The cornerstone of making AI a success in healthcare operations is to integrate it with pre-existing tools in such a way that it works in the background. 

That advice comes from a recent installment in a multipart series at Becker’s Hospital Review which intends to examine elements of AI in the hospital setting such as AI education, best use cases, seamless implementation and the benefits and opportunities machine learning offers healthcare organizations.

For starters, the writer says, healthcare organizations just beginning to grapple with AI need “to have a concrete understanding of what AI is and how it works.”

Not surprisingly, having a clear grasp of AI terminology is critical.  For example, the article defines “artificial intelligence” as “intelligent behaviors commonly associated with humans but exhibited by machines and applied to tasks like problem-solving, automatically completing forms or parsing medical images to recommend diagnoses.”  

Machine learning, by contrast, is “an application of AI that uses algorithms to find patterns in data without instruction. Machine learning automates a system's ability to learn, so it can improve from experience without being programmed for each task it completes.”

Part of machine learning involves “natural language processing,” which is “a computer's attempt to interpret written or spoken language. Because language is so complex, computers must carefully parse vocabulary, grammar and intent while allowing for variation in word choice when processing language, which is why programmers often take multiple AI approaches to natural language processing.”

On a more operational level, the article defines “robotic process automation” as  a “type of AI that entails training software algorithms to mimic how an employee would complete a specific task. These tools are often equipped with computer vision, or the ability for a machine to perceive and interpret visual or text-based imagery. Robotic process automation models are trained by ‘watching’ the human user perform that task and then directly repeating it.”

While clinical uses may spark the most interest in AI, it’s important to remember the administrative possibilities, as well. For example, the writer notes, some of the most opportune tasks for automation lie in the revenue cycle. “Checking patient eligibility and benefits, for example, is one step in the larger patient intake process that is easily automated. It often requires copying and pasting data as well as simple yet time-consuming interactions with predetermined applications — tasks that don't necessarily need intense human oversight.”

With automation, the writer concludes, healthcare organizations can accomplish more and scale their workforces. Understanding how AI works is just the first step to process improvement.