Managing prior authorizations in radiology has long been a labor-intensive process—especially when it starts with stacks of faxed orders. While fax remains a secure method for transmitting patient information, the manual handling of these documents slows everything down, introduces errors, and impacts patient care.

Imaging centers often receive hundreds of faxes each day, requiring staff to open, label, sort, and rekey information into their EHRs before prior authorization can even begin.

As Charu Nevatia, Associate Vice President of Automation at Infinx, explains, the challenge has never been about receiving the data—it’s about making sense of it.

Traditional optical character recognition (OCR) could only extract text from fixed, predictable formats, leaving providers with little recourse when faced with varied templates, handwritten notes, and incomplete forms. The result? Time lost, increased denials, and a frustrating experience for staff and patients alike.

From OCR to AI Agents

The arrival of large language models (LLMs) and advanced AI agents has changed the game. Unlike legacy OCR, AI agents can extract and interpret data from unstructured or inconsistent formats. Whether an order is typed, handwritten, or partially obscured, the AI agent applies learned context to identify key details like patient name, modality, urgency, and referring physician.

Even more importantly, these AI agents go beyond raw extraction. By combining intelligent data capture with configurable business rules, the system can automatically:

  • Classify orders by modality (e.g., MRI, CT, ultrasound)
  • Flag stat or urgent cases for immediate attention
  • Route documents to the correct team or queue
  • Identify missing fields before the case advances

This orchestration mimics what a skilled staff member would do—only faster, at scale, and with a consistent level of accuracy.

Handling High Volumes with Confidence

For imaging centers processing 700–800 faxes a day, AI-based intake automation is a force multiplier, with accuracy rates of 80–88% for fully automated intake without human intervention. The remaining cases—often those with poor image quality or incomplete information—are flagged for human review. This “human-in-the-loop” approach ensures that automation never sacrifices accuracy for speed.

In one example, the AI agent identified stat orders within seconds, routing them into a high-priority queue with alerts for immediate action. Similarly, modality-based routing allowed MRI specialists to receive only MRI orders, reducing handoffs and processing delays.

Built-In Safety Nets

Accuracy is critical in healthcare workflows, and automation is designed with safeguards in mind. In scenarios where document complexity could lead to misclassification—such as multi-page faxes containing orders for different patients—the system automatically routes the case for human validation. Poor-quality or illegible handwriting is similarly flagged, ensuring that no order proceeds downstream without confirmation.

Seamless Integration with Existing Systems

Integration is another hurdle that can slow technology adoption, but modern AI intake platforms work with a variety of setups. For organizations using Epic Radiant or other RIS/EHR systems, the automation can integrate via APIs or HL7 endpoints to create or update orders, attach documents, and avoid duplicates. Where direct integration isn’t possible, robotic process automation (RPA) can replicate human data entry into the system, minimizing disruption to existing workflows.

Why Intake Automation Matters for the Entire Revenue Cycle

Automating the intake process isn’t just about efficiency—it’s about preventing downstream issues that cost time and revenue. Inaccurate or incomplete information at intake can trigger delays in prior authorization, claim denials, or even missed appointments.

By capturing and validating key data up front, providers can:

  • Reduce prior authorization denials
  • Accelerate scheduling and patient access to care
  • Improve claim accuracy and reimbursement rates
  • Free staff to focus on higher-value tasks rather than repetitive data entry

Prior authorization remains one of the top causes of denials. Reducing errors at the very start of the process directly improves both the patient experience and the bottom line.

The Path Forward

The transition from manual fax handling to AI-powered document intake represents more than just an operational upgrade—it’s a shift in how radiology practices can think about scalability, accuracy, and patient care. With the technology now mature enough to handle real-world complexity, and the flexibility to fit into diverse tech stacks, the opportunity for transformation is here.

For imaging centers and specialty practices still buried under paperless-but-manual fax workflows, the future isn’t just about going digital—it’s about going intelligent. And that future starts at intake.

Want to dive deeper into this topic? Watch the webinar on-demand here.