In December 2017, Amazon, JPMorgan Chase and Berkshire Hathaway announced the formation of a new healthcare company which would use technology to provide high-quality healthcare to patients and families more simply, and at a more reasonable cost.
In 2016, U.S. per-person healthcare expenses were $10,348, more than double that of other first-world countries that offer universal health coverage ($4,752 in Canada, $4,600 in France, $4,708 in Australia, and $4,192 in the UK). Despite these costs, U.S. medical care is not altogether accurate or safe; medical errors kill more Americans annually than AIDS and motor vehicle incidents. Yet somehow modern medicine has escaped large-scale reform from automation and systems engineering.
According to the story in Forbes, Amazon has the potential to change this market. Its decision is modeled after tech giants like Alibaba and Tencent, which have been experimenting with employee healthcare software in China for many years and whose initial targets included online medical advice, drug tracking systems and more recently, artificial intelligence.
However, if Amazon intends to succeed where other industry giants have failed, it is essential for it to build infrastructure that can leverage cutting-edge medical technologies. As the saying goes, “If you want something you have never had, you must be willing to do something you have never done.” In 2018, this means Amazon needs to implement AI software for continuous analytics of automatically structured big data and advanced research technology.
According to The New York Times, over 130 Chinese tech companies were applying AI to increase efficiency and accuracy in overburdened Chinese hospitals. An example use case included the use of machine learning to identify diabetic retinopathy, which extends the capacity of Chinese ophthalmologists who are overburdened. Only 20 eye doctors are available for every 1 million persons, half of what is found in the U.S.
California workers’ compensation now limits medical care to “evidence based medicine.” Contrary to popular belief, experimental science in medicine is relatively new. Before the introduction of evidence-based medicine in the 1990s, the majority of Western medical advice was based on observational science and expert opinion. Evidence-based medicine has signified a historical shift in the way Western medical doctors view and treat patients.
What is concerning to many health experts is that more than half of current treatments may not be evidence-based. Historically, some very common – and invasive – procedures have turned out to have no benefit or even to be harmful. For instance, a 2018 study showed that stent placement for heart disease, a procedure that can cost up to $14,000, works no better than a placebo to increase exercise tolerance on a cardiac stress test.
If a healthcare technology disruptor that introduces AI with continuous analytics of automatically structured big data can display statistically significant observational evidence that is more likely to be accurate, the same system can be easily adapted to facilitate large-scale experimental validation studies in clinical trials. This could be disruptive, efficient and beneficial for medicine.