Revenue Intelligence
Denial management that catches the pattern, not just the claim.
Working denials one at a time recovers some of the money. It does not tell you that the rate moved, which payer moved it, or which workflow change caused three weeks of avoidable rework.
Starts from an 835 remittance file. No EHR integration required, and nothing to build first.
The problem
Your team is working denials. Nobody is working the cause.
Most denial management is claim-level and reactive by design: a denial arrives, someone codes it, someone appeals it, and the money either comes back or it does not. That work is necessary and your team is probably good at it.
What that process cannot do is tell you why the volume changed. A payer quietly re-scopes a prior-authorization policy, or a service line starts documenting differently after a staffing change, and the effect shows up as more claims in the queue — not as a signal that something upstream broke. Everyone works harder, the rate stays elevated, and the root cause survives.
The organizations that get denial rates down are not the ones with the fastest appeal workflow. They are the ones that notice the pattern early enough that the fix is a process change rather than a recovery project.
- Denial volume is discussed at the claim level; denial rate is discussed monthly
- Nobody can attribute a rate change to a payer, reason code or service line without a custom pull
- The same reason code has been in the top five for four quarters and no one owns the cause
- Appeals capacity is the constraint, so low-value denials are written off unexamined
- Underpayments and downcoded claims are invisible, because nothing was technically denied
- You find out a payer changed a policy when your team notices the pattern, not when it changed
Capabilities
What Vizier surfaces in denial data
Vizier evaluates your remittance and claim data continuously and raises what changed, ranked by what it is worth — instead of producing another denial report for someone to read later.
Rate movement, attributed
Denial rate change caught as it develops and narrowed to the specific reason codes, payers and service lines driving it, so the conversation starts at the cause rather than the total.
Payer behaviour change
When one payer starts adjudicating differently — new documentation demands, re-scoped policy, slower turnaround — it separates from the rest of your payer mix and gets flagged.
Reason code concentration
Which codes account for the movement, and whether they cluster around a single upstream process such as prior authorization, eligibility or medical necessity documentation.
Preventable versus unavoidable
Separating denials that reflect a fixable process problem from those that are a normal cost of the payer mix, so effort goes where it changes something.
Recovery prioritization
Which denial cohorts carry enough value and enough overturn likelihood to be worth your team's limited appeal capacity this week.
Quantified exposure
What the pattern is worth annualized where the evidence supports an estimate, so the biggest number gets attention rather than the loudest meeting.
What a finding looks like
A denial report would have shown this as a larger number in a bar chart, and the team would have worked the extra claims.
The finding says something more useful: this is one payer, one specialty, two code ranges, and the high overturn rate points at documentation rather than coverage. That is a fixable process problem, and it is worth $680K a year.
One payer's prior-authorization denials tripled after a policy change nobody was notified of.
- Prior-authorization denials for this payer rose from 41 to 129 per month.
- The increase is confined to one payer; the same codes are stable across the rest of the mix.
- 94% of the affected claims are orthopedic procedures in two CPT ranges.
- Overturn rate on appeal for this cohort is high, which suggests a documentation gap rather than a coverage change.
Confirm the payer's current prior-authorization requirements for the two affected CPT ranges, then review what the scheduling team is submitting against them.
The reporting gap
Why denial reporting rarely finds the cause
Denial reporting is built to summarise what was denied. That is a different question from what changed and why, and the summary format actively hides the answer.
- Aggregation conceals concentration — a stable overall rate can contain one payer deteriorating badly.
- Top-N reason code lists are stable by construction, so a code that tripled looks the same as one that did not.
- Monthly cadence means roughly two claim cycles ship under the same broken condition before anyone sees it.
- Claim-level workflow optimises recovery, not prevention — the queue gets cleared, the cause persists.
- Underpayment and downcoding never appear, because nothing was formally denied.
What this replaces
This replaces the root-cause project you keep deferring
Most revenue cycle teams already know they should be doing root-cause analysis on denials. It gets deferred, not because anyone disagrees, but because it takes an analyst several days per question and the appeal queue is on fire today.
So the analysis happens once a year, usually as a consulting engagement, produces a deck, and the findings are stale within a quarter because payer behaviour moved on.
Vizier does the detection and the first layer of attribution continuously. Your team spends its time on the fix rather than on establishing what needs fixing.
- The annual denial root-cause consulting engagement, and the deck that is stale by Q2
- Analyst days spent cutting denial data by payer, code and service line to answer one question
- The recurring monthly denial report that summarises without explaining
- Manual payer-policy monitoring across a dozen portals nobody has time to check
- Writing off low-value denial cohorts unexamined because there is no capacity to look
Who this is for
One denial pattern. Three different reasons to act on it.
Revenue Cycle
What changed in denials, and which payer or workflow caused it?
- Denial deterioration attributed to specific codes, payers and service lines
- AR ageing and payer behaviour shifts detected as they emerge
- Root-cause investigation you can keep pulling on in plain language
CFO / Finance
How much is the denial pattern costing, and how much of it is preventable?
- Revenue leakage surfaced with the exposure quantified
- Reimbursement and payer performance movement, early
- Financial impact ranked so the biggest number gets attention first
Analytics / Data
How do we stop absorbing a denial data pull request every week?
- Consistent definitions so two leaders asking the same question get the same answer
- Self-service investigation that does not generate another ticket queue
- Governance, access control and audit logging that survive review
Getting your data in
Start from the remittance file you already produce.
Denials are usually the fastest domain to stand up, because the data already exists as a file and already leaves your building on a schedule.
- A monthly 835 remittance file — enough on its own to surface reason code and payer concentration.
- A claims or charge extract, to connect denials back to service line and provider.
- An AR ageing export, to see how denial cohorts are ageing and where recovery is decaying.
01
Upload
CSV, Excel, or an export you already produce. Drop it in and Vizier reads it. This is where most organizations start, and it is enough to see real findings against your own numbers.
02
Scheduled
A recurring feed over secure transfer, on whatever cadence your team already runs. No one re-uploads anything by hand, and nothing about your source systems has to change.
03
Connected
Direct read-only connectivity to your EHR or source systems via FHIR R4, HL7 v2, or vendor APIs. Vizier reads; it never writes back.
Connect your EHR when you’re ready. See supported systems.
Security and governance
The page your CIO will ask for
Security questions get answered before a demo, not after procurement stalls.
HIPAA compliant
PHI handled under HIPAA Security Rule safeguards.
BAA included
Executed within one business day, on every plan.
Encrypted throughout
AES-256 at rest, TLS 1.3 in transit.
Read-only access
Vizier reads from source systems. It never writes back.
Role-based access control
Scoped permissions with SSO available.
Audit logging
Every query logged with account, timestamp and result size.
Tenant isolation
Your data is segregated from every other customer's.
SOC 2 Type II audit underway
Not yet certified. Report available under NDA on completion.
FAQ
Questions buyers ask
Is this denial management software or denial analytics?
Analytics, deliberately. Vizier does not work claims, submit appeals or replace your denial management workflow — your billing system or clearinghouse does that, and probably does it well. What Vizier does is tell you the pattern changed, which payer and codes caused it, whether it is preventable, and what it is worth, so the team working the queue is working the right things and someone is fixing the cause.
What data do you need to start?
An 835 remittance file is enough to begin. Add a claims or charge extract and denials connect back to service line, provider and procedure, which is where most root causes actually live. Direct connectivity to your billing system or EHR makes it continuous rather than periodic — worth doing, but not a prerequisite.
Can it tell the difference between a preventable denial and an unavoidable one?
It separates cohorts by the evidence available — whether the pattern is concentrated in one payer or spread across the mix, whether it aligns to a specific process point such as prior authorization or eligibility, and how the cohort behaves on appeal. A high overturn rate on a specific cohort is strong evidence of a fixable upstream problem. Vizier presents that evidence and the recommended place to look; the clinical and operational judgement stays with your team.
We already have denial dashboards. What does this add?
Dashboards tell you what your denial rate is. The question that costs money is whether it changed, where the change is concentrated, and what caused it — and answering that from a dashboard means an analyst and a few days, every time. Vizier does the detection and attribution continuously, so the first time you hear about a shift is while it is developing rather than at month end.
Does it cover underpayments as well as denials?
Where the remittance data supports it, yes — underpayments and downcoded claims are visible in the same data and are often larger in aggregate than outright denials, precisely because nothing triggers a workflow when a claim is paid at less than expected.
How quickly would we see something useful?
Usually in the first working session, because the starting data is a file you already have. What takes longer is the part that matters — deciding which findings to act on and who owns the fix — and that is your team's work, not ours.
Related reading
See what Vizier finds in your denial data.
Bring one remittance file. Thirty minutes is enough to know whether the pattern in it surprises you.
Start with the data you already have. Connect your EHR when you’re ready.