding of its nature.
It needs to be understood because this is a global problem and he is definitely not truly Schrodinger's pet because he really is there.
And this is the publication which you can have the. Tails off of.
It's the first known study to address perspectives of health, debt and non-use.
It's informing the parliamentary inquiry on research, integrity and the Wellcome Trust Initiative on understanding patient data.
So that's my my cut. And just thank you very much and your questions.
And that's a picture of where I'm from in Swansea, where I work in Swansea.
I don't live there actually, but I'll leave the catch up for you.