Let’s be honest, talking about AI can sometimes feel like trying to read a different language. One minute it’s machine learning, the next it’s agentic AI, and somewhere along the way someone drops RPA into the mix like we’re all supposed to nod knowingly. If you’ve ever felt like you’re smiling through a conversation about AI while secretly Googling terms under the table... you’re not alone.
So, let’s break it down – plainly, simply, and with a dash of clarity.
Think of these as the bedrock. The OGs. The tools that paved the way for everything else on this list.
Machine Learning
This is where it all starts. Machine Learning (ML) is like teaching a kid to ride a bike, except the kid is an algorithm, and instead of scraped knees, it collects data and learns from it to make better predictions or decisions over time. RCM Example: Predicting which accounts are most likely to result in denials and what issues are likely to cause the denial, so you can work them smarter, not harder.
Predictive Modeling
This is ML’s superpower cousin. It takes historical data, finds patterns, and says, “Here’s what’s probably going to happen next.” Whether it’s predicting patient no-shows or claim denials, this tool is all about looking ahead. RCM Example: Forecasting cash collections based on service mix, historical reimbursement, payer behavior, and claim status trends.
Deep Learning
The overachiever in the AI family. Deep learning uses layered neural networks to process data and solve complex problems like recognizing speech, analyzing imaging, or generating eerily accurate text. It powers some of the most advanced AI applications out there. RCM Example: Extracting and interpreting complex information from scanned medical records to support accurate coding.
These tools aren’t just smart, they’re strategic. They don’t just crunch data, they take action.
Generative AI
You’ve definitely heard of this one – hello, ChatGPT. Generative AI creates new content from scratch, whether that’s writing, images, code, or even clinical summaries. It’s the creative mind of the AI family. RCM Example: Drafting appeal letters that are customized to the denial reason, payer, and documentation, without requiring a staff member to start from scratch.
Agentic AI
Agentic AI isn’t your standard rule-follower. It takes initiative, makes decisions, and pursues goals autonomously. It’s like giving your AI a mission and watching it get it done without checking in every five minutes. RCM Example: Automatically identifying a missing authorization, checking payer requirements, and initiating outreach – all without a human triggering it.
Decision Intelligence
The grown-up version of decision support. This combines AI, analytics, and domain expertise to help people and systems make smarter, faster, data-driven decisions. Think of it as AI helping you choose wisely and not just quickly. RCM Example: Determining whether to appeal or write off a claim based on likelihood of success, cost to collect, and contract terms.
These tools bring AI to life in ways users can see and interact with.
NLP (Natural Language Processing)
This is how computers learn to speak human. NLP enables systems to understand, interpret, and respond to language – whether it’s pulling details from a patient note or powering a chatbot that actually answers your questions. RCM Example: Extracting structured data (like diagnosis codes or procedures) from unstructured physician notes to improve claim accuracy.
Smart Bot
Some of the most common interactions being labeled as “AI assistants” today are really just familiar workflows with a modern interface. While they may look new on the surface, many are simply repackaged versions of the HL7 and X12 messages your EHR has been using for years—just delivered in a more conversational format. RCM Example: Helping a registrar verify demographics, check eligibility, and obtain benefit information during patient intake.
Digital Assistant
The glow-up version of an API Assistant. More advanced, more personalized, and capable of handling complex interactions. This is the AI that remembers your name and gets things done. RCM Example: Guiding a scheduler through the selection of the best provider to meet the needs of a specific patient, ensuring that insurances will be in network for a visit, and connecting patients to financial assistance options based on their interaction.
Assisted AutomationWhen your Digital Assistant grows up and starts really contributing around the house, you are in the world of Assisted Automation. These solutions ask for your feedback and then complete half of your work for you while you sleep. RCM Example: Presenting you with four different ways to resolve a denial and then completing the option you select, asking if a new insurance should be billed and then updating registration and sending a claim.
Computer Vision
If NLP is about reading and writing, computer vision is about seeing. It helps machines interpret images and video – whether it’s reading scanned documents, recognizing patterns in radiology, or verifying IDs. It's like giving AI a pair of (very sharp) eyes. RCM Example: Scanning explanation of benefits (EOBs) or remittance advice documents to extract payment details and automate posting.
Not everything needs a neural network. Sometimes, you just need a reliable digital workhorse.
RPA (Robotic Process Automation)
The behind-the-scenes powerhouse. RPA doesn’t “think” like AI – it just follows rules. But when you need repetitive, rule-based tasks done quickly and without errors (think: claim status checks or medical record uploads), RPA shows up, does the job, and never needs a coffee break. RCM Example: Automatically logging into payer portals to check claim status, upload documentation, and update your system faster than any human ever could.
IPA (Intelligent Process Automation)
This is RPA with a brain. IPA combines automation with AI, machine learning, and analytics to handle more complex, judgment-based processes. It's what happens when automation grows up and starts solving problems, not just executing steps. RCM Example: Orchestrating a full denial workflow—flagging missing clinical info, initiating a bot to retrieve documents, drafting an appeal, and routing it to a human only if needed.
Self-Healing Automation
This is the gen Alpha of RPA. These bots were born to compute, assess, correct, and continue with their day. When Gen AI and RPA come together the result is automation that can correct itself when the world around it changes - say goodbye to human intensive maintenance of your digital workforce. RCM Example: Working across an EHR and payer website to fully manage the billing and denial process, and when a system upgrade occurs, finding the error, updating its code, and continuing the work without human intervention.
The modern AI toolbox can sound like a lot – but once you understand who does what, it becomes a powerful cheat sheet for solving real-world problems. Whether you’re creating, predicting, automating, or optimizing, there’s a tool built for the job.
At VisiQuate, we’re not just collecting buzzwords, we’re putting them to work in ways that move the needle on healthcare outcomes and financial performance. Want to talk about how this toolbox could help your team right now? Just ask your account team – we’re always up for a conversation (AI and otherwise).
