Fallback Protocol

The protocol must activate on explicit, measurable thresholds rather than ambiguous states. Triggers include:

• Confidence score falling below a calibrated threshold (e.g., < 95%)
• Out-of-distribution detection flagging an input vector as anomalous
• System timeout exceeding a predefined latency SLA (e.g., > 500ms)
• Schema validation failure when extracted data violates FHIR resource constraints

Each trigger is logged with a trace ID for downstream auditability.

When control transfers to a human reviewer, the protocol must preserve the full execution context to avoid forcing the operator to restart from scratch. This includes:

• The original unstructured input (e.g., the radiology report text)
• All intermediate model outputs and their associated confidence scores
• The specific trigger reason and the point in the pipeline where the fallback occurred
• A pre-populated review form with the model's best-effort extraction

This minimizes cognitive load and accelerates the time-to-correction.

A fallback protocol must handle transient failures gracefully without duplicating work. Idempotency keys ensure that:

• A network timeout that triggers a fallback does not create a duplicate review task when the original request eventually succeeds
• The system can safely retry low-confidence inputs after a model update without corrupting the audit trail
• Human corrections are applied exactly once to the downstream record

This is critical for maintaining data integrity in the clinical record.

Falled-back items must be intelligently routed into the review queue based on clinical urgency, not just FIFO order. The protocol integrates with skill-based routing to:

• Assign STAT radiology findings to an on-call radiologist immediately
• Route routine prior authorization fallbacks to a batch review queue
• Escalate items that have been in the queue beyond a defined service level agreement

This prevents alert fatigue by ensuring critical cases surface first.

Every fallback event is a training signal. The protocol must feed corrections back into the active learning loop:

• Human-corrected spans are logged as ground truth for parameter-efficient fine-tuning
• The error taxonomy tag applied by the reviewer is linked to the originating trigger condition
• Correction propagation applies the fix to semantically identical inputs in the batch

This ensures the fallback rate decreases over time as the model learns from its edge cases.

Every transition from AI control to human control must be recorded in an immutable audit trail. The protocol logs:

• The exact timestamp of the fallback trigger
• The identity of the human operator who assumed control
• The diff view of changes made during manual correction
• The final disposition (approved, corrected, rejected)

This provides a complete chain of custody for compliance with HIPAA and FDA SaMD regulations.

The predefined probability score below which a model's prediction triggers a fallback protocol. A threshold of 0.85 means any output with less than 85% confidence is routed to a human queue.

• Calibrated thresholds reflect true empirical likelihood, not raw softmax scores
• Setting thresholds too high increases review burden; too low risks undetected errors
• Dynamic thresholds can adjust based on clinical severity or task criticality

A technique that identifies inputs fundamentally different from the model's training data, triggering a fallback before a nonsensical prediction is made. Unlike simple confidence scoring, OOD detection catches semantic mismatches.

• Uses methods like Mahalanobis distance or energy-based models
• Prevents silent failures on novel document formats or rare conditions
• Essential for maintaining Straight-Through Processing integrity

A system design principle where functionality is partially reduced rather than completely lost during failure. In fallback protocols, this means the AI may still provide partial structured output with flagged uncertain fields rather than aborting entirely.

• Allows reviewers to correct specific spans rather than redo entire documents
• Preserves audit trail continuity
• Contrasts with hard-fail approaches that discard all AI work

The automated prioritization of fallback-flagged items based on urgency, clinical severity, or model uncertainty. Ensures the most critical cases reach human reviewers first.

• High-acuity findings (e.g., suspected malignancy) jump the queue
• Low-confidence but low-risk items may batch for end-of-shift review
• Integrates with skill-based routing to match complexity with expertise

A deployment strategy where a new model runs silently alongside production, logging predictions for human comparison without triggering live fallbacks. Validates confidence threshold calibration before go-live.

• Measures true fallback rate without operational impact
• Identifies threshold tuning needs using real-world data distributions
• Builds reviewer trust through transparent pre-deployment performance data

An architectural safeguard that automatically halts AI processing when failure rates exceed a defined threshold within a time window. Prevents cascading system degradation during model outages or data pipeline corruption.

• Three states: closed (normal), open (all traffic routed to humans), half-open (testing recovery)
• Protects against alert fatigue from repeated low-quality outputs
• Common in microservice-based clinical AI deployments