If you haven’t already made it your personal assistant, you’re probably sick to death of hearing about AI. If that’s the case, no matter how understandable, you’re unfortunately out of luck. The buzz around AI isn’t going anywhere. But with its inescapable popularity, it’s critical to note that AI isn’t the silver bullet many people treat it as. Sure, AI can do a lot, but acknowledging what it can’t do is equally as important.
AI and machine learning go hand in hand, to the point where they’re being used interchangeably. This is a common misconception. While AI refers to the general ability of computers to mimic human thoughts and actions, machine learning is a subset of AI, referring to the technologies and algorithms that help systems identify patterns and make decisions through data and experience.1 To make it simpler, machine learning is one of the blocks AI is built on, a tool to achieve the goal of AI itself.
Just as machine learning is the tool to achieve functioning AI, AI is a useful tool for us to achieve specific goals of our own. But believing it can solve everything is a recipe for disappointment.
AI and Healthcare
Healthcare is a uniquely complex industry. It’s not just about data and processes—it’s about people. Every patient interaction involves nuance, empathy, and ethical decision-making. The same goes for dealing with every employee, employer, broker, and everyone else involved in the process along the way. While AI can crunch numbers and recognize patterns at lightning speed, it can’t understand context in the way a human can.
There are additional important considerations like data privacy, security, and regulatory compliance as well. These delicate areas often require the kind of judgment and oversight that only experienced professionals can provide. So no, AI isn’t going to replace anyone in the healthcare industry, but it can give us better tools to do our jobs more efficiently and effectively.
Claims Processing
Anyone in the healthcare system knows claims processing is one of the most resource-intensive (and often frustrating) parts of the business. AI can help automate the repetitive steps, like data entry or eligibility checks. It can even flag potential errors or detect patterns of fraud, helping reduce costly mistakes and speed up reimbursements. That means fewer delays, fewer headaches, and more time to focus on other important areas.
Data Handling and Decision Support
Healthcare generates an enormous amount of data, and most, if not all, of that data is vital. AI can help make sense of that data, offering insights that support clinical decision-making or operational improvements. Instead of dealing with an overwhelming volume of information that takes hours to comb through, AI can process that information more efficiently and accurately, helping deliver timely decision-making for stakeholders.
However, humans still have the final say. With sensitive patient information making up the bulk of that data, having human oversight and considerable protection in place is crucial. AI can provide a flashlight of sorts into the obscurity of healthcare, but it’s up to us to decide where we shine it.
The future of healthcare is working with AI. Once we begin to view AI as a partner, we can open the door to smarter, more efficient care that moves the industry in the right direction.
