70% Faster Claim Turnaround Time (CTT) | Claim Management Automation

About SuperBill

SuperBill is a San Francisco-based ​​medtech company helping healthcare providers manage insurance claims, medical billing & process reimbursements. They provide solutions for healthcare providers (clinics, hospitals) and individual clients. 

If superbill intake delays your submissions by even one day, this case applies directly to your operation.

Challenge | Document-to-Claim Bottleneck

Before our cooperation, their RCM team reviewed each superbill manually. 

SuperBill’s staff was spending ~120 hours per week converting unstructured documents into claim-ready records. CPT/ICD codes, provider details, patient demographics, modifiers - it all required tedious work.

Every day, the team handled ~300–500 uploaded files, and weekly intake regularly exceeded ~2,000 documents, ranging from PDFs and scanned superbills to emailed images and screenshot invoices. 

Operationally, the consequences were very familiar to other RCM cases:

• incorrect data,
• lost documentation
• delays before submission,
• more requests back to patients for missing fields,
• inconsistent code capture that required revision,
• and rising aging across the reimbursement pipeline.

On top of that, the manual workload varied claim by claim.

SuperBill quickly realised that if they wanted to scale, they needed to significantly improve turnaround time (TT). 

Staffing wasn’t sufficient nor sustainable in this case. 

At peak volume, their RCM team carried a backlog of ~400–700 claims waiting for document review before they could even enter the claim engine. 

Claims that should have been prepared the same day were often delayed by several days extending the overall reimbursement timeline for providers relying on the platform.

Volume increased faster than staff capacity, and manual review became the gating factor for throughput. 

Out-of-network reimbursement already carried longer A/R cycles compared to in-network claims, and any delay at the document stage compounded that problem. 

In short:

Claim management automation was inevitable.

Solution | Claims Management Automation

The goal wasn’t to read documents faster–it was to remove document reading from the critical path entirely by leveraging AI.

We deployed an AI automation layer that took every incoming document and converted it into a claim-ready record. 

Our AI agent reads PDFs, images, and structures the data so it fits SuperBill’s claim format and each payer’s rules. It’s able to read data like: 

• patient demographics,
• provider identifiers,
• service dates and locations,
• CPT/ICD codes and modifiers,
• units, charges, and payment information.

This removed the need for staff to open files, extract codes, or rebuild claim headers by hand.

Most of the workload now moves straight through automation. 

About 95% of documents pass end-to-end without intervention. Only 5% are routed to specialists for missing fields or edge cases. 

The large manual prep queue is gone. The team works only on exceptions instead of touching every claim. On top of that, the agent runs continuously. 

New uploads are processed in 2–5 minutes, even on days with 300–500 documents coming in from patients and practices. 

That keeps claim intake steady, prevents backlogs, and stabilizes daily throughput. 

The automation also applies payer-specific formatting so claims enter the billing system in a clean, submission-ready state, reducing rework and downstream corrections.

Results

Earlier submission directly translated into earlier reimbursement – without adding staff.

By removing manual claim-prep tasks at the front of the process, SuperBill was able to focus on the KPI that drives reimbursement speed: Claim Turnaround Time (CTT)

Our AI automation cut the document-to-claim cycle from ~24 hours to ~7 hours, reducing claim turnaround time by about 70%. The manual intake queue shrank from roughly 500 pending claims waiting on document review to around 150 at any given time. 

While individual documents are processed in minutes, end-to-end claim readiness reflects batching, validation, and system handoffs across the intake window.

That had a direct financial effect: faster prep meant earlier submission, which meant earlier reimbursement.

On top of that, standardizing claim inputs reduced variance across payers, lowering audit and resubmission risk.

Daily throughput increased from ~250 claims per day to ~425, so the team could keep up with volume spikes without building a backlog. 

Error rates tied to document reading and formatting dropped by ~35%, which meant fewer claims sent back for fixes or rework.

Employees no longer had to interpret every file or rebuild claim data from scratch. Only about 1–5% of documents now need exception review. 

Once inputs are structured and consistent, the entire claims cycle becomes more stable – all without adding headcount or forcing providers to change how they send documentation.

Summary

SuperBill’s case shows the power of document-to-claim automation.

When agentic AI handles the extraction and structuring of unstructured inputs, RCM staff can process more claims with fewer delays, reducing turnaround time without changing upstream systems or adding resources.

If your team is slowed down by converting patient documents, superbills, or PDFs into claim-ready data, document automation can materially accelerate submissions and stabilize reimbursement timelines.

You don’t need new portals or new tools. You just need an Agent between the documents and your claim engine.