AI in Cardiology Triage: A Practical Checklist

1) Define Clinical Use‑Case & Scope

• Specify the triage scenario (e.g., ED chest‑pain, ward heart‑failure alerts, outpatient arrhythmia risk).
• Clarify target population and workflow touch‑points where AI intervenes.
• Define the decisions supported: flag high‑risk, prioritize consults, trigger tests, or escalate care.

2) Data Readiness & Quality

• Secure relevant streams: vitals, ECGs, labs (troponin, BNP), history, imaging, prior admissions.
• Check completeness, timeliness, accuracy; mitigate missing or delayed inputs.
• Ensure interoperability across EHR, telemetry, PACS, alerting; enforce privacy and security.

3) Model Selection & Validation

• Match architecture to data (e.g., DL for imaging, ensembles for multimodal risk).
• Validate locally for sensitivity, specificity, PPV/NPV in your population.
• Assess fairness (age, sex, race, SDoH) and plan for recalibration and drift monitoring.

4) Workflow Integration & Human‑in‑the‑Loop

• Embed scores/alerts in ED software, ward lists, or consult dashboards.
• Define ownership: who receives, acts, and escalates; provide clear SOPs.
• Maintain clinician control with overrides, manual review, and fail‑safes.

5) Change Management & Training

• Engage cardiology, ED, nursing, IT/informatics, and telemetry staff early.
• Educate on model behavior, limitations, and correct interpretation.
• Publish pathways and SOPs; communicate value: faster risk ID and better resource use.

6) Monitoring & KPIs

Instrument your deployment with operational and clinical metrics.

7) Regulatory, Ethical & Governance

• Comply with regional regulation (e.g., FDA/CE) and institutional governance.
• Clarify consent, transparency, and data ownership; document liability boundaries.
• Appoint a clinical champion and governance group for oversight and risk management.

8) Scalability & Future Proofing

• Design to extend across sites and indications (ED → wards → remote monitoring).
• Accommodate new biomarkers, devices, and modalities; budget for retraining and MLOps.
• Explore complements: wearables, home telemetry, predictive long‑term CV risk.

Quick Start: 10‑Point Readiness Scan

Resumen en Español (breve)

Este checklist práctico ayuda a equipos clínicos a implementar triaje de cardiología con IA de forma segura: define el caso de uso, asegura datos de calidad (ECG, troponina/BNP, historia), valida el modelo localmente con métricas clínicas, integra alertas en el flujo de trabajo con supervisión humana, forma a los equipos, mide KPIs (tiempo a cardiología, eventos, reingresos), cumple requisitos regulatorios y planifica escalabilidad y MLOps.