Artificial Intelligence and The Future of Health
Article written by Ebele Mogo, Doctor of Public Health, Community and Behavioural Health.
Artificial intelligence, the science of engineering high-level computational abilities, is rising to the fore of our conversations on big data. Discussions on artificial intelligence span from optimistic accounts of how robots will make our lives easier, to fears about what this will mean for our jobs and data privacy. Either way, artificial intelligence is no longer for futuristic movie scripts alone. It is now a growing reality.
Health is not left behind in this conversation. You may have heard of Google’s Deep Mind Project that started a collaboration with the UK’s National Health Service (NHS). By analyzing the large database of the NHS, Deep Mind intends to support clinicians to improve treatment plans. Machine learning algorithms have been to used to improve the life of at-risk children through more timely monitoring. Ambitious projects like Google Brain aim to predict medical events that will happen to you in the future.
What does artificial intelligence mean for the future of healthcare?
First, let us review the current health challenges of our day. The United Nations’ Sustainable Development Goal (SDG) 3 is to “ensure healthy lives and promote wellbeing for all at all ages”, with priorities like infectious diseases and maternal mortality as well as the growing problem of non-communicable disease deaths including injury and pollution-related diseases. Health systems are changing their payment models to reflect a focus away from services rendered to real value in the form of behavioural health change and improved health outcomes. There is also a stronger focus on inclusiveness as you may have seen reflected in SDG 3 with its repetitive use of the word “all”. We need to ensure that health benefits accrue to all and throughout their lifecourse.
Let’s take a look at a few ways that artificial intelligence could support these goals.
Artificial intelligence will have the unique ability to quickly integrate data from the evidence with various aspects of patients’ lives – their genes, their lifestyle, their medical history. This information could be used to create evidence-based treatment options for the patient at a very high level of precision. With the still significant problem of infectious diseases and maternal and infant deaths, the ability of artificial intelligence to monitor patients in real time, integrating this information with their histories and the evidence can support clinical decision making.
Informing prevention and behaviour change
The value proposition for artificial intelligence is not limited to treatment. With a growing focus on prevention as non-communicable disease rates rise around the world, artificial intelligence could be deployed to engage patients in healthier behaviours outside the clinic to ensure they improve their behaviours and hence their outcomes. This could yield a much needed reduction in healthcare costs.
Improving population health
Issues of equity are integral to improving population health as we see reflected in the Sustainable Development Goals. Equity needs to be informed with the evidence. Artificial intelligence can be used to feed and mine data records, aggregating this information at a high level and informing plans to improve aspects of the places where people live, work and play, for better health and access to care. In India, such high level analytics are already being used to inform responses to pollution.
As the new and complex field it is, there are various moving parts that will need to be addressed. You can imagine that one is privacy – making sure that patient records are safely mined and stored. Another is data quality. For a field that relies heavily on data, the right data infrastructure are necessary as a foundation for intelligent computation.This includes the need to break down data silos so that various sources of data about a patient’s life can be securely and quickly integrated. Each aspect of the moving target – policy around big data, data privacy, data integration, precision medicine – will need to be optimized, measured and improved. However, if early indications are anything to go by, artificial intelligence will take on ever more complex public health decisions, hopefully saving lives and money.
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