Use case
Use case3 min readBy The Data Workers Team

Capacity and patient flow data, run by agents

ADT events, bed boards and staffing data arrive at different speeds, so census and boarding dashboards are wrong when they matter most. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. ADT events, bed boards and staffing data arrive at different speeds, so census and boarding dashboards are wrong when they matter most.
  • •What changes. Agents keep ADT and bed data streaming, verified and reconciled, and catch feed gaps in minutes.
  • •What stays with your people. Operational alerts to clinicians: they change care decisions.
  • •The result. Morning huddles run on census that was verified an hour earlier.

The problem today

ADT events, bed boards and staffing data arrive at different speeds, so census and boarding dashboards are wrong when they matter most.

What changes with Data Workers

Agents keep ADT and bed data streaming, verified and reconciled, and catch feed gaps in minutes.

The workflow: Real-Time Streaming agent (register_stream_schema) → Quality Monitoring agent (get_anomalies) → Incident Debugging agent (diagnose_incident) → Autonomous Data-Conductor (verify_data).
Fig. 1:The workflow: Real-Time Streaming agent (register_stream_schema) → Quality Monitoring agent (get_anomalies) → Incident Debugging agent (diagnose_incident) → Autonomous Data-Conductor (verify_data).

The workflow, step by step

  1. 1.The Real-Time Streaming agent calls register_stream_schema and keeps ADT streams in step.
  2. 2.The Quality Monitoring agent calls get_anomalies and catches census gaps.
  3. 3.The Incident Debugging agent calls diagnose_incident and traces feed failures.
  4. 4.The Autonomous Data-Conductor calls verify_data and verifies census before huddles.

The impact metrics we move

  • •ADT feed gap caught: from about 4 hours to minutes
  • •Census reconciled: from about 8 hours to 15-20 min
  • •Boarding report: from about a day to under 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

  • •ADT streams are checked continuously, so census is right in real time.
  • •Bed and staffing data reconcile hourly, so boarding numbers are trusted.

Where your people stay in charge

Operational alerts to clinicians always go to a named approver, because they change care decisions. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. ADT freshness 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:
  • •ADT feed gap caught: from about 4 hours to minutes
  • •Census reconciled: from about 8 hours to 15-20 min
  • •Boarding report: from about a day to under 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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