Program Structure

Module 1: Foundations of Telemedicine and AI

• Overview of telemedicine services and digital care models.
• Understanding the role of AI in supporting remote healthcare delivery.
• Identifying safe and high-impact AI use cases in telemedicine.

Module 2: Digital Health Data and Remote Monitoring

• Types of telemedicine data including vitals, questionnaires, and wearable signals.
• Designing meaningful alerts and monitoring thresholds.
• Managing noisy and incomplete real-world health data.

Module 3: AI for Virtual Triage and Symptom Analysis

• Designing symptom screening workflows for virtual care.
• Rule-based versus AI-assisted triage approaches.
• Risk management using escalation rules and clinician oversight.

Module 4: Clinical Decision Support in Telemedicine

• Supporting follow-up planning, reminders, and chronic care check-ins.
• Understanding risk scoring and early warning indicators.
• Communicating AI suggestions responsibly to clinicians.

Module 5: AI-Assisted Clinical Documentation

• Automating visit summaries and follow-up instructions.
• Structuring clinical notes for clarity and audit readiness.
• Reducing administrative workload while maintaining accuracy.

Module 6: Patient Engagement and Personalized Digital Care

• Designing digital onboarding, reminders, and education workflows.
• Personalizing communication based on patient risk and behavior.
• Improving accessibility and trust in digital health interactions.

Module 7: Telemedicine Operations and Service Analytics

• Optimizing appointment scheduling and reducing wait times.
• Monitoring service quality and unresolved virtual visits.
• Using patient feedback and analytics for continuous improvement.

Module 8: Privacy, Ethics, and Compliance in Digital Health

• Protecting patient data through consent, access control, and secure storage.
• Addressing bias, fairness, and transparency in AI systems.
• Ensuring safe deployment and ongoing monitoring of AI tools.

Module 9: Designing an AI-Enabled Telemedicine Solution

• Building an end-to-end telemedicine workflow with AI support.
• Defining clinical safety, operational, and patient experience metrics.
• Planning deployment, monitoring, and system updates.

Final Project

• Design an AI-powered telemedicine solution for a chosen healthcare use case.
• Define patient journey, data requirements, risk controls, and evaluation metrics.
• Example projects include remote monitoring alert systems, virtual visit summarization workflows, or safe triage chatbot designs.