Days in AR tells you how old your receivables are; payment velocity tells you how fast money is actually arriving and how reliably — the question a client cares about most when cash flow is tight. But payments post into each client's PM system and route through clearinghouses, so the firm has no consolidated turnaround view. This dashboard tracks cash velocity across the book so account managers can show clients not just what was collected, but how quickly.
What this dashboard answers
It answers the cash-flow questions behind collections: how much cash posted this period, how long is it taking payers to pay after submission, what share lands within 30 days, and how much auto-posts electronically versus needing manual handling? The overview shows total cash posted, dollar-weighted days to payment, paid-within-30 %, ERA/electronic remit %, and transaction count, with a days-to-payment histogram and a remit-source donut.
The metrics that matter
Weighted days to payment is the velocity headline, dollar-weighted so large payments drive it. Paid-within-30 % is the reliability measure — a high share means predictable cash flow, a low one means the client should expect lumpiness. ERA/electronic remit % matters for the operation's own efficiency: paper checks and patient payments cost more to post than auto-posted electronic remits, so a low ERA share both slows cash and raises cost to collect. Patient-responsibility collected isolates the slowest, hardest-to-collect bucket.
Why the data is trapped across the systems
Payment timing requires the submission date (from the PM system) and the posting date (from the remit, often routed through Availity or Waystar), joined per claim, per client. Each PM system holds only its own client, and the clearinghouse holds only the transit data — so 'how fast are my payers paying across the book' is a question no single system answers. It gets reconstructed by exporting and matching submission and payment dates by hand.
How to read it
Read weighted days to payment by payer, slowest first — that's the list of payers stretching your clients' cash flow. The paid-within-30 % by payer shows reliability, and the days-to-payment histogram reveals whether the book is consistently quick or has a slow tail. A low ERA % is an efficiency opportunity worth chasing. The sample uses entirely synthetic data, so there is no PHI.
Metrics it tracks
| Metric | What it means |
|---|---|
| Total Cash Posted $ | Payments posted in the period across all clients. |
| Weighted Days to Payment | Dollar-weighted submission-to-payment turnaround — SUM(payment × days) ÷ SUM(payment). |
| Paid Within 30 Days % | Share of payments received within 30 days of submission. |
| ERA / Electronic Remit % | Share of payments arriving as electronic remits that auto-post. |
| Payment Transaction Count | Count of payment-posting rows in the period. |
| Patient-Responsibility Collected $ | Payments sourced from patient balances rather than payers. |
Used by: Billing-company owners and account managers
Frequently asked questions
How is payment velocity different from days in AR?
Days in AR measures how old open receivables are right now; payment velocity measures how long paid claims took from submission to payment. Velocity is forward-looking cash-flow reliability — useful for telling a client when to expect money — while days in AR is a snapshot of what's still outstanding.
Why track ERA / electronic remit percentage?
Electronic remits (ERAs) auto-post, while paper checks and patient payments need manual posting that costs more and posts slower. A higher ERA share means faster cash and lower cost to collect, so it's both a velocity and an efficiency lever — and a concrete improvement to recommend to clients.
Can I show this per payer and per client?
Yes. The template models payment-posting data with payer, client, and remit-source slicers, so you can show each client their payer-by-payer turnaround and the share paid within 30 days. The sample uses synthetic data so you can see the finished velocity view first.
Build this report on your own data
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