Healthcare automation › Denial management
Denial management automation
Most denial queues are worked in the order they arrived, which means the recoverable dollars sit behind the unrecoverable ones. We automate the triage, assemble the appeal packet from the documentation the payor actually asks for, and hand your team a queue sorted by what is worth their time.
Book a teardownThe problem
Because it is worked first-in-first-out by people who have to re-derive the same judgment every time. The recoverable dollars and the write-offs sit in the same list, and nothing in the workflow distinguishes them until a human has already spent twenty minutes.
Three specific things make denial work expensive out of proportion to its value:
Triage is manual and repeated
Deciding whether a given reason code on a given payor for a given service line is worth appealing is a judgment made hundreds of times a month, usually from memory, usually inconsistently.
Appeal assembly is clerical
Finding the order, the notes, the medical-necessity documentation and the original submission is retrieval work rather than clinical work, and it is where most of the hours go.
Nobody is measuring what wins
Without appeal outcomes tracked by reason code and payor, next quarter’s triage is the same guesswork as this quarter’s.
The result is a queue where aging dollars quietly become write-offs, and where the team’s own sense of “we appeal everything worth appealing” cannot be checked against data.
Scope
Ingestion and classification of remittance data, recoverability scoring against your own history, appeal packet assembly, submission and tracking. A person still reviews and signs every appeal that goes out.
The row that compounds
Automating appeals recovers money you already lost. Feeding root causes back upstream stops the denial happening again. At the diagnostics laboratory, the 40% reduction in denials came mostly from the upstream fixes rather than from winning more appeals.
Where documents are unstructured we read them with a model rather than a brittle template, with clinical text de-identified before it reaches one. How we handle that.
Proof
At a national cancer diagnostics laboratory, denials fell 40% and appeal success improved 70% within the first year. The program has run continuously since 2018.
Two things drove those numbers. Consistency: every submission built the same way removed a class of avoidable rejections entirely. And prioritization: the team stopped spending equal effort on recoverable and unrecoverable denials.
Cash flow improved as a second-order effect. Faster appeal turnaround pulls revenue recognition forward, which mattered more to the finance side than the headcount avoidance did.
Measured 2018 to 2026 against the client’s pre-automation manual workflow.
Read the appeals case study →Engagement
Four stages. The first is free and produces a written artifact you can use whether or not you hire us.
01
Teardown
Thirty minutes on your denial rate, top denial codes and top payors. You get a one-page opportunity map ranked by recoverable dollars per hour of effort.
02
Baseline your own outcomes
One to two weeks. We pull your denial and appeal history and build the recoverability picture from your data. Most clients learn something here that changes their triage before any software ships.
03
Automate triage, then assembly
Triage first, because it is lower risk and immediately reorders the queue. Appeal assembly second, attended, so your team reviews every packet while trust is being built.
04
Close the loop upstream
Root-cause reporting into eligibility, authorization and coding, so the denial stops being generated. This is where the 40% reduction comes from.
Objections worth raising
Denial automation is easy to oversell. These are the four pushbacks we get most often, answered straight.
Can software really decide what is worth appealing?
It can rank, and ranking is most of the value. It cannot make the final call on a clinically complex denial, and we do not build it to.
The score comes from your own outcomes: this code, this payor, this service line, appealed this way, won or lost. That is a better basis than experience alone because it is measured, and a worse basis than a senior coder’s judgment on any single unusual case. So the queue is sorted by score and a person still decides. Where the score and the coder disagree, we log it. That disagreement is how the scoring improves.
Will auto-generated appeals get rejected as boilerplate?
They would if they were boilerplate. An appeal that recites a generic medical-necessity paragraph is worse than no appeal, because it burns the timely-filing window.
What we assemble is the payor-specific documentation set with the actual clinical record attached, plus a cover letter citing the specific denial code and the specific evidence answering it. And a human signs it. If your appeals currently go out as a template with a name swapped in, automation is not your first problem.
Our denial data is a mess. Does this not need clean data first?
No, and waiting for clean data is how these projects never start. The baseline stage exists precisely because the data is messy, normalizing electronic and paper remits into one denial record is part of the work.
What we do need is a few months of history and access to it. If denials are only recorded as free text in a work-queue note, we will say that the first project is capture rather than automation, and scope accordingly.
We already have a clearinghouse denial module.
Then sometimes you should not add anything, and we will tell you that. Clearinghouse modules are good at classification and reporting on the claims that flow through them.
Where they tend to stop is appeal assembly, payor-specific documentation, unstructured and paper remits, and scoring against your own outcomes rather than a generic benchmark. If your bottleneck is the twenty minutes per appeal spent gathering documents, that is not a module problem and no configuration screen fixes it.
Questions we get asked
What is the difference between a denial and a rejection?
A rejection never entered adjudication. It failed a format or eligibility check and can usually be corrected and resubmitted. A denial was adjudicated and refused, so it needs an appeal rather than a correction. Treating them the same is one of the most common sources of wasted effort. Longer answer.
Do you work the denials for us, or build the system?
We build and run the automation; your team keeps the clinical judgment and the payor relationships. We are not a billing outsourcer, and we do not want to be the reason your institutional knowledge leaves the building.
Which denial codes do you start with?
Whichever ones carry the most recoverable dollars in your data. Usually a short list of five to ten codes accounts for the majority. We identify them in the baseline stage rather than assuming.
How does this interact with prior authorization work?
Directly. A large share of denials trace back to a missing or mismatched authorization, so the two programs share root causes. Most clients start with one and add the other within a year. Prior authorization automation.
Can you publish benchmarks for our segment?
Not yet. We hold eight years of production data across payor categories and are preparing anonymized aggregate benchmarks, pending a client-consent review. Aggregate and de-identified is normally fine, and “normally” is not a standard we are willing to rely on.
What does it cost?
Fixed-fee for the first workflow, with an optional maintenance retainer. We give you a number after the baseline stage, when we know what the recoverable pool actually looks like rather than guessing at it.
Related reading
Service
Prior authorization automation
Where a large share of your denials are actually created, before a claim ever goes out.
See the service →Proof
The appeals case study
How a productized appeal framework turned individual wins into a repeatable process.
Read it →Five minutes
ROI calculator
What the manual version costs you a year in staff time, with the assumptions in the open.
Run it →Two minutes
Readiness check
Eight questions on whether your denial workflow is ready to automate, and where to start.
Take it →Next step
Thirty minutes on your denial rate, your top codes and your top payors. You leave with a one-page map of recoverable dollars ranked by effort, yours to keep either way.
Book a teardown