Option 1: 90-Minute Introductory Session – “Understanding AI’s Impact on Healthcare Administration”
Target Audience
Non-clinical healthcare professionals (administrators, managers, support staff).
Objective
To provide a foundational understanding of AI and its current/potential applications in non-clinical healthcare settings.
Content
Module 1: Demystifying AI
- What is AI? Key terminology (machine learning, deep learning, natural language processing).
- Brief overview of AI’s history and evolution.
- Focus on AI concepts relevant to non-clinical roles.
Module 2: AI in Non-Clinical Healthcare:
Exploring current applications:
Administrative automation (e.g., scheduling with tools like Lumeon, billing).
Operational efficiency (e.g., supply chain management using platforms with AI-powered forecasting).
Data analysis for resource allocation.
Emerging trends and future possibilities.
Module 3: Ethical Considerations (Non-Clinical Focus)
Data privacy and security in AI implementation.
Bias in algorithms and its impact on healthcare access.
Responsible AI adoption in administrative processes.
Format
Lecture with Q&A
Learning Outcomes
Participants will gain a basic understanding of AI and its relevance to their non-clinical roles in healthcare, enabling them to participate in discussions and identify potential areas for AI application.
Option 2: Half-Day Workshop – “Leveraging AI for Enhanced Healthcare Operations”
Target Audience
Non-clinical healthcare professionals (administrators, managers) seeking to explore practical AI applications.
Objective
To equip participants with the knowledge and skills to identify and implement AI solutions for operational improvement.
Content
Module 1: AI Fundamentals Recap & Expansion
- Brief review of key AI concepts.
- Deeper dive into specific AI techniques relevant to operations.
Module 2: AI for Operational Efficiency:
- Process automation with AI:
- Robotic Process Automation (RPA) in administration.
- AI-powered workflow optimisation.
- Predictive analytics for resource management:
- Forecasting demand and staffing needs (e.g., using tools with time series forecasting).
- Optimising supply chain logistics.
Module 3: Data-Driven Decision Making with AI:
- Using AI for data visualisation and reporting.
- Identifying trends and patterns in healthcare data.
- Improving decision-making through AI-powered insights.
Module 4: Introduction to Prompt Engineering (for relevant tasks):
- Basics of prompt engineering for large language models (LLMs).
- Using LLMs like Google Gemini or OpenAI’s ChatGPT to generate reports or summaries.
- Ethical considerations in operational AI.
Format
Interactive workshop with case studies, group discussions, and hands-on exercises.
Learning Outcomes
Participants will be able to identify opportunities to apply AI to improve healthcare operations, understand the process of implementing AI solutions, and analyse the ethical considerations involved.
Option 3: Full-Day Intensive – “Implementing AI Solutions for Non-Clinical Healthcare Transformation”
Target Audience
Non-clinical healthcare leaders, managers, and analysts involved in strategic planning and implementation.
Objective
To provide a comprehensive understanding of AI implementation, from strategy to execution, with a focus on driving organisational transformation.
Content
Morning Session
Module 1: Strategic AI Planning
- Developing an AI strategy aligned with organisational goals.
- Identifying high-impact AI use cases.
- Assessing organisational readiness for AI adoption.
Module 2: Advanced AI Applications in Healthcare Administration
- AI for patient experience enhancement (non-clinical aspects).
- AI in financial management and revenue cycle optimisation.
- AI for risk management and compliance (non-clinical).
Module 3: Deep Dive into Prompt Engineering and LLMs for Healthcare
- Advanced prompting techniques for tools like Claude
- Building AI-powered tools for specific non-clinical tasks.
Afternoon Session
Module 4: Data Governance and Infrastructure for AI
- Ensuring data quality, security, and accessibility.
- Building a robust data infrastructure to support AI initiatives.
Module 5: Change Management and Organisational Adoption
- Strategies for overcoming resistance to change.
- Building an AI-ready culture within the organisation.
- Training and upskilling the workforce for AI-enabled roles.
Module 6: Project Implementation and Measurement:
- Developing a roadmap for AI project implementation.
- Measuring the ROI of AI initiatives.
- Monitoring and evaluating AI performance.
Format
Intensive workshop with presentations, group projects, case study analysis, and expert panel discussions.
Learning Outcomes
Participants will be able to develop an AI strategy for their organisation, lead the implementation of AI solutions, manage the organisational change associated with AI adoption, and measure the success of AI initiatives.
Testimonial
