We can all recognize that COVID-19 has given us a clear reminder of the increasing cost of healthcare, both locally and globally. With premiums rising, the baby-boomers aging, and diabetes mellitus, the most costly disease in the world, affecting ten percent of the global populace, the rising healthcare cost is a matter that is close to home for most of us. But what is the reason for this phenomenon? In my opinion, the two principal contributors are the implementation and integration of new and emerging technologies. These typically require new and suitable infrastructure, training and onboarding, and most importantly, large scale roll-outs. On the flip-side, the application of AI is a promising approach to drive down these costs. Therefore, the onus is on innovators, industry leaders, and governments to make healthcare more affordable for the everyday person. The sooner we adopt non-traditional methods and solutions, the sooner we can get closer to solving this ever-present problem.
According to statista.com, the US currently has a health expenditure of 18% of the GDP, the highest in the world. This figure has almost quadrupled since 1960, and we can expect a similar trend globally. The generally accepted proposition is that the application of AI can significantly reduce these growing expenditures while improving healthcare quality and access. Although there is no consensus for determining the criteria for cost-benefit effectiveness, generous efforts have been proposed toward making a case for, or against, AI as a cost-effective approach.
For example, one source estimates that $200B is wasted annually in administrative activities that health care payers and providers are unable to reduce on their own. In this case, the two identified sources of waste were human error and fraudulent billing activities, as described in the article here. Another source simply concluded that more research needs to be done to determine if AI is a feasible cost-reducing solution. This may seem like a self-evident claim, but an important one nonetheless. The full article can be read here. Finally, another source presented four questions that need to be asked when leveraging AI and financial risk. The full article can be read here. In summary, there are a lot of different opinions and predictions brought forth. A lot of productive discussions are taking place. I'm all for that, as long as we turn those discussions into some form of tangible solutions.
While it is difficult to confidently put a figure to the costs saved from the application of AI in healthcare, we should also consider the costs incurred by the lack of better solutions. We have an obvious problem at hand, and fortunately, we also have the tools and knowledge. The question is, can we think outside the box and re-imagine what affordable and quality healthcare look like? I acknowledge that this may seem a bit philosophical. But the way I see it is that we can either talk the talk or walk the talk. We can learn from our most recent experience of COVID-19 and mitigate future risks, or we can accept things as they are. So my final takeaway is this, so far, AI is at the forefront of so many groundbreaking technologies. Healthcare should not be an exception. We don't have to re-invent the wheel but re-adapt and re-purpose what already exists.