Review Cadence

A zero-latency intervention model where AI outputs are queued for immediate human verification before any downstream action occurs. This cadence is mandatory for high-acuity clinical decisions such as medication reconciliation or critical lab value extraction.

• Latency Budget: Typically < 60 seconds per task
• Use Case: Prior authorization for urgent procedures
• Trade-off: Maximizes safety but creates reviewer bottleneck risk during peak volumes

A deferred processing model where AI outputs accumulate in a queue and are reviewed in aggregated batches at defined intervals, typically every 15 to 60 minutes. This cadence balances straight-through processing rates with manageable reviewer workloads.

• Batch Window: 15-60 minute cycles
• Use Case: Clinical document classification and routing
• Advantage: Enables skill-based routing and workload leveling across distributed teams

A daily batch review cadence where all AI-generated outputs from a clinical shift are queued for retrospective audit. This model is appropriate for low-risk administrative coding and documentation integrity tasks where immediate action is not clinically required.

• Review Window: Once per 8-12 hour shift
• Use Case: ICD-10-CM code validation for billing
• Risk: Delayed error detection may propagate downstream before correction

A dynamic cadence model where review frequency is determined by the model's calibrated probability score. High-confidence predictions bypass human review entirely, while low-confidence outputs are routed to real-time or near-time queues based on clinical risk thresholds.

• High Confidence (>0.95): Straight-through processing
• Medium Confidence (0.80-0.95): Near-time batch review
• Low Confidence (<0.80): Real-time mandatory review
• Key Metric: STP Rate vs. error escape rate

A statistical sampling cadence where a random subset of both reviewed and auto-processed outputs undergoes comprehensive quality audit on a weekly or monthly basis. This cadence detects concept drift and reviewer drift that may not be visible in real-time operations.

• Sampling Rate: 5-10% of total volume
• Use Case: Inter-annotator agreement measurement
• Output: Updates to error taxonomy and retraining priorities

An asynchronous escalation cadence activated by specific triggering conditions rather than a fixed schedule. When two reviewers produce discrepant annotations or a model encounters an out-of-distribution input, the item is immediately routed to a senior adjudicator.

• Trigger: Inter-annotator disagreement or low-confidence flag
• Use Case: Ambiguous clinical entity linking
• Outcome: Establishes golden dataset references for future model training

The automated prioritization of review queue items based on urgency, clinical severity, or model uncertainty. Effective triage enables a risk-stratified review cadence where critical findings are surfaced for immediate review while low-acuity items are batched.

• Stat triage: Real-time alert for findings like suspected pulmonary embolism.
• Routine triage: Daily batch for chronic condition coding.
• Prevents alert fatigue by ensuring reviewers only see high-value notifications at high frequency.

The percentage of clinical documents processed entirely by AI without human intervention. This is the primary metric that review cadence aims to maximize safely.

• An STP rate of 85% means only 15% of cases require human review, shifting cadence from continuous to periodic batch.
• Inversely correlated with review burden.
• Monitoring STP rate drift over time signals concept drift in input data, requiring cadence recalibration.

A deployment strategy where a new AI model runs silently in parallel with production, logging predictions for human comparison without affecting live operations. This decouples review cadence from real-time clinical risk.

• Reviewers can audit shadow predictions on a delayed, asynchronous cadence (e.g., weekly batch).
• Enables safe measurement of inter-annotator agreement between model and human before go-live.
• Critical for calibrating the initial confidence threshold that will govern live review cadence.

A state of desensitization caused by excessive low-value notifications, leading clinicians to ignore or override critical warnings. Review cadence is a primary lever to combat this phenomenon.

• Shifting from real-time interruptive alerts to a summarized daily batch review reduces cognitive disruption.
• Interface design must support the chosen cadence; a real-time alert requires immediate context, while a batch review needs efficient diff view and bulk operations.
• The goal is to make every human review event high-signal and actionable.

A predefined procedure that gracefully transfers control to a human when an AI model encounters a low-confidence input or system failure. This protocol defines the maximum acceptable review latency.

• For real-time cadence systems (e.g., diagnostic decision support), fallback must be instantaneous with a queued human operator.
• For batch cadence systems (e.g., retrospective coding), fallback items are simply flagged for the next scheduled review window.
• Designing fallback protocols requires mapping each AI function to its clinical time-sensitivity.