Course Information

Introduction

The integration of Artificial Intelligence (AI) into healthcare has revolutionized the way medical professionals approach decision-making processes.
AI technologies, such as machine learning and predictive analytics, have enabled the analysis of vast datasets to uncover patterns and insights that were previously unattainable.
This advancement has led to improved diagnostic accuracy, personalized treatment plans, and enhanced patient outcomes.
For healthcare executives, understanding and leveraging AI is no longer optional but essential to remain competitive and deliver high-quality care.​

Despite the promising benefits, the adoption of AI in healthcare presents several challenges.
One significant hurdle is the lack of understanding among executives regarding AI capabilities and limitations.
This knowledge gap can lead to unrealistic expectations, misinformed decisions, and potential resistance from clinical staff.
Moreover, integrating AI into existing workflows requires careful planning to ensure seamless adoption without disrupting patient care.​

Another critical concern is the ethical implications of AI deployment.
Issues such as data privacy, algorithmic bias, and transparency must be addressed to maintain patient trust and comply with regulatory standards.
Healthcare executives must be equipped to navigate these ethical considerations, ensuring that AI applications align with organizational values and legal requirements.​

The rapid evolution of AI technologies also necessitates continuous learning and adaptation.
Healthcare leaders must stay abreast of emerging trends, such as explainable AI and federated learning, to make informed strategic decisions.
Failing to do so may result in missed opportunities for innovation and improvement in patient care.​

Real-world examples underscore the transformative potential of AI in healthcare.
For instance, AI-driven diagnostic tools have demonstrated remarkable accuracy in detecting diseases like diabetic retinopathy and certain cancers, often surpassing human experts.
Additionally, predictive analytics have been employed to identify patients at risk of hospital readmission, enabling proactive interventions that reduce costs and improve outcomes.​

This course is designed to empower healthcare executives with the knowledge and skills necessary to harness AI effectively.
By exploring the intersection of technology, ethics, and leadership, participants will be prepared to drive AI initiatives that enhance organizational performance and patient care.​

Objectives

By attending this course, participants will be able to:

Analyze the current landscape of AI applications in healthcare and their impact on clinical and administrative processes.

Evaluate the ethical, legal, and regulatory considerations associated with AI implementation in healthcare settings.

Design strategic plans for integrating AI solutions into existing healthcare infrastructures.

Implement AI-driven decision-making tools to enhance operational efficiency and patient outcomes.

Assess the risks and challenges of AI adoption, including data security and workforce implications.

Apply principles of change management to facilitate organizational acceptance of AI technologies.

Develop metrics for monitoring and evaluating the performance of AI initiatives within healthcare organizations.

Who Should Attend?

This course is ideal for:

Healthcare Executives and Administrators seeking to understand and implement AI strategies to improve organizational performance.

Clinical Directors and Managers aiming to integrate AI tools into patient care processes.

Health IT Professionals responsible for deploying and managing AI technologies within healthcare systems.

Policy Makers and Regulators interested in the governance and ethical considerations of AI in healthcare.

Consultants and Advisors working with healthcare organizations on digital transformation initiatives.​

This course is suitable for intermediate to advanced practitioners with a foundational understanding of healthcare operations and a keen interest in technological innovation.

Training Method

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a 7” Tablet containing a copy of the presentation, slides and handouts
• Post-assessment

Program Support

This program is supported by:
* Interactive discussions
* Role-play
* Case studies and highlight the techniques available to the participants.

Daily Agenda

All training sessions are conducted from Monday to Friday, following the standard working week observed in the United Kingdom and European Union. Saturday and Sunday are official weekends and are not counted as part of the course duration.
Daily Schedule (Monday to Friday)
- 09:00 AM – 10:30 AM Technical Session 1
- 10:30 AM – 12:00 PM Technical Session 2
- 12:00 PM – 01:00 PM Technical Session 3
- 01:00 PM – 02:00 PM Lunch Break
* Note: Lunch is provided if applicable and subject to course delivery arrangements.
- 02:00 PM – 04:00 PM Self-Study & Reflection
* Participants are expected to engage in guided self-study, reading, or personal reflection on the day’s content. This contributes toward the CPD accreditation and deepens conceptual understanding.

☕ Coffee and refreshments are available on a floating basis throughout the morning. Participants may help themselves at their convenience to ensure an uninterrupted learning experience.

Week 2

Day 6:
Integrating AI into Clinical Decision Support Systems
* Analyzing the role of AI in enhancing clinical workflows and decision accuracy.
* Designing AI models to support diagnostic and treatment decisions.
* Evaluating the integration of AI with existing EHR systems.
* Reviewing case studies on AI-enabled decision support tools in hospitals and clinics.

Day 7:
AI in Population Health and Public Health Intelligence
* Leveraging AI for disease surveillance, prevention, and health forecasting.
* AI applications in managing chronic disease and behavioral health trends.
* Using machine learning for social determinants of health (SDoH) analysis.
* Real-world applications of AI in pandemic preparedness and resource allocation.

Day 8:
AI Governance, Ethics, and Regulatory Compliance
* Developing frameworks for ethical governance of AI in healthcare.
* Understanding local and global AI regulatory landscapes (e.g., GDPR, HIPAA).
* Navigating liability, accountability, and transparency in AI decision-making.
* Engaging patients, staff, and boards in responsible AI adoption.

Day 9:
Monitoring, Evaluating, and Optimizing AI Performance
* Establishing KPIs and success metrics for AI implementation in healthcare.
* Creating dashboards and analytics tools to monitor AI efficacy.
* Performing audits and continuous improvement of AI systems.
* Applying lessons learned from failed or underperforming AI initiatives.

Day 10:
Capstone Workshop – Strategic Roadmapping and Future Readiness
* Group presentations of strategic AI projects tailored to real-life healthcare challenges.
* Facilitated peer review and expert feedback sessions.
* Discussion on emerging technologies and AI trends in digital health.
* Guidance on continuing education, professional development, and certification pathways.