AI and Digital Technologies: Pioneering Healthcare Transformation

Aim

This course provides a practical overview of how AI and digital technologies are transforming healthcare delivery. Participants learn how AI, data platforms, telehealth, IoT, digital therapeutics, and automation improve clinical and operational outcomes—along with the governance and implementation basics required for real-world adoption.

Who This Course Is For

• Healthcare administrators, hospital leaders, and operations teams
• Clinicians and clinical informatics professionals exploring digital health
• HealthTech founders, product managers, and implementation teams
• Data/AI professionals working on healthcare systems and analytics
• Researchers and students in biomedical and digital health domains

Prerequisites

• No coding required
• Basic understanding of healthcare workflows is helpful (EHR, diagnostics, patient journey)
• Interest in technology-enabled healthcare improvement

What You’ll Learn

• Healthcare transformation drivers: access, quality, cost, and patient experience
• AI use-cases: clinical decision support, imaging, triage, and predictive analytics
• Digital health stack: EHR, data platforms, interoperability concepts, and APIs
• Telehealth and remote monitoring: workflows, quality measures, and limitations
• IoT and wearable signals: data capture, reliability, and patient adherence
• Digital therapeutics and personalization concepts
• Automation: scheduling, documentation support, coding, and operational optimization
• Implementation basics: change management, adoption, and impact measurement
• Governance: privacy, security, safety, bias, and responsible AI practices

Program Structure

Module 1: The Digital Health Landscape

• Healthcare delivery models and transformation goals
• Key digital health components and how they connect
• Measuring impact: clinical and operational KPIs

Module 2: AI for Clinical Decision Support

• Predictive analytics and risk stratification
• AI in imaging and diagnostics (overview)
• Clinical workflow integration and safety checks

Module 3: Data Platforms, Interoperability, and Analytics

• EHR basics and healthcare data types
• Interoperability concepts and integration pathways
• Dashboards for population health and operations

Module 4: Telehealth and Remote Patient Monitoring

• Teleconsultation workflows and triage models
• Remote monitoring programs and alert design
• Quality, safety, and patient engagement

Module 5: IoT, Wearables, and Smart Care Environments

• Wearable sensors and continuous health signals
• Data reliability, calibration, and noise handling
• Care pathways enabled by real-time monitoring

Module 6: Digital Therapeutics and Personalization

• Basics of DTx and evidence expectations (overview)
• Personalized care plans and engagement strategies
• Outcome tracking and adherence support

Module 7: Automation and GenAI in Healthcare Operations

• Automation in scheduling, documentation support, and patient communication
• GenAI risks: hallucination, privacy, and safe-use guardrails
• Operational KPIs for automation effectiveness

Module 8: Implementation and Governance

• Adoption planning, change management, and stakeholder alignment
• Privacy, security, and responsible AI requirements
• Measurement framework: clinical outcomes + operational ROI

Tools & Templates Covered

• Digital health use-case prioritization matrix
• Implementation checklist (workflow + training + adoption)
• Impact measurement plan (clinical + operational KPIs)
• Governance checklist (privacy, security, safety, AI ethics)

Outcomes

• Identify high-impact AI and digital health opportunities across care pathways
• Understand core digital health components and how to integrate them
• Create an implementation and measurement plan for healthcare transformation
• Apply governance practices for safe and compliant adoption

Certificate Criteria (Optional)

• Complete learning checkpoints
• Submit a short transformation plan (use-case + workflow + KPIs + governance)