Dr Philip Payne, Director of the Institute of Informatics and the Robert J. Terry Professor in the Division of General Medicine and Professor of Computer Science and Engineering at the School of Engineering and Applied Science, Washington University in St Louis, sheds light on how precision medicine can help provide best healthcare through AI, machine learning and data collection
Much has been written about the promise of precision–or personalised– medicine. This approach to healthcare utilises all the data we can collect about individuals to measure their characteristics, including lifestyle, genes, the biological basis for their health and wellness, and how they present during an office visit.
The biggest challenge facing us in delivering precision medicine is turning data into action. We have massive advances underway in artificial intelligence, machine learning, and the use of mobile computing and sensors, as well as any number of other data sources to help providers better understand our patients and our communities. There is a lot of information coming at us at an astonishing cadence. Now is the time when we have to turn that data into actionable insights for as many patients as possible.
To turn data into action, we must understand all levels of data, from individual behaviours and lifestyle to environmental factors, and the clinical presentation of health as well as disease. As we use these data to make better decisions for individuals, we can often improve the quality, safety, and cost of the care they receive.
Ultimately, if we can process all of this information and translate it into action, we have an opportunity to treat people when they are sick, and also to take care of them and ensure that they don’t get sick in the first place. However, there is much work to be done to ensure this information is inclusive of all people.
Studies have shown us that disease risk, as well as response to drug therapy, varies greatly across different populations. Starting in 2009, a series of studies identified the fact that more than 96 per cent of the data that we were using to understand the genomic basis of disease was derived from individuals of western European ancestry. Of course, the patients that we see in our clinics and hospitals are not just from western European ancestry. They come from across the globe, from a variety of races and ethnicities. It’s critical that the data we collect to inform precision medicine reflects that diversity, and that we engage partners from all over the world in this undertaking.
India is making some interesting moves to implement broader access to health insurance and healthcare for all parts of their population. Given the size of India’s population and the complexity of delivering care at that scale, in many ways, a country like India could leapfrog the United States in terms of thinking about data driven approaches to improve wellness in the population. Their goal of creating more accessible, better quality healthcare for all of their citizens will only work if they can manage demand, and the only way to manage demand is to keep that population healthier in the first place. You can think about that from a couple of difference perspectives.
For example, India has some of the highest penetrance of mobile devices of any country in the world. Such devices open up all kinds of interesting ways of intervening and working with patients to understand their health status, and to maintain that status, through their devices that are personal and consumer-oriented.
Additionally, if broader access to healthcare insurance and care expands in India via Ayushman Bharat, if you have a population where the vast majority of individuals have access to healthcare, and if there is an increasing use of electronic health records, which is also happening in India, you have a scenario in which you can build very large cohorts of patients. From those cohorts, you could build their genomic signature alongside clinical data. Using both clinical and genomic data will allow researchers to ask and answer questions at a scale that we can’t readily ask in our siloed healthcare system in the United States.
I also think India has an opportunity to rethink how to train healthcare providers to deliver that care. They are going to need many more healthcare providers. Those healthcare providers are perhaps going to have to see and manage much larger patient cohorts than we deal with in the United States. That’s where digital health becomes really interesting. It could augment human capabilities to make those providers more effective and allow them to practice at the maximum capacity they can to ensure that important access to high-quality healthcare.
We also need to think about the ethical, legal, and social implications of doing so. Increasingly, we’ve come to understand that we need laws and ethical standards that allow us to not only collect the data that we need to understand how people respond to therapy or what their risks of disease are, but also to make sure that that understanding is not used to discriminate against individuals.
These are just of few of the issues that my colleagues and I at Washington University in St. Louis recently had the opportunity to discuss with healthcare experts and practitioners in India. During our Forum for India, held in Mumbai, we talked over opportunities, challenges and best practices with friends from a variety of corporate, academic and clinical backgrounds. During these face-to-face sessions, we were able to connect, collaborate, and further strengthen partnerships.
Ultimately, the most beneficial approach to delivering healthcare is not treating people while they are sick, but actually promoting health and wellness. This is an opportunity to take an individual’s unique risk of developing a disease and intervene before they become sick, so that both patients and providers alike can be focused more on well-care as opposed to sick-care. It is something we must work toward on a global scale, and I look forward to further engagement with my colleagues in India to do just that.
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