Clinical Decision Support & AI Safety Design Checklist

Clinical Decision Support & AI Safety Design Checklist

Purpose: Help teams design, pilot, deploy, monitor, and govern clinical decision support (CDS) and clinical AI so tools improve patient care without introducing new error modes, bias, workflow disruption, or clinician distrust. Use this checklist during design reviews, pilot planning, deployment gating, and post‑deployment monitoring.

How to use this checklist

Work through sections before moving to the next phase. Record evidence, owners, and acceptance criteria for each item. Where helpful, attach screenshots, test logs, or clinical validation reports. This checklist is intentionally human‑centered: items emphasize clinician ownership, transparency, and reversible deployments.

1. User‑Centered Design & Workflow Fit

• Define users and decision context: List roles (e.g., ED physician, nurse, pharmacist), timing (real‑time, retrospective), and decisions the CDS supports.
• Map current workflow: Document baseline steps, data sources, handoffs, and time pressures. Identify where the CDS inserts and why it helps.
• Design for minimal disruption: Ensure recommendations are concise, actionable, and placed where decisions are made. Avoid adding extra clicks unless clear benefit.
• Explainability and provenance: Show key reasons for every recommendation (data inputs, rules, risk score, confidence). Include links to primary evidence or guideline references where possible.
• Safe default behaviors: Define clear defaults, explicit opt‑ins/outs, and visible clinician control (accept, defer, override with reason).
• Accessibility and clarity: Test text size, colors, iconography, and language for clarity under real clinical conditions.
• Usability acceptance criteria: Observe N clinicians in a simulated use case; median task time and error rate should not worsen more than X% vs baseline (set numeric thresholds locally).

2. Clinical Validation & Evidence

• Define intended use and exclusion criteria: Clear clinical population, settings, and contraindications for use.
• Internal validation: Test model/rule performance on representative local data. Report metrics (sensitivity, specificity, PPV, NPV, calibration) with confidence intervals.
• External validation: If available, validate on geographically or temporally distinct datasets or peer results.
• Prospective pilot evaluation: Prefer prospective evaluation under real workflow conditions before broad rollout. Define primary outcomes (clinical process, safety, patient outcome) and measurement period.
• Bias and fairness checks: Stratify performance by age, sex, race/ethnicity, language, payer, and other relevant subgroups. Flag performance gaps and mitigation plans.
• Acceptance criteria: Predefine thresholds for go/no‑go (e.g., no subgroup performance drop >10% compared to baseline). Document statistical power and sample size for the pilot.

3. Alert Fatigue & Notification Strategy

• Prioritize critical alerts: Reserve interruptive alerts for time‑sensitive, high‑harm situations. Use passive or silent notifications for low‑impact guidance.
• Rate limiting and suppression: Implement logic to avoid repeated alerts for the same clinical event or patient within a defined window.
• Actionable content: Every alert should include: the problem, suggested action, urgency, and expected next step (or link to order/action).
• Override logging and review: Capture override reasons and schedule periodic review of high‑frequency overrides to detect design problems.
• Acceptance criteria: Monitor alert volume per clinician/day; set improvement targets (e.g., reduce unnecessary interruptive alerts by X% during pilot).

4. Safety, Fail‑Safe, and Fallbacks

• Clearly define failure modes: What happens if data is missing, model unavailable, or inputs are inconsistent?
• Fail‑safe defaults: In uncertain conditions, prefer human review rather than automated action. Avoid silent incorrect recommendations.
• Rollback and emergency stop: Have documented, tested procedures and responsible owners who can pause or revert the CDS in minutes when needed.
• Testing of rollbacks: Exercise rollback monthly during early operations until stable.

5. Logging, Audit, and Data Governance

• Event logging: Log inputs, outputs, timestamps, user actions (accept/override), version IDs of model/rule, and unique patient/context identifiers (pseudonymized if needed).
• Immutable audit trail: Ensure logs are tamper‑resistant and retained per policy (e.g., regulatory, risk management). Define retention windows and access controls.
• Privacy and security: Apply least privilege, encryption in transit and at rest, and de‑identification where feasible for analytics.
• Monitoring dashboard: Build metrics for throughput, latency, error rates, and clinical response patterns (see Monitoring Pack below).

6. Deployment Gating & Staged Rollout

1. Alpha (lab): Functional tests, safety cases, and end‑to‑end simulated scenarios.
2. Beta (limited clinical pilot): Real clinicians in constrained settings. Collect usability data, clinical metrics, and overrides.
3. Canary (small real population): Gradual rollout to a small percentage of users or locations with active monitoring and rapid rollback capability.
4. Full rollout: Only after meeting predefined performance, safety, and adoption criteria.

For each stage, document duration, sample size, primary/secondary endpoints, and stop/go criteria.

7. Monitoring Pack (Operational Metrics & Clinical Safety Signals)

Maintain a live dashboard with:

• Technical health: uptime, latency, data ingestion errors, exception rates.
• Use metrics: number of recommendations, acceptance rate, override rate, time to action.
• Clinical performance: outcome metrics tied to the CDS (e.g., time to antibiotic, adverse event rate, readmission rate) with control comparisons where possible.
• Fairness monitoring: performance by subgroup and any drift signals.
• Safety signals: unexpected increases in adverse events, unanticipated clinician workarounds, or frequency of critical overrides.
• Feedback loop: clinician‑reported issues, categorized and triaged weekly. Ensure feedback routes to owners and product/design teams.

8. Governance, Roles & Documentation

• Clear owners: Product owner, clinical champion (senior clinician), data scientist/engineer, informatics lead, and compliance/risk contact.
• Decision authority: Define who can approve pilots, scale rollouts, and execute emergency stops.
• Change control: Version control for rules/models with documented change logs and impact analysis before updates.
• Training & communication: Provide short, role‑specific training prior to pilot and just‑in‑time reminders in the EHR UI. Publish a one‑page quick‑start and escalation contacts.
• Regulatory alignment: Document whether the CDS is subject to medical device regulation in your jurisdiction; keep regulatory documentation and clinical evaluation files up to date.

9. Continuous Improvement

• Regular reviews: Weekly during pilots, then monthly for the first 6 months post‑rollout, then quarterly. Review monitoring pack, clinician feedback, and open issues.
• Data‑driven updates: Plan controlled A/B tests for meaningful changes; use post‑deployment evaluation rather than only offline metrics.
• Knowledge preservation: Archive rationale for design choices so future teams understand context when updating models or rules.

10. Quick Acceptance Checklist (for gating decisions)

• Has a clinical owner signed off on intended use and pilot plan?
• Are validation metrics (internal/external) documented and acceptable?
• Is alert strategy defined and tested for the target setting?
• Is there an immutable audit trail and monitoring dashboard in place?
• Are rollback and emergency stop procedures tested and owned?
• Is a plan for clinician feedback, training, and governance published?
• Are fairness and subgroup analyses performed, with mitigation plans for gaps?

Example artifacts to attach

• Clinical validation report (metrics, datasets, subgroup analysis)
• Pilot protocol with endpoints and sample size
• UI screenshots and interaction flows
• Monitoring dashboard screenshot and definitions
• Rollback playbook and contact list

This checklist is meant to be adapted. Use local thresholds, governance structures, and regulatory requirements to refine acceptance criteria. When possible, pair this checklist with an Interactive pilot template (collecting pilot dates, owners, endpoints, and status) and a monitoring dashboard that reads logged events so teams can act quickly on safety signals.