Use case
Use case3 min readBy The Data Workers Team

Revenue cycle data, run by agents

Denials, charge capture and AR reports pull from the EHR, billing and clearinghouse systems, and every month-end someone reconciles them by hand. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Denials, charge capture and AR reports pull from the EHR, billing and clearinghouse systems, and every month-end someone reconciles them by hand.
  • •What changes. Agents tie charges, claims, remits and denials together on every load and trace leakage to its source.
  • •What stays with your people. Write-offs and financial reporting inputs: they hit the P&L.
  • •The result. Leakage is found weekly, not at month-end.

The problem today

Denials, charge capture and AR reports pull from the EHR, billing and clearinghouse systems, and every month-end someone reconciles them by hand.

What changes with Data Workers

Agents tie charges, claims, remits and denials together on every load and trace leakage to its source.

The workflow: Connectors agent (get_connector_status) → Quality Monitoring agent (run_quality_check) → Incident Debugging agent (get_root_cause) → Data Context & Catalog agent (define_business_rule).
Fig. 1:The workflow: Connectors agent (get_connector_status) → Quality Monitoring agent (run_quality_check) → Incident Debugging agent (get_root_cause) → Data Context & Catalog agent (define_business_rule).

The workflow, step by step

  1. 1.The Connectors agent calls get_connector_status and lands billing and clearinghouse files.
  2. 2.The Quality Monitoring agent calls run_quality_check and ties charges to claims and remits.
  3. 3.The Incident Debugging agent calls get_root_cause and traces denial spikes.
  4. 4.The Data Context & Catalog agent calls define_business_rule and governs denial and AR definitions.

The impact metrics we move

  • •Charge-to-claim tie-out: from about a week to 15-20 min
  • •Denial spike traced: from about 3 days to ~1 h
  • •AR aging report: from about 2 days to ~1 h
Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.
Fig. 2:Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.

What goes on autopilot

  • •Charges, claims and remits are tied on every load, so leakage is visible the week it happens.
  • •Denial categories are governed, so finance and RCM agree.

Where your people stay in charge

Write-offs and financial reporting inputs always go to a named approver, because they hit the P&L. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Charge-to-remit tie-out for one hospital.
  • •First week. Agents run at L1, observing only, and show the fixes they would have made, with receipts.
  • •By week two. The routine work above runs on its own; anything sensitive stays with a named approver.
  • •At day 30. We review the impact metrics against your baseline:
  • •Charge-to-claim tie-out: from about a week to 15-20 min
  • •Denial spike traced: from about 3 days to ~1 h
  • •AR aging report: from about 2 days to ~1 h
  • •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.

Related: The health systems and providers hub · The full team playbook

Related Posts

Use case

Account intelligence data, run by agents

Account data is split across CRM, billing, product usage and enrichment vendors that identify accounts differently. Here is how Data Workers runs it instead.

Use case

Accruals and prepaids data, run by agents

Accrual schedules live in spreadsheets, and missed or stale accruals surface in review. Here is how Data Workers runs it instead.

Use case

Agent governance and audit, run by agents

AI agents act on data with no consistent approval, record or rollback. Here is how Data Workers runs it instead.

Use case

Agentic controllership, run by agents

Controllers spend the month chasing reconciliations, approvals and evidence across ERP, billing and spreadsheets. Here is how Data Workers runs it instead.