AI, Healthcare's Inflection Point

Accenture recently published a study showing Artificial Intelligence (AI) in healthcare as growing more than 10x in the next five years to around $6.6 billion at a compounded rate of over 40%. This study also suggests that AI could save healthcare $150 billion. This could be an extraordinary development as the US spends over $3 trillion a year on healthcare, which represents 18% of the GDP. And yes, nearly one third of all healthcare costs are entirely unnecessary.

We believe healthcare is at an interesting inflection point. Human intelligence alone has not been reign in the healthcare costs. The good news... technology has a unique opportunity to address this systemic challenge given the rise in EMR adoption along with the recent advances in AI.

EMR Adoption

The Health Information Technology for Economic and Clinical Health (HITECH) Act, was enacted as part of the American Recovery and Reinvestment Act of 2009, and was signed into law on February 17, 2009, to promote the adoption and meaningful use of health information technology. As a result, the US has experienced massive adoption in less than a decade. As of 2016, over 95% of hospitals eligible for the Medicare and Medicaid EHR Incentive Program have achieved meaningful use of certified health IT. This is a crucial element as the majority of healthcare providers now have the very foundation to take AI to the next level. More importantly, data output continues to double every year, which will allow AI to become far more accurate and effective.

AI Acceleration

First, it is important to note the distinction between analytics and AI. To date, healthcare analytics has been a predominantly hypothesis driven method that utilizes a question-first approach. While this can be valuable, it often leads to more questions and often creates a state of analysis paralysis.

AI, on the other hand, can surface meaningful, and otherwise unknown insights using unsupervised learning. In other words, machines can offer greater insights associated with complex data relationships that the human mind cannot possibly comprehend. This new approach using a combination of human intelligence and machine learning could unlock a whole new realm of possibilities in healthcare. Beyond the immediate applications to improve clinical and operational efficiencies, AI has the potential to completely redefine the current paradigm thus personalizing optimal care delivery for the specific needs of each patient.

Potential Applications

Life Sciences: There are a number of interesting applications for pharma and biotech companies. At minimum, AI could help speed up clinical trials by producing required content for regulatory bodies while also provide more sophisticated testing to determine if a patient will experience adverse reactions to specific drugs.

Insurance: Health Plans stand to gain quite a lot when leveraging AI in their environment. For example, AI could help expedite claim approvals, more accurately decipher exception management, and improve fraud detection. With these combined improvements, you can imagine that general workflow and operational efficiencies will produce further savings.

Clinical Care: Two of the leading areas that healthcare executives are focused on could be addressed with AI. This includes minimizing unwarranted clinical variation and finding innovative approaches to expense reduction. This will be very important to not only bend the cost curve, but to improve outcomes in population health management initiatives.


While the potential impact for AI is significant, we must temper that excitement with some of the initial barriers. Consider two immediate hurdles, transparency and accessibility.

Transparency: As with any new innovation, it is often met with a bit of skepticism. Most of this is well-founded, whereas some of it is rooted in the apprehension to change. This notion of transparency has been crucial for all other important innovations including the birth of electricity. We must willing to share the AI methods for solving these problems with the broader healthcare community. This will foster trust and alleviate any concerns with the proverbial “black box”. The more democratic we can make this process, the greater chance we collectively have to realize the benefits.

Accessibility: AI will reach a pivotal tipping point when it becomes accessible to the masses. That is to say, you don’t have to be a data scientist, nor do you have to make a considerable investment to reap the benefits. We are already seeing this proliferation in AI as it is becoming an extension of our daily lives with smartphones, personal assistants (Google Home, Amazon Alexa), etc. We expect to see a similar shift in healthcare when voice and image recognition are met with decision support via any mobile device. This will be a game changer not only for clinicians, but also for every healthcare consumer.

So what do you make of AI in healthcare? We see this as a moment where AI could truly usher in a massive transformation that improves the lives for every individual regardless of their socioeconomic status. With a real collaborative spirit, the possibilities are endless. That's incredibly exciting and encouraging.