47% faster denial resolution at a multi-hospital system — RCM workqueue automation
A multi-hospital health system had $34M in denied and underpaid claims stuck in manual workqueues, with an average 23-day resolution cycle. pronix.ai deployed an agentic RCM workqueue that classified denials, drafted appeals, gathered evidence and tracked status — cutting resolution time by 47% and recovering $11M in the first year.
- Client
- Multi-hospital health system, 18 hospitals
- Industry
- Healthcare Providers
- Platform
- Epic · Kore.ai Agent Platform · Workday Financials
*Representative outcome; results vary by client, scope and platform configuration.
The challenge
Denied claims were growing 12% year over year and the PFS team was hiring contractors just to keep the queue flat. Prior automation only handled clean claims; the hard work — denial reason analysis, appeal evidence, payer-specific follow-up — was still manual.
Our approach
Denial-intent classifier
An agentic layer read EOB/ERA data, Epic denial reason codes and payer contracts to categorize every denial into one of 14 action types — no-pay, underpayment, coding edit, prior-auth, COB and more.
Evidence assembly agent
For each appeal, the agent gathered the clinical note, authorization record, contract terms and prior correspondence — packaged into a payer-ready appeal packet in under two minutes.
Human-in-the-loop on high-value cases
Denials above $25K or involving clinical validation were routed to a senior PFS analyst with the appeal pre-drafted and evidence attached — review time dropped from 45 to 8 minutes.
Payer follow-up cadence
The agent tracked payer SLA windows and auto-escalated stalled appeals before they aged past the filing deadline — first-year recoveries included $4.2M in appeals that would have expired.
“We stopped paying people to read EOBs. They now review exceptions and negotiate — that's the job we hired them for.”
Illustrative case study. Scenarios, metrics, quotes and client details are representative composites based on Pronix engagements and industry benchmarks unless a named client is shown with written consent. Outcomes vary by client, scope, data quality and platform configuration. Nothing on this page is a guarantee, warranty or professional advice. See our Terms of Use for the full disclaimer.
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