What is the future of artificial intelligence (AI) in healthcare?
It’s a big question that almost every medical professional has had cause to ask recently, and the answer is even bigger. In fact, at this moment, the answer is something along the lines of, “We don’t exactly know yet, but it’s going to be monumental.”
There are, of course, current applications for AI being used and developed today that we can look at to inform our prediction of how AI will be used in healthcare in the future, and that’s exactly what we’re going to cover here. By the end of this article, we will have an answer to our question.
And we’ll craft that answer using Gartner research to identify healthcare use cases for AI (content available to Gartner members).
AI solutions for doctors
AI applications currently being used to serve providers and improve their performance and experience do so by helping to “enhance human performance on cognitive tasks.” For example, AI is improving diagnostics and treatment plans by analyzing data and applying machine learning and/or algorithms to provide decision support or even make decisions themselves.
Two existing applications that fall into this category are:
➤ Diagnostic imaging interpretation: Using deep learning programs and categorization technology, AI-enabled imaging systems are now equipped with algorithms for faster and more accurate image reading, including x-rays, MRI exams, and CT scans. Not only does AI imaging technology improve a doctor’s performance with a more accurate diagnosis, but it’s also a valuable tool in combating the shortage of imaging specialists in the healthcare market.
➤ Precision health: A new approach to healthcare that focuses on preventative care based on data collected from genetic information, wearable devices, and advanced electronic health systems. Using information such as a patient’s lifestyle, environment, and biometric data, AI-enabled precision health tools can identify potential risks and suggest preventative interventions.
As these two examples illustrate, artificial intelligence is currently making life easier for medical providers, and it will continue to do so as these applications are further developed and iterated into new areas.
With this knowledge, we can begin crafting our answer to the original question:
AI solutions for patients
With a growing focus on patient experience and engagement in healthcare, it’s natural that AI has been adapted to serve patients as well as providers. Through the use of AI-enabled interfaces, patients now have more power and control over their own care paths.
Two examples of patient solutions using artificial intelligence are:
➤Virtual health assistants: Using augmented reality, cognitive computing, speech and body recognition software, a virtual persona is created for patients to engage with. These virtual health assistants are able to provide a personalized experience in which patients can ask questions and learn how to better manage their health.
➤Healthcare bots for customer service: These are interactive chatbots that use natural language processing (NLP), sentiment analysis, and concept extraction algorithms to respond to patient statements and questions around administrative things such as bill payment, appointment scheduling, or medication refills.
We know that patients do a better job of managing their own chronic conditions and that outcomes improve when they are engaged with their care, but staying in regular communication with individual patients adds a time-consuming burden to already overwhelmed medical providers.
With these AI tools, patients can achieve positive results while providers are granted more time to focus on other tasks. Thus, we can expand our answer to what the future of AI will be:
AI solutions for administrators
Finally, AI algorithms can go a long way towards automating administrative processes, eliminating waste, and reducing bottlenecks by better managing all of the data that a medical practice produces.
Currently, AI is being used to great effect in the realm of revenue cycle management and optimization. Two examples of that are:
➤Real-time physician documentation improvement: Using specialized, real-time decision support models at the point of care, physicians can increase the accuracy, comprehensiveness, and efficiency of their note taking.
➤Computer-assisted coding: Programs using NLP and machine learning can suggest medical codes based on clinical documentation. Coders can use these suggestions to amend or validate their codes and ensure the highest reimbursement rate possible.
And with that, we’re able to account for administrative tasks in the formation of our answer:
How to overcome barriers and prepare for the future of AI in healthcare
According to Gartner, “The AI/smart machine era will be the most disruptive in the history of IT … Eventually, these advances will redefine what it means to be a physician and a patient.”
However, as these advances are made, Gartner has also identified current challenges to adopting AI. The most notable one is a lack of trust in AI-based technologies from medical providers.
Those medical providers who fail to recognize the benefits of AI are in for a wake-up call.
As Gartner puts it, “Physicians must begin to trust the use of AI so they are comfortable using it to augment their clinical decision making. There is simply so much information available to use when making medical and diagnostic decisions that it is truly beyond the cognitive capabilities of the human brain to process it all.”
AI has been and is being tested on a large scale by massive healthcare delivery organizations, and the beauty of operating an independent medical practice is that you get to watch these giant providers as they test out technology before it becomes ubiquitous (and before patients start expecting to see that tech in smaller practices).
Look to these providers as they incorporate AI-powered technology more and more, and use their examples to figure out what applications will be the most beneficial to you.