Maxine leads the Diverse Data initiative at Genomics England, which aims to reduce health inequalities in genomic medicine by ensuring patients, regardless of their background, receive the same quality of genomics-enabled personalised medicine, supported by the latest research on people like them. Maxine is an organiser in the Turing’s Health Equity Special Interest Group where she primarily focuses on promoting data diversity in genomics. Maxine is also the co-founder of One HealthTech – a global, volunteer-led, grassroots community that supports and promotes under-represented groups in health innovation. OHT has over 20,000 contributors worldwide across 20 Hubs which have collectively delivered over 1000 events, projects, campaigns and initiatives improving diversity in healthtech. She also set up Data Science for Health Equity, a community of practice that brings together those with expertise in data science and health inequalities to connect and collaborate on cutting-edge domains in health. She has been part of a number of communities and committees including being a Non-Executive Director for the Eastern Academic Health Science Network, a member of the World Economic Forum’s Global Shapers, and the British Computer Society (Health Exec) and the DeepMind Health Independent Review Board.
Healthcare makes up on ~10% of our health, with the other 90% being genetic, behavioural and social factors. Despite this, much of the health data research is dominated by healthcare data only, such as a clinical trials, physiological measures or electronic health record data, or makes use of only a narrow set of other determinants, like genetic factors of self-reported by behaviours. If we are to realise the hype and hope of advance analytics and AI for personalised, predictive and preventative healthcare, we need a better understanding of the causes of disease, and how different determinants of health interact. Citizens’ digital footprints are increasingly being captured as a by-product of daily living activities in consumer data companies, yet there is currently no way by which to make use of these data, in combination with other sources, for health and care purposes at scale.
Maxine’s work focuses on understanding how we can re-draw the boundaries of what we define as “health data” by making use of financial, geospatial, social or transactional consumer data to infer the health status of populations, and find scalable ways to use existing datasets to better understand social determinants of health. Much of this work involves collaborating with large private companies to understand how to safely, ethically and openly repurpose consumer data for public health research.
With the increasing digitisation of health, care and life sciences, there is huge potential to transform the way we predict, prevent, treat and understand health and disease. However, the data-ification of healthcare and research is not without its risks. Huge swathes of the population are missing in health datasets, we see countless examples of algorithms automating and encoding societal biases in health and we still live in a world with enormous inequalities in resources, funding and skills to support the digital revolution in health.
Maxine’s research interests lie in ensuring data science is as fair and equity-enhancing as possible in health, where she is currently focused on this in the context of genomics. Studies of human genetics have largely been on populations from WEIRD (Western, Educated, Industrialized, Rich, Democratic) countries which has resulted genomic insights that are not generalizable to all populations. Most studies, trials and papers conclude with a call to action to recruit and use more diverse genomes, and yet the proportion of non-European ancestries in genomic studies is diminishing. To address this gap, we must work across the whole pipeline of genomic research and health care delivery, from the populations we work with and the data we collect, to the analyses we carry out and the availability of genetic testing.
Prior to joining Genomics England Maxine’s research looked at applying machine learning methods to health, care and life science challenges, and also explored the ethical impacts these approaches had. Maxine was a
Research Associate and Fellow working between The Alan Turing Institute, The Health Foundation and the University of Oxford where she primarily worked with Professor Chris Holmes. Her postdoc focused on exploring the boundaries of what we define as “health” data, and how we can infer how healthy or sick people are, based on their financial, geospatial, social or transactional consumer data. Prior to this she completed a PhD at UCL’s Institute of Health Informatics (and was a Turing Enrichment Student) where her work looked at using novel statistical methods to detect early signs of dementia in electronic health records. She also completed an MSc in Health Policy, Planning and Financing (LSE & LSHTM) and a BSc in Biomedical Sciences, Neuroscience & Pharmacology (UCL).