Catch the denial before you send the claim.
Your reports explain last month. Zaxis reads the same data forward — scoring every claim for denial risk before it leaves the building, ranking your A/R by what is actually recoverable, and telling you what next quarter's cash looks like.
No signup to see your number. The audit runs entirely in your browser.
You already earned this revenue. The loss happens after the visit.
Practices rarely lose money on the care they deliver. They lose it in the gap between submitting a claim and getting paid — denials that were preventable, A/R that ages past the point of collection, and underpayments nobody flagged because nobody was comparing.
~10%
of claims are denied on first submission. Each one is rework, delay and write-off risk.
$25+
of administrative cost to rework one denied claim — before any revenue impact.
3–5%
of net revenue lost to preventable denials at practices without predictive analytics.
Industry estimates, used for framing only. The numbers that matter are yours — the audit below runs on them.
Denials, aging A/R, and silent underpayments
- Denials. Missing authorization, coding mismatch, eligibility errors. Most are preventable — but teams find out only after the payer says no.
- Aging A/R. Collection probability falls with every week a claim sits. Days in A/R is the clearest single indicator of a stressed revenue cycle.
- Underpayments. Payers reimburse below contracted rates. With no variance analysis, the shortfall is invisible and quietly accepted as normal.
Two dimensions can't tell you what happens next
Most practices already have a PM export, a spreadsheet, or a static dashboard. They describe the past. They cannot tell you which claim in today's batch will be rejected, what next quarter's collections look like, or which single fix returns the most money.
That gap — between reporting what happened and predicting what happens next — is the entire reason Zaxis exists.
How the platform closes itFind out what your revenue cycle is leaking. It takes about forty seconds.
Move three sliders. The tool models preventable denial value, rework cost and cash tied up in aging A/R using the same arithmetic our analysts apply to client data — and it shows you every assumption, so you can argue with it.
- Runs entirely in your browser. Nothing is uploaded, nothing is stored.
- No email address required to see the number.
- Then, when you want the real thing: send a de-identified denial sample and we return a full analysis, free.
Your practice
Estimated annual recoverable revenue
$0
Denied claim value / year$0
Rework admin cost / year$0
Three views of the same dollar.
Every claim exists in three states at once: what claims like it have historically done, what it is doing right now, and what it is about to do. Most tools show you two of the three.
Historical
Denial history by payer, reason code and CPT. Collection trends across quarters. Payer behaviour you can take into a contract negotiation.
Real-time
Live claim status from your EHR/PM. Today's bottlenecks, today's unworked queue, and KPI monitoring nobody has to build a report for.
Predictive AI
Denial risk scored before submission, cash forecast with a confidence range, and a ranked list of what to fix first for the most money back.
The software, the experts and the team — one accountable partner.
Most vendors sell one of these and leave the rest to you. Start where the pain is; expand when the data says so.
Zaxis RCM Intelligence
The platform: executive dashboards, Power BI integration, KPI monitoring and the predictive engine that scores denial risk before submission.
See the platformZaxis RCM Consulting
Senior consultants for denial root-cause analysis, workflow audits and financial performance reviews. People who have run billing departments, not generalists with a template.
See consultingZaxis Resource Provider
Trained RCM specialists, onshore and offshore, who work inside your existing process for A/R follow-up, posting and denial work — capacity without headcount.
See staffingFewer denials. Faster cash. A forecast you can staff against.
Prediction is worthless unless it changes what someone does on Monday morning. Every Zaxis output is written as an action with a dollar value attached to it.
- A predicted-denial worklist your biller clears before submission, ranked by recoverable dollars
- Payer-level intelligence showing exactly where each one underpays or slow-pays you
- 30/60/90-day cash forecasts with a confidence range, not one fragile number
- A five-minute Monday view for the owner: what moved, what broke, what it cost
- Independent verification that your in-house or outsourced billing is performing
Analysis, not another dashboard
Anyone can chart last month. We tell you which of next month's claims will fail, and what fixing them is worth.
Software plus people
The platform finds the leak; our consultants and specialists close it. One partner, one line of accountability.
HIPAA-first, not HIPAA-later
Encryption in transit and at rest, role-based access, audit trails, and a BAA signed before any PHI moves.
You keep your billing team
No rip and replace. Zaxis sits alongside your current process and makes it measurable.
From first email to live platform in about 30 days.
The order matters: we find the money before we ask you to buy anything.
Free predictive analysis
You send a de-identified sample of denial and A/R data — or we model your specialty from benchmarks. De-identified data needs no BAA, and there is no commitment attached.
Twenty-minute read-out
We walk your numbers: denial drivers by payer, days in A/R, and an estimate of what a clean-claim cycle would recover. You keep the findings either way.
Scoped proposal
A priced engagement across platform, consulting and staffing, mapped to the specific leaks we found — with the expected return sitting next to each line.
Connect and go live
BAA executed, 835/837 connection established, dashboards configured to your specialties and payers. Value in week one, fully live in about 30 days.
Denial patterns are specialty-specific. So is the model.
A behavioral health denial looks nothing like an orthopedic one. Zaxis is tuned per specialty — payer mix, common reason codes, authorization rules, and the CPT lines that actually carry your revenue.
A nine-provider specialty group, six months in.
The sequence we look for: denial rate falls first, days in A/R follows, and recovered revenue lands in the second quarter as the worklist compounds.
−48%
denial rate
−11 days
days in A/R
+$214K
recovered revenue, annualized
91% → 98%
first-pass rate in four months
Illustrative demo data from an anonymized engagement. Results depend on payer mix, specialty and baseline. Ask us for the methodology behind every figure — we will send it.
$412K
net collections
31.4
days in A/R
4.7%
denial rate
97.8%
clean claims
Predicted denial queue · pre-submission
#82931 Missing auth · Aetna92%$1,840
#82914 Code mismatch · United87%$960
#82898 Eligibility · BCBS64%$415
The Monday-morning view: four numbers, then the queue that changes them.
What people push back on, and what we say.
Four objections come up in almost every first call. None of them have a clever answer — only an honest one.
"We already have reporting."
You almost certainly do, and we will not pretend otherwise. The question is whether it tells you which claims are about to fail. If it does, you do not need us. If it does not, that is the entire gap we fill — and the free audit will show you its size in dollars.
"Our billing company handles this."
Then you should be able to see how well. Practices routinely hire us as an independent measurement layer over an outsourced biller. Several billing companies then license the platform themselves, because being measured well is a competitive advantage.
"AI in healthcare is mostly marketing."
Frequently, yes. So we publish the model: what it trains on, what it scores, what it cannot do. Every prediction carries the factors driving it, and we report predicted-versus-actual back to you monthly. A score you cannot interrogate is a score nobody should act on.
"We don't have time for another implementation."
Nothing changes in your workflow to start. We read 835 and 837 files your clearinghouse already produces. The first read-out needs a data export and twenty minutes of your time — not a project plan.
The questions finance leaders actually ask.
Is Zaxis HIPAA-compliant?
Yes. Data is encrypted in transit and at rest, access is role-based, and every access event is written to an audit trail. We execute a Business Associate Agreement before any protected health information changes hands. For the free analysis we prefer a de-identified sample, which requires no BAA at all. Full detail is on our security page.
Do we have to replace our billing company or in-house team?
No. Zaxis is built to sit alongside whoever bills for you today. Many practices hire us specifically to verify that their existing billing — internal or outsourced — is performing. If you later need extra capacity, our specialists plug into the same workflow rather than replacing it.
What data do you need to start?
For the free analysis: a de-identified export of denials and A/R covering the last 90 days, or simply your specialty and payer mix if you would rather we model from benchmarks. For the live platform: a standards-based connection using the 835 remittance and 837 claim files your clearinghouse already generates.
How does the denial prediction actually work?
The model learns from your own adjudicated history — payer, CPT, modifier, place of service, authorization status, eligibility response, rendering provider and prior denial reason — and scores each pending claim's probability of rejection before submission. Every score names the factors driving it, so a biller can act on it or overrule it rather than trusting a number blindly.
How long until we see something useful?
The first read-out lands within a few business days of receiving your sample. The platform is typically live in about 30 days, and most clients find something actionable in week one — usually a payer or a reason code nobody had isolated before.
Are the numbers on this site real client results?
No, and we will not pretend they are. Every figure here is illustrative demo data or drawn from an anonymized engagement, and every industry statistic is labelled an estimate. We never expose a client name or client data in marketing. The only numbers that should convince you are your own — which is exactly what the free analysis produces.
What does it cost?
Pricing depends on claim volume, provider count and which of the three pillars you use. The analysis and the read-out are free, and the proposal that follows puts expected recovery next to the cost so you can judge the return before committing to anything.
See your revenue from a completely new angle.
Send 90 days of de-identified billing data. We will run a free predictive analysis and show you exactly what Zaxis would have caught — before you decide anything.
One screen. Three dimensions of your revenue.
History shows you the pattern. Live status shows you the work. Prediction shows you where the money goes next. Zaxis holds all three at once, so a decision no longer needs three exports and a spreadsheet.
Dashboards built around decisions, not around data types.
Each view answers one question for one person. If a chart doesn't change what somebody does, it doesn't ship.
Executive snapshot
Net collections and month-over-month movement, days in A/R, denial rate, clean-claim rate — plus the one number that moved most, explained in a sentence.
Denials command centre
Denials by payer, reason code and service line, sorted into fixable families — eligibility, missing information, documentation, coding — with the workable subset separated from what is genuinely lost.
A/R recovery worklist
Open balance ranked by expected recovery, with aging bucket, payer, timely-filing runway and a recommended next action on every line.
Payer performance
Payment lag, denial rate, underpayment variance and net collection ratio per payer — the evidence you bring to a contract renewal.
Cash-flow forecast
30/60/90-day projected collections with a confidence range, driven by your aging profile and each payer's actual behaviour rather than a straight line.
Predicted-denial queue
Tomorrow's claims scored today: risk percentage, driving reason, dollar value, and whether it can still be fixed before the batch goes out.
$412K
net collections
31.4
days in A/R
4.7%
denial rate
97.8%
clean claims
Predicted denial queue · pre-submission
#82931 Missing auth · Aetna92%$1,840
#82914 Code mismatch · United87%$960
#82898 Eligibility · BCBS64%$415
Illustrative demo data. No real patient or client identifier appears in any Zaxis demo.
How prediction is produced — and why you can argue with it.
A score nobody can interrogate is a score nobody acts on. Every prediction Zaxis makes carries its reason, its confidence, and the history it learned from.
Learn from your outcomes
The model trains on your own adjudicated history — payer, CPT, modifier, place of service, rendering provider, authorization status, eligibility response and prior denial reason. Your payers, your patterns; nothing pooled with another client.
Score before submission
Each pending claim receives a denial probability and the top factors behind it, so a biller sees "missing auth, this payer, this CPT" instead of an unexplained number.
Rank by recoverable dollars
Risk alone isn't a priority. Risk multiplied by claim value, adjusted for how often that denial type is actually overturned, is.
Report accuracy in the open
Predicted versus actual comes back to you monthly. When the model is wrong we say so and retrain — accuracy that is never shown is accuracy nobody should trust.
Measured figures come from your file. Modelled figures — recovery factors, forecasts — are labelled as assumptions inside the interface itself, never folded silently into a total.
Boring where it should be boring.
Healthcare data earns no points for novel infrastructure. Standards in, encrypted at rest, access logged, reconciled to source.
- Ingestion. 837 claim files and 835 remittances from your EHR/PM — the same standards your clearinghouse already speaks.
- Analytics layer. Built on Power BI, so your own team can extend the model without waiting on us.
- Access. Role-based permissions: an owner, a manager and a biller each see a different surface of the same data.
- Audit. Every access and export is logged. Reconciliation runs on every refresh — charges minus payments minus adjustments minus balance must equal zero.
- Agreements. A BAA is executed before any PHI moves. De-identified evaluation samples need none.
Where your data lives
Encrypted in transit and at rest, isolated per client, retained only as long as your agreement specifies.
What we never do
Pool your data with another client's, train a shared model on identifiable records, or publish your name as a reference without written consent.
Integrations
Works with the major EHR and practice-management systems through standard file exchange. If it produces an 835 and an 837, we can read it.
Exports
Every table exports to Excel and CSV with the derived columns intact, so your accountant checks the same arithmetic you do.
What a live account looks like.
$412K
net collections per month, up 8.2% MoM
31.4
days in A/R, improved by 6.1
4.7%
denial rate, down from 9.1%
97.8%
clean-claim rate
Denials by payer — June
| Payer | Denied value | Share |
|---|---|---|
| Aetna | $21.4K | 34% |
| United | $15.2K | 24% |
| BCBS | $11.8K | 19% |
| Cigna | $8.3K | 13% |
| Medicare | $6.4K | 10% |
| Total | $63.1K | 100% |
A/R aging — $287K outstanding
| Bucket | Share | Read |
|---|---|---|
| 0–30 days | 52% | Healthy |
| 31–60 | 24% | Normal lag |
| 61–90 | 13% | Work now |
| 90+ | 11% | Filing risk |
Cash-flow forecast for the same account: Q3 projected $1.24M ± $40K.
All figures on this page are illustrative demo data. No real client data or client name is ever shown.
Bring 90 days of data. Leave with your own numbers.
We run the analysis free and show you what the platform would have caught, before you commit to anything.
Every service, measured by what it returns.
We don't sell hours. Each service below is tied to a number on your dashboard — denial rate, days in A/R, first-pass rate, net collection ratio — and reported against it every month.
Insight, expertise and capacity — from one partner.
Buy one, or let the findings decide. Most engagements start with the platform because it costs the least to prove.
RCM Intelligence
The platform: role-based dashboards, KPI monitoring, denial and A/R analytics, and the predictive engine.
See the platformRCM Consulting
Senior consultants for denial root-cause work, workflow audits and financial performance reviews — typically four to six weeks, ending in a written remediation plan you own.
Discuss an engagementResource Provider
Trained RCM specialists, onshore and offshore, embedded in your workflow for A/R follow-up, posting, denial work and front-desk verification.
Talk about capacityWhat we actually run for practices.
Each can be delivered as software only, as a managed service, or as trained staff working inside your process.
Medical billing
The problem: claims go out with errors nobody catches until the remittance comes back weeks later.
What we do: charge-capture review, scrubbing against payer-specific rules, submission, and remittance posting with variance flags on every short payment.
Measured by: first-pass acceptance rate and net collection ratio.
Revenue cycle management
The problem: the cycle is split across front desk, coders, billers and a vendor, and nobody owns the whole number.
What we do: end-to-end ownership from eligibility through final payment, with one dashboard every role reads.
Measured by: days in A/R and total net collections.
Denial management
The problem: denials get worked one at a time in the order they arrive, with no loop back to the cause.
What we do: classify every denial into fixable families, appeal the workable set, and fix the upstream cause so the same denial stops recurring next month.
Measured by: denial rate and appeal overturn rate.
Accounts receivable recovery
The problem: aged A/R gets worked by age, so small recent claims get attention while large old ones cross filing deadlines.
What we do: rank open balance by expected recovery, work it in that order, and surface timely-filing deadlines before they pass.
Measured by: A/R over 90 days as a share of total.
Medical coding
The problem: under-coding leaves money behind and never shows up as a denial; over-coding invites audit exposure.
What we do: certified coders review documentation against CPT, ICD-10 and modifier rules, with a feedback loop to providers on recurring gaps.
Measured by: coding-related denial rate and per-encounter yield.
Credentialing & enrollment
The problem: a provider starts seeing patients before enrollment completes and the claims are simply unbillable.
What we do: payer enrollment, revalidation tracking and CAQH maintenance, with expiry dates monitored well ahead of time.
Measured by: claims denied for enrollment or credentialing reasons.
Eligibility verification
The problem: eligibility errors are the cheapest denial to prevent and among the most common to receive.
What we do: verification before the visit, with coverage, benefits and patient responsibility confirmed and recorded against the encounter.
Measured by: eligibility-family denials per 1,000 claims.
Prior authorization
The problem: in procedure-heavy specialties, a missing authorization is the single most expensive avoidable denial.
What we do: authorization requirements checked per payer and CPT, requests tracked to approval, expiry monitored against the schedule.
Measured by: authorization-related denial value.
Payment posting
The problem: posting errors hide underpayments and quietly make every downstream report wrong.
What we do: ERA and manual posting with contractual-variance detection, so a short payment is flagged rather than absorbed.
Measured by: posting accuracy and identified underpayment value.
Patient billing & support
The problem: patient responsibility is a growing share of revenue and the hardest part to collect.
What we do: clear statements, payment plans, and a staffed line for billing questions answered in your practice's name.
Measured by: patient A/R and self-pay collection rate.
Practice analytics & reporting
The problem: reports exist, but nobody reads them because they answer no question anyone asked.
What we do: role-specific dashboards plus a monthly written read-out of what moved and what it cost.
Measured by: whether decisions actually change — we ask you every quarter.
RCM staffing & virtual resources
The problem: hiring a biller takes three months, and turnover resets the clock.
What we do: trained specialists onshore or offshore working inside your systems, supervised by our team, scaled up or down monthly.
Measured by: cost per worked claim and backlog burn-down.
Let the data pick the service.
Every engagement we've run started the same way: we analysed the denial file, and the file told us which of these lines mattered. The audit takes a minute and costs nothing; the full analysis takes a de-identified export.
Run the free auditIf denials are the pain
Start with denial management and the platform. Fastest measurable return, and the root-cause loop stops the same denial recurring.
If cash is the pain
Start with A/R recovery and the forecast. Ranking open balance by expected recovery usually moves days in A/R within one cycle.
If capacity is the pain
Start with staffing plus analytics, so added capacity is directed by the worklist rather than by whoever shouts loudest.
How working with us actually goes.
Can we buy just the analytics and keep our biller?
Yes, and many clients do exactly that. The platform is the entry point for most engagements. Consulting and staffing exist for when the data shows a gap your team doesn't have the capacity to close.
How are you priced?
Platform access is priced on claim volume and provider count. Managed services are typically a percentage of collections. Staffing is a monthly rate per specialist. Every proposal puts expected recovery next to cost, so the return is visible before you sign.
What is the minimum commitment?
Consulting engagements run four to six weeks. Platform and managed services start on a term you're comfortable with — we would rather earn the renewal than lock you into it.
Who owns the data and the dashboards?
You do. Every table exports, and if you leave you take the full line-level history with you.
Do you work with billing companies as well as practices?
Yes. For billing companies the platform is usually white-labelled reporting across the whole book, which turns analytics into a retention and margin advantage instead of a cost line.
Find out which service you actually need.
Run the audit, or send a de-identified sample. The findings tell us both — and the read-out is free either way.
A denial in behavioral health looks nothing like one in orthopedics.
Payer mix, authorization rules, documentation requirements and the CPT lines carrying your revenue all change by specialty. So does the model. Here's what we tune for each — and where the money usually hides.
Where each specialty leaks
Family practice
High volume, low dollar per claim, thin margins. The leak is usually eligibility errors and preventive-visit coding rather than large individual denials.
Watch: eligibility denials · annual wellness coding · modifier 25 pairs
Urgent care
Walk-in volume means eligibility gets verified after the fact, if at all. Multi-site groups also carry place-of-service and registration inconsistencies between locations.
Watch: eligibility · place of service · self-pay conversion · observation coding
Mental health
Session limits, authorization renewals and telehealth modifiers drive most denials. Low per-claim value makes manual appeals uneconomic, so prevention matters more here than almost anywhere.
Watch: authorization expiry · session caps · telehealth POS and modifier · panel credentialing
Behavioral health
Carve-out payers behave differently from the medical plan printed on the same card. Zaxis separates them so the denial pattern becomes legible instead of averaged away.
Watch: carve-out routing · medical-necessity documentation · level-of-care authorization
Physical therapy
Visit caps, therapy thresholds and plan-of-care recertification produce recurring, predictable denials — exactly the kind a pre-submission model catches cheaply.
Watch: KX modifier · visit limits · recertification dates · timed-code units
Orthopedics
Large dollars per claim, so one missing authorization is expensive on its own. Global-period rules and implant billing add avoidable complexity on top.
Watch: prior authorization · global period · bundling edits · implant and device billing
Cardiology
Diagnostic testing carries strict medical-necessity and coverage-determination rules. The denial usually originates in the diagnosis code, not the procedure code.
Watch: LCD/NCD necessity · professional vs technical split · monitoring frequency limits
Dermatology
Cosmetic-versus-medical determinations and pathology billing produce a distinctive denial profile, alongside meaningful self-pay volume that needs its own collection path.
Watch: medical-necessity documentation · lesion counts and sizing · pathology coordination
Internal medicine
Chronic care management and complex E/M levels are commonly under-coded. That never appears as a denial — only as revenue that was never billed in the first place.
Watch: E/M level distribution · CCM and RPM eligibility · annual wellness capture
Pediatrics
Vaccine administration, well-child schedules and Medicaid plan variation drive most of the friction, compounded by frequent coverage changes mid-year.
Watch: vaccine and administration pairing · Medicaid plan rules · EPSDT schedules
Dentistry
Dental-to-medical cross-billing is where recoverable money usually sits, particularly for surgical and sleep-related procedures that qualify under medical benefits.
Watch: medical cross-coding · frequency limits · pre-determination handling
Home health
Episode-based billing, face-to-face documentation and certification periods make timing errors the dominant cause of denial rather than clinical coding.
Watch: certification windows · face-to-face documentation · RAP and final claim timing
Multi-specialty clinics
The real problem is aggregation: one blended denial rate hides the two service lines actually causing it. Zaxis reports by service line first and practice second.
Watch: service-line separation · shared-provider attribution · cross-site payer variation
Billing companies & RCM firms
Your differentiator is proof. Zaxis gives you book-wide benchmarking and white-labelled reporting that makes your performance visible to the practices you serve.
Watch: per-client margin · book-wide denial patterns · client retention reporting
Not listed? We support 30+ specialties. The tuning process is identical: your historical outcomes teach the model how your payers behave.
A generic model produces generic advice.
If a tool tells an orthopedic group and a pediatric practice the same three things, it has read neither one's data. Specialty tuning isn't a feature line for us — it's why the predictions are usable at all.
30+
specialties supported
110+
reason codes mapped to fixable families
Per payer
rules, filing deadlines and behaviour tracked separately
Find out what we'd expect to see in yours.
Tell us your specialty and payer mix and we'll send the denial pattern we'd expect — then test it against your own data, free.
What is your revenue cycle leaking this year?
Five inputs you already know. The tool models preventable denial value, rework cost and the cash sitting in aging A/R — using the same arithmetic our analysts apply to client data, with every assumption published below it.
Drop your file in. The dashboard builds itself.
Export your denial or charge detail from your PM system, drag it onto the panel below, and the full analysis appears in about thirty seconds — aging, payers, service lines, reason codes, recovery projection. Your file is parsed inside this browser tab and never leaves your computer.
Denials Command Center
Loads on demand so the page stays fast. Nothing is sent to a server — the parser, the charts and the export all run locally in this tab.
Works offline. Best on a laptop or desktop — the dashboard is dense by design.
For a denial or reason-code export
Denials by reason code, payer, service line and aging, with every code mapped to a fixable family, a workable-denials view, and a priority plus recommended action on each line. No recovery projection here by design — a denial file can't support one honestly.
For a charge or production detail export
The full revenue-cycle picture: aging buckets, payer and financial class, service line, provider and facility, collection months, unbilled backlog and write-offs — plus a recovery projection where every factor is visible and adjustable rather than hidden in the model.
0
bytes uploaded — parsing happens in your browser tab
~30s
from dropped file to a complete dashboard
110+
reason codes mapped to fixable denial families
$0.00
identity check: charges − payments − adjustments − balance
Columns your file doesn't contain are hidden rather than estimated. Measured figures come from your data; modelled figures are labelled as assumptions. Finished analysis exports to a branded Excel workbook, or shares as a locked read-only copy.
Size the problem from five numbers instead.
Same arithmetic our analysts use, with every assumption published underneath. Useful for a board slide before you export anything.
Move the sliders to match your practice
Don't know a figure exactly? Estimate it. This model is built to size the problem, not to price an engagement — being roughly right is enough to decide whether the real analysis is worth twenty minutes.
Estimated annual recoverable revenue
$0
Annual billed volume$0
Denied claim value / year$0
Denied claims / year0
Preventable share of denials$0
Recoverable on rework$0
Rework admin cost avoided$0
Cash tied up above 30-day A/R$0
First-pass gap—
Value per claim submitted$0
Cumulative recovery, first 12 months (ramped)
Month 1 → 12 · year-one total $0
How the model works
Annual billed = claims × average allowed × 12. Denied value = annual billed × your denial rate. We treat 60% of denied value as preventable and assume 55% of that is collected on rework — deliberately conservative against published RCM benchmarks.
Where rework cost comes from
Each denied claim carries $25 of administrative cost to rework: an industry-standard estimate covering staff time only. It excludes the revenue impact of denials that are never reworked at all, which is usually the larger number.
What it deliberately does not do
It does not read your payer contracts, model underpayment variance, or account for timely-filing risk on aged claims. Those require your actual data — which is what the free analysis covers.
The estimate is arithmetic. The analysis is your data.
Send a de-identified sample of denials and A/R for the last 90 days. Within a few business days you get a written read-out and a twenty-minute walkthrough — free, whether or not you ever become a client.
- Denial drivers ranked by payer, reason code and dollar value
- Days in A/R by bucket, with the claims closest to timely-filing write-off
- A recoverable-revenue figure built on your actual collection ratios, not national averages
- The three fixes we would make first, and what each one is worth
De-identified data requires no BAA. If you would rather send identifiable data, we execute a BAA first — always, without exception.
Request your free analysis
What the AI actually looks for.
On most healthcare websites, "AI" means a chatbot. Here it means a model trained on your claim outcomes that produces a ranked list of claims to fix, each with its reason attached.
Denial risk scoring
Every claim scored before it leaves the building, using payer, CPT, modifier, place of service, authorization and eligibility signals drawn from your own history.
Underpayment variance
Compares what each payer actually paid against what your contract implies, per CPT — turning silent shortfalls into a negotiating position.
Cash-flow projection
30/60/90-day collections projected with a confidence range, built from your aging profile and each payer's real payment lag.
Worklist ranking
Open A/R sorted by expected recovery rather than by age, so the team works the $4,200 claim before the $61 one.
Timely-filing alerts
Claims approaching each payer's filing deadline surface before they cross it, while appeal is still possible.
Reconciliation trail
Charges minus payments minus adjustments minus balance ties to zero on every run. If a number can't be traced to a source row, it doesn't reach the dashboard.
Reporting versus prediction.
Where a standard dashboard is genuinely enough, we say so.
| Question a practice asks | Standard PM report | Zaxis |
|---|---|---|
| What did we collect last month? | Yes — this is what they're for. | Yes, with trend, payer mix and variance. |
| Which claims in today's batch will be denied? | No. | Scored before submission, with the driving reason. |
| Which payer is quietly underpaying us? | Rarely — needs manual contract comparison. | Variance per payer per CPT, flagged automatically. |
| What will we collect next quarter? | No, or a straight-line guess. | Forecast with a confidence range from real payment lag. |
| What should the team work first? | Usually sorted by claim age. | Sorted by expected recovery in dollars. |
| Is our billing company performing? | You see their numbers, not a benchmark. | Independent measurement against your own history. |
Start with your own numbers.
The estimator gives you a size. The free analysis gives you the answer. Neither costs anything.
Everything we know about denials, written down.
Guides, benchmarks and worked methods for people who actually run a revenue cycle. If something here asks for an email address, it's because we're sending you a file worth opening.
What we publish, and why.
Each guide exists because the same question came up in enough first calls to be worth writing once, properly.
The twelve denial families — and which are worth appealing
A working taxonomy that groups reason codes into fixable families, with the realistic overturn rate for each and the point at which an appeal costs more than it recovers.
In preparation · request early access below
Days in A/R by specialty: what good actually looks like
Benchmark ranges by specialty, with the payer-mix caveats that make a single national number misleading for any individual practice.
In preparation · request early access below
Working aged A/R by expected recovery, not by age
The prioritization method, the arithmetic behind it, and a spreadsheet you can run yourself before buying anything from anyone.
In preparation · request early access below
What a denial prediction model actually uses
The signals, the limits, and how to tell a real model from a rules engine with a marketing name attached to it.
In preparation · request early access below
Prior authorization: the pre-submission checklist
Authorization requirements by specialty, expiry tracking, and the documentation that actually survives an appeal.
In preparation · request early access below
How a nine-provider group cut denials 48% in six months
An anonymized walkthrough of the sequence: measure, classify, prevent, re-measure — including what didn't work.
In preparation · request early access below
Take a template, not a sales call.
Tools you can use immediately, whether or not you ever talk to us. We send them in one email with the files attached — no drip sequence.
- Denial classification worksheet — group your last 90 days into fixable families
- A/R aging review template — bucket, payer, expected recovery, filing runway
- KPI definitions sheet — exactly how we calculate gross and net collection ratio, first-pass rate and days in A/R, so comparisons mean something
- Early access to each guide above as it publishes
One email, files attached. Unsubscribe in a click and we honour it immediately across every list.
Get the template pack
Reading about denials is useful. Seeing yours is better.
The audit tool takes a minute. The free analysis takes a de-identified export and a few business days.
We'd rather show you your numbers than our software.
Zaxis Health builds predictive business intelligence for the healthcare revenue cycle, and backs it with consultants and specialists who can act on what it finds. Every relationship starts the same way: we analyse your data, free, and tell you what we see.
Built from the reporting side of the problem.
Zaxis grew out of revenue-cycle analytics work — building the dashboards, reconciling the exports, and sitting with practice owners as they discovered that a number they'd trusted for a year didn't tie to anything.
The same conversation kept repeating. The information needed to prevent a denial almost always existed before the claim went out: in the eligibility response, in that payer's history with that CPT, in a prior denial nobody had grouped. It simply wasn't looked at in time.
So we built the layer that looks at it in time, then added the people who fix what it finds. That's the whole company — prediction, plus the capacity to act on it.
Mission
Make preventable revenue loss visible early enough that a practice can still prevent it.
Vision
A revenue cycle where denials are an exception somebody predicted, not a backlog somebody inherited.
How we measure ourselves
Denial rate, days in A/R and net collections at our clients. Not seats sold.
Six positions we won't trade away.
These aren't values on a wall. Each one is a rule that shapes what the product is allowed to show you.
Measured and modelled are different words
Figures read from your file are labelled measured. Figures produced by an assumption — recovery factors, forecasts — are labelled modelled, in the interface, every time. Blurring the two is how dashboards lose trust permanently.
Every total must reconcile
Charges minus payments minus adjustments minus balance equals zero, on every refresh. If a figure can't be traced back to a source row, it doesn't appear on screen.
No client is ever named
Every reference in our marketing is anonymized and every number is illustrative. If you become a client, you won't find yourself on this website unless you ask to be.
A prediction must carry its reason
A risk score with no explanation is a number nobody acts on. Every score names the factors driving it, so a biller can agree with it or overrule it on the evidence.
We don't fabricate what we can't see
If your file lacks a column, the dashboard hides that dimension rather than estimating around it. Missing data is stated plainly, never quietly filled in.
Your team stays your team
Zaxis is designed to make an existing billing operation measurable, not to replace it. Independent verification is a perfectly good reason to hire us.
Who does the work.
Revenue-cycle consultants who have run billing operations, certified coders, and analysts who build the models — not a general software team with a healthcare vertical bolted on.
- Senior RCM consultants leading denial root-cause and workflow engagements
- Certified coders reviewing documentation against payer-specific edit rules
- Data analysts who reconcile every dashboard to the source file before it ships
- Trained RCM specialists, onshore and offshore, for A/R follow-up and posting capacity
You'll meet the people who'd actually run your account on the first call — not a sales engineer who hands you off afterwards.
HIPAA as a default, not a setting
Encryption in transit and at rest, role-based access, and audit trails on every access and export.
BAAs before PHI
Executed before any identifiable data moves, and again at close before onboarding. De-identified evaluation samples require none.
Data isolation
Client data is never pooled, and identifiable records are never used to train a shared model.
Standards-based integration
835 and 837 file exchange with your EHR/PM. No scraping, no shared credentials.
Insight first. Software second. Always in that order.
We open every relationship by analysing the prospect's own denial data and giving the findings away. If the numbers don't justify working together, we say so — and you keep the analysis.
Free
predictive analysis before any commitment
20 min
read-out walkthrough on your findings
~30 days
to a live platform, value in week one
Zero
client names published, ever
Start with the part that costs nothing.
Send a de-identified sample. We'll tell you what we find, whether or not it leads anywhere.
The boring answers, in writing.
If you're evaluating a vendor that touches claim data, you need specifics rather than a badge. Here is exactly how Zaxis handles your data, what we will and won't do with it, and what we ask you to sign before anything sensitive moves.
What happens to a file you send us.
Evaluation: de-identified by default
For a free analysis we ask for a de-identified export — no names, medical record numbers, dates of birth or addresses. De-identified data isn't PHI, so no BAA is required and your compliance review stays short.
If identifiable data is involved, the BAA comes first
We execute a Business Associate Agreement before any protected health information changes hands — during evaluation and again at close before onboarding. No exceptions, no "we'll paper it later".
Transfer over a secure link, never email attachment
We send an encrypted upload link. Please don't attach claim data to an email or to any form on this website.
Storage, isolation and retention
Data is encrypted in transit and at rest, isolated per client, and retained only as long as your agreement specifies. Evaluation data is deleted on request, and by default once the read-out is delivered and accepted.
The technical and administrative controls in place.
Encryption
In transit and at rest, using industry-standard algorithms. Credentials and keys are held in managed secret storage, never in application code or spreadsheets.
Role-based access control
Access is granted by role and scoped to the minimum needed. An owner, a manager and a biller each see a different surface of the same dataset, and access is reviewed when roles change.
Audit trails
Every access and every export is logged with user, timestamp and record scope. Logs are available to you, not just to us — you can see who looked at what.
Standards-based integration
We ingest 837 claim files and 835 remittances from your EHR/PM or clearinghouse. No screen scraping, no shared logins, no browser plugins reading your PM system.
Per-client isolation
Your data is never pooled with another client's, and identifiable records are never used to train a model that any other client benefits from.
Personnel
Staff who touch client data are trained on HIPAA obligations, work under confidentiality terms, and are granted access only for the accounts they support.
What we will never do.
- Sell, rent or share your data with a third party for their own purposes
- Pool your data with another client's for benchmarking without a separate written agreement
- Train a shared model on identifiable records
- Publish your name, logo or figures as a reference without written consent
- Show real patient or client identifiers in any demo, screenshot or marketing asset
Ask us for the BAA in advance
We'll send our standard agreement before the first call if that speeds up your review. We're also comfortable executing yours.
Sub-processors
We'll name every sub-processor with access to client data on request, along with the safeguards in place with each.
Breach notification
Notification obligations, timelines and contacts are set out in the BAA, in line with HIPAA requirements.
Offshore resources
Where offshore specialists support your account, they work under the same agreements, access controls and audit logging as onshore staff. We'll tell you who and where before anyone is assigned.
Straight answers.
Is a BAA required for the free analysis?
Not if the sample is properly de-identified, which is what we ask for and what we prefer. If you'd rather send identifiable data, we execute a BAA first.
Where is data stored?
In encrypted storage within the United States, isolated per client. We'll confirm the specific environment and sub-processors in writing during your review.
Can we delete our data at any point?
Yes. Evaluation data is deleted on request and by default after the read-out. Client data is deleted or returned per the terms of your agreement at termination.
Do you use client data to improve the product?
Models are trained per client on that client's own history. We do not train a shared model on identifiable client records, and we do not use your data to benefit another account.
What about the AI Audit Tool on this site?
It runs entirely in your browser using the numbers you type. Nothing is transmitted to us, stored, or logged — there is no server call at all. You can verify that by opening your browser's network tab while you use it.
Send your compliance questions before the demo.
We'd rather clear the review early than discover it three weeks into a proposal.
Twenty minutes, your numbers, no slide deck.
Most demos are a product tour. Ours is a walkthrough of what we found in your data — which means the useful version starts with a de-identified sample, not a calendar invite.
Book a demo or request an analysis
Reach us
hello@zaxishealth.ai — replies within one business day.
Hours
Monday to Friday, 9:00–18:00 ET. Analysis requests are picked up the next business morning.
Prefer to look first?
The AI Audit Tool gives you a sized estimate in about a minute, with no contact details required.
We reply and scope
One short email confirming which data would be most useful for your specialty.
You send a de-identified sample
Ninety days of denial and A/R detail over a secure link. No BAA needed while it stays de-identified.
We walk the findings
Twenty minutes on your numbers. You keep the read-out either way.
Asked and answered.
Do I need to send patient data to get value from the first call?
No. A de-identified export — no names, no medical record numbers, no dates of birth — is enough for the analysis, and it needs no BAA. If you'd rather send nothing at all, we can model your specialty from benchmarks and still show you the shape of the problem.
Is the demo a sales pitch?
It's a walkthrough of findings. If the findings don't justify working together, we'll say so on the call, and you keep the analysis.
We already have a billing company. Is this awkward?
Not at all — independent measurement is one of the most common reasons practices call us. Several billing companies have ended up licensing the platform themselves after seeing the reporting.
How quickly can we start?
Analysis within a few business days of receiving a sample. Live platform in about 30 days from a signed agreement and a working data connection.
What happens to the data I send?
It's used solely to produce your analysis, stored encrypted, and deleted on request. We never pool it with another client's data or use identifiable records to train a shared model. Full detail is on our security page.
Privacy notice
This notice covers information collected through zaxishealth.ai. Protected health information handled under a client engagement is governed by the Business Associate Agreement and our security commitments, not by this notice.
Effective 2 August 2026
What we collect
Only what you send us. If you complete a form on this site, we receive the fields you filled in — typically your name, work email, practice name, specialty, provider count and any notes you add. We don't require any of it to browse the site or to use the AI Audit Tool.
What the AI Audit Tool collects
Nothing. The estimator runs entirely in your browser. The figures you enter are never transmitted to us, never stored, and never logged. If you subsequently ask for a full analysis, that request goes through the contact form like any other.
How we use it
To reply to you, to prepare an analysis you requested, and — if you asked for the template pack — to send the files and occasional revenue-cycle guides. We do not sell, rent or share your information with third parties for their own marketing.
Email preferences
Every commercial email we send includes a working unsubscribe link. We honour opt-outs promptly and suppress the address across all of our sending domains and sequences, not just the list you unsubscribed from. You can also reply to any message and ask to be removed.
Please don't send patient data through this site
No form on zaxishealth.ai is intended for protected health information. If you need to send claim or denial data, ask us for a secure transfer link and we'll set one up — with a Business Associate Agreement in place first if the data is identifiable.
Retention and deletion
Enquiry records are kept for as long as needed to answer you and maintain a record of the conversation, and deleted on request. Evaluation data sent for an analysis is deleted on request and, by default, once the read-out has been delivered and accepted.
Your choices
You can ask us what information we hold about you, ask for it to be corrected, or ask for it to be deleted. Email hello@zaxishealth.ai and we'll action it. Depending on where you live, you may have additional statutory rights, and we'll honour those too.
Changes to this notice
If we change how we handle information, we'll update this page and the effective date above.
Contact
Questions about this notice or about how we handle data: hello@zaxishealth.ai.
That page isn't here.
The link is broken or the page has moved. Nothing has been lost from your revenue cycle — just from our URL structure.
Try one of these.
The platform
Dashboards, prediction and how the three axes fit together.
Services
Denial management, A/R recovery, billing, coding and staffing.
Specialties
What we tune for orthopedics, behavioral health, urgent care and 30+ more.
AI Audit Tool
Size your revenue leak in about a minute, free.
Security & HIPAA
Encryption, BAAs, isolation and audit trails, in writing.
Contact
Book a demo or request a free predictive analysis.