Cutting insurance claim rejections for a clinic group
A payer-integrated e-claims platform withhybrid AI pre-validation for amulti-branch clinic group, cutting claimrejections from around 18% tounder 5%.
The Challenge
Coders keyed insurance claims by hand into one payer portal after another, and only learned a claim was wrong when it bounced back weeks later, often after the resubmission window had closed. Close to one claim in five came back rejected, each one a hit to cash flow and a case a coder thought was already finished. With every branch working its own queue, no one could see which payers were slow or which errors kept repeating.
Errors surfaced only on rejection
Coders keyed claims by hand into one payer portal after another and only learned a claim was wrong weeks later, often after the resubmission window had already closed.
No view across branches
Every branch worked its own queue in its own portal. No one could see what was outstanding, which payer was slow, or which denial reasons kept recurring across the group.
Checks had to be defensible
This is patient and insurance data, and the rules belong to the payers and regulators. A tool that guessed at coding or invented a requirement would be worse than none at all.
Our approach
Every build follows the same software development life cycle, from requirements and design through build, testing, and support. Each phase is planned, demoed, and signed off before the next begins, so quality is engineered in rather than checked at the end.
Discovery & requirements
We ran discovery across coding, billing, and reception and wrote a testable specification covering every claim state, transition, and rejection reason, with patient-data handling designed in from the start.
(Outcome):
Architecture & design
We designed hybrid validation: a deterministic rules engine for the auditable checks and an LLM layer grounded in the payer rulebook, behind role-based access and append-only audit logging.
(Outcome):
Build
We built the platform on the regulated payer channels, the hybrid validator, and one cross-branch worklist, demoing each before moving on.
(Outcome):
Testing & UAT
We tuned the validator's precision and recall against a large set of historically adjudicated claims before go-live, then ran user acceptance testing with the billing team, with a coder in control of every submission.
(Outcome):
Deployment & support
We rolled out branch by branch, each site stabilising before the next came online, with drift monitoring as payers change their rules.
(Outcome):
Outcomes
Claim rejection rate cut from around 18% to under 5%
Claims submitted the same day they are coded, clearing a standing five-day backlog
Roughly 80% of claims auto-validated and cleared before submission, with the rest routed to a coder with an explained flag
Revenue recovered on previously lapsed claims, now caught and resubmitted inside the payer window