A pharmacy can dispense everything correctly and still not get paid. Third-party reimbursements lag, claims reject at adjudication, and patient copays go uncollected at the counter — and because volume is high, even a small leak compounds fast. This dashboard tracks the money from billed to collected: what's outstanding, how fast it's aging, what's rejecting and why.
What this report answers
Are we collecting what we billed, and what's stuck? The Collections Overview page leads with outstanding AR, collected amount, collection rate, rejected claim rate, copay collected, and aged AR over 60 days, then breaks open balance by aging bucket and by PBM and trends the collection rate by month. The Rejections & Aging page lists open and rejected claims and ranks rejections by reason.
The metrics that matter
Collection rate — collected divided by billed — is the headline, the share of billed dollars that actually lands. Aged AR over 60 days is the early-warning line; the older a third-party balance gets, the less likely it pays. Rejected claim rate is the upstream cause: a spike usually traces to eligibility, prior-authorization, or coverage issues that can be fixed at the point of adjudication. Copay collected closes the loop on the patient-responsibility dollars that slip away at busy counters.
Why the data is trapped across the systems
Collections live across the divide between what was billed (the dispensing system — PioneerRx, Computer-Rx) and what was actually paid (the PBM remittance, 835/CSV). Open balance is the difference, aged by date and grouped by PBM. Rejection reasons sit in the adjudication response. No single screen totals open AR by aging bucket, trends collection rate, and ranks rejection reasons together — so balances quietly age toward write-off.
How to read it
Work the aging buckets first: balances sliding into 60+ days are the at-risk dollars to chase now. Read open balance by PBM to see which payers create the friction, and the rejection-reason donut to fix the most common adjudication failures upstream. Watch the collection-rate trend — a downward drift means more is billed than is landing, usually rejections or slow third-party pay. Copay collected reminds you the counter is part of collections too.
The sample uses synthetic claims, balances, and rejection reasons — no real PHI or remittances.
Metrics it tracks
| Metric | What it means |
|---|---|
| Outstanding AR | Open third-party and patient balance across all claim rows. |
| Collected Amount | Dollars paid across all claims in the period. |
| Collection Rate | Collected ÷ billed — the share of billed dollars actually paid. |
| Rejected Claim Rate | Rejected claims ÷ all claims — share rejected at adjudication. |
| Copay Collected | Patient copay collected at point of sale. |
| Aged AR over 60 Days | Open balance in the 61–90 and 90+ aging buckets. |
Used by: Owner-pharmacists and office managers chasing unpaid third-party AR, copay balances, and rejected claims
Frequently asked questions
What is a good collection rate for an independent pharmacy?
Collection rate is collected divided by billed — the share of billed dollars that actually lands. Higher is better, and a downward trend usually points to rising rejections or slow third-party payment. The report's value is spotting which PBMs and which aging buckets drag the rate, not matching a single benchmark.
Why does third-party AR aging matter so much?
Pharmacy claim volume is high and third-party balances that age past 60 days are increasingly unlikely to be collected. Watching aged AR over 60 days by PBM lets the office work the most at-risk dollars before they age out, instead of discovering write-offs after the fact.
Can I build this on my own billing data?
Yes. Export your billed claims, PBM remittances, and copay records and use this as a template — we model billed-vs-collected, aging, and rejection reasons into a Power BI report. The sample uses synthetic data so there's no PHI here.
Build this report on your own data
Clone this Independent pharmacies template — describe it and we’ll generate sample data so you can try it free, or upload your own export. You get a fully modeled, branded Power BI project that opens in Power BI Desktop.
Use this as a template →