Use case
Use case3 min readBy The Data Workers Team

Clinical quality data, run by agents

Quality measures rebuild from abstracted and EHR data quarterly, and safety signals surface late. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Quality measures rebuild from abstracted and EHR data quarterly, and safety signals surface late.
  • •What changes. Agents keep measure inputs current, document lineage and flag safety signals as data lands.
  • •What stays with your people. Submitted quality measures: they are public and regulated.
  • •The result. Quality teams see measures monthly with lineage ready.

The problem today

Quality measures rebuild from abstracted and EHR data quarterly, and safety signals surface late.

What changes with Data Workers

Agents keep measure inputs current, document lineage and flag safety signals as data lands.

The workflow: Data Ingestion agent (get_connector_status) → Quality Monitoring agent (run_quality_check) → Data Context & Catalog agent (trace_cross_platform_lineage) → Autonomous Data-Conductor (verify_data).
Fig. 1:The workflow: Data Ingestion agent (get_connector_status) → Quality Monitoring agent (run_quality_check) → Data Context & Catalog agent (trace_cross_platform_lineage) → Autonomous Data-Conductor (verify_data).

The workflow, step by step

  1. 1.The Data Ingestion agent calls get_connector_status and lands EHR and abstraction data.
  2. 2.The Quality Monitoring agent calls run_quality_check and checks measure inputs.
  3. 3.The Data Context & Catalog agent calls trace_cross_platform_lineage and documents lineage.
  4. 4.The Autonomous Data-Conductor calls verify_data and verifies measures.

The impact metrics we move

  • •Measure refresh: from about 3 months to 1 day
  • •Lineage for a measure: from about a week to minutes
  • •Input gap detected: from about 2 weeks 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

  • •Measure inputs are checked on every load, so no surprises at submission.
  • •Lineage is generated, so reviews take hours.

Where your people stay in charge

Submitted quality measures always go to a named approver, because they are public and regulated. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Top quality measures refreshed monthly.
  • •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:
  • •Measure refresh: from about 3 months to 1 day
  • •Lineage for a measure: from about a week to minutes
  • •Input gap detected: from about 2 weeks 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

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