Artificial Intelligence 101

Artificial Intelligence 101: 5 Terms to Add to Your AI Playbook

This blog post was originally published on Nov 11, 2018 and has been updated to incorporate the latest insight on AI.

Artificial intelligence capabilities are quickly becoming a necessity for today’s healthcare organizations to survive and thrive in a rapidly changing and competitive environment. But where to begin with healthcare AI and automation? The technology behind AI solutions may be complicated, but understanding the basics shouldn’t be.

For healthcare systems 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 terms that healthcare leaders should be familiar with as they explore how AI can benefit their organization:

1. Artificial intelligence:

Artificial intelligence can be a catch-all term for many different technologies that attempt to simulate intelligent behaviors commonly associated with humans, from voice recognition to pattern detection to process automation. In theory, true AI should be able to think like and interact with other humans seamlessly. With advanced Computer Vision, Machine Learning skills, along with fundamental RPA capabilities, artificial intelligence can increase efficiency, capacity and intelligence to provide both immediate and long-term value to a healthcare organization.

AI solutions have the possibility to transform healthcare tasks like claims statusing, automatically completing forms, parsing medical images, and recommending diagnoses. When reading about or researching artificial intelligence, it is helpful to know more specifically about what technologies make up the building blocks of artificial intelligence.

2. Robotic process automation:

Robotic process automation (RPA) is another building block of AI that entails training software algorithms to mimic how an employee would complete a specific task – only faster and more accurately. RPA models are trained by “watching” a human user perform a task and then directly repeating it. These tools are often equipped with computer vision, or the ability for a machine to perceive and interpret visual or text-based imagery.

RPA is one of the most basic but powerful artificial intelligence tools that healthcare can take advantage of today. Many of the backend operational tasks that bog down our systems are robotic, repetitive tasks that can easily be automated through RPA, such as claim checks or prior authorizations.

3. Computer Vision

Computer Vision, or CV technology, enables a computer to “see” and interact with a user interface the same way a human would. Simple RPA solutions may tout their CV capabilities, but AI developed specifically with the dynamic environments of healthcare in mind are built to work in Citrix environments, read scanned documents, and provide accurate and fast recognition of the information it’s seeing – even when UI changes and updates occur.

4. Natural language processing:

Computers operate in code; humans with language. Natural language processing (NLP) is a computer’s attempt to bridge this gap by interpreting written or spoken language. Natural language processing is a much more difficult task than it might seem. 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 NLP.

Natural language processing is typically paired with other AI technologies, for example, using NLP to extract data from clinical notes and patient records plus ML to use that data to prepopulate ICD codes.

5. Machine learning:

Machine learning is one type 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. Using historical data already in your system, a machine learning model is “trained” on relevant examples so that it can apply those learnings to future data inputs.

Machine learning is one of the most important pieces of advanced AI solutions available in the market today. And as more data becomes available for research, the more sophisticated models can be developed. For a deeper dive on this advanced technology, check out our Machine Learning series.

Giving Artificial Intelligence the Turing test

British computer scientist Alan Turing was one of the first to work on artificial intelligence. He developed a machine during the second world war that could converse with humans. He also firmly believed that true AI should be indistinguishable from a human.

This idea became known as the “Turing Test”, and it is still used today: it is the test of a machine’s ability to successfully converse with a human evaluator in such a way that a third party isn’t able to determine which is the human and which is the machine. The Turing test picks out the most sophisticated AI from more basic solutions that are merely a simulation of human intelligence.

Implementing AI at Your Health System

In healthcare, some of the most opportune tasks for artificial intelligence are the numerous behind-the-scenes operational and administrative workflows. These workflows are often repetitive, high-volume, rules-based tasks, which are perfect candidates for intelligent automation. These routine processes are bogging down our health system, increasing costs, reducing efficiencies, and leading to burnout. With healthcare AI, you can let your humans be humans, and let AI automate the robotic processes that too often fill their days. Health systems 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, ease clerical burdens for employees, and get employees refocused on patient work, not paperwork.

“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 of Olive said during a recent presentation.

Olive is the AI workforce your organization needs to take advantage of healthcare AI at scale to transform your healthcare system. From the revenue cycle to the supply chain, Olive works across an enterprise to save healthcare organizations money and time. With artificial intelligence, healthcare can accomplish far more with their existing resources. To learn more about artificial intelligence and its place in healthcare, check out our AI and Automation for Healthcare white paper.

This article was originally posted on Becker’s Hospital Review.



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