CDS & Clinical AI Pilot Safety Checklist

CDS & Clinical AI Pilot Safety Checklist

Use this checklist to structure a safe, human-centered pilot of clinical decision support or clinical AI. The list is intentionally practical: each item includes the expected evidence or acceptance criteria you should be able to point to before moving forward. Adapt items to local workflows, policy, and regulatory needs.

Pre-deployment (must be completed and documented before go‑live)

1. Clinical validation cases and acceptance criteria

   Evidence: a curated set of representative positive, negative, and edge-case cases with expected outputs and clear pass/fail criteria. Acceptance criteria include sensitivity, specificity, PPV/NPV, calibration, or clinical outcome proxies appropriate to the use.

   • Document the dataset, inclusion/exclusion rules, and why cases represent real-world use.
   • Define minimum acceptable performance thresholds and failure modes that require stopping the pilot.
2. Integration test plan

   Evidence: test scripts and results for EHR order flows, alerts, documentation, APIs, SSO, and data mapping.

   • Confirm that orders/alerts fire in correct context and that suggested actions are recorded clearly in the chart.
   • Confirm that timestamps, provenance, and tool version are logged with each recommendation.
3. Data privacy, security & auditability

   Evidence: completed privacy impact assessment, data-sharing agreements, and security review (including logging and retention policies).

   • Confirm telemetry and recommendation logs are retained for post-hoc review and RCA.
4. User training and communication plan

   Evidence: short role-based training materials, script for launch communications, and clear guidance on when to accept, modify, or override suggestions.

   • Explain the tool's intended use, known limitations, and how clinicians should document overrides.
5. Clinical governance & owner

   Evidence: named clinical owner(s), escalation path, and approval from relevant committees (e.g., patient safety, informatics, legal).

6. Pilot scope and stopping rules

   Evidence: clear scope (units, clinicians, patient cohorts), pilot duration, sample size targets, and explicit rollback/stop triggers.

   • Example stop triggers: unanticipated increase in adverse events, >X% unexpected override rate with adverse outcomes, major data mapping failure, or clinically unacceptable drop in sensitivity for a key subgroup.
7. Monitoring plan (metrics & dashboards)

   Evidence: list of performance metrics, sampling cadence, who monitors them, and how alerts are handled.

   • Examples: alert volume, actionable rate, clinician acceptance/override rate, time-to-action, false positive/negative rates, subgroup performance, and downstream clinical outcomes when available.
8. Explainability & clinician-facing rationale

   Evidence: one-line rationale for each recommendation, links to the underlying rule/logic/model version, and guidance for when to distrust the suggestion.

9. Testing for bias and subgroup performance

   Evidence: analysis showing model/rule performance across relevant demographic and clinical subgroups, with planned mitigation if disparities are identified.

10. Incident response & escalation plan

    Evidence: documented steps for responding to safety incidents, responsible parties, timelines for investigation, and how to communicate with affected patients and clinicians.

11. Regulatory & legal review

    Evidence: confirmation that applicable regulatory reviews (e.g., FDA, local regulators), contracts, and consent requirements have been addressed or a plan exists to address them.

Deployment controls (day of go‑live)

• Limit initial rollout to a small controlled cohort (e.g., single unit, volunteer clinicians).
• Enable enhanced logging/telemetry and link logs to patient/context identifiers for follow-up reviews.
• Communicate and staff a rapid-response contact (on-call clinical lead) for the first 72 hours.
• Ensure clinicians can easily override recommendations and that overrides are logged with reason.
• Deploy feature flags / circuit breakers so the tool can be quickly turned off or scaled back without code changes.

Post-deployment monitoring & reviews

1. 30/60/90-day safety reviews

   Evidence: structured review notes covering quantitative metrics, qualitative clinician feedback, adverse events, and recommended changes. Each review should record decisions (continue, adjust, pause, or stop).

2. User feedback log

   Evidence: a captured, time-stamped feedback log tied to users and cases (free-text plus structured tags). Triage process for safety-related feedback must be defined.

3. Drift detection and data-quality monitoring

   Evidence: scheduled checks for data schema changes, distributional shifts, input missingness, and performance drift. Define thresholds that trigger investigation or rollback.

4. Periodic model/rule revalidation

   Evidence: plan for revalidation cadence (e.g., quarterly) and when retraining or rule updates require a new pilot.

5. Audit & traceability

   Evidence: ability to reconstruct the exact recommendation shown to a clinician (inputs, model/rule version, timestamps) for any patient encounter.

Acceptance criteria & examples

Define measurable acceptance criteria prior to starting the pilot. Examples:

• Alert positive predictive value >= 30% in the pilot cohort.
• Clinician override rate <= 40% for first 30 days without associated adverse event signal.
• No statistically significant drop in detection of condition X compared to baseline.

Quick pilot-plan template (copy & adapt)

Fill these fields before go-live:

• Pilot name: e.g., "Sepsis Early Warning Pilot - Med ICU"
• Scope: units/clinicians/patient criteria
• Start/End dates:
• Clinical owner: name and contact
• Success metrics: primary metric and 2 secondary metrics
• Stop/rollback triggers:
• Monitoring cadence: daily for first 7 days, then weekly

Example monitoring metrics

• Raw alert count per 100 encounters
• Actionable alert rate (alerts leading to clinical action)
• Clinician acceptance / override rate and documented reason
• Time-to-action following recommendation
• Model/rule performance by subgroup (age, sex, comorbidities)
• Number of incidents linked to the CDS/AI

Practical guidance & common pitfalls

• Aim for small, measurable pilots. Avoid full-scope launches without evidence.
• Track qualitative clinician stories alongside metrics — numbers alone miss workflow friction and safety signals.
• Make the rollback procedure fast and well-practiced; slow or bureaucratic rollbacks increase risk.
• Log every override with a short reason — aggregated reasons reveal systematic problems.

How to use this checklist

1) Before a pilot, complete the pre-deployment items and store evidence (test reports, approvals, training logs, validation datasets).

2) On go-live, enable logging, limit scope, and ensure a named on-call clinician is available.

3) Run structured 30/60/90 reviews and keep a persistent safety & feedback log. Use the acceptance criteria to make an evidence-based decision to expand, adjust, or stop.

Tip: Consider packaging this checklist into an interactive form or audit collection so teams can capture evidence, timestamps, and decisions directly in the system for future audits and learning.