Rules-based systems sound great until reality hits. A missing cover sheet, a handwritten note, or an unusual layout can throw those systems into chaos. And that’s how referrals get missed.
In this mini webinar, we walk through why AI-powered intake tools outperform traditional rules-based systems. You’ll learn how Referral AI recognizes what’s actually a referral, even when it’s buried in a messy fax, an email, or a PDF. More importantly, you’ll see how it helps your team capture every referral faster without constant rework.
What you’ll learn:
✅ Why rules-based systems struggle with real-world referrals – Time stamp: 1:49
✅ How Referral AI identifies referrals—no matter the format – Time stamp: 3:24
✅ What happens when referrals are incomplete (and how AI handles it) – Time stamp: 8:17
✅ How AI improves auditing, compliance, and intake visibility – Time stamp: 9:47
✅ What implementation looks like—even for agencies with custom EMRs – Time stamp: 10:29
Plus, real answers to common intake questions like how to handle homegrown EMRs and how AI fits into your existing workflow – Time stamp: 9:19
WEBINAR TRANSCRIPT
Cortney Swartwood [00:05]
Hey everyone, and welcome to our final session in the mini webinar series. I’m Cortney Swartwood, Senior Marketing Manager here at WorldView. Thanks for joining today’s webinar, “Smarter Than Rules: How AI Knows What’s Actually a Referral.”
Before we dive in, a quick rundown. On your screen, you’ll see the presentation, a Q&A window, and a survey. You can move and resize these however works for you. Please drop all questions in the Q&A window, not the chat. That helps us track them and get them answered quickly. You can also submit technical issues there.
The survey window lets you share feedback and request more information about our services. You’ll also see a resources icon at the bottom to download today’s slides.
This session is being recorded, and you’ll get a link tomorrow to watch it on demand or share with your team.
Today’s session is one we’ve been looking forward to. We’re breaking down what it actually means when we say our AI is “smarter than rules.” We’ll look at how AI compares to systems based on fixed rules and how it recognizes what’s actually a referral—even when things are messy. Nolan Craig is leading the session today. Nolan, take it away.
Nolan Craig [01:49]
Thanks, Cortney. I’m Nolan Craig, Regional Director of Sales at WorldView. I’ve been with WorldView for nearly 14 years, consulting with agencies on everything from medical records and order management to intake workflows.
Let’s start by talking about the limits of rules-based systems. These systems rely on structured logic like, “If it says XYZ on page one, send it to this person.” That works until something changes. If the sender adds a cover page, writes something by hand, or switches the layout, those rules break down.
When that happens, referrals get misrouted—or missed entirely. The intake team ends up reviewing documents manually anyway, which defeats the purpose of automation. Rules-based systems don’t handle the real world very well, especially in post-acute care where every referral looks different.
So what does “smarter than rules” really mean? Referral AI handles this completely differently. It doesn’t need templates or fixed rules. It looks at the entire document, identifies patterns, and understands what a referral is—even if it’s buried on page three or handwritten.
This is possible because it’s trained on thousands of real-life referral examples. It understands intent, not just structure. And the best part? It gets smarter over time. If your team flags something the AI missed or provides feedback, it learns and improves.
That means less time spent sifting through documents and more time focused on patient care. AI can detect referrals hidden inside long fax packets, emails, PDFs, or portal downloads. It automates the identification and prioritization so that nothing slips through the cracks.
Referral AI keeps intake moving even during high-volume times, improving both efficiency and patient satisfaction.
Here’s how it works in action. Referral AI, powered by Kno2, takes referrals from any source, extracts key details—like patient info, diagnosis, urgency, and payer—and routes them into your EMR or intake system. It flags high-priority referrals so your team knows exactly what to work on first.
This reduces manual work, speeds up the intake process, and gets patients admitted faster. It’s not about replacing your process. It’s about making it smarter and faster.
In summary, rules-based systems are rigid. They require constant coding of every possible scenario, and they fall short in the real world. AI-based systems like Referral AI are flexible, adaptive, and learn over time. This leads to better visibility, faster intake, and better patient care. Referral AI is built specifically for home health and hospice, so it’s designed to meet the needs of agencies like yours.
Cortney Swartwood [07:15]
Awesome. Thanks, Nolan. It’s really helpful to see how this compares to other tools out there. Let’s jump into some questions before we wrap up.
First question: Can Referral AI distinguish between actual referrals and other types of documents?
Nolan Craig [07:39]
Absolutely. Referral AI is trained to recognize the small details that separate real referrals from things like visit notes or general correspondence that might come in on the same fax line. That means your intake team only focuses on actionable items.
Cortney Swartwood [08:03]
Perfect. Next question: What happens if a referral is missing some key information? Can the AI still help?
Nolan Craig [08:17]
Yes, it can. This is where AI really shines. Referral AI will flag incomplete referrals while still pulling in any usable data. For example, if the diagnosis is there but the payer is missing, it’ll still mark it as a referral and highlight what’s missing. That way, your team knows exactly what to follow up on without starting from scratch—or worse, losing the referral.
Cortney Swartwood [09:03]
That’s great. No one likes having to do double work. Next question: Is there a learning curve for teams using Referral AI?
Nolan Craig [09:19]
Not really. Referral AI is designed to fit right into your existing workflows. Training is simple, and our support team is there to help every step of the way. If you’re already using WorldView, it feels even more intuitive.
Cortney Swartwood [09:38]
Awesome. Next one: Can this AI help with auditing or compliance?
Nolan Craig [09:47]
Yes. Referral AI extracts structured data that helps ensure all the necessary information is captured right away. It reduces gaps and helps keep you audit-ready. It’s like doing a quality check before it ever hits the chart. Plus, leadership and compliance teams get better visibility.
Cortney Swartwood [10:20]
Perfect. Last question: We have a homegrown EMR. Can we still use Referral AI?
Nolan Craig [10:29]
Absolutely. While we have strong integrations with EMRs like Homecare Homebase, Axxess, and KanTime, we also have a lot of experience setting up integrations with other systems, including homegrown EMRs. Our team handles that process to make sure it goes smoothly.
Cortney Swartwood [10:54]
Perfect. And if anyone missed it, Cody’s session last week was all about EMR integrations, so check out the recording on the website if you want to dive deeper into that.
Nolan, thank you so much for walking us through this today. And thanks to everyone who joined—not just for today, but throughout the entire series. We hope this helped you better understand how to support your intake team.
Please complete the last two questions of the survey and hit submit when you’re ready. If we missed any questions, we’ll follow up within 24 to 48 hours. And if anything else comes to mind, Nolan’s email is on the screen—feel free to reach out.
Thanks again, and we look forward to connecting with you soon.
Nolan Craig [11:54]
Thanks, everyone.
Cortney Swartwood [11:54]
Bye, everyone.