AI Shifts Revenue Cycle From Reactive to Predictive
Artificial intelligence is moving hospital revenue cycle management away from chasing down unpaid claims toward predicting and preventing denials before they happen, according to Brian Kenah, chief technology officer at EnableComp.
Kenah identified three distinct areas where AI is already delivering results in revenue cycle operations.
Making PDFs Operational
The first involves document intelligence-extracting actionable information from unstructured content like contracts, fee schedules, and state regulations that hospitals traditionally stored as static PDFs.
"What AI is capable of doing is making all that content operational in a very, very effective and efficient manner," Kenah said.
Revenue cycle staff can now read payer contracts, determine expected reimbursement amounts, extract fee schedule line items, flag variances, and reconcile explanations of benefits automatically. This goes beyond simple document automation-it turns a library of PDFs into real-time decision-making capability.
Connecting Disconnected Systems
The second win involves intelligent integration that eliminates manual data retrieval. "Humans have been the glue between systems," Kenah said.
As APIs become more prevalent and AI Agents & Automation capabilities stabilize, hospitals can now use agentic orchestration to pull data from multiple sources and make decisions without human intervention. This reduces manual labor and pushes information processing upstream to automated systems.
Spotting Patterns Before Problems Arise
The third area is predictive intelligence. AI uses Data Analysis to identify root causes of denials, spot shifts in payer behavior, and flag claims approaching filing deadlines.
"We can move from this concept of 'chase to collect' to a lot more around predicting and prevention," Kenah said. "It really changes the way in which the hospital performs revenue cycle management."
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