Rivio Diagnostic/ Revenue cycle

R$ 55.6 million a year at a hospital that was within the industry average.

At a high-complexity private hospital, the diagnostic measured, across five fronts, how much revenue the institution produced but could not bill.

Period: Jul to Dec 2025
Claims: 66,524
Scope: 5 fronts of the cycle
Annual opportunity: R$ 55.6 million

Real data from a Rivio benchmark diagnostic. Hospital and payers anonymized. The figures on this page are identified opportunity, measured on real claims, not revenue already recovered in cash.

The diagnostic happens while the hospital is running. No routine is suspended, no team leaves its work, and the operation keeps billing throughout the four weeks of assessment.

01/ How the diagnostic is done

04 Weeks, in parallel with the operation
02 Profiles working together
07 Areas interviewed in depth
05 Revenue cycle fronts
00 Hospital routine interrupted

Who does the work

Two profiles sitting together, because reading a high-complexity claim requires skills that rarely sit in the same chair.

The three skills show up in the same claim, often in the same item. That is why the pair of profiles works side by side throughout the assessment.

Technology and conversation, in the same assessment

Two tracks run in parallel over the four weeks, and it is where they meet that produces usable results. An out-of-pattern indicator only becomes action when someone knows which day-to-day decision produced it.

Neither track closes the diagnostic on its own: the database reading measures the size, and the interviews explain the cause that makes the number actionable.

Why the task is big

A high-complexity claim carries thousands of items, checked against dozens of contracts and three market price tables whose effective dates change throughout the year. At this institution, 66,524 claims went through billing in six months. Checking item by item, across all of them, every month, is a volume no team can absorb by hand, and that is exactly where technology comes in.

Data governance

Hospital billing data is sensitive information with a defined purpose. The assessment runs inside that perimeter: contracted scope, a claim audit and billing purpose, access monitored by the institution itself, and results demonstrated on a small batch before any conversation about expanding.

What comes out at the end

Opportunities you can act on right away, with the current operation and the current team. Each front points to where there is revenue to recover, what it is worth and which process adjustment unlocks it. The questions the diagnostic answers:

01How many items are provided to the patient but never reach the invoice, and what is that worth per month?
02Which denial reasons keep recurring, and how much of each could be prevented before submission?
03How much time does each billing stage take, from discharge until the claim reaches the payer?
04Does the price, package and supply master data match what the contracts set?
05How wide is the gap between this operation and hospitals that have already automated the cycle?

02/ The depth

2,500+

Rules in the audit base
4,658

Claims in the test batch
66,524

Claims with measured timing
1.74s

To read an entire claim

The assessment goes down to the claim item, not the claim. One acetaminophen too few in an ICU daily rate, a dressing that was never billed, a package with the wrong percentage. That is the level where revenue is lost, and that is the level the diagnostic has to examine to produce a defensible number.

Five fronts cover the entire cycle, from the moment the patient is authorized to the reconciliation of what the payer paid.

Front 1 Process mapping across 7 pillars
Front 2 Underbilling, by reprocessing claims
Front 3 Denials, by history and ANS reason
Front 4 Billing pipeline, stage by stage
Front 5 Master data against contracts and market tables

Two mechanical measurements

A plug-in reads the management system and produces two things no one has to estimate.

What the plug-in measures in the database

The clock on each billing stage, and how closely the master data matches what the contracts set.

Neither measurement depends on anyone remembering anything: one reads the timestamp of each stage, the other compares each master data line with the document that governs it.

The test batch

The agents start out operating on a real batch. They reprocess claims the hospital had already closed and sent for billing, reading each one against a set of rules adapted to the hospital’s contractual reality. Rivio’s full base has more than 2,500 rules, divided into professional fees, supplies, OPME (implants and special materials), medications, packages, fees, procedures, daily rates, exams, medical gases, dressings and nutrition; this diagnostic used the subset the institution’s contracts and operation call for. When a rule finds a discrepancy between what was performed, what was documented and what was contracted, the agent flags the item with the evidence attached. In this diagnostic, that came to 4,658 claims.

Reprocessing claims that are already closed is the toughest test there is, and that is on purpose. Everything the agents find there had already gone through the hospital’s process and been signed off as ready by competent people. What shows up is what only shows up when the reading goes down to the item, in every claim, without getting tired.

03/ What we found

This is a good hospital. That is what makes the result uncomfortable.

The institution’s average time to payment was 75.4 days, practically the market’s, which Anahp measures at 73. Revenue produced but not billed stood at 5.25% a month, within the 5% to 8% range the industry knows and treats as normal. No indicator was outside the pattern Brazilian private healthcare accepts.

And it is from an operation like this that R$ 55.6 million a year came out.

R$ 55.6M

In additional revenue identified over twelve months
5.25%

Of the month’s revenue in items provided and never billed
94%

Of the claims analyzed had some discrepancy
40 days

Possible reduction in the average time to payment

Opportunity identified on real claims, measured on the ERP database. See the methodology note.

Where the R$ 55.6 million comes from

Four fronts with a measured financial value, on an annual basis.

The four fronts add up to R$ 55.6 million. Front 1, process mapping, produces a maturity diagnostic with no financial value of its own.

What the audit found in claims already closed

The underbilling front reprocessed claims the hospital had already closed and sent for billing. The reading took 1.74 seconds per claim. Doing the same review by hand would have cost 641 hours that month, the work of an entire team dedicated solely to rereading claims already signed off as ready.

From the reprocessed batch to the value found

Each step is what was left of the previous one after the rule base was applied.

Projected over twelve months, the month’s value reaches R$ 26 million, the equivalent of 5.25% of the institution’s monthly revenue.

The rules that found the most revenue

Values for the month analyzed, across 7,978 discrepant items identified.

Rule	Value found
No evidence the medication was used	R$ 840,609.61
Price lower than negotiated in the contract	R$ 518,900.19
Quantity billed higher than quantity used	R$ 265,706.69
Price higher than negotiated in the contract	R$ 161,168.20
Enteral diet underbilled relative to what was administered	R$ 96,589.27
Oxygen not charged	R$ 65,297.56
Infusion pump not billed	R$ 12,572.43

In a global ICU isolation daily rate, under a closed package, 7 units of acetaminophen were administered and only 4 reached the claim. Even in a claim that is 100% package, what is charged must reflect what was administered.

Where denials are born

Rivio contractually commits to reimbursing the hospital for the denials included in its coverage. The analysis of the history identified the recurring reasons, the weight of each one and which of them fall under that coverage: when an appeal is not recovered from the payer, Rivio reimburses the hospital for the denied amount.

It is this coverage that takes the final denial rate from 4% to 2.5%, a 37.5% reduction.

Five reasons that account for most denials

ANS table codes, weighted by value and by number of occurrences.

Code ANS	Reason	% Value	% Qty
3040	Technical denial	17.6%	22.0%
1705	Amount submitted too high	13.8%	14.4%
1707	No information on the pricing table	10.2%	11.3%
1714	Service amount above the table	7.4%	20.6%
2001	Invalid supply	4.2%	4.4%

Forty days between discharge and the money

With the plug-in on the ERP database, the diagnostic measured the actual time of each stage and compared it with Rivio’s benchmark of seven days between discharge and a claim ready to submit, already validated at partner hospitals.

The average time to payment falls from 75.4 to 35.2 days, and within it, billing time falls from 34.2 to 7 days.

Average time to payment, today and with the Rivio standard

The bar splits into billing time, which is under the hospital’s control, and the time for appeals, reconciliation and payment.

The gain concentrated in billing time, which goes from 34.2 to 7 days, is the part of the cycle that depends on internal process.

Where the days are, stage by stage

Time measured at each billing transition, against the reference of hospitals that already run on Rivio.

The four stages add up to 36.2 days, and the cycle measured end to end is 34.2 days, because some stages overlap in time.

The biggest gap is in the first stage, closing the claim, which accounts for 23.6 of the days.

Billing inside and outside the accrual period

The accrual period is the month in which the claim was produced: 65% of what the hospital produced between July and December 2025 was billed within the same month.

Value produced from July to December 2025, by month and by billing destination.

Closed within the period: 65%

Rivio standard: 90%

In 2025, 6% of what was produced was only billed in 2026, about R$ 9 million held back.

Process maturity, pillar by pillar

The seven-pillar mapping measured the distance between the hospital’s operation and the standard of hospitals that have already automated the cycle. The overall score came to 49 out of 100. The data existed at the institution; what was missing was preventive action and integration to attack the root cause of what repeated every month.

Maturity assessment, seven pillars of the revenue cycle

Score from 0 to 5 against the Rivio standard. Technology and systems was flagged as the number one priority.

Technology and systems came out as the number one priority: the teams worked in fragmented tools, without full TISS integration between them.

Master data, where the error only shows up as a denial

By cross-checking the ERP database against contracts, the hospital’s own tables and market tables (Brasíndice, Simpro, CBHPM), the diagnostic measured six master data fronts and arrived at a score of 33 out of 100, with no automatic review of effective dates, contracts or tables.

Among packages, 50 ran with no link to the official table, 29 had a mismatched value and 107 applied a percentage outside the rule of the contract itself. Adding both ends, R$ 2,263,130.75 in revenue billed below the contracted amount and R$ 836,869.25 in denial risk from billing above it.

Brasíndice

3.4of 5
Packages

1.8of 5
Supplies

1.6of 5
Fees

1.5of 5
Procedures

1.0of 5
Prices

0.6of 5

The joint injection and viscosupplementation group has its own contractual rule of 70% over the table. The master data had been applying the standard package 30%, across 1,725 lines and 107 packages.

04/ What this means

R$ 55.6M

Of revenue already produced by the hospital
0

Additional patients to treat
0

Additional beds to open

None of this R$ 55.6 million depends on treating one more patient, opening one more bed or hiring one more person.

This revenue has already been produced by the hospital. There was a physician, there was nursing, there were supplies consumed and there was people’s time. The cost already left the bank account. What got lost along the way was the billing.

And the reason it got lost is simple arithmetic: reviewing all of this by hand, every month, takes a team no hospital keeps on standby. The math of human labor never added up, and the technology able to do this reading at scale has only now arrived.

Recovered revenue becomes an on-call physician hired, a bed opened, new equipment, a test that comes back the same day. Efficient hospitals save more lives, and that is why every real is worth measuring.

Methodology note

Nature: Benchmark diagnostic. The figures are opportunity identified on real claims, not revenue already recovered in cash. Realizing each front depends on implementation, on process change and on the hospital itself.
Source: Extraction of billing data from the institution’s management system, reprocessing of real claims by the AI audit, and mapping interviews with the people responsible for each area.
Data handling: The assessment runs on the institution’s billing database, accessed under a signed non-disclosure agreement (NDA) and for the purpose of medical claim auditing, the same purpose as the hospital’s internal audit. Sensitive patient data — CPF (Brazilian taxpayer ID), insurance card number and name — is anonymized, and the data travels with layers of security and encryption. No information that identifies a patient is part of this publication.
Period: July to December 2025 for the pipeline analysis, with 66,524 claims. One reference month for the underbilling analysis, with 4,658 claims.
Scope: Five revenue cycle fronts: process mapping, underbilling, denials, billing pipeline and master data. The audit’s rule set is adapted to each hospital’s reality.
Annualization: Underbilling, denials and billing time start from the month or reference period measured and are projected over twelve months. Every indicator on this page is annualized, except master data, which comes from the historical series of the period analyzed.
Not included: Revenue actually recovered, gains already realized in cash, commercial terms, implementation cost and return on investment. Nothing on this page represents results delivered.
Market comparison: The average time to payment of 73 days and the underbilling range of 5% to 8% of revenue are industry references (Anahp). The seven-day benchmark between discharge and submission comes from hospitals that already run on Rivio.
Anonymity: Hospital and payers anonymized. The institution’s total financial volumes, coverage percentages and contract terms are not disclosed.

Indicator definitions

Underbilling

Revenue produced by the hospital that never reached the invoice, because an item was missing or billed below the contracted amount.
Final denial

The portion of the billed amount the payer refuses for good, after the appeal process has been exhausted.
Time to payment

Average time to payment. The time between the patient’s discharge and the money in the hospital’s account.
Time to bill

Average time to bill. The time between discharge and a claim ready to submit to the payer, the part of the cycle under internal control.
Closing within the period

The percentage of the value produced in a month that is actually billed within that same month.
Maturity score

A standardized score from 0 to 100 that measures the distance between the operation assessed and the standard of hospitals that have already automated the cycle.