Days in accounts receivable is one of the most watched metrics in lab finance. When it climbs, the pressure is immediate: accelerate collections, increase follow-up, add staff, work the queue harder.
That response makes sense when the problem is operational. In many labs, however, aging AR isn't primarily an operational problem. It's a data problem. Working the queue harder doesn't fix a transmission error, a reporting blind spot, or a payor underpayment that auto-cleared out of the workflow.
This page covers both sides: the operational levers that genuinely move days in AR, and the data and system issues that keep it elevated no matter how hard the billing team works.
What Days in AR Is Actually Telling You
Days in AR measures how long it takes, on average, to collect payment after a service is rendered. The calculation is straightforward: divide your total outstanding AR by your average daily charges.
For independent labs, a healthy benchmark is generally under 40 days. Many labs run higher — and the gap between where they are and where they should be is almost always explained by a combination of factors, not a single cause.
The important question isn't just “how do we lower this number?” It's “why is it this high?” Because the answer determines whether operational changes will actually move it.
Operational Reasons AR Ages And What Helps
These are the factors most labs already know to address. If you have solid controls here and your AR is still elevated, the problem is likely elsewhere.
Slow claim submission
Every day between service and claim submission is a day added to your AR. Labs with manual charge entry workflows or batch submission schedules often carry unnecessary lag before claims even reach the payor.
What helps: Same-day or next-day claim submission. Automated charge capture directly from LIS order completion where possible.
Incomplete front-end verification
Insurance verification failures at the front end create downstream billing problems: wrong payor, wrong member ID, inactive coverage. These claims reject, require manual correction, and re-enter the queue days or weeks later.
What helps: Real-time eligibility verification at order entry. Systematic review of registration errors by ordering source.
Denial management backlogs
Unworked denials age silently. In high-volume labs, denial queues can grow faster than staff can work them, particularly when appeal deadlines create urgency that pulls attention away from older accounts.
What helps: Denial prioritization by dollar amount and filing deadline. Root-cause analysis of denial patterns to address systematic issues rather than working claims individually.
Slow payor reimbursement
Some payors are simply slow. State Medicaid programs, certain managed care plans, and some workers' compensation payors routinely take 60–90 days or longer to adjudicate clean claims. This isn't always fixable, but it should be understood as a structural contributor to your AR rather than a collections failure.
What helps: Payor-level AR aging analysis. Separate benchmarking for slow payors so they don't distort overall performance metrics.
Why AR Stays Elevated Even When Operations Are Running Well
This is the part that doesn't show up in most AR reduction guides because most AR reduction guides assume the billing system is showing you everything.
It usually isn't.
The following causes of elevated AR are systematic, invisible to standard reporting, and won't respond to operational improvements because they're not operational problems. The list is a representative sample, not the full set; actual lab examples of each, with how they were found, live on the case studies page.
Claims that auto-cleared without payment
Billing systems use auto-clearing rules to manage denial workflows. When a payor posts a remit with a denial code but no payment, and the system auto-clears that denial on posting, the claim vanishes: not on the no-activity report (it has remit activity), not on the error report (the denial cleared). It ages in a blind spot no standard report is designed to surface.
Transmission errors creating false write-offs
When a system integration drops or corrupts a data field during transmission, the billing system processes based on what it received, not what was intended. The result is often accounts flagged incorrectly and removed from active AR. Write-offs that shouldn't be write-offs are invisible in AR aging, because they've already left the AR.
HL7 errors that look like normal denials
Labs receiving orders from multiple EMR or ordering systems are exposed to HL7 transmission errors that present as ordinary claim rejections. Without source-level analysis, these get worked individually, keeping denial rates elevated and clean claim rates suppressed with no one recognizing they share a single fixable root cause.
Submission routing that fails silently
“Submitted” inside the billing system isn't the same as “accepted” at the clearinghouse. When routing or enrollment is wrong for a subset of payors, claims can leave marked as submitted, get rejected at the gateway, and never come back as a denial. Internally they look active and age normally. Externally, the payor never received them.
Eligibility selecting the wrong plan
Real-time eligibility doesn't always pick the correct primary when a patient has multiple active coverages. Claims go to the wrong plan, get denied or paid as secondary, and the resulting balance shifts to the patient. AR ages while the billing team works individual accounts, with no indication the underlying issue is in how the eligibility response is being interpreted.
Stale LCD and policy edit tables
Edit tables that aren't refreshed against current LCDs and payor medical policies quietly drift behind the live coverage rules. Denials climb on high-volume tests with diagnoses that are now valid under the current policy, AR ages while staff appeal them individually, and the pattern looks like a coding problem when the actual issue is internal configuration.
NCCI bundling suppressing reimbursement
Procedure-to-procedure edits don't always present as denials. Sometimes they show up as a $0 line on an otherwise paid claim. The claim closes. The contracted reimbursement on the suppressed line is gone, and nothing in standard AR reporting flags it, because the claim itself isn't aging.
Tests that were never linked to a CPT
New assays can go live in the LIS and result normally without ever producing a claim, because no CPT mapping was added in the billing system. The orders never reach the billing queue. They don't show up in AR aging at all, but the revenue is gone just the same.
Payor underpayments normalized over time
When a payor consistently reimburses below contracted rates, billing teams often adapt, adjusting mental benchmarks for what “normal” payment looks like from that payor. Underpayments get posted, the contractual adjustment gets written, and the account closes. Without systematic comparison of actual payments against contracted rates, the gap is entirely invisible in AR aging, because the accounts closed.
A Different Way to Think About Reducing Days in AR
Most AR reduction efforts focus on speed: faster submissions, faster follow-up, faster collections. That's worth doing. Speed alone, however, doesn't recover money that's already been removed from AR incorrectly, and it doesn't surface claims sitting in reporting blind spots.
A complete approach to reducing days in AR has two components:
- Operational: Making sure clean claims go out fast, denials are worked systematically, and follow-up is prioritized effectively.
- Investigative: Making sure the reporting is actually showing you everything: that auto-clearing logic isn't hiding unpaid claims, that system integrations are transmitting correctly, and that payor payments are being validated against contracted rates.
Most labs have the operational side covered, at least partially. The investigative side is where the biggest recoveries tend to be found.
Signs the Problem May Be in Your Data
A few indicators that data or system issues may be contributing to your elevated AR:
- AR that ages despite active follow-up — accounts that billing staff keep working but never resolve
- Write-off rates that have been climbing gradually with no clear cause
- Clean claim rates that seem lower than expected given your coding quality
- Reconciliations between systems that never quite tie
- Payor payment trends that feel off but don't trigger any specific alert
- A persistent sense that the numbers don't add up, even when nothing specific flags
None of these are proof of a data problem. Any of them, however, is worth investigating beyond what standard reporting can show.
If Your AR Is Elevated and You're Not Sure Why
Operational improvements only go so far when the root cause is in the data. A Revenue Recovery Audit is designed specifically to find what standard AR reporting misses: the blind spots, the transmission errors, the payor patterns that have been quietly costing you money.