Meet CIRCLE: the engine behind the execution.
the closed loop
CIRCLE
patent-pending
Downstream payer decisions are the reward signal for upstream prevention.
Every remittance makes the next claim smarter. Without downstream truth, upstream prevention is just rules in a spreadsheet.
Signal in
Clinical documentation and claims flow into the engine. Every payer adjudication flows back: what was paid, denied, adjusted, and why, from remittance data.
Decisions out
Coding suggestions, pre-bill denial-risk flags, payer-drift alerts, appeal drafts, and recovery work queues.
Outcomes as the reward signal
The payer's actual decision on each claim scores every upstream prediction. Prevented denials, won appeals, and recovered underpayments count for the model. False positives count against it.
Tuned, governed
False-positive rate and reviewer flip rate are measured monthly per payer and category. Deviation triggers retraining; noisy categories get elevated to humans.
A claim flagged high-risk that then paid in full without appeal counts against the model as a false positive. Prevented denials, won appeals, and recovered underpayments count for it.
One loop. Four stations. One command view.
CIRCLE drives the Denial Autonomy Suite's four stations and converges everything at Revenue Recovery Central. A recovery finding downstream becomes a prevention rule upstream.
A learning loop you can audit, not a black box you hope about.
The sophistication is not the model; it is the measurement around it. Neurex publishes the governance, not just the results.
A false positive, defined
A claim flagged high-risk that subsequently paid in full without appeal or resubmission counts against the model. No grading on a curve.
Measured monthly
False-positive rate and reviewer flip rate, per payer and per category. Deviation triggers a retrain.
Humans elevate
Any category with a reviewer flip rate above 30% is auto-elevated for human review, and thresholds calibrate so the model gets quieter where it is noisy.
Flagged high-risk, then paid in full without appeal or resubmission.
Categories with a flip rate above 30% are auto-elevated for human review.
thresholds calibrate so the model gets quieter where it is noisy
Horizontal AI is retrofitted. Neurex is native.
General-purpose AI learns from the internet. CIRCLE learns from adjudication outcomes on the tenant's own claims: tenant-scoped learning on de-identified data, isolated and governed.
PHI is processed under executed BAAs for operational claims work. Model learning uses de-identified, tenant-scoped data. A human stays in the loop on consequential actions, with audit-ready trails throughout, HIPAA-aligned operations, and SOC 2 Type II readiness. Security at Neurex
One engine. Two ways to engage.
Providers engage Neurex directly as Service-as-a-Software: no seats to license, outcomes you measure in dollars. RCM and HIM service organizations embed the same intelligence inside their own platforms via API.
Integration architectureWatch it learn from your own payers.
Start with a pilot where you set the success criteria. Every claim CIRCLE touches is scored by the payer's actual decision.
CIRCLE is patent-pending · SOC 2 Type II readiness · HIPAA-aligned