Quick answer: The ROI of automating denial management for an oncology practice comes from two sources: recovered revenue on high-dollar drug claims that would otherwise be written off, plus the staff hours reclaimed from manual rework. A simple way to model it is denials worked per month × average oncology claim value × the recovery-rate lift, minus the platform cost. Because oncology claims are so valuable and so many denials currently go unworked, even a modest improvement in recovery rate usually clears the cost — the math tends to favor automation faster in oncology than in lower-dollar specialties.
Why the ROI math is different in oncology
Denial automation pays back faster in oncology than almost anywhere else, and it comes down to claim value. A primary-care denial might be worth $90; an oncology infusion denial can be $14,000. When the claims you're recovering are that large, the revenue side of the ROI equation dwarfs the software cost.
The other half of the story is how many denials currently go unworked. Industry data from Change Healthcare found that roughly 65% of denied claims are never reworked — they age out and the revenue is lost. In a primary-care practice that's a frustrating leak. In oncology, where each abandoned claim can be five figures, it's a serious financial hole.
Denial rates are also climbing. Initial denials hit 11.8% in 2024, and 60% of medical groups reported higher denial rates than the year before. For an oncology practice, that trend means the cost of doing nothing is rising every year.
How to build the ROI model
A defensible ROI model rests on numbers you can pull from your own billing system, not vendor promises. Four inputs do most of the work.
- Monthly denial volume. How many claims get denied each month. Pull 90 days and average it.
- Average oncology claim value. Your denied claims skew high because of drug costs — use the actual average of denied claims, not your overall average.
- Current recovery rate. What percentage of denied claims you actually recover today. If 65% never get worked, your recovery rate on the unworked pile is zero.
- Hours per appeal. A quick time audit — most oncology appeals run 30 to 60 minutes of skilled billing time.
The recovered-revenue line is the big one: (denials newly worked because of automation) × (average claim value) × (recovery rate on those denials). The labor line is secondary but real: reworking a denied claim costs between $25 and $181 depending on complexity, and automation reclaims most of that. Subtract the platform cost and you have your net.
The recovered-revenue side
The largest ROI driver is working denials that used to be abandoned. If your practice denies 300 claims a month, works 100, and lets 200 age out, the 200 unworked claims are pure opportunity. Even recovering a fraction of them — at oncology claim values — is substantial.
Here's the leverage: up to 79% of denials are overturned on appeal when they're actually worked. The denials weren't unwinnable; there just wasn't enough staff time to get to them. Automation removes the time constraint by drafting the appeals and assembling the documentation, so the previously-abandoned claims get worked.
Multiply a few dozen recovered five-figure infusion claims a month by even a conservative recovery rate, and the recovered-revenue line alone usually exceeds the platform cost several times over. This is why oncology practices tend to hit payback faster than the practice down the street running lower-dollar claims.
The reclaimed-staff-time side
The second ROI source is labor, and it's where the soft benefits live. Manual denial work is slow: a biller reconciling a chemo denial, hunting through the chart for documentation, and writing an appeal can spend 30 to 60 minutes on a single claim. Automation compresses that to a quick review of a drafted appeal.
That doesn't usually mean cutting staff — it means the same team works far more denials, and the hours shift to higher-value work. Billers move from clerical assembly to the complex appeals, peer-to-peer prep, and payer escalations where their expertise actually changes outcomes. For practices struggling to hire and retain experienced oncology billers, getting more output from the team you have is its own return.
There's a burnout dimension too. Denial rework is the least satisfying part of the billing job. Automating it tends to improve retention — a real but hard-to-quantify line on the ROI ledger.
What an oncology practice should expect — and the honest caveats
A realistic expectation: the recovered-revenue line shows up within the first few billing cycles as previously-abandoned denials start getting worked, and the labor savings build as the team adjusts. For most specialty practices and PE-backed MSO oncology groups, the recovered revenue on high-dollar claims is the line that clears the cost, with labor savings as upside.
The honest caveats matter for a credible business case. Implementation takes time — integrating with your EHR and billing system is real work, usually measured in weeks. Not every denial is recoverable; genuine medical-necessity disputes and payer policy exclusions won't reverse no matter how good the automation is. And the ROI depends on actually redeploying reclaimed staff time toward recovery rather than letting it evaporate.
This is the workflow Honey Health's Denial Management agent is built around — working the denials, drafting the appeals, and, because it sits alongside the Prior Authorization and Eligibility agents, fixing the upstream causes so the denial volume shrinks over time. Preventing a denial is worth more than recovering it, and the compounding effect of prevention is what makes the multi-year ROI stronger than a one-time recovery bump.
Frequently asked questions
How quickly does denial automation pay for itself in oncology?
Most oncology practices see the recovered-revenue line cover the platform cost within the first few billing cycles, because high-dollar claims that used to age out start getting worked. The exact timing depends on your denial volume, average claim value, and how much of your denial pile currently goes unworked. The higher your abandoned-denial rate today, the faster the payback.
What's the biggest driver of ROI — recovered revenue or staff savings?
Recovered revenue, by a wide margin, in oncology. Because denied infusion and chemo claims are so valuable, working claims that used to be written off produces returns that dwarf the labor savings. Staff-time reclamation is real and worth modeling, but it's the secondary line, not the headline.
Do we have to lay off billers to realize the ROI?
No, and most practices don't. The return comes from working more denials and recovering more revenue with the same team, not from cutting headcount. The reclaimed hours typically shift toward complex appeals, peer-to-peer prep, and payer escalations — higher-value work that further improves recovery.
How do we measure ROI after we go live?
Track three metrics against a documented pre-automation baseline: denial recovery rate, total recovered revenue, and aged-denial dollars written off. If recovery rate and recovered revenue climb while write-offs fall, the automation is delivering. Capture the baseline before go-live, because without it the improvement is unprovable.
Is prevention or recovery the better ROI lever long-term?
Prevention, over a multi-year horizon. Recovering a denial gets back revenue you already earned; preventing one avoids the rework cost entirely and gets you paid on the first pass. A platform that both works denials and fixes their upstream causes compounds — the recovery bump shows up first, but the shrinking denial volume is what makes the long-run return stronger.
