Batch Processing

Batch processing executes one workflow across many input rows.

CSV schema design

Use one row per case and one column per variable. Example headers:

• case_id
• member_id
• date_of_birth
• payer_name
• service_date

Keep values normalized before upload.

Batch workflow

1. Upload CSV.
2. Map each column to a workflow variable.
3. Validate mappings on a sample row.
4. Launch canary batch.
5. Expand to full volume when success thresholds are met.

Operational guardrails

• Start with 5 to 20 rows to validate field mapping.
• Monitor completion and error rates after first 50 runs.
• Pause rollout if error taxonomy shifts unexpectedly.

Pilot metrics to monitor

• Completion rate (target commonly >= 90 percent)
• Error category distribution
• Mean and p95 runtime
• Support minutes per completed case
• Cost per workflow run

Export and reconciliation

Use pilot export endpoints and jobs tables to reconcile:

• Total submitted rows
• Terminal run counts
• Flagged vs completed outcomes
• Missing outputs requiring reprocessing

Recovery strategy

If a batch fails partially:

• Export failed rows.
• Fix root cause in workflow or mapping.
• Re-run only failed rows using the same batch ID convention.