NPI Provider Affiliation & Movement Intelligence

Use this Actor to analyze npi provider affiliation and movement and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Turn recurring health datasets into npi provider affiliation & movement intelligence with snapshot deltas, confidence-scored GO/WARN/BLOCK decisions, and agent-ready recommended actions.

Why use this Actor

Teams following health changes need repeatable evidence and prioritized actions instead of repeatedly inspecting raw rows by hand. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Current-versus-previous snapshot comparison with deterministic deltas.

• Gen2 confidence-scored GO/WARN/BLOCK decision contract.

• Agent-ready recommended actions instead of raw rows.

• Dataset-ID inputs for recurring Apify workflows.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• health monitoring and prioritization.
• zero direct intelligence recurring workflows.
• competitive research and executive briefs.
• AI-agent decision pipelines.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.002 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Output

The Actor returns one structured intelligence report with normalized provider counts, affiliation and movement deltas, the strongest observed changes, confidence signals, an executive GO/WARN/BLOCK decision, and an agentAction for downstream automation. The default Dataset keeps the result auditable and easy to hand off to dashboards, alerts, research workflows, or AI agents. The report focuses on decision-relevant movement patterns rather than forcing users to manually reconcile every raw provider row across snapshots.