Customer case study · Healthcare
Recovering missed billables with automated RVU audits
The automated audit surfaced billables that manual review did not consistently find, identifying an estimated $75,000 per year in recoverable RVUs while saving several hours of physician time each month, with every write verified out of band and zero silent incorrect writes.
Monthly physician RVU recovery audit
Partner engagement; institutional endorsement pending
≈$75,000
estimated recoverable billables identified per year
≈5 hrs
manual audit work saved each month
0
silent incorrect writes reported as done
The challenge
High-value work hidden inside a manual audit
RVU audits took several hours every month and still did not consistently surface every missed billable.
The job required collecting evidence from the existing EMR, comparing it with monthly RVU spreadsheets, and preparing the findings for review and recovery.
The workflow
Let automation handle the repetition
- 01Load the month’s RVU report spreadsheets.
- 02Navigate the relevant EMR records and collect the clinical-note evidence needed for the audit.
- 03Programmatically compare documented procedures with the RVUs that were credited.
- 04Write the findings to an analysis spreadsheet and generate a copy-ready recovery email.
The result
More complete audits without spending physician hours collecting and reconciling the data by hand
The automated audit surfaced billables that manual review did not consistently find, identifying an estimated $75,000 per year in recoverable RVUs while saving several hours of physician time each month, with every write verified out of band and zero silent incorrect writes.
Methodology
How the result was measured
A fixed methodology, so the result reads as evidence rather than a headline: what was measured, over what period, how recovery is defined, how it was attributed, and the full governed-run counts.
- Study and observation period
- January 6 to June 30, 2026 (6-month engagement). Figures are the monthly steady-state average across the window.
- Records reviewed
- ≈480 cardiology electrophysiology encounters per month (one physician’s billable volume). Each encounter is one governed reconciliation run.
- Baseline methodology
- A parallel manual audit of a stratified 120-encounter sample by the practice’s certified coding reviewer established the baseline capture rate and the corrected coding for each sampled encounter. OpenAdapt output was compared against that adjudicated baseline.
- Definition of “recovered”
- Recovered here means IDENTIFIED and queued: a recovery opportunity that OpenAdapt flagged and wrote to a review workbook and recovery email for the billing team to submit. It does NOT mean submitted, approved by a payer, or collected. Dollar figures are estimated recoverable value at the point of identification, not booked revenue.
- Attribution method
- An opportunity is attributed to OpenAdapt only where it flagged a differential the baseline sample confirmed and the billing team had not already queued. Results are reconciled against the existing worklist to exclude double-counting of opportunities that would have been caught anyway.
- Manual effort, before and after
- Before: roughly 6 physician and coder hours per month to run the audit by hand. After: roughly 1 hour per month, spent on exception review of halted runs and QA sampling. Net reduction of about 5 hours per month.
- Application and surface
- The practice’s EMR and RVU spreadsheets. GUI last-mile navigation and write, where no supported billing API reaches the step.
- Deployment mode
- Runs in the customer-controlled Windows environment, on the practice workstation over RDP as deployed. No PHI leaves the customer boundary.
- OpenAdapt and pack versions
- OpenAdapt Flow v1.23.0, RCM cardiology workflow pack v0.4.2.
- Identity and effect contract
- Each run binds to the patient and encounter identity, read out of band before any write. The effect contract asserts the specific coding change intended for that encounter, and nothing is treated as done until that asserted effect is confirmed against the persisted record.
- Verifier implementation
- Out-of-band read-back. After a write, the verifier re-reads the persisted field through an independent path and compares it against the asserted effect. Disagreement or ambiguity halts the run and routes it to a human. A run is never reported successful on an unverified write.
Governed-run counts
Every run, accounted for
Monthly steady-state counts. Verified, halted, and failed runs reconcile to the total. Verified rate 99.2%, at $0.03 per record. The number that matters most for a clinical write is the last one.
A halt is a designed outcome, not a failure: it means the verifier could not confirm the effect, so a human reviews it instead of a wrong value silently reaching the record.
Scope of this evidence
A partner engagement, not an institutional endorsement
This work is a partner engagement with a US cardiology electrophysiology practice and a collaborating board-certified cardiac electrophysiologist. A physician advisor collaborating on the workflow is not the same as an institution endorsing OpenAdapt. No institutional endorsement is claimed or implied; institutional endorsement is pending. The customer is kept anonymized by request and by policy.
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