Radiology intake has never been simple. Even highly digital, “high tech” organizations find themselves buried under messy multi patient faxes, handwritten order digitization challenges, and scattered workflows that slow everything down. In a recent Office Hours session, Stuart Newsome sat down with Neelam Yadav, Associate Product Manager at Infinx, to break down how AI-powered document capture can modernize radiology intake automation while keeping Epic workflows intact and familiar for staff.
Why messy intake problems persist
Despite years of investment in EHRs, radiology teams are still highly dependent on referring physicians who fax orders from a wide range of EMR systems. Those orders arrive in different formats, with inconsistent quality, and often in multi patient batches. Many referring clinics still rely on handwritten orders because they lack robust electronic ordering infrastructure.
Historically, OCR based automation struggled in this environment. It required structured, predictable layouts and often broke when formats varied. That meant staff had to manually review, key, and sort most referrals, consuming valuable time and increasing the risk of errors.
Neelam explained that Infinx’s Document Capture AI changes this dynamic by treating each referral as a full document to be understood in context, not just as a collection of fixed fields. The system is trained specifically for referral management automation, so it can handle variable layouts, multi patient faxes, and handwritten content while still extracting accurate data.
Making sense of bad handwriting without putting patients at risk
Handwritten orders are a major pain point, especially when handwriting is barely legible even to humans. Document Capture AI tackles this with image enhancement and pattern recognition that significantly improves its ability to interpret low quality scans and handwriting.
However, Neelam emphasized that AI should not be making risky decisions alone. Infinx uses a confidence scoring model that evaluates every extracted field. If the confidence falls below a configured threshold, the order is automatically flagged and routed into a human review queue.
This human in the loop design keeps the majority of documents fully automated while ensuring that edge cases, poor quality scans, or ambiguous fields get human attention. Staff are not wasting time on clean, high confidence orders but can focus on the small percentage that truly require oversight. The result is higher throughput without compromising safety or data quality.
Safeguarding Epic workflows with context aware extraction
One Epic specific concern radiology leaders often raise is field misclassification. For example, if a referral number is accidentally placed in the authorization field, an exam might fall off a work queue and never be worked, creating both clinical and revenue risk.
Neelam explained that Document Capture AI is trained with referral specific context, so it learns the difference between referral numbers, authorization numbers, and other identifiers as they appear in the document and across the workflow. It is not simply scraping the first number it finds near a keyword.
On top of that, Infinx implements field level validation rules and pattern checks when integrating with Epic. If something looks out of pattern, the system flags it for review before updating Epic. This creates a second layer of protection that makes the mapping “bulletproof” and prevents the silent failures that worry operations and revenue cycle teams.
Designing Epic integration that fits existing workflows
Because most radiology organizations using Infinx are on Epic, the integration strategy is critical. Neelam described a structured approach that starts with understanding how each organization currently updates Epic, which interfaces are live, and how intake staff work today.
From there, Infinx configures HL7 based integration and uses supported mechanisms like base64 encoded document uploads to attach images and referral documents correctly in Epic. The goal is not to force a new workflow, but to slide automation into the existing patterns so that staff continue to live in Epic while Document Capture AI does the heavy lifting behind the scenes. This approach supports seamless Epic EHR integration without disrupting day-to-day operations.
Infinx also builds traceability into its platform. For every document, radiology teams can see which patient and order in Epic were updated, along with the full lifecycle of that document. This makes audits, root cause analysis, and operational tuning much easier.
Phased rollout and change management that actually works
Change management can be more intimidating than the technology itself. Many radiology leaders do not want their entire intake team pulled into meetings that are not relevant to their role, and they worry about overwhelming staff with sudden change.
Neelam recommends treating Document Capture AI like a new team member. Start with the highest volume, highest pain workflow, usually order intake, and then launch with a subset of volume and a small, focused group of users. Review 100 percent of the AI’s work at first, refine mappings and rules, then gradually reduce audits as confidence grows.
Once the initial use case stabilizes, organizations can expand to additional workflows or regions in a phased manner. This staggered rollout reduces resistance, keeps meetings role specific, and builds internal champions as teams see early success.
Preventing duplicates before they hit Epic
Duplicate orders and duplicate patient creation are another recurring headache for both scheduling and revenue cycle teams. They often occur when referring offices resend orders, when demographics are slightly mismatched, or when intake staff accidentally create a new patient instead of linking to an existing one.
To reduce this, Infinx is building a duplicate detection layer into Document Capture AI. The system scans configurable lookback windows to identify whether a similar order already exists. If it detects a likely duplicate, it flags the document and can either prevent it from being sent to Epic or route it for human review with a duplicate warning. This reduces wasted work, prevents clutter in Epic, and protects downstream revenue cycle processes.
Modernizing without disruption
Radiology intake will always be complex, but it does not have to be chaotic. By combining context aware AI, confidence based routing, robust Epic integration, and thoughtful change management, radiology teams can significantly modernize intake without disrupting the workflows their staff rely on every day.
If you want to see how AI-powered document capture can streamline your Epic based radiology intake while protecting data quality and revenue, request a demo if you’re interested.