Four ways AI can make healthcare more efficient and affordable
Arnaud Bernaert, Head of Partner Development, Europe & North America, Head of Healthcare Industry,Member of the Executive committee, World Economic Forum Geneva
Emmanuel Akpakwu, Project Lead, Value in Healthcare, World Economic Forum Geneva
According to OECD estimates, 20% of healthcare spend is wasted globally. The United States Institute of Medicine believes the figure is more like 30%. Using both estimates, the top 15 countries by healthcare expenditure waste an average of between $1,100 and $1,700 per person annually.
To put this into context, the average waste per-person across the top 15 countries is 10–15 times more than the average amount spent by the bottom 50 countries on healthcare, who currently spend an average of around $120 per person. Even more concerning is the fact that the underlying reasons for this waste include preventable and rectifiable system inefficiencies such as care delivery failures, over-treatment, and improper care delivery.
Technologies such as artificial intelligence (AI) can help minimise such inefficiencies, ensuring substantially more stream-lined and cost-effective health ecosystems.
There has been much debate in the past decade around the potential applications of AI-driven technologies in a number of industries, including healthcare (if you weren’t blown away by Google’s recent lifelike AI assistant, then you’re most likely a time traveller from the future).
While we are not yet at the stage of autonomous robots doing your house chores and driving you to work — the traditional perception of AI — there is strong evidence pointing to a number of ways in which AI can help tame healthcare costs.
1. Guiding treatment choice
In today’s world, being able to effectively and accurately harness the power of data enables more efficient decision-making across most industries. Healthcare is no different. As healthcare providers begin to move towards a standardised format for recording patient outcomes, large sets of data will become available for analysis by AI-enabled systems which can track outcome patterns following treatment and identify optimal treatments based on patients’ profiles. In doing so, AI empowers clinical decision-making and ensures the right interventions and treatments are customised to each patient, creating a personalised approach to care. The immediate consequence of this will be a significant improvement in outcomes, which will eliminate costs associated with post-treatment complications — one of the key drivers of cost in most healthcare ecosystems across the world.
2. More efficient diagnosis
Repetitive, uncomplicated tasks such as the analysis of CT scans and certain tests can be performed more accurately by AI-enabled systems, reducing physician error and enabling early diagnosis and interventions before conditions become critical. As an example, an Israeli start-up has developed AI algorithms that are equally or more accurate than humans when it comes to the early detection of conditions such as, for example, coronary aneurysms, brain bleeds, malignant tissue in breast mammography and osteoporosis.
According to a recent article in Wired, AI has demonstrated 99% accuracy and is 30 times faster in reviewing and translating mammograms, enabling much earlier detection of breast cancer than humans are capable of. In cases such as osteoporosis, which costs the UK’s National Health Service approximately £1.5 billion annually (and that excludes the high costs of social care), the detection of vertebral fractures — an early indicator of impending osteoporosis which is commonly missed by human diagnosis — can substantially reduce the cost of this condition to health services.
3. Clinical trials optimisation and drug developmentAI has the potential to enable faster development of life-saving drugs, saving billions in costs that can be transferred to health ecosystems. Most recently, a start-up supported by the University of Toronto programmed a supercomputer with an algorithm that simulates and analyses millions of potential medicines to predict their effectiveness against Ebola, saving costly physical tests and — most importantly — lives, by repurposing existing drugs.
In clinical trials, AI can optimise drug development using biomarker monitoring platforms — biomarkers allow for gene-level identification of diseases — and millions of patient data points, which can be analysed in seconds from a drop of blood using at-home devices.
4. Empowering the patient
AI has the potential to truly empower us as individuals to make better decisions regarding our health. Vast numbers of people across the world already use wearable technology to collect everyday information, from their sleep patterns to their heart rate. Applying machine learning to this data could inform people at risk of certain diseases long before that risk becomes critical. Mobile apps are already providing granular-level patient profile information that could help people living with specific chronic conditions to better manage their disease and live healthier lives. All of this can lead to healthier populations and a reduction of the overall cost burden.
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These examples represent a small fraction of what is possible when the full potential of AI is leveraged in the delivery of healthcare. The possibilities can neither be underestimated nor overemphasized, and cooperation between public and private sector industry stakeholders is vital if this potential is to be realised. As global populations live longer and the prevalence of chronic disease increases, the rising cost of healthcare will continue to remain an important topic amongst healthcare stakeholders. Perhaps it’s time to call in the machines.
Originally published at www.weforum.org.
