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.
Healthcare
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 priority01 When this matters
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
Each transition connects technical work to a result your organization can see.
Software designed around data models
→ Software designed around clinical workflowSampled, manual chart and charge review
→ Automated review across the full populationRefund demands months after payment
→ Documentation that survives a payer auditRevenue leakage discovered by the payer
→ Discrepancies caught before the claim goes out03 A practical path
The work changes as we learn. Clear decision points keep scope, investment, delivery, and ownership aligned.
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.
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.
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.
Findings become prioritized fixes, the checks run continuously instead of once, and your team keeps the dashboards, thresholds, and runbooks when the engagement ends.
04 Expertise involved
Design, build, and support important software products through one integrated senior delivery team.
Build reliable data foundations that make analytics, AI, operations, and reporting easier to trust.
Use historical patterns, forecasts, and optimization models to understand performance, anticipate outcomes, and choose practical next actions.
Maintain and improve critical software through a standing team with visible priorities and operating responsibility.
05 Common questions
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.
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.
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.
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.
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.