When a claim fails because of a missing field or outdated insurance detail, it is easy to blame the billing process, but it is incomplete or inconsistent information captured at registration that is often the root cause. This seemingly innocuous oversight may be the reason why AI adoption in revenue cycle management (RCM) has been slow. AI can accelerate repetitive work, but it depends on clean, trustworthy data to deliver the results teams expect.
How Integris Health and Experian Health Approached the Front-End Problem
Healthcare IT Today sat down with Clarissa Riggins, Chief Product Officer at Experian Health, and Amy Trogdon, Vice President of Patient Access at Integris Health to explore how AI can strengthen front-end data quality and why that accuracy is the gateway to broader AI adoption in RCM. Their work highlights how AI is most valuable when it shapes the workflow itself, not when it is layered on top of broken or inconsistent processes.
Key Takeaways
- Reducing Denials Starts With Accurate Patient and Coverage Information. Most denials stem from front-end data quality issues rather than billing failures. Poor data at this step leads to errors downstream which erodes trust in the process and underlying technology.
- Direct Integration with Epic Work Queues Is Key. Keeping tools inside the EHR reduces friction, increases staff confidence, and accelerates adoption.
- Healthcare Wants AI But Adoption Is Stalled. Interest is high, but trust, workflow fit, and data quality remain barriers.
Reducing Denials Starts With Accurate Patient and Coverage Information
The strongest AI models still struggle when the underlying data is incomplete or inconsistent. That is why the biggest failure points in claims often begin well before billing.
“Claims denials can be traced all the way back to the very beginning and the front end of the process,” said Riggins. “Things like missing patient data, eligibility errors, inaccurate or outdated insurance information.” Trogdon sees the impact daily. “Having incorrect information, not having prior authorizations, missing patient data, that really does contribute to denials on the backend.”
AI can help registration teams catch these errors in real time, but only when organizations treat front-end data quality as a core RCM function rather than an administrative step.
Direct Integration with Epic Work Queues Is Key
Even the best-designed AI tools falter when they require staff to change how they work or jump between systems.
Experian’s Patient Access Curator was intentionally built to sit inside Epic’s workflows, which created an immediate sense of familiarity for users. “We’re integrating directly into the workflow, into the work queue so that they don’t have to keep tabbing across the different systems… we’re trying to make it easy and frictionless,” explained Riggins. Trogdon added, “Having to go from one system to another just provides that interruption to the workflow that can lead to errors.”
Riggins and Trogdon both argue for eliminating “swivel-chair” inefficiencies in healthcare – where staff are asked to swivel in their char (literally and figuratively) from one system to another, just to get a job done. Highly integrated technologies not only improves accuracy, it also acts as an accelerant for AI adoption. When the technology fits the workflow, staff begin to trust the output rather than question it.
Healthcare Wants AI But Adoption Is Stalled
Experian’s latest State of Claims: 2025 Report shows a striking gap.
“67% of respondents have high hopes for technology and AI to address claims denials,” shared Riggins. “However, only 14% have adopted… there’s still a little bit of lack of trust.” That lack of trust is often rooted in earlier experiences where tools added complexity instead of removing it.
Trogdon notes that trust builds gradually as staff see consistent, accurate results. Early AI wins often start small: verifying demographics, checking eligibility, identifying active coverage – before expanding to more complex tasks. As the technology proves itself, teams become more comfortable letting AI take on work that used to consume hours of manual effort.
Why Front-End Precision and Workflow Fit Matter for AI Adoption
The themes from this conversation point to a hidden reality of RCM. Clean data fuels adoption. Workflow fit sustains it. And both create the conditions where AI can meaningfully reduce denials and administrative strain.
When registration teams enter accurate patient and coverage information, case managers gain clarity at discharge, staff spend less time correcting preventable errors, and patients move through the system with fewer delays.
AI in RCM is not stalled because the models are immature. It is stalled because the foundation they depend on, data quality and workflow trust, has not been strengthened. When those pieces align, the technology begins to feel less like a leap and more like a natural next step.
Learn more about Integris Health at https://integrishealth.org/
Learn more about Experian Health at https://www.experian.com/healthcare/
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