Program Structure

Module 1: Digital Health Informatics — The Real Ecosystem

• Digital health components: EHR/EMR, LIS, RIS/PACS, telehealth, wearables, apps.
• Clinical workflows: where data is created and where decisions happen.
• Why integration matters: avoiding “AI in isolation.”
• Common failure points: poor data, workflow mismatch, and low trust.

Module 2: Healthcare Data Types & Quality (What AI Learns From)

• Structured data: vitals, labs, medications, diagnoses, procedures.
• Unstructured data: clinical notes, discharge summaries, reports (NLP overview).
• Time-series streams: ICU monitoring, wearables, remote patient monitoring (RPM).
• Data quality issues: missing values, coding variation, bias, and leakage risks.

Module 3: AI Use Cases in Digital Health

• Risk prediction: readmission, sepsis alerts, chronic disease progression (workflow view).
• Triage and prioritization: decision support, not decision replacement.
• Clinical documentation support: summarization, coding assistance (safe boundaries).
• Population health: hotspots, risk stratification, and outreach targeting.

Module 4: Interoperability & Integration Essentials (Conceptual but Practical)

• Why standards exist: consistent data exchange and safer integration.
• HL7/FHIR basics (concept): what they represent and typical integration flows.
• Terminologies overview: ICD, SNOMED, LOINC (why coding consistency matters).
• Data pipelines: ingestion → cleaning → modeling → deployment → monitoring.

Module 5: Building AI-Enabled Clinical Workflows

• Where AI fits: alerts, dashboards, order suggestions, escalation flags.
• Human-in-the-loop design: making clinicians the final decision point.
• Reducing alert fatigue: thresholds, prioritization, and explainability basics.
• Designing for adoption: usability, trust, and clinical relevance.

Module 6: Model Validation, Safety & Performance Monitoring

• Clinical validation vs technical accuracy: what matters in practice.
• Bias and fairness: how models can harm underserved groups.
• Drift monitoring: when models degrade due to changing populations or protocols.
• Documentation: model cards, audit trails, and change management basics.

Module 7: Privacy, Security & Responsible AI in Healthcare

• Protected health information (PHI) mindset and safe handling habits.
• Consent, de-identification basics, and secure data sharing concepts.
• Ethical boundaries: transparency, accountability, and explainability.
• Regulatory awareness (overview): why compliance planning matters.

Module 8: Applied Case Labs (Integration Scenarios)

• Case A: AI triage for emergency department flow.
• Case B: Remote patient monitoring for diabetes/hypertension.
• Case C: Hospital readmission risk scoring integrated into discharge workflow.
• Case D: NLP-based clinical note summarization with safety checks.

Final Project

• Create an AI + Digital Health Integration Blueprint for a chosen use case.
• Include: data sources, workflow map, AI output design, validation plan, privacy safeguards, and monitoring plan.
• Example projects: sepsis early warning dashboard, RPM workflow for cardiac patients, AI-assisted discharge planning, population health risk stratification plan.