We do not add labor to the revenue cycle. We add intelligence.
Revenue cycle management for health systems does not fail for lack of people. It leaks in the seams between entities, between clinical documentation and the claim, between the payer contract and the posted cash. Adding headcount widens the same seams. Intelligence closes them.
The 4-A model, one operating core
Four disciplines wrap a single core. None of them is more staff. Each one turns effort that a human used to spend into signal a system can act on, so the people you already have work the highest-value queue first.
Augmented Intelligence
Models read the chart, the remit, and the contract, and surface the coding, the underpayment, and the appealable denial before a person opens the account.
Automation
Status checks, eligibility, claim edits, and follow-up touches run without a keyboard, so human hours land only where judgment changes the outcome.
Analytics
Every account carries a risk price. Work is sequenced by expected recovery and days-to-cash, not by alphabetical worklists or the oldest queue.
Assurance
Governance, audit trails, and coding accuracy at 95% or higher keep every entity clean across one shared compliance and reporting spine.
The intelligent revenue cycle pipeline
Capabilities are only worth what they move. The rail below maps each stage to the moment revenue is actually secured, from the account being priced for risk, to the claim being protected, to the cash being accelerated into the system.
Risk-priced
- Eligibility, benefits, and authorization verified up front
- Every account scored for denial and underpayment risk
- Documentation and coding checked against the claim it will produce
Revenue-protected
- Edits and clean-claim logic applied before submission
- Denials routed to the right specialist by root cause
- Appeals drafted from payer policy, not from templates
Cash-accelerated
- Follow-up sequenced by expected recovery and aging
- Underpayments detected against contracted rates
- Cash posted, reconciled, and reported across every entity
Four levers, measured per $1B net patient revenue
A CFO does not buy capabilities, a CFO buys movement on a small number of levers. Here is how the model reads at scale. The figures below are illustrative industry benchmarks used to show the shape of the math, not a named client's actuals.
On $1B of net patient revenue, one point of first-pass improvement removes roughly $10M of billed volume from the rework loop before it ever denies.
Experian State of Claims 2025 puts first-pass denials at 11.8%. Lifting the overturn rate on that pool converts written-off revenue back into posted cash.
Automation carries the volume, not new hires. Cost to collect falls as basis points of net patient revenue while throughput rises.
Risk-sequenced follow-up pulls cash forward. Each day of AR released on $1B NPR frees roughly $2.7M of working capital back into the system.
Illustrative benchmark math for a multi-billion-dollar net-patient-revenue academic medical center. Actual results depend on payer mix, contract terms, and baseline. No client-specific figures are shown.
Guidelines we hold ourselves to by name
Intelligence only counts if it is measured the way finance already measures. We report against the frameworks your board and your audit committee recognize.
HFMA MAP Keys
The industry standard for revenue cycle performance. Clean claim rate, denial rate, cost to collect, and AR days are reported as HFMA MAP Keys so every entity in the system is compared on the same definitions, not on locally invented metrics.
Experian State of Claims 2025
First-pass denials sit at 11.8%, and the cost of reworking those claims compounds across a system's volume. Our model attacks the denial before it happens and the rework after it does, rather than staffing up to absorb it.
System-agnostic, one book or three
No rip-and-replace, ever
Academic medical centers run on a single EMR across many entities, and that investment stays exactly where it is. Our intelligence layer integrates on top of the system you already run, so nothing gets ripped out and no clinical workflow changes.
- Sits on your existing single-EMR environment, system-agnostic by design
- Maps cleanly to a multi-entity or HoldCo structure without co-mingling data
- One reporting spine, entity-level books, consolidated system view
- Scales from one book to three without a new implementation each time
Faculty practice + hospital, single EMR
Same intelligence layer, own book
Consolidated to the parent view
Add intelligence to the revenue cycle you already run
Bring us your HFMA MAP Keys and your entity structure. We will show you where revenue is leaking between the seams, and model the levers per $1B of net patient revenue before you change a single system.
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