Using AI OCR to verify insurance and boost RCM

How AI OCR enhances accuracy and benefits revenue cycle management

In a complex, fast-paced healthcare environment, boosting administrative efficiency while accounting for accuracy can make or break a practice’s financial health – and that process can start as early as initial insurance verification. Insurance errors often contribute to claim denials, delayed payments and frustrated patients. But AI-powered Optical Character Recognition (OCR) technology is reshaping this process to deliver better outcomes for providers and patients alike.

By moving beyond the limitations of traditional OCR, AI OCR capabilities are helping improve insurance data accuracy, automate manual tasks, and support healthier revenue cycles for ambulatory practices.

Here’s how.

What is OCR? 

Optical Character Recognition, or OCR, is a technology that transforms printed or handwritten text from paper documents into machine-readable digital data. German inventor Gustav Tauschek devised first concepts for OCR developed in the early 20th century¹ – since then, OCR has long offered healthcare providers an alternative to manual data entry.

Historically, OCR focused on converting text into a digital format in the form of telegraph coding. Over time, expansion and adoption led to OCR systems that improved on digitization and encryption by making printed text machine-readable.

Early OCR solutions eased the burden of scanning and storing paper documents but struggled to interpret the contextual meaning of the data. Staff still needed to manually verify extracted data in key fields—insurance numbers, policyholder names, coverage dates.

Over the years, incremental improvements enabled OCR to better recognize various fonts and styles of handwriting, accelerating the digitization of insurance cards, claim forms, and other critical documents. However, these systems remained largely reactive tools rather than intelligent interpreters of complex insurance data.

What is the difference between traditional OCR and AI OCR?

While traditional OCR passively converts text, AI OCR actively engages with the content and analyzes it to pull out relevant information and tag it. AI OCR uses machine learning algorithms to extract, categorize, and contextualize insurance information, significantly reducing errors and the need for human intervention.

Imagine traditional OCR as a scanner that reads text aloud. AI OCR, in contrast, acts like a smart assistant that not only reads but comprehends what is important—whether it’s distinguishing between a policy number and a group ID or interpreting handwriting from a patient-submitted insurance card.

This leap moves insurance card data extraction from a manual chore to a streamlined, automated workflow that improves accuracy and, ultimately, speeds up claims processing.

How the AI OCR insurance verification capability in athenaOne® improves accuracy and efficiency

Two standout capabilities in the AI-native athenaOne platform—Automated Insurance Selection and Insurance Mapping—highlight how innovative technology can reduce administrative tasks and help speed up the revenue cycle process, from initial patient intake to claims submission.

While traditional OCR passively converts text, AI OCR actively engages with the content analyzes it to pull out relevant information and implement tagging.

Automated Insurance Selection

Patients increasingly prefer sharing their insurance information digitally, through digital options like self-check-in kiosks and online portals. With the Automated Insurance Selection feature in athenaOne, practices can harness the power of AI to streamline insurance verification right at the point of patient entry.

When a patient uploads an image of their insurance card to the patient portal or scans the card at the front desk during check-in, machine learning models within the AI-native athenaOne process the image instantly. After digitizing the card, the models extract key details and validate them against existing patient records. Once the models cross-reference the details on the card with the existing patient record, they automatically suggest the correct insurance for billing.

The results are compelling: Customers leveraging Automated Insurance Selection saw a 35% reduction in insurance-related rule hold rates and a 7.4% reduction in patient insurance-related denials.² Fewer claim denials and holds get practices paid the full amount, faster. That means less lost revenue and reduced administrative follow-up.

Automated Insurance Selection can transform what used to be a manual, error-prone process into a fast, data-driven workflow that helps enhance accuracy while freeing staff to focus on patient care.

Insurance Mapping

Another frequent challenge in ambulatory care is integrating patient and charge data from disparate external systems. Mismatches in insurance information across systems can cause confusion, inaccurate billing, and delays.

AI-driven Insurance Mapping in athenaOne tackles this challenge by automatically translating and aligning incoming demographic and charge data with the appropriate insurance in the system. By eliminating manual data wrangling, practices can onboard new patients and import data without breaking workflow or sacrificing accuracy.

According to athenahealth data, the Insurance Mapping feature saved customers over 3,600 staff hours previously spent on manual insurance mapping.³ That time savings can directly translate into lower operational costs and faster, more accurate information on claims.

Greater accuracy in insurance verification boosts revenue cycle management

At its core, insurance verification accuracy is a linchpin for effective revenue cycle management (RCM). Successful insurance verification ensures claims are submitted with correct insurance information, reducing potential rework caused by insurance-related rejections or denials. This can help accelerate cash flow and minimize costly administrative overhead.

With AI OCR automating much of the data extraction and verification, there’s less guesswork. Rather than add to clinicians’ administrative burden by having them manually add these inputs, practices can delegate insurance verification to administrative staff. Automating the detail entry process allows staff to focus on upskilling rather than troubleshooting insurance discrepancies, while still allowing them to flag any potential errors. Their engagement with the outputs can also help AI models learn and improve over time.

The Automated Insurance Selection and Insurance Mapping features are complemented by the proprietary rules engine in the athenaOne. That rules engine scrubs claims for potential errors and flags them prior to submission. Scrubbing can also help identify payer-specific checks to help ensure adherence to individual insurer requirements and prompt smart, timely alerts for claim follow-ups based on the payer. This proactive approach to claims management can bolster clean claim rates, improve reimbursement speed, and help practices achieve healthier, more predictable revenue cycles.

Leveraging smarter AI to reduce manual labor and improve outcomes

Technological evolutions and AI enhancements in healthcare should always complement providers. athenahealth’s vision for an AI-native platform harnesses an intelligent layer of machine learning across different workflows, freeing up time for clinicians to dedicate to patient care and practicing medicine.

By harnessing AI OCR for insurance verification, athenahealth can empower healthcare providers to reduce administrative burdens, lower claim denials, and improve both operational efficiency and patient satisfaction.

Ready to unlock the full potential of AI-powered healthcare? Discover how athenahealth’s innovative AI solutions can elevate your practice’s revenue cycle management and patient journey.