Implementation of artificial intelligence in health services

Addressed: Health professionals, Commandments,  health administrative, and nurses.
Language: Catalan
Format: Online tutoring
Calendar: October 30,- December 18, 2024
Duration: 30h

General objectives :

• Dote health professionals with the knowledge and skills needed to effectively and responsibly leverage artificial intelligence in clinical settings.

Specific objectives :

• Understand the foundations of AI and its applications to health care.
• Ethical and regulatory considerations should be considered to improve patient care.
• Make clinical decisions to ensure patient safety and data privacy.

Content

1. Introduction to AI in health services (2 h)

• The situation of health systems and the need of transformation of patient care.
• Historical evolution of health services and key milestones
• AI overview and machine learning.
• AI challenges and opportunities to transform health
• Ethical considerations, regulations and regulations
• Current trends of emerging technologies

2. Health data management (3 h)

• Evolution of health information systems
• Privacy and security of data
• Quality and analysis of data
• Electronic health records (EHR)
• Patient data connected and the use of Raw date
• Data integration and interoperability

3. Fundamentals of machine learning (4 h)

• Supervised, non-supervised and booster learning
• Operation of the elaboration of algorithms
• Evaluation and validation of the model
• Cross-validation techniques
• Hyperparameters tuning

4. AI for medical image (3 h)

• Evolution of the medical image and its diagnostic and therapeutic impact
• Computational vision
• AI applications in radiology and pathology
• Segmentation, classification and image triage
• Practical cases and practical examples

5. Processing of natural language (PNL) (3 h.)

• Introduction to NLP
• Analysis of medical text data
• Clinical documentation and coding
• Analysis of feelings and mood in the patient’s history
• Virtual health shippers and assistants

6. Preaching analysis and support for clinical decision-making (3 h)

• Preacher modeling
• Risk stratification and prediction of patient results
• Clinical decision support systems
• Case studies and applications in the real world

7. Research by new drugs and genomic medicine (2 h)

• AI in drug discovery
• Genomic data analysis
• Custom and pharmacogenetic medicine
• Improving prevention, early diagnosis and diagnosis with biomarkers
• Drug development case studies

8. Telemedicine and remote patient monitoring (2 h)

• Telehealth and telemedicine technologies
• Remote patient monitoring with AI
• Telehealth regulations and challenges
• Cases of use and success cases

9. IA in organizational processes and sustainability (2 h)

• Hospital management and optimization of processes and resources
• Supply chain and inventory management
• Preaching maintenance in medical equipment
• Motivation and well-being of professionals
• Automation of AI-driven processes
• Strategies for developing environmental sustainability plans

10. Ethical and regulatory considerations (2 h)

• Ethical aspects in the implementation of AI
• Regulatory frameworks
• Biaix and fairness of AI algorithms
• Practical cases and good practices

11. Implementation and adoption (2 h)

• Define governance models when implementing AI
• Promote collaborative and participatory leadership
• Integration of AI into clinical and organizational flows and processes
• Analyze the challenges and opportunities in the implementation of AI
• Change management and promote continued training of professionals
• Practical case of successful AI adoption

12. Future trends and emerging technologies (2 h)

• The future of AI in health systems and organizations
• Analyze the impact of social, demographic, economic and political changes
• Emerging technologies (quantum computing, AI in wearables, etc.)
• The potential for research and innovation in AI in the field of health
• Preparing for what will come. How to navigate stormy waters.