AI vs. Machine Learning vs. Deep Learning vs. NLP
by XSOLIS Insights, on Feb 5, 2023 2:47:29 AM
Artificial intelligence (AI), machine learning, deep learning, natural language processing (NLP), and more. These related concepts are broadly valuable for modern businesses, in general, and for healthcare utilization management in particular. However, they can also be confusing without some straightforward definitions. Depending on your role within your organization, you may have even been asked a question like, “What’s machine learning vs. deep learning?”
We’ve all heard about “big data” in healthcare – that 30% of the world’s data volume is generated by the healthcare industry. Yet, in today’s challenging environment for healthcare operations, there is increasingly more emphasis on turning data into actionable insights. Specialized AI techniques, such as machine learning, deep learning, and NLP help make that happen.
While clinical applications of these technologies are often in the headlines, they can also be used to tackle operational challenges – unlocking powerful data insights to improve staff efficiency, shoring up revenue integrity in the face of rising costs, and reducing friction associated with transition of care coordination. Let’s take a closer look at AI vs. machine learning vs. deep learning vs. NLP, along with some examples, to better understand their intent, purpose, and benefits.
What is AI?
Encyclopedia Britannica offers a straightforward and concise definition: “Artificial intelligence (AI) [is] the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”
AI is a very broad and complex concept. Significant expertise is needed to build, maintain, and improve effective and relevant AI.
AI is the foundation of the other terms we’ll define. Knowing nothing else, if someone asked you to explain machine learning vs. deep learning vs. AI vs. a neural network, you could confidently say AI is the broader field under which machine learning, deep learning, and NLP lie – you may even think of AI as a tent or umbrella that envelops the other terms, and it is at the heart of all three other concepts.
What is Machine Learning?
Machine learning refers to the application and study of algorithms that make sense of data. Rather than humans explicitly creating rules to solve problems, such as classification or regression, machine learning offers a much more efficient way to derive these rules from the consumption of raw data. There are three different types of machine learning: supervised learning, unsupervised learning, and reinforcement (semi-supervised) learning. Supervised learning is by far the most common, and relies on labeled data to provide direct feedback to the algorithm. A machine learning algorithm continually leverages data to improve its performance. By drawing on new and larger data sets, it can become more accurate and useful over time.
In the context of utilization management, machine learning can learn to identify patterns in patient populations. That can help predict the proper patient status, including obvious inpatient or outpatient cases. This frees up administrative staff and clinicians to focus on complex edge cases that need additional attention.
What is NLP?
NLP can be seen as a specialized domain of AI and machine learning, where the goal is to enable machines and humans to communicate using natural (i.e., human) language. This language can encompass many forms, including written text (both typed and handwritten), spoken word, and even vision (e.g., American Sign Language). All these inputs are considered unstructured data. When focused on text, NLP aims to include not only the words and sentences themselves, but the implicit context they may offer as well.
In a healthcare setting, effective NLP can extract the most useful and relevant information about a patient from large amounts of written text. XSOLIS’ UM solution, CORTEX®, goes further and provides additional insights by comparing text parsed through NLP to other situationally relevant cases in its database.
What is Deep Learning?
Deep learning is also a subset of machine learning. It involves complex, multi-layered neural networks with the goal of simulating the learning process of the human brain, IBM explains. According to XSOLIS Senior Director of Data Science, Jason King, this may even be a bit of a misnomer. A deeper network has the capacity to learn more complex patterns, and when successful, the result is algorithms that can better understand and use unstructured data. Interestingly enough, he adds that early neural networks were inspired by birds but have significantly diverged from those systems. The main difference between deep and machine learning to keep in mind – they use different types of data. ML uses structured, labeled data in its algorithms while deep learning can also ingest and process unstructured data as well.
An AI-Driven Solution for Precision UM
At XSOLIS, we’re proud to help providers and payers realize a more efficient and less burdensome approach to UM driven by purpose-built AI. That means saving time, reducing spend, and delivering better outcomes for patients and plan members. Our clients are ushering in a new future – one where technology can serve as a bridge between payers and providers, with shared views, objective real-time data, and predictive analytics to make jobs and lives easier.
You don’t have to understand everything about what goes into our Care Level Score or be able to recite these definitions to reap the benefits of using advanced technologies. Take it from Humana’s Angie Frame, whose team has achieved up to 83% faster approval processes by using CORTEX with Precision UM automation capabilities: “I don’t understand all of the things in the secret sauce, but I’ve learned over five years (and multiple audits) that I can trust it. The numbers are right, and it works.”
Learn more today about how your team can also apply the power of data science to streamline processes and remove manual work.
Hear from our Director of Data Science, Jason King, about each of these AI-related terms.