Money that never gets billed never gets collected, and lag-to-bill is where it quietly disappears — charges stuck in coding, waiting on a provider's signature, or held for eligibility while the timely-filing clock runs. Because each client's encounters sit in a different PM system, the firm can't see the unbilled pile across the book. This dashboard surfaces it before it turns into a write-off.
What this dashboard answers
It answers the charge-capture questions that precede AR entirely: how much have we captured but not yet billed, how long are charges taking to go out, and how much is at risk of blowing a filing deadline? The overview shows unbilled dollars, dollar-weighted lag-to-bill days, the share submitted within 5 days, timely-filing at-risk dollars, and unbilled encounter count, with a lag-bucket histogram and an unbilled-status donut.
The metrics that matter
Lag-to-bill has to be dollar-weighted so a few large held charges aren't averaged away by many small ones that went out fast. Charges-submitted-within-5-days is the throughput target — the higher it is, the less revenue is exposed to filing limits. Timely-filing at-risk dollars is the alarm: every dollar in it is collectible revenue one missed deadline away from a guaranteed write-off. Missing-charge count catches the encounters where the charge was never captured at all — pure leaked revenue.
Why the data is trapped across the systems
Unbilled charges live in the front end of each client's PM system — athenahealth, AdvancedMD, eClinicalWorks and the rest — in coding queues, sign-off holds, and eligibility holds. Those are the screens a billing firm rarely sees aggregated, because each system shows only one client and the firm logs in elsewhere to do the actual billing. Pulling the unbilled work-in-process across clients is a manual export, so lag-to-bill problems usually surface only after the money is already late.
How to read it
Watch the lag-bucket histogram for a fat tail at 11-20 and 21+ days — that's where charges are getting stuck. The unbilled-status donut tells you why (in coding, pending sign-off, held for eligibility), which points at the fix. Timely-filing at-risk dollars by client is the work-now list. The sample uses entirely synthetic data, so there is no PHI.
Metrics it tracks
| Metric | What it means |
|---|---|
| Unbilled Charge $ | Charges captured but not yet submitted to a payer. |
| Avg Lag-to-Bill Days | Dollar-weighted days from service to submission — SUM(charge × lag) ÷ SUM(charge). |
| Charges Submitted Within 5 Days % | Share of encounters submitted within 5 days of the date of service. |
| Timely-Filing At-Risk $ | Unbilled dollars sitting near the payer's filing deadline. |
| Unbilled Encounter Count | Count of captured-but-unbilled encounter rows. |
| Missing-Charge Encounter Count | Completed encounters with no charge captured yet — leaked revenue. |
Used by: Operations directors and account managers
Frequently asked questions
What is lag-to-bill and why does it matter?
Lag-to-bill is the time from date of service to claim submission. Long lags expose revenue to timely-filing limits and delay every downstream collection. Tracking it dollar-weighted — so big held charges count for more — shows where charge capture is bottlenecked before the money ever reaches AR.
What causes charges to sit unbilled?
The usual holds are incomplete coding, encounters waiting on a provider's sign-off, and charges held for eligibility verification — plus encounters where no charge was captured at all. Breaking unbilled dollars down by status points to whether the fix is coding throughput, provider workflow, or front-end registration.
How does this differ from the AR aging report?
AR aging tracks claims that have been billed and are waiting to be paid. Lag-to-bill tracks revenue earlier in the cycle — encounters that haven't been billed yet at all. Catching leaks here prevents them from ever becoming aged AR or timely-filing write-offs, so the two reports cover consecutive stages of the same pipeline.
Build this report on your own data
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