The Revenue Is Already Earned. Intelligent Automation Is How We Collect It Faster.

The practices that pull ahead aren’t billing more than anyone else. They’re collecting faster, cleaner and with far less manual effort. Here’s how, and why it matters more now than ever.

Samantha Wagner is EVP and Chief Administrative Officer at Advantum Health.

Let me start with the conclusion. Community oncology practices do not have a revenue problem. They have a speed and accuracy problem. The money is already earned. It sits in claims that move too slowly, get keyed by hand, bounce back as denials and quietly get underpaid. What separates the practices that thrive is working harder on their billing. It is intelligent automation doing the repetitive work flawlessly, so revenue arrives faster and people are freed to make the decisions only people can make. That shift is the whole game, and I want to walk you through it.

What I See in Practice After Practice

When I sit down with a practice administrator, the story is almost always the same. Talented people are spending their days on work that software should be doing. Checking eligibility by hand. Re-keying prior authorizations into payer portals. Calling to check claim status. Posting payments line by line. None of it is clinical. All of it is slow. And every hour spent on manual tasks is an hour not spent on the denials and appeals where real money is recovered.

The cost is bigger than it looks. The administrative drag alone is staggering. The American Medical Association’s 2024 Prior Authorization Physician Survey found that physicians average 39 prior authorization requests a week and lose 13 hours of staff and physician time to them. In oncology, where a single drug claim can carry a five- or six-figure value, that slowness is not just frustrating. It is expensive.

no I agree with you on the emphasis part, but unsure how to highlight without this looking like a bag of skittlesJun 16Claude responded: The fix here isn't a new color, it's using a different lever.Analyzing gradient colors and brand alignment detailsAnalyzing gradient colors and brand alignment detailsThe fix here isn't a new color, it's using a different lever. Emphasis comes from contrast, and you have four kinds: hue, value (lightness), weight, and size. The card is already spending its whole hue budget on the gradient, so adding green or any second accent is what tips it into Skittles territory. The move is to leave hue alone and create the emphasis with value, weight, or a structural anchor instead.
Let me show you what that looks like rather than just describe it. First I'll sample your actual gradient so the comparison reads true.Evaluating emphasis treatments to avoid visual chaosEvaluating emphasis treatments to avoid visual chaosGradient sampled: it runs from your blue through a purple midtone to coral, so the canvas is already doing a lot of color work. Here are three ways to make the payoff land without adding to that, none of which introduces a competing hue.Ran 2 commands, created a file, viewed a fileRan 2 commands, created a file, viewed a fileAll three live on your gradient and none adds a competing color.
A, value only. Same heavy white type throughout, but the setup drops to about 55 percent opacity so the payoff reads as the one solid line. It's just flipping the fade that's already on the current card onto the correct words. Zero new ink, the most understated, and impossible to make gaudy.
B, navy anchor. The payoff sits in a navy knockout bar. Navy is already on the card in the headshot pill, so this reads as a deliberate motif rather than a new color, and it ties the quote visually to the attribution block below it. This is the strongest emphasis of the three and my pick if you want the phrase to genuinely stop the eye.
C, green underline. Green is already on the card in the "BLOG" and "READ THE FULL PIECE" accents, so a single underline is consistent rather than Skittles. The catch you can see here: when the phrase wraps, the underline splits into two segments and looks a little broken. It cleans up only if the whole phrase sits on one line, which is harder to guarantee.
My recommendation is B for impact or A for restraint. Both are bulletproof on the no-new-color test, and B has the bonus of rhyming with the navy headshot pill. C is fine but the wrapping makes it the fussiest to keep clean.
If you tell me which one you want, I can build it into the full vertical card, though I'll need the headshot file and ideally the exact gradient stops to match your version pixel for pixel rather than the approximation I sampled here.Emphasis options comparisonImage · PNG DownloadYou said: Let's revise a couple of lines.Let's revise a couple of lines...the one that says harder billing. what is that??Jun 23Claude responded: Fair flag, it's vague.Decoded vague phrasing and excavated clearer alternativesDecoded vague phrasing and excavated clearer alternativesFair flag, it's vague. What I was reaching for is the contrast with the old thesis: the winners don't succeed by doing more billing or pushing harder on the billing side, they win by collecting what's already earned, faster and cleaner. "Harder billing" compressed that into something that doesn't actually mean anything. It also risks the wrong read, like you're dunning patients more aggressively, which is the opposite of the point.
Cleaner ways to say it:

"What separates the practices that thrive is not how hard they bill. It is how fast and cleanly they collect." (my pick, sets up the whole speed theme)
"is not billing more. It is collecting more of what they have already earned, faster."
"is not working their billing harder. It is letting automation do the repetitive work so collections move faster."

I'd go with the first. It's the tightest and it tees up the speed argument that follows.
One thing to keep in sync: the same phrasing shows up in the LinkedIn caption ("The fix isn't billing harder"), so I'll fix it there too with the matching language once you lock the wording. The caption version would become something like "The fix isn't billing harder. It's collecting faster," or we can drop "billing harder" entirely.
What's the second line you want to revise?You said: Next point of feedback from the creator of the presentation the blog was built on: "RPA is not AI- lets talk about true AI where the system helps the user make…Next point of feedback from the creator of the presentation the blog was built on: "RPA is not AI- lets talk about true AI where the system helps the user make intelligent deciwions that reduce time spent."Jun 23Claude responded: That's a sharp correction, and Samantha's right.Reconciled SME feedback distinguishing RPA from true AI decision supportReconciled SME feedback distinguishing RPA from true AI decision supportThat's a sharp correction, and Samantha's right. My draft folded RPA and machine learning together under one "intelligent automation" umbrella, which blurs exactly the line she's drawing. RPA isn't intelligent. It follows rules. The real AI story is decision support: the system reading patterns and handing your people the answer so they spend less time figuring out what to do. That's a stronger and more honest claim, and it gives the brand a POV most vendors can't credibly make, because a lot of them slap "AI" on what is really just RPA.
Here's the section rewritten to draw that distinction. New heading, since the old one ("Intelligent Automation, Not Just 'AI'") now undersells the AI point Samantha wants to make.

RPA Does the Work. AI Sharpens the Decision.
Let me clear up a distinction the industry blurs constantly. RPA is not AI, and that difference is the whole point.
Robotic process automation, or RPA, handles the high-volume, rules-based work that eats your team's day: checking eligibility, submitting prior authorizations, checking claim status, posting payments. It follows rules. It is fast and it never tires, but it is not intelligent. Plenty of vendors put an "AI" label on exactly this kind of automation. It isn't AI. It is a very good robot.
Real AI does something different. It helps your people make better decisions, faster. By reading patterns across thousands of claims and payer behaviors, it can tell your team which claims are likely to deny and why, which denials are worth working first, where an underpayment is hiding and what the next best step is. The expert still makes the call. But instead of spending an hour digging to figure out what to do, they are handed the answer in seconds. That is what reduces time spent, and it is where the real leverage lives.
So we use both, deliberately. RPA clears the busywork. AI sharpens the judgment. Our people own the decisions that need a human. The right technology in the wrong hands still produces the wrong results: automation without intelligence just makes mistakes faster, and intelligence without expertise is a recommendation no one acts on. Put all three together and you get cleaner claims, faster cash and a team spending its time where it counts.

One accuracy check before I bake it in: the AI examples I listed (denial prediction, worklist prioritization by recoverable dollars, underpayment detection, next-best-action) are standard, credible decision-support capabilities, but I'm describing the category, not confirming what your platform actually does. If Samantha wants this tied to specific EVA capabilities, tell me which ones and I'll make the examples match rather than generic.
I'll also fold in the other fix while I'm in there: "harder billing" becomes "not how hard they bill. It is how fast and cleanly they collect," per the line you flagged. And both changes need to sync to the LinkedIn caption, which currently has the same conflation ("intelligent automation. Not 'AI' as a buzzword"). I'd rework the caption's middle to: RPA takes the repetitive work off your team, and AI helps them decide faster on what's left.
Want me to rebuild the doc and refresh the caption now with this copy, or tweak the wording first?You said: can you please provide alt text for the in-blog graphicscan you please provide alt text for the in-blog graphics2:53 PMClaude responded: Here's alt text for the two in-blog graphics, written to convey the actual data (so screen readers and AI crawlers get the substance, not just "chart") with th…Evaluated alt text best practices for two data graphicsEvaluated alt text best practices for two data graphicsHere's alt text for the two in-blog graphics, written to convey the actual data (so screen readers and AI crawlers get the substance, not just "chart") with the keywords woven in naturally. AP style, percent spelled out.
Graphic 1, "The Scale of the Problem"
Advantum Health infographic on the community oncology revenue gap, showing four figures: an 11.8 percent average initial claim denial rate in 2024, 34 percent of cancer patients facing a treatment denial, 3 to 4 percent of net patient revenue lost to denials and underpayments, and about $14,000 in revenue at risk per private payer denial.

Why Getting Paid Faster Is the Real Story

Speed is margin. Every day a clean claim sits unsubmitted, every week a denial goes unworked, every underpayment that ages past the appeal window, is cash you earned and did not collect.

The math is simple. A practice running 55 to 70 days in accounts receivable is financing its own payers, interest free. A practice that runs under 35 days has the same revenue working for it twice as fast. Same care delivered. Same codes billed. A radically different cash position. When I talk about getting money faster, this is what I mean. Not chasing volume, but collapsing the time between care delivered and dollars in the bank.

Intelligent Automation, Not Just “AI”

Let me clear up a distinction the industry blurs constantly. RPA is not AI, and that difference is the whole point.

Robotic process automation, or RPA, handles the high-volume, rules-based work that eats your team’s day: checking eligibility, submitting prior authorizations, checking claim status, posting payments. It follows rules. It is fast and it never tires, but it is not intelligent. Plenty of vendors put an “AI” label on exactly this kind of automation. It isn’t AI. It is a very good robot.

Real AI does something different. It helps your people make better decisions, faster. By reading patterns across thousands of claims and payer behaviors, it can tell your team which claims are likely to deny and why, which denials are worth working first, where an underpayment is hiding and what the next best step is. The expert still makes the call. But instead of spending an hour digging to figure out what to do, they are handed the answer in seconds. That is what reduces time spent, and it is where the real leverage lives.

So we use both, deliberately. RPA clears the busywork. AI sharpens the judgment. Our people own the decisions that need a human. The right technology in the wrong hands still produces the wrong results: automation without intelligence just makes mistakes faster, and intelligence without expertise is a recommendation no one acts on. Put all three together and you get cleaner claims, faster cash and a team spending its time where it counts.

What Makes Advantum Different

Plenty of vendors will sell you software. Fewer will own the outcome. What sets us apart comes down to three things.

We are built around prevention, not cleanup. 

Most revenue is lost upstream, in eligibility, authorization and documentation, so that is where we concentrate our automation and our attention. Catching a problem before it becomes a denial is worth far more than appealing it after the fact.

We specialize in oncology. 

Oncology billing is its own discipline, with infusion coding, add-on codes, modifier rules and high-dollar drug claims that punish generic approaches. Our automation and our coders are tuned to it. 

We close the loop. 

Every denial we work teaches the system. Back-end denial patterns feed back into front-end edits and documentation prompts, so the same mistake does not happen twice. That compounding is where the real efficiency lives. 

The result is what every practice leader actually wants. Cleaner claims. Faster cash. Fewer write-offs. And a team freed up to do the work that genuinely needs a human.

A Quick Self-Audit

If you want to know where you stand, measure against high performers on six numbers. These are the metrics that separate top-quartile oncology practices from the rest:

First-pass acceptance rate: high performers clear 95 percent; laggards sit below 85 percent.

Days in accounts receivable: high performers stay under 35; laggards run 55 to 70.

Net collection rate: high performers exceed 98 percent; below 95 percent signals leakage.

Underpayment recovery: systematic and monthly for high performers; ad hoc or nonexistent for laggards.

Authorization denial rate: under 4 percent for high performers; above 15 percent is a warning.

Denial overturn rate: high performers overturn more than 75 percent; below 50 percent leaves winnable money on the table.

That last number matters more than most practices realize. A denial is rarely final. In radiation oncology, research published in JAMA Network Open found that the majority of insurance denials are overturned on appeal. If you land on the lagging side of three or more of these, the gap is not theoretical. It is quantifiable, and most of it is recoverable.

Advantum Health benchmark table comparing lagging community oncology practices with high performers across six revenue cycle metrics: first-pass acceptance rate, days in accounts receivable, net collection rate, underpayment recovery, authorization denial rate and denial overturn rate. High performers clear a first-pass rate above 95 percent, stay under 35 days in AR, exceed a 98 percent net collection rate, recover underpayments systematically each month, hold authorization denials under 4 percent and overturn more than 75 percent of denials.

Where I Would Start

The practices that win the next decade will be the ones that collect what they have already earned, faster and with less friction. The technology to do that exists today.

If any of this sounds like your practice, I would start where we always start: with a revenue diagnostic. It is a focused lookback at your denials, underpayments and infusion code gaps across your highest-volume payers, and it shows you exactly where the recoverable revenue sits and how much faster you could be collecting it.

Learn more about our prior authorization and eligibility verification services, or read how we approach denial management. When you are ready, let’s talk.