FLG: Could you introduce yourself and the work you do?
I am currently a Health Informatics Director at AstraZeneca. I started my career as a lab coat-wearing immunologist, but was fortunate enough to get interested in computers and programming at just the right time. After spells of using my data science skills in infectious disease and translational medicine, I joined AstraZeneca to focus on Real World Evidence (RWE). RWE is non-traditional, non-experimental data obtained outside a clinical trial, for example from hospital records, insurance claims and fitness trackers. My team and I analyse these data to generate valuable insights that aren’t otherwise available, like identifying potential new patient populations and predicting the course of a disease.
FLG: What motivated you to pursue a career in this field?
For me, it’s always been about the science: being able to work on challenging problems that have a beneficial impact. I’ve been lucky to spend my career doing this.
FLG: What has machine learning and artificial intelligence been used for so far, and what successes has it seen?
AI and machine learning have the potential to transform the way we discover and develop new medicines. At AstraZeneca we are embedding AI across our R&D with the aim of gaining a better understanding of the diseases we want to treat, identifying new targets for novel medicines, speeding up the way we design, develop and make new drugs, recruiting for and designing better clinical trials and driving personalised medicine strategies.
In terms of an example of success, the rapid adoption of high-quality Electronic Health Records (EHRs) represents a vast, rich, and highly relevant data source that has a huge potential to improve clinical trial implementation. Federated EHR technology is unlocking new opportunities to enhance clinical research and transform the way we do clinical trials. The technology has the potential to refine or replace many clinical trial processes including patient identification, selection, trial conduct, and capture of data. More than 100 AstraZeneca clinical trials are already benefitting from a federated EHR research platform that improves trial set up and enrollment timelines.
FLG: Are there any challenges in artificial intelligence and do you see any future potential developments that may affect its use in the future?
Today we are generating and have access to more data than ever before. Data and analytics have the potential to transform drug discovery, but the true value of scientific data can only be realised if it is ‘FAIR’, or Findable, Accessible, Interoperable and Reusable. Ensuring we have the foundations for data science and AI is critical to our continued success. It takes hard work to get data in the right shape, embed the right governance, implement the right analytics tools, and, most importantly, to get that data into the hands of the right people to yield transformational benefits. That is why at AstraZeneca, a concerted, cross-company effort around data and analytics is so important. Bringing the right stakeholders together is key.
FLG: What do you think the future of machine learning and artificial intelligence holds?
AI and big data have the potential to revolutionise how we can predict and prevent disease before patients get ill, improve how we treat patients and change how we make scientific discoveries and develop new medicines. However, the healthcare sector is behind many other sectors in making the most of data. Around 80% of health data is unstructured. Effective partnerships to enable the collection and sharing of ‘FAIR’ health data will provide the foundation of sustainable and efficient healthcare systems.
FLG: What do you think is the most exciting thing happening in the genomics field worldwide right now?
There are many exciting developments in the field of genomics at the moment. One example that we are part of is the Whole Genome Sequencing project, one of the most ambitious sequencing programmes ever undertaken – sequencing the entire genetic code of all 500,000 participants in the UK Biobank health research resource. Its long-term aim is to enable the scientific community to better understand, diagnose, treat and prevent life-changing diseases, such as cancer and diabetes. Collaborating with leading institutions is central to our approach of transforming drug discovery and development through genomics.
