Captured, Extracted, and Still Sitting in a Queue: How provider revenue cycle teams move correspondence from a scanned image to the right patient account, and automate the work that follows

Correspondence arrives in your revenue cycle continuously. Remittance advice, Explanation of Benefits (EOB) forms, payer denial letters, refund requests, and patient payments come by mail, by fax, and in a single prepared file from your bank. Your team opens each one, identifies what it is, searches several systems for an account number that is often missing, and keys the result into another application before anything can post.

Two problems have to be solved, and most projects plan for only one. Reading the correspondence accurately is difficult on its own, because a payment stub, a denial letter, and a discharge summary can sit in the same file, some as clean scans and some as faxed copies. Clean data is only the halfway point. It still has to reach the right patient account and trigger the next step, whether that is posting the transaction, routing the denial, or starting the refund.

Join Naviant and ABBYY for a panel discussion on automating revenue cycle correspondence end to end. We will walk through one lockbox batch as a working example, show where classification and extraction carry the volume, and then follow the data into the systems of record and the downstream processes that depend on it.

Key Learnings:

• What mixed correspondence actually looks like inside one batch, and why classification has to come before extraction. A payment stub, a denial letter, a discharge summary, and an EOB can arrive in the same file, each bound for a different worklist.
• How confidence scoring decides what processes automatically and what a person reviews, what happens to a document that falls below the threshold, and how those review decisions make the next batch easier.
• What it takes to move validated data into your patient accounting and clinical systems, including the interface, mapping, and indexing decisions most projects underestimate, and how to sequence them.
• How downstream work gets automated once the data carries context, including refunds and denial routing, and where your staff stay in the loop on the exceptions that need judgment.