AI pushes into healthcare: 5 terms to add to your AI playbook

There is immense potential for artificial intelligence and automation in healthcare, such as its application for clinical care recommendations or executing repetitive, high-volume tasks within a hospital's revenue cycle.

Through automation, healthcare organizations can help their staff accomplish more in their days. Understanding how AI works is an essential first step to process improvement.

In this multipart series, Becker's Hospital Review will examine AI in the hospital setting, including AI education, best use cases, seamless implementation and the benefits and opportunities machine learning holds.

Defining Artificial Intelligence

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. In other words, let AI supplement and optimize employees — not replace them.

For healthcare organizations just beginning their journey with AI, it is crucial to have a concrete understanding of what AI is and how it works. Here are five key components that personnel should be familiar with:

1. Artificial intelligence: 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. In theory, true AI should be able to think like and interact with other humans seamlessly.

2. Machine learning: 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 l task it completes. A machine learning model is "trained" on relevant examples from diverse data sources.

3. Natural language processing: 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.

4. Robotic process automation: 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.

5. Turing test: The original test of a machine's ability to successfully converse with a human evaluator in such a way that a third party wouldn't be able to determine which was the human and which was the machine. Developed by Alan Turing in the 1950s, the Turing test picks out the most sophisticated AI from AI that is merely a simulation of human intelligence. In other words, AI that passes a Turing test is indistinguishable from a human.

Making AI fit

In healthcare, some of the most opportune tasks for automation lie in the revenue cycle. Workflows marked with repetitive, high-volume, rules-based tasks, which are also more prone to human error, are the best candidates.

Some of the processes organizations want to automate are lengthy and complex, and often consist of numerous steps. 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. Automating these tasks can free up employees' schedules so they can dedicate time to activities that require critical thinking, problem solving and creativity, thereby allowing organizations to scale their workforce and ease clerical burdens for employees.

Olive, a healthcare AI and robotic process automation company, devised an AI solution that accomplishes many of the same tasks as an employee. For example, Olive can log into the EHR, extract specific information from a report and use it to complete an assigned task, such as claims adjudication or prescription prior authorization. Hospital and health systems' workforces, as a result, are then left to accomplish work that requires uniquely human capabilities.

"AI should be implemented in a way, in healthcare, where you can't really tell the difference between your artificial intelligence and a human," Sean Lane, CEO and co-founder of Olive said during a recent presentation.

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

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