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BRIEF DESCRIPTION

The course will present an introduction to Digital Health and Artificial intelligence fundamentals to understand its application in health care decision making tools.

Aims:

• Understanding the Evolution of Digital Health
• Fundamentals of Artificial intelligence and Machine learning 
• Understanding how works an automated clinical decision support System
• Know the Current ethical issues related to automated decision support tools
• Feel prepared to contribute to the advancement of digital health in decision support

SUMMARY

Module 1: Introduction to digital health and CDSS

Fundamentals of digital health and CDSS independently.

• Section 1.1: Definition and context of DH 
• Section 1.2: CDSS definition

Module 2: Current CDSS

State of art of CDSS, going deeper in this elements.

• Section 2.1: Core components (theory)
• Section 2.2: Types of CDSS
• Section 2.3: Development of a CDSS

Module 3: Artificial intelligence, Machine learning in CDSS

Introduction to AI and ML to put in context with Digital health in general and then with CDSS.

• Section 3.1: Fundamentals of AI in Digital Health
• Section 3.2: Machine learning in CDSS

Module 4: CDSS implementation

Rationale for implementing a CDSS

• Section 4.1: Planning customization
• Section 4.2: Integration with existing systems
• Section 4.3: Training and Change Management
• Section 4.4 Data security
• Section 4.5 Evaluation of the implementation

Module 5: Data Privacy and ethical considerations 

This module reviews the elements and protocols related to data security.

• Section 5.1: Importance of Data Security
• Section 5.2: Privacy Considerations

Module 6: Challenges and Future Trends in CDSS

Think of how CDSS will evolve and define the possible barriers that can jeopardize this process.

Module 7: Successful Cases

Enumerate different examples of successful cases of CDSS implementation.

AUTHOR AND COORDINATOR

Author

Luis Marte

Coordinator

Santi Fort