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Health data science is transforming the future of medicine: from better understanding diseases and developing treatments, to shaping AI-driven healthcare solutions. However, this means that questions of trust and ethics are more important than ever. How can health data be used responsibly to improve patient care while protecting privacy? And how do we ensure that the benefits are shared fairly across society?

Professor Mark Lawler is a renowned scientist whose work is recognised internationally, having published over 200 papers in journals such as the Lancet and New Medicine. He is the Associate Director of Health Data Research Wales-Northern Ireland and is Scientific Director of DATA-CAN, the UK Health Data Research Hub for Cancer.

In this interview, Public Engagement and Communications Intern Ope Awofadeju spoke with Mark about what ethical and trustworthy health data science means to him and the future of cancer care.��

What first inspired you to work in data science and cancer care?��

My entry into the cancer-data space was a mix of chance and timing. I studied genetics as an undergrad, which naturally led to cancer research during my PhD—so cancer became a central focus early on. The health data side began when I moved to Belfast about 12 years ago. I was invited by Professor Paddy Johnson, a great mentor, and what started as a visiting professorship evolved into a permanent role at Queen’s University, where I was encouraged to build data science capacity.��

The project itself focused on cancer inequalities in which we mapped disparities in cancer outcomes across Europe. At that time, around 2012-2013, it was the largest analysis of its kind. But we did not just want to simply produce a publication but rather desired to drive real change in policy. One of the things we developed was the Europe Cancer Patient’s Bill of Rights, which detailed what patients could expect from their health systems through Europe.����

So, I suppose in a way, part of what led me here was chance or maybe coincidence. I advise young people to not be too strict on what you want to do as you may end up in something different which is more interesting. Something which you might not have explored if you were rigid in terms of what your career structure was going to be.��

How are you using health data to solve problems in cancer care?��

One example that I’m quite proud of was our work with Bowel Cancer UK. They approached us with a serious inequality affecting patients with bowel cancer which was that patients were being treated with a drug called Tuxomal, under then English protocol, were not allowed treatment breaks- that even doctors recommended due to the drug’s toxicity. We wanted to compile some strong evidence that would work to challenge that policy, doing things such as comparing continuous treatment with intermittent treatment- where patients take breaks- to see if there was any impact on survival. There wasn’t.����

What turned out to be the tipping point, was a health economic analysis we conducted that revealed that switching to an intermittent treatment could save the NHS around £2 billion. As a result, the NHS changed their protocol, allowing treatment breaks and marking a proud moment in my career as a real difference was made in these patient’s lives. We even won Research Impact of the for this and for me, this serves as a great example for how data can solve real life problems.

I advise young people to not be too strict on what you want to do as you may end up in something different which is more interesting.

What is the biggest challenge facing your research?��

One strong example is the policy change around continuous vs. intermittent treatment of Tuxomal. It took 18 months to gather the evidence, but nearly five years to see it implemented. This highlights a major challenge: turning evidence into policy isn’t just about data—it requires persistence, lobbying, and getting the message to the right people.��

Another challenge is data access. While the UK has excellent data, the systems to access it are slow and overly restrictive. Patients often assume their data is already being used to improve care. As cancer survivor, Jackie Gaff once said, “We thought you were doing it already.” Covid showed how quickly data governance can adapt when public health demands it. What I mean by this is that privacy rules were relaxed, enabling rapid diagnostics and vaccine development without breaches or complaints. It proved we can strike a better balance between privacy and public good.��

What makes you optimistic about the future of cancer care?��

I think what makes me optimistic is the increase in teamwork in this area. I like to use the phrase “we need to compete not against each other, but against cancer” which is our common enemy. Working together does not just make sense from a practical perspective but also means you bring together different brains, who emphasise things differently which can only be a good thing in relation to cancer research.����

Our better use of resources also makes me optimistic especially when working with a health system that is under pressure. I have written a lot about this in the , as I believe we must reconsider how care is delivered. Lastly, I’m also encouraged by the integration of digital tools into clinical care. Through , we developed a unified digital record system linking GPs and hospitals. After a year, it saved £44.4 million, mostly through time efficiencies. Three years later, savings exceeded £120 million. Innovations like this show we’re moving in the right direction.��

What do you hope the long-term impact of your work will be?��

My hopes for the long-term impact of my work lie in how we use our data to tackle some of the biggest challenges in healthcare, not just in improving treatment, but preventing disease altogether. I believe that algorithms specifically will play an important role in helping flag risk factors which will help us guide people toward healthier lifestyle choices and behaviours.����

Looking ahead to 2045, I think healthcare will look completely different. We’ll be treating and preventing diseases in the community. Access to health records on our phones like what’s recently being done in Northern Ireland gives us the opportunity to have a population wide view and opens up exciting possibilities in the future.

Ethics is not just about involvement, it is about fair benefit. If a study does not clearly benefit patients, the health system, researchers and the industry then it probably is not worth doing.

What does ethical and trustworthy health data science mean to you?��

I think that has to be the prerequisite for everything we do particularly because we are working with data and people always question who’s using their data. So ethical health data science starts with strong governance and genuine public involvement. The public’s involvement can range from giving advice on how to make the projects better to even holding the power to veto projects.������

But ethics is not just about involvement, it is about fair benefit. If a study does not clearly benefit patients, the health system, researchers and the industry then it probably is not worth doing. We often have to make difficult decisions in this space, but when they’re made transparently, with everyone around the table, we can make such calls with confidence. It is all these things put together that enable that trust and which allow health data science to be ethical.����

What skills do you believe are essential for the next generation of health��data scientists?����

I believe that data skills are important for everyone working in healthcare, which is why I would advocate for teaching data science to undergraduates in medicine and health professions. We also need people with foundations in biostatics and bioinformatics; some come from data science backgrounds and move into medicine whilst other make the opposite transition.����

Both routes are valuable and allow for diversity of thought and therefore unique perspectives to be brought into the field. I believe our work is all about pattern recognition, but technical skills are not enough. There is a need for the next generation to have digital and data literacy with our systems being digital and for even the public to adapt in this same regard.��

In short, we have to invest in skills not just for future data scientist, but across the entire health ecosystem.