Icarus/Outcomes/Healthcare technology and revenue-cycle auditing

OUT / 02

Healthcare

Find the billing errors before the payer does.

Clinically led healthcare technology and revenue-cycle auditing that catches coding and documentation gaps before a payer recovery audit turns them into recoupments.

Discuss this business priority

01 When this matters

You may need this outcome when:

Payers now run post-payment recovery programs continuously, and a recoupment demand can arrive a year after the money was booked and spent. Meanwhile your clinical and billing systems fight the people using them: documentation takes longer than it should, workarounds have become routine, and nobody can say with confidence how much revenue is exposed to coding and charge-capture errors that a recovery audit would find.

02 The required shift

The business process must improve with the technology.

Each transition connects technical work to a result your organization can see.

01

Software designed around data models

Software designed around clinical workflow
02

Sampled, manual chart and charge review

Automated review across the full population
03

Refund demands months after payment

Documentation that survives a payer audit
04

Revenue leakage discovered by the payer

Discrepancies caught before the claim goes out

03 A practical path

Move from the current problem to a working solution.

The work changes as we learn. Clear decision points keep scope, investment, delivery, and ownership aligned.

01

Clinical and operational review

We sit with the actual workflow, not the process diagram, and identify where the system creates work instead of removing it. This review is led by a BSN-RN with over ten years of critical care practice.

02

Data and access assessment

We map where clinical, coding, and billing data originates, who may access it, how long it is retained, and where that flow creates clinical or financial risk.

03

Automated audit build

We build the analysis that reviews every encounter rather than a sample: charge capture against documentation, coding consistency, duplicate and unbundling patterns, and the timing rules that recovery auditors use to claw payments back. The same evidence that prevents an error also defends the claim if it is challenged later.

04

Remediation and operation

Findings become prioritized fixes, the checks run continuously instead of once, and your team keeps the dashboards, thresholds, and runbooks when the engagement ends.

05 Common questions

Questions to answer before you invest.

What does having a Registered Nurse on the team actually change? +

Healthcare software usually fails on workflow, not code. Our healthcare engagements are led by a BSN-RN with more than ten years of critical care experience, so the review starts from how care is actually delivered and documented. That perspective is uncommon among technology firms and it changes which problems get solved first.

Can this reduce what we lose to payer recoupments? +

That is the point of auditing the full population rather than a sample. Recovery audits, whether from a commercial payer, a RAC contractor, or a payment-integrity vendor, look for repeated patterns: a code that documentation does not support, unbundling, duplicate charges, or timing that breaks a payer rule. Those patterns are exactly what continuous automated review surfaces first, while you can still correct the claim or the template producing it. For claims already paid, the same analysis tells you where you are exposed before a demand letter does, and the retained evidence supports an appeal rather than a write-off.

Can you help with SOC 2 as well? +

Yes, and it has its own path. See SOC 2 readiness and attestation support for how we prepare the controls and evidence, then connect you with licensed CPA firms in our network to perform the examination.

How is an automated revenue-cycle audit different from what our billing team already does? +

Coverage. Manual review examines a sample, usually after a payer has already flagged something. We are a technology company, so we review the entire population of encounters with rules and models running continuously. That finds systematic patterns, such as a documentation template that consistently under-supports a code, which sampling is unlikely to catch and which compounds into retroactive adjustments.

We are not a healthcare organization. Is this relevant to us? +

This is one of several outcome paths, not our whole business. The same engineering, data, and cloud teams serve clients across industries. Healthcare simply happens to be an area where we hold a clinical credential in addition to the technical one.