Lifting First-Pass Claim Acceptance to 97% with AI-Assisted Denial Prevention
Regional home health agency (~1,100 active patients) · Southeast U.S.
81% → 97%
First-Pass Acceptance
64%
Fewer Denials
$1.1M
Annual Revenue Protected
9 weeks
Time to Full Impact
How an AI-assisted, human-reviewed pre-submission scrub lifted a home health agency’s first-pass claim acceptance from 81% to 97% and cut denial rework.
The Challenge
A regional home health agency serving roughly 1,100 active patients was stuck with a first-pass claim acceptance rate of about 81%. Nearly one in five claims was rejected or denied on first submission, and the billing team spent most of its week reworking claims reactively rather than preventing errors. When we analyzed a full quarter of denials, the pattern was clear: the vast majority were preventable. Late or mismatched Notice of Admission (NOA) filings, PDGM coding that did not align with the OASIS assessment, and eligibility lapses accounted for most of the loss. The team had the expertise to catch these issues, but no systematic way to flag them before claims went out the door. Accounts receivable was climbing as reworked claims aged.
Our Solution
We layered an AI denial-prediction step on top of a certified-coder review workflow. Every claim was scored for denial risk before submission using the agency’s own historical denial patterns, payer edits, and code-to-OASIS consistency checks. High-risk claims were routed to an AAPC-certified coder for correction before they were ever submitted — the model flags, a human decides. Alongside the pre-submission scrub, we added daily NOA monitoring so no admission slipped past the five-day window, eligibility re-verification close to each date of service, and a denial root-cause feedback loop that fed recurring issues back into intake and coding. The result was a prevention system rather than a rework treadmill: fewer claims failed, and the ones flagged were fixed while there was still time to fix them.